National Academies Press: OpenBook

Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary (2011)

Chapter: 3 Healthcare System Complexities, Impediments, and Failures

« Previous: 2 Engaging Complex Systems Through Engineering Concepts
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

3

Healthcare System Complexities, Impediments, and Failures

INTRODUCTION

The extent to which health care for Americans is timely, efficient, and appropriate for a given individual is determined by the characteristics of the delivery system. Moving to a learning healthcare system will require the identification of specific areas where system complexities slow or inhibit progress and the development of solutions geared toward overcoming impediments and failures.

Workshop discussions considered a number of process inefficiencies, structural barriers, and system failures that are significant impediments to quality and that preclude the delivery of highly effective, highly efficient, evidence-based health care. In the second workshop session, the focus turned to the areas of underperformance that may need the most attention and correction from an engineering perspective. Presenters in this session examined select obstacles inherent in multiple healthcare system components and certain flawed processes that particularly affect the generation and application of evidence. One goal of the session was to frame suggested ideas for how systems engineering might address some of health care’s most troublesome shortfalls.

This chapter begins with an overview of the healthcare culture. In his presentation William W. Stead, chief information officer of Vanderbilt University Medical Center, described the current healthcare environment as being characterized by competition, misaligned incentives, and inherent distrust among stakeholders. Throughout health care, Stead sees competing cultures at loggerheads—as exemplified by the tensions among consum-

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

ers who want high service and low out-of-pocket costs, payers who want to select risk and limit cost, and purchasers who want more value at the lowest cost. Looking to a future that will be defined by individualized medicine, Stead suggested that tomorrow’s opportunities may not be fully realized without fundamental changes in the healthcare culture. Education for health professionals is only one area that needs reform. Another requirement will be to move from the business of managing episodes of care to the business of caring for patients and populations. He added that similar fundamental reforms will need to be engineered into the business models of virtually every healthcare stakeholder—in payment mechanisms, and, notably, in the role of the individuals in managing their own care.

Speaking from her perspective as a cardiologist and health policy analyst, Rita F. Redberg, director of Women’s Cardiovascular Services at the University of California, San Francisco, noted that a marked proliferation in new diagnostic and treatment technologies has resulted in a precipitous increase in healthcare costs. Moreover, limited integration in the design of systems for health information technology (HIT) and technologies such as imaging systems has allowed their misuse and overuse, thus impeding their ability to improve healthcare quality. Redberg surveyed the current landscape of diagnostic and treatment technologies available for heart disease and offered suggestions for systemically evaluating and using these technologies in ways that improve care and reduce costs. She proposed that more systematic data collection and the development of more prospective registries would lead to better-informed decisions in health care.

Addressing a concern that was raised throughout the workshop about the need for more robust data collection and mining capacities, Michael D. Chase, associate medical director of quality, Kaiser Permanente Colorado, asserted that the U.S. healthcare system has not fully leveraged clinical data to improve health outcomes. Impediments to full use of the data include limited data access, a problem that is exacerbated by inadequate adoption of electronic health records (EHRs) and lack of data standards. As health care has become more complex, the lag in the sophistication of data applications in evidence generation has become more acute. Engineering principles, Chase suggested, could help those in charge of health care manage various complex processes and increase the use of data for clinical decision support. Chase offered examples and suggestions concerning how key delivery systems could be better integrated into healthcare systems in order to address critical areas in health care. For example, Chase proposed a patient-centered, population health–based view grounded in the principle of getting the right information to the right member of the healthcare team—including the patient—at the right time during the workflow or decision-making process. Chase presented a model that takes a broad look at decision support opportunities across a continuum of patient needs,

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

available healthcare professionals, tools and systems, and an extended time line for patient care.

Amy L. Deutschendorf, senior director for clinical resource management at Johns Hopkins Hospital and Health System and principal of Clinical Resource Consultants, also observed that there has been an escalation in system and patient complexities throughout the current healthcare environment. The crush of information, a plethora of new technologies, increased regulatory oversight, an aging population, and heightened consumer awareness and expectations have all contributed to the disorganization, fragmentation, and discontinuity of patient care. Consequently, she argued, effective care coordination and linkage have become even more important. Deutschendorf spoke of the need for processes that ensure patient-centered alignment of care. One application is a care delivery process with communication models and systems that can ensure the accurate and timely transfer of patient information throughout the healthcare continuum. Deutschendorf suggested a number of other changes, including more clarification, definition, and distinctions between acute patient care and ambulatory care; better management of consumer expectations; and increased communication and collaboration between caregiving team members. Because models of care need to be based more firmly on evidence, she proposed that rigorous research be conducted to determine which care delivery models can yield appropriate safety outcomes and the highest possible quality outcomes.

Speaking from his perspective as chief executive officer (CEO) of the University of Pennsylvania Health System (UPHS), Ralph W. Muller discussed areas of successful transformation in administration and business systems at his institution. He highlighted projects on patient registration, billing, and revenue cycle management, and he discussed how each was transformed in order to be more effective. He also described a project that examined how UPHS inpatient and outpatient operations were improved through a combination of systems analysis, reporting systems, incentive alignment, and continuous change management. In discussing lessons learned in several areas of day-to-day practice—as well as from significant, documented results—Muller illustrated how engineering-specific interventions can change systems of care. In recounting examples of reform at UPHS, Muller also highlighted elements of a methodology for conceptualizing change in the face of entrenched health cultures. He offered specific lessons learned about using data and analysis to identify opportunities and motivate change, redesign workflows and restructure roles, integrate information technology, establish goals and monitor performance, and create meaningful incentives.

The final speaker in the second session, Eugene C. Nelson of the Dartmouth–Hitchcock Medical Center, said that we will need a healthcare system information environment that provides critical knowledge that can

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

be used to effectively manage individuals over time, evaluate and improve the quality and value of clinical practice, and facilitate basic translational and outcomes research. Nelson described a successful transformative activity at the Dartmouth–Hitchcock Spine Center that designed, tested, and refined patient-centered “feed-forward” and “feedback” data systems, which are built into the flow of healthcare delivery in order to support patient care and generate information and knowledge concerning entire patient populations. Nelson detailed the issues and concerns that motivated the project, discussed the challenges of designing the systems, and described their positive impacts on system effectiveness and patient satisfaction. He also outlined a promising approach for creating sustainable feed-forward data systems based on the formation of “collaboratories,” or professionally organized networks for advancing health care and healthcare research.

HEALTHCARE CULTURE IN THE UNITED STATES

William W. Stead, M.D., Vanderbilt University Medical Center

This paper begins with three observations about the culture of health care in the United States. First, that culture is centered on individual expert health professionals; their behaviors reflect the way they are selected, the way they are educated, and what it takes to survive in their work environment. These cultural roots of the health professions must be addressed if change in health care is to be realized. Second, the culture of health care in this country is one of a clash among competing forces. Stakeholders work against each other to obtain advantage for themselves at the expense of others. If we are to achieve meaningful improvement, this competitive clash needs to be transformed into a competition to work together to achieve the right results for the patient. Third, today’s health care faces discontinuous, disruptive change. The way health professionals make decisions will not scale up to handle the data load that is resulting from biological discoveries in genomics, proteomics, and other areas. This last observation is good news. As the health professions and other stakeholders realize that they cannot escape disruptive change, we will have a once-in-a-century chance to test better approaches to health care. Building on these observations, this paper contrasts the current healthcare culture with a future culture in which care is delivered through systems approaches.

The Culture of the Health Professions

The culture of the health professions is rooted in their education. In the first phase of that education, the scientific basis of health and disease and the scientific method are taught. The goal is for each professional to have

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

a current fact base and to know the method by which facts are discovered. This phase of education is preparation to act on what is known, interpret new literature, and learn from practice. By way of analogy, at the end of this phase, students have learned how the car works and how it is built, but they have no idea how to plot a path from point A to point B. In the second phase of education, students learn practice through an apprenticeship model in which they are mentored by a variety of individual experts. To continue the analogy, in this phase students learn the many ways to use the car to get from point A to point B and which ways work best. The third phase of education extends throughout the career as learning continues through practice and reading. If something unusual is seen in a patient or something new is tried on the chance that it might work, case reports are written to share observations. When the effects of alternative approaches are sought, a trial is conducted and the results written up. However, learning remains individual. Each health professional seeks to be the best expert at caring for the cases he or she sees.

The culture of the health professions is influenced by the way decisions are made. The reasoning of health professionals, because they are experts, takes place through the recognition of patterns. A person with fever, cough, infiltrate on a chest X-ray, and an elevated white count is suspected of having pneumonia, while a low white count causes concern that the immune system is overwhelmed. These conclusions are based on the entire picture, in much the same way that a constellation in the night sky is recognized. There is no systematic processing of data and calculation of combinatorial probabilities as is done by a novice in a learning situation. In addition, the data used to make decisions are imprecise. Many measurements used in clinical practice are correlative measures, not direct measurements of the substance itself. For example, nephrologists used to measure serum creatinine, an indicator of renal function, by the light absorption of a compound formed by the adduct formation between creatinine and the picrate ion. Other compounds were absorbed at the measured frequency, causing falsely elevated measures. At a time when the sensitivity of the test was ±0.3, the threshold for treating transplant patients for rejection was a change of 0.3. In other words, physicians erred on the side of treatment with a toxic drug because treatment had to be started early to save the transplant. That kind of reasoning was used regularly, in the face of uncertainty, in life and death situations, under an oath that says “do no harm.”

The culture of the health professions has also been shaped by the exponential increase in biomedical knowledge and technology. This overload is handled through specialization and subspecialization. In the process, some are learning more and more about less and less, while the rest are learning less and less about more and more. The workflow requires large amounts of multitasking, is interruption driven, and is nontransparent. There is no

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

chance to sit and reflect. Compensation models reward piecework, procedures, and technology. Health professionals do their best to deliver exceptional care despite the “system.” Time is the most limited resource.

The combination of these internal roots and external pressures has led the culture of the health professions to become one in which circumstances that conflict with quality health care are accepted. Variability in practice is accepted as well. The best experts are sought out and expected to disagree. What other industry would report success if there were a shift in performance on a recommended practice from 60 to 80 percent of cases? If 5 practices need to be followed for each patient with a condition, and each is performed correctly 80 percent of the time, the probability that all 5 will be done correctly for a given patient is just 33 percent. The health professions’ culture accepts process improvement targets that are far lower than necessary to have the desired effect on clinical outcomes.

Autonomy is a goal of training. Challenges from those lower in the hierarchy are not acceptable. The conditions under which health professionals function lead to increased self-confidence and cynicism (Gray et al., 1996). The fragmentation in care results in less of a sense of responsibility. Although everyone knows the healthcare system is broken, each individual believes his or her own practice is quite good. Data showing the variability in practice are met with surprise. By and large, health professionals are passionate about doing the right thing and are attempting to provide care for patients despite the system. Most of the time, they do a good job. The trouble is that most of the time is insufficient to avoid the quality problems that are ubiquitous in health care.

 

The Clash Among Competing Forces

The culture of the health professions is just one of many cultural challenges to achieving better health care. The healthcare system in the United States is a clash among competing forces; it is not a system. Health professionals, for example, focus on payment for services and autonomy. Care facilities seek high-margin services and low supply costs. Suppliers focus on intellectual property protection and volume. Meanwhile, consumers seek accessible services and low out-of-pocket costs. Payers pursue the right to select risk and limit cost. Purchasers want more value at the lowest cost.

As Porter and Teisberg (2006) point out, the different stakeholders compete in a zero-sum game. The only way a payer can reduce costs for a purchaser, such as an employer, is to negotiate with the provider to take less or force the consumer to receive less. Because employers are working outside of the direct care process instead of improving that process, they add administrative overhead. As the other stakeholders respond, the increase in

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

overhead is compounded, and the system becomes more expensive and less workable for the patient.

This clash among stakeholders raises several cultural barriers to quality health care. Incentives are not aligned. Providers are paid more if they overuse resources and if they provide poor care leading to rework. They are paid less if they provide such good care that other care is not necessary. They are paid more for technical and episodic tasks and little for cognitive, coordinative work. Healthcare CEOs have limited power given the autonomy of health professionals and the competition among hospitals for physicians.

The stakeholders distrust each other. Although individuals trust their own physicians, they do not trust the “system” (Norris, 2007). They are the ball in the healthcare ping-pong match. They are forced to change health plans regularly as employers and government seek to control costs. A Medicare beneficiary sees a median of two primary care providers and five specialists per year, and Medicare beneficiaries with multiple chronic diseases see up to 16 health professionals (Pham, 2007).

The culture of health care accepts waste. In his keynote address, Brent C. James outlined the data. Administrative overhead in U.S. health care may be as high as 40 percent. Thirty percent of the care provided may be unnecessary; as much as 70 percent may be preventable. Given the rapidly escalating cost of health care, tension exists over the cost of new technology, which has accounted for half of that increase in recent decades. Can we afford ever better technology? Does the increased cost of health care hurt the economic competitiveness of the country by increasing the cost of everything we do?

Finally, the culture accepts poor outcomes on a population basis. In the United States, 109 deaths per 100,000 patients each year are attributable to health care, as compared with 65 in France (Nolte and McKee, 2008). Yet France’s per capita healthcare spending is about half that of the United States.

Toward a New Healthcare Culture

Even if today’s health care provided acceptable quality and access at an affordable cost, the healthcare culture would face disruptive, discontinuous change because of the inevitable demise of expert-based practice (IOM, 2009a). Cognitive research shows that a human can handle from five to nine facts in a single decision (Miller, 1956). Even with today’s clinical descriptions of phenotype, the number of facts bearing on a decision already can exceed this capacity, contributing to the overuse, underuse, and misuse of medical care. The additional data from structural genetics will probably push us into the range of ten facts per decision. Full data on a person’s

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

functional expression may create a ten-fold increase in the facts per decision, and data on proteins may add a second ten-fold increase. Imagine a primary care provider trying to cope with such a massive amount of data in a 15-minute encounter. Clearly a new paradigm for clinical decision making will be necessary. This inescapable change will create a once-in-a-century chance to rethink roles—and therefore culture—in health care.

Table 3-1 contrasts the current culture with a possible future culture in which systems approaches to health and health care are used to deliver the desired results every time. In the current culture of a clash among forces, people attempt to fix the nonsystem by layering fix on top of fix from the outside. Each fix adds complexity and cost without changing the fundamentals of care delivery. The goal should be a future culture in which the system is continuously refined from the inside out. In this culture, people are recruited and educated to know their limits, to trust the system and their teammates, and to expect perfect collective performance or correction with each failure. Care is coordinated around populations, and the care delivered is right for the individual through systematic use of evidence (IOM, 2009b). Each individual is a data point in a population database. Providers are taught to practice in multidisciplinary, high-performance teams, using simulation to perfect their skills and outcomes to guide course corrections (IOM, 2007). Coordinated care is paid for and, on the basis of the value, delivered.

In the process of shifting toward this vision or other possible futures, health professionals must strive to preserve the best of the current culture. Most people engaged in health care are passionate about what they are do-

TABLE 3-1 Comparison of Current and Possible Future Healthcare


Current Culture Future Culture

•   Layer fix on fix from outside

•   Improve from the inside out

•   Trust oneself; provide care despite the system

•   Know one’s limits; trust the system and one’s team

•   Care safe for the masses

•   Right care for the individual

•   Manage episodes of care

•   Care for populations and the patient as a whole

•   Expert-mediated use of evidence

•   Systematic use of evidence

•   Each patient is an experiment with n = 1

•   Each patient is a data point in a population

•   Learn in disciplinary silos

•   Learn in teams

•   Learn by applying science through practice

•   Learn from simulation and outcomes

•   Pay for piece work and process steps

•   Pay for coordination and outcomes


Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

ing and about being in health care. Every day, in every hospital or clinic, there are people who go far out of their way to help their patients, despite the ecology in which they work. That passion must be preserved. At the same time, changes must be made to roles, education, decision-making processes, payment structures, and the way success is measured—in short, to the professional and business models of every stakeholder in the system.

DIAGNOSTIC AND TREATMENT TECHNOLOGIES

Rita F. Redberg, M.D., M.Sc., University of California, San Francisco

A marked proliferation of new diagnostic and treatment technologies has resulted in a precipitous increase in the costs of health care. Moreover, despite the potential of these technologies to improve the quality of health care, the limited integration in system design for such technologies as HIT; laboratory, radiology, and imaging systems; and monitoring and surgical equipment has allowed their misuse and overuse. This paper surveys the current landscape of diagnostic and treatment technologies available for treatment of heart disease and examines how they might be evaluated and employed more systematically to improve care and reduce costs.

In the late 1970s, John Eisenberg and Sankey Williams at the University of Pennsylvania were studying the behavior of the house staff with the goal of changing their routine daily lab test ordering for inpatients. However, Eisenberg and Williams’s daily reminders to the house staff to order only those tests that would affect patient management were not successful in reducing the number of daily lab tests ordered. It was difficult to be criticized for ordering too many tests as one could also be criticized for omitting a potentially useful test. All of the incentives in medical training lean toward ordering more tests, and how the additional information improves patient care receives little consideration. This philosophy is ingrained in the culture and reinforced by patient demands and the public’s perception that more care means better care.

At the time of the study, healthcare expenditures were on the order of 8 percent of the U.S. gross domestic product (GDP), and everyone expected that if healthcare expenditures reached 10 percent of GDP, things were going to change. Yet today, 30 years later, healthcare expenditures are at about 17 percent of GDP, the Medicare Trustees Report predicts that Medicare will be insolvent by 2012, and people are still speculating about when things are going to change. At least there is now some cause for optimism that some meaningful changes will take place that will lead to healthcare resources being spent more wisely. This paper examines what factors might drive such changes. The focus is on four of the main drivers of healthcare

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

costs: demographics, limited quality measures, the third-party payment system, and technology growth.

In terms of demographics, as we live longer we become victims of our success. The population includes more older people, who, on average, make more intense use of healthcare resources than do younger people. At the same time, quality measures are limited, and it is quite challenging to measure and reward good-quality care. The result is a massive healthcare system in which some of the care is of good quality and some of bad quality. Additionally, the third-party payment system insulates some of the main drivers of healthcare costs (patients and physicians) from the actual cost of care. When one enters a store to make a purchase, the cost is clearly marked, and one can judge the value of the item relative to one’s budget. In health care, by contrast, the cost to the consumer is generally unknown, and out-of-pocket costs are not related to the actual cost of care and often not related to the patient’s own consumption of care. Of course, health care is a different kind of commodity from such purchases as appliances. However, a system in which copayments are the same for a very expensive and a very inexpensive test encourages increased consumption of health care without consideration of value. Generally, patients who receive a great deal of health care pay no more than those who receive only a little. A similar situation exists at the physician level. When our hospital’s house staff is asked about the prices of the tests they order in the context of a discussion about why they are ordering a test and how the patient is going to benefit from its results, physicians rarely know what the tests cost. In an academic medical center, the costs of testing and new technology are invisible because doctors are removed from the payment system and insulated from the cost of health care. Similarly, house officers are often shocked to learn of the difference in cost between the latest fourth-generation antibiotic and older generics.

Of all the factors that drive up healthcare costs, however, the growth of technology can be singled out as most significant. Technology, of course, has many benefits. Numerous examples exist of advances in technology that have led to great improvements in health care. However, before a new technology is embraced, a technology assessment should be performed to determine whether it will yield actual patient benefits that outweigh any possible risks. This point is best illustrated by randomized controlled trials. The current healthcare system does not emphasize the need for evidence of benefit before widespread diffusion of new technology.

Today we are seeing a rapid proliferation of technologies for both diagnosis and treatment. A major example is imaging, whose rates have increased dramatically in the past few decades. For example, cardiac imaging used by cardiologists has increased by 24 percent per year over the past decade. Looking just at Medicare data from 1999 to 2003, cardiac

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

imaging increased 45 percent. Computed tomography (CT) scans represent the largest part of the cardiac imaging increase; CT scans of various body parts, excluding the head, have increased by 85 percent (MedPAC, 2007). In 2005, the estimated cost for all imaging was $100 billion (Farnsworth, 2005).

It is fair to say that the benefit to patients of this increase in imaging remains unclear. There have been no tremendous declines in mortality or improvements in health outcomes that are clearly related to the increase in imaging. So what is driving the increased use of imaging? Certainly, the technology has gotten better. Pictures are much clearer, for instance. And the technology has also become easier to use. Furthermore, imaging-related entrepreneurial activity, such as freestanding CT centers, has grown, and once one has made a capital investment in a very expensive CT scanner, the incentive to use it is great. Defensive medicine, such as ordering a specific test because of concern about being sued, is always mentioned as a driver of healthcare costs in relation to technology advances. Patient demand for the use of new technologies has also increased. Patients read about these advances on the Internet, hear about them in the media, are bombarded with related direct-to-consumer advertising, and request use of the technologies from their doctors.

Pictures are very powerful, and people are driven by images they see in the media. A recent collection of media clips, for example, showed a cover story in Time magazine about a CT angiogram, with the headline “How to stop a heart attack before it happens.”1 Yet how these tests could prevent a heart attack is unclear. Tests appear to have become confused with prevention, but the link between the two remains undetermined. Most prevention is based on lifestyle changes—such as better diet, increased physical activity, and smoking cessation—that individuals can make to reduce their risk of disease. If people eat a heart-healthy diet, exercise regularly, and do not smoke, they can reduce their chance of having a heart attack by 50 percent. They can also get a CT scan, but doing so is not going to change their chance of having a heart attack. It is possible, of course, that taking the test might make a person more likely to eat a healthy diet, exercise, and not smoke, but there are no data indicating this is the case. Still, patients appear to hear the message that getting such tests can prevent a heart attack. When people say they are doing something for prevention, they are usually talking about getting some kind of test.

Medicare data show a tremendous increase in the use of all cardiac imaging modalities. CT has seen the biggest increase, followed by magnetic resonance imaging and then positron emission tomography. Looking at these data, one can certainly understand why the Medicare Payment Advi-

 

images

1Time Magazine, September 5, 2005.

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

sory Committee is so interested in assessing the use of imaging, since it has been a huge driver of the increase in Medicare costs per beneficiary.

Take as an example cardiac CT angiography, the technology that makes it possible to visualize the coronary arteries noninvasively. The pictures are impressive, but there are currently no data on associated clinical outcomes, so there is no way to know whether the information from the images can be used to affect patient health.

There are data, however, showing that the increased use of CT scans poses a significant radiation risk. David Brenner recently wrote in the New England Journal of Medicine that some 62 million scans are done annually in the United States and that this number is increasing every year (Brenner and Hall, 2007). It is estimated that 2 percent of all cancers in the United States are attributable to radiation from CT scans and that some 3 million additional cancers can be expected in the next decade because of increased use of CT scans. The obvious question, then, is how the benefits from these additional CT scans can be weighed against the associated risks. In the Medicare system, a new test tends to be used first in high-risk patients and then, as it becomes more accepted, to be used more frequently, in lower-risk patients, and repeatedly. This pattern explains the dramatic increase in the use of CT scans.

Two years ago the Medicare Coverage Advisory Committee (MCAC) evaluated data related to cardiac computed tomographic angiography (CCTA). Although most Medicare decisions are local, if the Centers for Medicare & Medicaid Services (CMS) issues a national coverage decision, it trumps all local decisions. Thus a meeting of MCAC is sometime convened to review the evidence concerning a procedure or practice. The typical process is that MCAC reviews all of the data and then votes on the evidence, after which CMS makes a decision on whether to extend or expand coverage.

Duke University was commissioned to perform the evidence review for CCTA. The conclusions of the technology assessment were that the benefits of CCTA were unproven. MCAC voted that the evidence on CCTA was insufficient to establish its benefit. However, CMS elected not to issue a national coverage decision following that meeting, which meant that coverage decisions were left to local carriers. There was tremendous interest in CCTA at that time. Colleagues from the American College of Cardiology (ACC) and the American College of Radiology (ACR) collaborated on draft text that could be used for local CCTA coverage decisions, based on the ACC and ACR consensus concerning indications for use of the technology. Just a few months after the Medicare coverage meeting in which the evidence was found to be insufficient, all 50 states had included CCTA in Medicare coverage by local decisions (Redberg, 2007).

Looking at some other diagnostic and treatment technologies, we are

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

likely to hear more about spinal fusion, implantable cardiac defibrillators, percutaneous coronary interventions (PCIs, also known as stents), and lap banding for morbid obesity. Defibrillators, which basically prevent sudden death by firing a shock to the heart, were the focus of an MCAC meeting in February 2003. After publication of the results of Multicenter Automatic Defibrillator Implantation Trial 2, Guidant and major makers of defibrillators petitioned CMS for expanded coverage, and CMS expanded implantable cardioverter defibrillator (ICD) coverage for primary prevention. Now a much larger potential pool of ICD recipients exists because primary prevention includes anyone who has had a heart attack and a certain amount of damage to the heart muscle—a much bigger group than actual survivors of heart attacks (secondary prevention). Because of this expansion, only 1 in 11 patients derives any benefit from ICD, where benefit is defined as the device having been activated and the patient having been saved from a potentially lethal rhythm. Recently a published Medicare data analysis showed no survival benefit (at 1 year) for patients with ICD compared with conventional therapy, after adjustment for age and comorbidity. Again, then, evidence is accumulating after practice patterns have been established that casts doubt on the rationale for widespread ICD use. However, ICDs are now part of the culture in electrophysiology. Even so, to some extent the data on benefits lag behind usage, particularly in subgroups such as elderly people and women (Lin et al., 2007), and ICDs are implanted in far more patients than will ever benefit from the device (Lin et al., 2008).

PCIs show similar trends. There is tremendous geographic variation in the use of PCI. We have done some work in collaboration with colleagues at Dartmouth looking at the use of PCI across the country and documenting its appropriate use. As one might expect, we found a great deal of geographic variation and data suggesting that much PCI use is actually inappropriate, or there was no documentation of ischemia prior to its use.

A key point of this paper, therefore, is that technology use often goes far beyond what the data show with respect to patient benefit. For example, it is estimated that more than one-third of all CT scans are unnecessary. Therefore, it is easy to discern a great deal of inefficiency in the system. The implication is that there is room for improvement in our culture, our practice, and our delivery of health care. A major step would be to begin more systematic data collection and to develop more prospective registries, such as the National Cardiovascular Data Registry at the ACC. Kaiser has large registries. More systematic data collection and analysis would lead to better-informed decisions. More randomized controlled trials—which will require more funding—is in order, as is the development of more observational data. It is important that these data be gathered, analyzed, and incorporated into practice guidelines and reimbursement. Changing practice

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

patterns is much more difficult after they have been established, even with the introduction of new evidence.

In addition, all these data must be more widely available. Currently, it can be very difficult to access large databases. More transparency is needed for these kinds of data.

Finally, there must be more consistent review of the evidence for clinical benefit prior to the routine use of new technologies. A change of culture is needed in this regard so that a technology does not see widespread adoption before the evidence review is complete. This is one crucial way to concentrate healthcare spending so that it yields the greatest possible benefit in actually improving health outcomes. Once a technology has been widely adopted, curtailing its use is extremely difficult, and there are many examples of this point in our healthcare system.

The healthcare system could benefit from a systems engineering approach whereby data collection and review are incorporated into the practice of medicine; the data collection is accessible, easily performed, and inexpensive; and with rapid turnaround, the data can be examined quickly. It is essential to align incentives and reward evidence-driven care.

A LOOK AT THE FUTURE OF CLINICAL DATA SYSTEMS AND CLINICAL DECISION SUPPORT

Michael D. Chase, M.D., Kaiser Permanente Colorado

To date, health care in the United States has not fully leveraged the available clinical data to improve the health outcomes of individuals and populations. From a technology and clinical data perspective, data too often are “locked away” on paper, in various applications, and in isolated databases. Too few practices and hospitals use electronic medical records (EMRs), and usability issues remain. Existing data standards are used inconsistently, as are interoperability standards. Thus the information needs of patients, physicians and care teams, organizations, and the healthcare system as a whole are not being met. Privacy and security concerns persist. More important, the culture of health care presents barriers to the effective use of the data and information. From a process perspective, the complexity of health care has dramatically increased. More people have chronic disease, more have multiple chronic diseases, and the treatments and technologies available have increased. In response to this increased complexity, health care has not taken full advantage of engineering principles that can be used to deal with complex processes. The healthcare environment, with its structure and financing, adds considerably to the barriers.

The above issues limit the effectiveness of clinical decision support. To create a more effective learning healthcare system, the healthcare estab-

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

lishment will need to direct more attention to various areas, including the people and culture, the processes, the data and technology, and the healthcare environment. Integrated delivery systems are well positioned to address these areas and can serve as a model for those that deliver care outside of such systems. Much work remains, however, before a learning healthcare system can be fully realized throughout the United States.

An editorial entitled “Is Information the Answer for Hypertension Control?,” written by Eric Peterson and published in the Archives of Internal Medicine (Peterson, 2008), commented on a paper in that issue of the journal that reported on a study of blood pressure control in a large population of patients with cardiovascular disease. The study found a blood pressure control rate of 95 percent, significantly better than what is usually seen. The editorial suggested that the system described in the paper provided a hint as to how electronic data systems may hold the key to achieving better blood pressure control in the future. It read:

For a moment, imagine you live in a world in which an integrated EMR system was the standard in most community practices … the blood pressure trajectories of hypertensive patients could be easily tracked … feedback reports could then quickly update busy caregivers regarding which of their patients fell short of treatment goals and needed closer follow-up. And as an intervention, such data could be used to provide various incentives for meeting blood pressure control goals…. Taken [one] step further, online pharmacy systems, linked to decision support, could also be used to proactively remind patients and/or alert their physicians if important therapies were consistently missed…. Therefore, in the future, ambulatory information systems could be applied both as a diagnostic tool and as an effective therapeutic intervention. (Peterson, 2008)

The purpose of this paper is to identify some of the barriers to fully realizing such a vision of a learning healthcare system and to discuss how they might be overcome.

The barriers to synthesizing and using information to support enhanced care delivery can be viewed in terms of four broad categories: people and culture, process, data and technology, and the healthcare environment. Challenges from the people standpoint include the prevailing culture of health care with its hierarchical, often physician-centric, and slow-to-evolve team-based approach to care. The clinical leadership needed to address the larger issues in health care is often lacking and not adequately fostered and valued. The skills and training required to use technology and information systems, as well as team skills, need further development. With respect to the process of care, health care has grown in complexity, thanks in part to its complex workflows. One of the major purposes of this workshop is to highlight the underuse of tools that could be adopted

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

from engineering, particularly as they apply to complex systems, such as tools for system design, analysis, and control. Because of the culture and structure of health care, an end-to-end, patient-centered view of the process is often lacking. Care is viewed at the departmental level or from a disciplines frame of reference, as opposed to a continuous view of the care process. As a result, problems with transitions of care between departments or venues of care are magnified.

The healthcare industry lags behind other industries in how information technology is used. Clinical information systems often are not integrated. Data are locked away in various applications, often still on paper, and in various databases. This lack of integration occurs even within organizations; it is far worse across organizations. Data standards and interoperability standards are used inconsistently. This situation is being addressed by a number of public and private organizations, including the Office of the National Coordinator for HIT, the American Health Information Community, the Healthcare Information Technology Standards Panel, and the Certification Commission for HIT. Usability issues remain, and there is continued concern about privacy and security issues. Finally, the healthcare system in the United States presents significant barriers. Most primary care is delivered by relatively small practices, and most specialty care is delivered by individual departments with, as noted above, a lack integration among the various care venues. Healthcare financing and reimbursement reinforce this fragmented care.

In considering what is required to provide clinical decision support that will enhance the care delivered to patients, one needs to take into account both an individual patient-centered view and a population view. Accomplishing this requires getting the right information to the right team member at the right time in the workflow or the decision-making process so as to trigger the right event for the care of an individual patient as well as for a population of patients. Another way of framing this point is to ask, “What sorts of information do the patient, the clinician, and the healthcare team need to meet their agreed-upon healthcare goals?”

A review of clinical decision support published in 2005 in the British Medical Journal concluded that “clinical decision support systems have shown great promise for reducing medical errors and improving patient care. However, such systems do not always result in improved clinical practice, for reasons that are not always clear” (Kawamoto et al., 2005). This observation suggests that we are dealing with a very complex system, one that is not sufficiently understood. Engineering expertise can be applied to better understand the process of care and the application of technology so as to improve the provision of effective clinical decision support.

On this same topic, a 2004 article in the Journal of the American Medi-

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

cal Informatics Association entitled “Some Unintended Consequences of Information Technology in Health Care: The Nature of Patient Care Information System-Related Errors” (Ash et al., 2004) cautioned against unintended consequences of information technology in health care. The paper pointed to potential errors in the process of entering and retrieving information, such as human–computer interface issues and cognitive overload, and it addressed the overemphasis on structured and complete information entry or retrieval. The paper also warned about errors in the communication and coordination process, including the potential for misrepresenting collective, interactive work as a linear, clear-cut, and predictable workflow; the possibility of misrepresenting communication as information transfer; decision support overload; and the loss of prior mechanisms for catching errors. The paper highlights the fact that we do not completely understand healthcare processes and do not fully recognize the disruption that occurs when technology is introduced—underscoring the need for engineering expertise in the process of designing a better learning healthcare system.

What are some general themes regarding effective clinical decision support? Clinical decision support should be carried out in the context of a planned care model—a model that is much more patient-centric, that takes into account process redesign and a team approach, and that is enhanced by information technology. This model differs significantly from the old one-doctor, one-patient, one-exam-room, paper-record model. In approaching clinical decision support, one needs to think broadly across the care team members, including the patient; across the continuum of care; and across the tools and systems available. Some decision support opportunities include

  • reference information and guidance—clinical evidence sources and guidelines,
  • direct-to-patient clinical decision support—availability of information,
  • relevant data presentation—attention to the human–computer interface,
  • documentation forms and templates—integration into the workflow,
  • order entry facilitator—integration of decision support at order entry,
  • protocol and pathway support—a way to facilitate the care process,
  • reactive alert and reminders—used judiciously, and
  • use of clinical data—clinical registries to support the planned care model.
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

Use of clinical data, including clinical registries in the context of team process redesign, is one particular area in which one can often see significant improvement of care at both the individual and population levels.

What might this effective use of clinical decision support look like? Imagine a patient time line that extends for a year. On that time line is a point that represents an encounter. If one enlarges that point, one can see what some of the decision support opportunities are, many of which are currently available at Kaiser Permanente. The systems underlying decision support includes the EMR, online tools that give patients access to their medical records, and clinical registries. The patient time line starts with the appointment process and may include an appointment. A patient questionnaire can be delivered, such as a health risk appraisal or a questionnaire specific to the patient’s condition or disease.

A preventive alert system is available that, at check-in, alerts the patient to needed interventions, thereby activating the patient as well as the care team as the patient moves through the healthcare system. At the rooming stage, the medical assistant can be reminded to address important risk factors, such as assessing smoking status and informing the patient about smoking cessation programs. An array of tools are available to clinicians during this encounter, including reminders for prevention issues, alerts for chronic care issues, and a variety of charting tools supporting the care process and facilitating data entry. Computerized physician order entry is a particularly powerful tool to facilitate clinical decision making. There are also alerts and reminders for those instances in which, for example, a physician may prescribe a medication to which the patient is allergic, that interacts negatively with another medication, or that is contraindicated for the patient’s specific condition. As this process unfolds, one can see that it would be very easy to overload one part of the system, such as the encounter in the exam room between physician and patient. That is why one needs to think across the continuum of care and across the care team members who are available to avoid creating a bottleneck in one part of the process.

At discharge, printing of patient instructions and other visit information can be available for reinforcement and later review by the patient or family members. Decision support can also be embedded in the pharmacy information system, thereby using the pharmacist as another team member in the care delivery process.

Finally, the enhanced system uses clinical registries, which apply data from the EMR as well as other clinical systems. This is one way to enable new models of care, including outreach to patients with needed interventions that can be done outside of the usual face-to-face visit. This availability of information allows all of the care team members, including patients and their families, to participate in the care being delivered.

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

Now let us look at a specific example of enhanced care enabled by information technology. The editorial mentioned at the beginning of this paper referred to a process of care that exists at Kaiser Permanente Colorado (KPCO)—the Collaborative Cardiac Care Service composed of Kaiser Permanente Cardiac Rehabilitation and the Clinical Pharmacy Cardiac Risk Service. It is a service whereby nurses and clinical pharmacists coordinate the provision of cardiac risk reduction activities in patients with cardiovascular disease by supporting and working collaboratively with patients, primary care physicians, and cardiologists. The focus is on activities that have been shown to improve patient outcomes. The service assists patients in managing and monitoring antiplatelet therapy, antilipid therapy, beta-blocker medication, angiotensin-converting enzyme inhibitor medication, blood pressure control, and diabetes management, if applicable. It also provides counsel and support on lifestyle changes. The service follows more than 12,000 patients with cardiovascular disease. Performance levels obtained in this population of patients include an average low-density lipoprotein cholesterol of 78 and average blood pressure of 126/72. More important is that the cardiac mortality of this population has been reduced by 73 percent. Also of interest is that the organization has seen a financial return because fewer patients with cardiovascular disease require rehospitalization or further cardiac interventions.

The development of the KPCO Cardiac Rehabilitation and Clinical Pharmacy Cardiac Risk Service addressed and overcame many of the barriers in the areas of people and culture, process, data and technology, and the healthcare environment that were reviewed earlier, resulting in superior clinical outcomes. The service has many of the characteristics that could be considered components of a model learning healthcare system. With regard to the people issue, KPCO has developed a culture of physicians, nurses, and clinical pharmacists working together and focused on the patient. That collaboration has extended to those who work in information technology. There has been effective clinical leadership on the part of clinical pharmacists, nurses, physicians, and information technologists in the establishment of these services. Clinical staff have focused roles and clear accountability and are trained in their roles, including use of the technology. In terms of process, clear, evidence-based guidelines and clinical pathways are agreed upon by all involved and modified as needed according to new research findings or internal learning. Alternative approaches to care and communication with patients have been more fully exploited with the use of phone contacts, mail, secured messaging, group visits, and direct patient Internet access to medical records, including laboratory results, medications, and patient instructions. There are clear hand-offs and communication with other team members, including primary care clinicians and cardiologists.

On the technology side, KPCO has been using EMRs for 10 years, a

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

necessary but not sufficient measure to support this kind of process. An information infrastructure is required with the capability to aggregate data that facilitate the identification and stratification of populations of patients into the clinical registry. This registry provides the real-time information needed by the team members to properly manage both individual patients and the population. It alerts the team members when needed interventions are due or when they have not been completed, thus ensuring long-term adherence to agreed-upon goals. The registry facilitates the tracking of the performance of the service, providing necessary feedback on its processes. A clearly defined clinical model from an engineering perspective informs the technology approach. Collaboration with information technology enables system adjustments as the clinical model transitions. KPCO is an integrated healthcare delivery system that allows a system-level view. The program design was not significantly constrained by the financing and reimbursement system that currently prevails in the United States.

Is information the answer? Yes, but it is only part of the answer. One cannot think about data and technology without also taking into account people and their culture, focusing on the process of care from the patient’s perspective, and addressing the healthcare environment. In sum, the challenge and opportunity for all who want to see an improved learning healthcare system is to address all of these interrelated components.

CARE COORDINATION AND LINKAGE

Amy L. Deutschendorf, M.S., R.N., APRN-BC, Johns Hopkins Hospital and Health System, Clinical Resource Consultants, LLC, Johns Hopkins University School of Nursing

The current healthcare environment is characterized by escalating systems and patient complexities. The proliferation of new medical information and technologies, increased regulatory oversight, an aging population, and heightened consumer awareness and expectations are all affecting the ability to provide coherent care for patients. The dismantling of traditional care delivery models as a result of cost constraints in the early 1990s has also contributed to the disorganization, fragmentation, and discontinuity of patient care. With as many as 20 healthcare providers per patient, the need for effective communication and collaboration has become more important than ever to achieve quality and safety outcomes. The National Quality Forum has identified care coordination as one of its top national priorities (National Priorities Partnership, 2008). This paper focuses on those structure and process factors that contribute to the current state of discontinuity and fragmentation in patient care. The critical factors in effective care delivery models are discussed, as well as the need for communication models

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

and systems that can provide the accurate and timely transfer of patient information throughout the healthcare continuum.

The current healthcare system evolved from the late 1980s and early 1990s with the broad penetration of managed care in an attempt to respond to economic pressures and manage rapidly escalating healthcare costs. Financial risk was transferred from the payer to the provider, with spending being more tightly controlled and facility service to the acutely ill being limited. To contain costs, acute care facilities sought strategies to improve efficiencies in care delivery, which resulted in widespread restructuring, reengineering, and redesign efforts. These changes had a significant effect on hospital systems, clinical staff, and resources. Hospitals could no longer afford uncoordinated patient care that resulted in ad hoc patient care decisions. Although some new patient care delivery models were proposed that centered care on patients and families, most were more closely related to industrial approaches geared to achieving efficiencies affecting the bottom line. Untested models were implemented without evidence of improved clinical quality outcomes, effective care delivery systems were frequently dismantled, and unskilled workers were substituted for professional staff. There was an exodus of experienced care providers, resulting in shortages in most healthcare disciplines. Ultimately, clinical quality and safety outcomes eroded as a result of a lack of understanding of the complexities of individual human responses to similar stimuli.

In addition to such changes in care delivery systems, other factors played a major role in creating the complexity of the current healthcare environment. New information and medical technology that must be translated into safe practice is proliferating at an extraordinarily rapid rate, making it nearly impossible to determine true priorities for implementation in evidence-based practice. As noted, each patient may have up to 20 healthcare providers, all generating assessments and treatment plans that must be coordinated and communicated. Multiple levels of care must be considered when patients are being transitioned out of the acute care setting, all with different rules for admission and reimbursement. Although the average length of a patient’s hospital stay has decreased by 23 percent over the past decade, the severity of illness has increased by 12 percent, necessitating improved assessment and monitoring strategies. Twenty-five percent of a hospital’s census may turn over in a 24-hour period, adding to increased patient care unit activity and the need for accurate coordination of services and resources. As many as 62 percent of hospitals report operating over capacity. The increased collection and public reporting of quality and safety data, sanctioned by regulatory agencies as a means of demonstrating organizational performance, is contributing to health systems’ administrative burden and threatens to distract caregivers from a focus on the bedside.

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

As the average length of stay decreased, it was estimated that half of American hospitals would close by the year 2000, but this projection underestimated the effect of the aging population. Today, it is not unusual for an 85-year-old with chronic health conditions to be living at home, still driving, with a spouse who also has chronic disease and has nowhere to go after a catastrophic illness. The health needs of the aging population were not fully anticipated when the Balanced Budget Act was passed in 1997, reducing Medicare payments to skilled and long-term care providers. (Some of these cuts were reinstated under the Balanced Budget Reform Act of 1999 and the Budget Improvement and Protection Act of 2000.2) As a result, elderly patients may consume more acute care resources and have longer lengths of stay while awaiting appropriate transition to another level of care because no public funds are available to support assisted living and long-term care.

Today the objectives of acute care are “stabilization and transition,” admitting only the sickest patients and focusing on preservation of their functionality. Although these objectives are significantly different from those of just 20 years ago, patient care delivery processes have not changed dramatically, even as the increased severity of illness demands significant transformation. As noted, it is not uncommon for a quarter of a large academic hospital’s patients to have a length of stay of 24 hours or less. The impact of this shortened length of stay combined with the increased severity of illness is that healthcare practitioners must accurately assess, evaluate, and treat patients during this time frame. There is a disparity between the expectations of the acute care environment and those of the regulatory agencies. The acute care setting is frequently viewed by regulatory bodies as the point of access for all current and historical patient problems, when such attention to patient needs is more appropriately the purview of the ambulatory care environment. The healthcare provider has a more limited exposure to the patient in the acute care setting than in any other setting. Yet it is expected that all biopsychosocial, economic, and developmental problems the patient has ever experienced will be addressed and documented in this setting, at the same time that the healthcare providers are employing preventive strategies and facilitating healthy behaviors in the future. More focus is required on how to improve access to ambulatory care departments, where the appropriate objectives are health promotion, illness prevention, and stabilization or improvement of function.

The complexity of patient populations has changed radically, in part because of increased life spans, greater prevalence of chronic illness, and expanding consumer expectations. Healthcare consumers are armed with

 

images

2 H.R. 2015 [105th]: Balanced Budget Act of 1997; H.R. 5661 [106th]: Medicare, Medicaid, and SCHIP Benefits Improvement and Protection Act of 2000.

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

Internet information and resources, arriving at appointments with their physicians well prepared with questions, background information, and specific suggestions about their preferred treatment. Often, they present their findings in the form of demands, having decided in advance what the best therapy is. Healthcare providers have not fully anticipated the implications of an elderly, sicker consumer and have not implemented methods to manage unrealistic expectations. Education needs to be provided actively to patients and families so they know that although they are likely to live longer, they will do so with chronic disease. They must be counseled about their responsibilities for healthy behaviors and understand that the current armamentarium of diagnostics and treatments cannot “cure” those chronic conditions, but will at least help preserve functional status. Preparing patients and families for realistic end-of-life decisions is more important than ever, given the increased healthcare costs associated with futile care.

The demand by payers, consumers, and purchasers for demonstration of outcomes has led to a greater prevalence of regulatory standards and oversight focused on improving quality and safety outcomes. Reduced practice variation through evidence-based care and fiscal responsibility through cost-effective strategies are expected to be transparent to the consumer and payer through the provision of specific and quantifiable information. Pay for performance, an incentive-based concept that rewards healthcare organizations that can demonstrate improvement as defined by outcome indicators, may create even more stress on an institution that has limited financial resources to divert to quality initiatives. Unfortunately, the number of mandatory initiatives and reporting requirements not only may tax an organization’s financial and human resources, but also may ultimately contribute to a lack of progress in reducing adverse events—or worse, create an increase in unanticipated serious outcomes. The “risk of abundant quality” may be described as a situation in which changes conceived as important and beneficial by all stakeholders are implemented but result in unexpected new hazards, including increased direct and indirect costs, new errors and adverse events, and lost opportunities elsewhere (Warburton, 2005).

An example is the increase in redundant pneumococcal vaccinations for hospital inpatients as acute care facilities attempt to comply with Joint Commission Core Measures. Although evidence regarding the safety of multiple revaccinations is inconclusive, increased adverse events have been reported (Shih et al., 2002). Additionally, processes such as pneumococcal vaccination, smoking cessation, and influenza vaccination are more appropriately applied and measured in the primary care setting.

At the same time, both public and private payers are devising new and inventive ways to avoid payment for services that have been provided. Although the goal is to give providers incentives to ensure the medical necessity of therapies and appropriate levels of care, the result has been

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

an expectation that providers will do more with less. Under the Medicare Modernization Act of 2003, it is expected that CMS will collect billions of dollars in perceived overpayments for services that were rendered in good faith by dedicated providers. The newest strategies to deny reimbursement for therapies for complications of illness perceived to have occurred in the hospital are ill conceived, for in many cases these complications were not truly preventable (Pronovost et al., 2008). The imperative to appeal reimbursement decisions has resulted in increased administrative burden and greater healthcare costs borne by the healthcare provider.

Few data showing true safety improvement have emerged over the past 8 years since the publication of the Institute of Medicine (IOM) report To Err Is Human (IOM, 2000). Failure to rescue, described as “death due to complications of serious illness and disease” (Silber et al., 1992), remains a serious problem. It is plausible that the ratio of safety–risk (adverse events related to the implementation of safety or quality initiatives) to safety–improvement may actually be increased as a result of the complexity of the environment, numerous and sometimes random regulations, and the lack of proven systems and processes to address the ways in which patients receive care. There needs to be a focus on the provision rather than the demonstration of quality care, with the application of research findings to support structures and processes linked to quality and safety outcomes.

These and related issues have combined to create a crisis in the way care is provided to patients. Operationally, care delivery may be defined as the way in which providers and services are deployed to meet patient and family needs over the continuum of care. Research that links clinical outcomes with patient care models is woefully lacking, and current models of care, characterized by a “siloed” mentality, continue to reflect the mindset of an industrial age. Fundamental processes of care have not changed to accommodate the complexities of healthcare systems and patient illnesses, which include rapid changes in condition and limited exposure. Provider shortages are evident in all specialty areas and are projected to worsen over the next 10 years, with demand significantly exceeding supply because of increased consumption of healthcare resources by an aging population. Patient care in the acute setting is frequently organized around physician service lines rather than the patient. Nurses and other providers have become task oriented as a result of the emphasis on productivity and the increased regulatory demands for documented compliance with standards. Staffing patterns that have been adopted because they are economically efficient, such as 12-hour nursing shifts, have led to care that is more fragmented and has less continuity. Patient care planning is unidimensional and uncoordinated as a result of poor communication among providers, patients, and families and across levels of care. Hand-offs between providers, from shift to shift and across transitions, are insufficient and frequently result

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

in poor patient outcomes caused by “lost” information. The medical record, which traditionally was the one repository of all patient information and which told the patient’s “story,” is often incoherent as organizations try to translate a paper record into an electronic format and end up with voluminous, redundant, and inefficient information technology solutions. Other technological solutions designed to improve safety outcomes, such as physician order entry, may in fact increase errors because of the lack of a systems approach to planning and implementation.

In the final analysis, the current healthcare environment reflects an overwhelming lack of coordination and continuity of care. The plan of care is often a “secret,” with different providers having discreet and important pieces of the puzzle that are known only to them and from which the patient and family are excluded. As noted earlier, the National Quality Forum has adopted care coordination as one of its national priorities. The forum defines care coordination as activities ensuring “that the patient’s needs and preferences for health services and information sharing across people, functions and sites are met over time” (NQF, 2009). New models of care delivery must be developed to reflect future objectives, as indicated in Table 3-2.

If care delivery systems are to be redefined to meet prospective healthcare demands for improved clinical and financial outcomes, there must be a dramatic change in healthcare culture from siloed to systems thinking. All patients should expect to have their care managed. Care management should reflect a systems model defined by a multidisciplinary, collaborative practice approach integrated into patient care delivery. The major elements of redefined care delivery systems must be centered on communication, collaboration, coordination, and continuity. New structures and processes must be built to support these elements.

Strategies must be implemented that support frequent, real-time, mul-

TABLE 3-2 Patient Care Delivery Transition


Old Approach New Approach

•   Focus is on the high-risk patient

•   Focus is on all patients

•   Episodic acute care is the priority

•   Continuity of care across the care continuum is the priority

•   Healthcare professionals work in isolation

•   Collaboration among healthcare team members is required

•   Care planning is conceptual

•   Care planning is aggressive and results oriented, and prevention is important

•   Provider infrastructure is fragmented, and information systems are not integrated

•   Provider infrastructure is fully integrated


Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

tidisciplinary communication with all patients, through all transitions, including the family as well as the patient. It has been reported that 70 to 80 percent of all healthcare errors are caused by human factors associated with interpersonal interactions (IOM, 2001). Even the simple approach of having all members of provider teams take part in daily multidisciplinary rounds with all patients can improve patient care planning, expedite care delivery, and reduce fragmentation. Care planning must be truly interdisciplinary, with all providers sharing information, contributing to the coordination of care, and being accountable for patient outcomes. Although it is essential that care planning be collaborative, one physician should be identified as “team captain” and be responsible for synchronizing consulting services and the provision of resources. This physician would also be responsible for providing a sensible interpretation of information to patients and families. Traditional paternalistic approaches by healthcare providers toward patients and families need to be replaced with partnerships that empower patient and family decision making.

New models of care must be based on evidence, must reflect intra- and interepisodic domains, and must include provisions for seamless transitions between episodes. Rigorous research is needed on the relationships between care delivery models and associated quality and safety outcomes for the appropriate levels of care. Quality and safety indicators should be measured in the correct environments so they do not distract care providers from the focus of the patient’s problem and the objectives of the care setting. A patient who is critically ill in the hospital is unlikely to benefit from smoking cessation education, for example, yet the provider is required to at least address this standard through documentation. Provider roles must be clearly defined and carried out according to patient characteristics and the required provision of services to improve efficiencies and avoid duplication. Procedures for ensuring competency should be consistently tested and implemented, and they should reflect the dynamic changes in medical information and technology. The idea of technology as a panacea for patient safety should be tempered with careful analysis, planning, and evaluation. Nurses and other bedside providers have become so reliant on equipment and electronic data that clinical correlation may be nonexistent, resulting in increased errors and adverse events (Bates, 2005; Karsh, 2004; Rotschild et al., 2005). Although technology is a necessary component of the armamentarium of adjunctive patient safety structures, its impact may be negligible if not actually detrimental unless there is proper clinical interpretation.

Processes of care must be realigned around the patient and family. Provider teams should be centered at the unit of care to improve communication and coordination and expedite care delivery. Traditional academic models that may result in as many as 10 medical services treating patients on 1 unit should be reevaluated so that similar patients and provider ser-

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

vices can be aggregated in single locations. Because patient conditions are rapidly changing, increasing the frequency of monitoring and surveillance is paramount to improving safety and reducing adverse events. Typical nursing processes, such as taking vital signs every 4 hours or offering assessments every 12 hours, are no longer necessary in an environment where the patient’s status is continuously changing. Assessments focused on the problem for which the patient was admitted—and which must be resolved to facilitate the next transition of care—should be performed frequently and should relate to evidence-based guidelines. Coherent documentation systems must be developed that do not merely translate a paper record into an electronic format, but rather reflect the patient’s story and make it possible for that story to be shared across levels of care. The implementation of disparate documentation systems that do not “talk” to each other should be discouraged, if not eliminated altogether.

To accomplish the overarching and major systemic changes in patient care delivery required to achieve true improvements in quality and safety, certain healthcare traditions must be addressed. These traditions exist in all disciplines and in each patient care environment. Whether they have to do with academic teaching rounds or nursing reports, they reflect structures that worked in an age when patients might have been admitted to the hospital for diagnostic tests, and an average length of stay might have been 7 to 10 days. Systems engineering principles should be implemented to engage departments and professionals in the creative thinking needed to address today’s patient populations in all care settings. True change can occur only with appropriate preparation that engages all stakeholders and addresses the system components that may be affected. The only constant in today’s healthcare system is change, and our ability to anticipate and plan, rather than react, will determine our ultimate success in the achievement of healthcare outcomes.

TRANSFORMING HOSPITALS THROUGH REFORM OF THE CARE PROCESS

Ralph W. Muller, M.A., University of Pennsylvania Health System

The care and service processes in American hospitals, the most complex institutions within the American healthcare system, need to undergo a transformation. Numerous reports have shown that complexity can be reduced and performance improved through careful evaluation of the systems underlying important care and administrative processes within hospitals. This paper focuses on three successful transformations within UPHS, each in an area that causes significant patient frustration:

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
  • patient billing—reorganizing billing systems for greater efficiency and improving reporting systems so as to be able to provide more effective feedback to employees;
  • patient access to physicians—reducing patient waiting times and easing the scheduling of physician appointments; and
  • in-patient progression—reducing complexity to streamline the course of treatment during hospital stays.

These case studies highlight several themes:

  • issue definition—defining the issue clearly to lay the groundwork for the fundamental transformation of work required to effect lasting change in complex systems that are built within entrenched cultures;
  • constant vigilance—monitoring progress and results on a daily basis to ensure that old patterns are not repeated; and
  • structured rewards—using incentives to reward improvement and maintain changes in a complex system.

Billing

A common patient complaint concerns hospital billing. Patients and their families often cannot understand their bills, question the fees charged, or object to long delays between the date of service and receipt of the bill. Often the tendency within the hospital is to blame the finance office, which sends the bill, but in fact the bill generated is the result of a multistep process that commences before the patient is even provided care. As shown in Figure 3-1, the typical hospital billing process is complex, and breakdowns can and do occur at many points. For example, if incorrect insurance information is collected on admission or if there is an error in medical chart abstraction defining the patient’s services, the final bill will be wrong.

Through a systematic review of the billing process, UPHS found that the component functions operate in silos, with no clear connection between the people who register patients at intake and those who prepare and send out the bill after discharge. This situation led to an enormous amount of rework and frustration among employees, who had limited tools with which to ensure that the right bill went to the right person at the right time.

Several corrective actions were taken. First, it quickly became apparent that there was a body of expertise around the billing of Medicare, Blue Cross Blue Shield, and other major insurance carriers. To interface effectively with each of these carriers, UPHS reorganized its billing function by payer, rather than by medical specialty. This redesign was complemented by

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

images

FIGURE 3-1 Revenue cycle engineering.

 

changes in information technology that helped organize and prioritize the work of frontline employees and their managers. In addition, productivity and quality measurements were built more explicitly into job descriptions, evaluations, and incentive systems for the staff.

Reporting systems that give feedback on performance on a daily, weekly, and monthly basis are critical. To this end, UPHS implemented a system that provides granularity of information, so that information at the level of the frontline employee completing a billing form can be evaluated by the supervisor, by the operating unit, and across the system. UPHS can summarize and drill down on particular billing information so that the information is presented at the transactional level to the frontline employee, but it can also be summarized for the chief financial officer and CEO as needed. This is a critical element added to the billing transaction system: the ability to aggregate underlying information to support different levels of review.

As a result of these changes in the UPHS billing system, annual recurring income improved by $57 million, or 2 percent of revenue—a considerable gain when hospital margins of 3 or 4 percent are difficult to secure. The process and system changes implemented also yielded productivity improvements equivalent to 20 staff.

Access to Physicians

A second transformation effort at UPHS focused on increasing patient access to physicians. With more than 1,000 physicians practicing at 150 sites, UPHS is a large regional provider of specialty physician services, and

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

it has experienced marked increases in demand for these services. As in other large physician groups that lack standardized processes, the increasing demand was challenging UPHS’s ability to serve patients. For example, a review of practice call records revealed that 22 percent of the total phone calls received (300,000 per year) were not answered by a person. Another service and efficiency issue was the high frequency of “appointment bumping,” or the cancellation of a visit by a physician or patient. Analysis revealed that making appointments with long lead times, which at UPHS were often 60 days or more, resulted in a 50 percent or greater likelihood that either the physician or the patient would cancel the appointment. Rescheduling patients after a cancellation required a great deal of extra work.

To tackle such service and efficiency issues, UPHS evaluated the full continuum of its care process, including access and scheduling of appointments, availability, patient flow during and after the visit, and follow-up. The evaluation engaged all caregivers. As noted above, for appointments scheduled more than 60 days in advance, there was a 50 percent chance that either the doctor or the patient would cancel, so scheduling appointments within 6 to 10 days of a request became a key focus. The result has been increased patient satisfaction, as well as less staff time spent rescheduling appointments. UPHS also found that when a physician cancels a patient’s visit, especially more than once, the chances are three to four times higher that the patient will miss the visit (Figure 3-2). UPHS educates its doctors about this statistic, and it has implemented a series of policy and process changes to reduce the frequency of cancellations.

Another focus of the effort to improve patient access to UPHS practices was an evaluation of capacity—again taking a systems approach to the processes of care. Understanding capacity use across all dimensions— examination rooms, providers (physicians, nurse practitioners), and clinical and clerical staff—helped pinpoint opportunities to increase outpatient capacity and address patient service problems. In some cases, there was a 50 percent difference between provider capacity and actual activity. For example, patient demand to see a doctor on a Tuesday or Wednesday significantly exceeded capacity, while there was excess room capacity on Friday afternoons. Incentives to encourage the use of rooms in off-peak periods have been instituted, and exam room, provider, and clerical staff capacity has been harmonized to reduce mismatches and increase effective capacity use.

In addition, patient intake processes have been redesigned so that the front office performs rapid check-in and collects all critical patient information via the EMR, including patient histories, medication management, and chief complaint lists. Patient flow within the practice has also been enhanced through the use of patient tracking systems and process changes

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

images

FIGURE 3-2 Impact of physician-initiated appointed rescheduling on patientinitiated cancellations and no-shows.

 

that encourage patients to complete all requests (e.g., prescription refills) during the visit, rather than in follow-up.

UPHS also tracks relevant metrics across various clinical departments. Sharing comparative data with physicians, most of whom strive to be among the best, has spurred internal competition to achieve and demonstrate improvement.

Inpatient Stays

As is the case with many acute care hospitals in the country, occupancy rates at UPHS are very high, with patients occupying 90 percent or more of the hospitals’ beds on average. The result is bottlenecks in the emergency room and difficulty in accommodating regional referrals. Because building new beds is expensive—approximately $2 million a bed in Philadelphia— UPHS focused on optimizing the flow of patients in its hospitals. This systems change has made it possible to treat admitted patients more expeditiously, which is both better for patients and their families and consistent with demands insurers are placing on hospitals.

The complex patient flow process was broken down into its component parts, with a focus on not only the steps just prior to discharge but also the activities that occur before and during the stay. The patient flow process encompasses the initial referral, insurance verification and the logistics of obtaining a hospital bed, medical management once the patient has been admitted (e.g., the turnaround of lab and imaging results and medication

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

management), planning for the discharge with the care team and family, and finally, turning around of the bed for the next patient.

UPHS undertook a major analysis of these processes and related bottlenecks, leveraging information technology to enhance the availability of information and streamline processes. An electronic board that tracks the status of inpatients in the hospital enables every doctor, nurse, resident, transporter, and housekeeper to access detailed information and keep track of each patient. The board displays easy-to-read icons that indicate whether a patient is in imaging, whether his or her lab results are available, whether prescriptions have been written, or whether the patient needs to be discharged. That information, once gathered by one caregiver, is now known by all, which saves time and frustration and enables caregivers to manage the process more effectively. Giving the critical information to staff members allows them to focus on being doctors, nurses, social workers, or transporters rather than wasting time tracking down information that is already available. This initiative created the equivalent of 17 new beds, avoiding $34 million in construction costs and improving the patient, family, and physician experience.

Lessons

The transformations described above offer several key lessons:

  • Use data and analysis to identify opportunities and motivate change. It is necessary to break down complex processes to understand their component parts, to identify where breakdowns occur, and to make all members of the team aware of the issues. In the billing process, for example, the critical process steps turned out to be at the front end rather than the back end. In increasing access to physicians, the critical element was to manage the balance based on the availability of the physicians, examination rooms, nurses, and clerical staff. To advance patient care processes inside the hospital, it was critical to track the key steps in the patient journey from admission to discharge, sharing information in real time with all caregivers.
  • Redesign workflows and restructure roles, integrating information technology. Each of these cases relied on redesigned workflows and restructured roles for the staff, with extensive use of information technology to facilitate the restructuring. For example, the restructuring of work in the patient billing process was supported by new tools that prioritize the daily work of frontline staff and aggregate decision support information for management at all levels.
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

    The physician access initiative was integrally associated with EMR implementation, which enabled a streamlined collection of patient information. The electronic patient board that tracks patients throughout the hospital stay and provides status information to all caregivers has been a critical element in improving the management of inpatient stays.

•   Establish goals and monitor performance in real time. In each of these efforts, critical metrics were identified and tracked on a daily basis. Any authorized staff member can access the same data at the appropriate level of granularity—per patient, per unit, or per hospital.

•   Create meaningful incentives for physicians, management, and staff. Efforts to redesign the care processes at UPHS were integrated into the overall management plan of the organization. For example, all UPHS administrators, including the CEO, academic department chairs, and every member of senior management, have related goals that are written into their individual and team plans. Metrics related to each of the processes discussed are reflected in incentive plans for middle managers as well. Consequently, for more than 1,000 of UPHS’s 13,000 employees, these processes are incentivized through compensation plans. Other recognition programs, such as quality awards, are also used to encourage doctors, nurses, and other clinical staff to move these transformation efforts forward.

 

The care transformation that has been achieved at UPHS is an example of how to manage the complex American healthcare system, one institution at a time, by bringing more accountability into the system.

A PERSPECTIVE ON PATIENT-CENTRIC, FEED-FORWARD “COLLABORATORIES”

Eugene C. Nelson, D.Sc., M.P.H., Elliott S. Fisher, M.D., M.P.H., and James N. Weinstein, D.O., M.S., The Dartmouth Institute for Health Policy and Clinical Practice at Dartmouth Medical School and Dartmouth–Hitchcock Medical Center

“It is important to note that clinical work doesn’t have to be done at the expense of scholarly work. They should be and need to be done together.”

James N. Weinstein, D.O., M.S.

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

This paper is intended to respond to a bold charge issued by the organizers of this workshop:

To highlight complexities in and impediments associated with generating clinical information and knowledge, as well as to reflect on systems changes or incentives that might address the various asymmetries and barriers to use of clinical data for health learning.

The paper responds to this charge by first briefly describing the nature of the problem; then explaining the fundamental idea, providing a case study to demonstrate how the idea can work in the real world; and finally, outlining a path forward for enacting the proposed solution, taking into account some of the impediments and complexities that may arise. As suggested by the above quotation from Weinstein, a basic premise is that the intelligent design of health information systems can unite clinical practice with clinical research and contribute powerfully to a learning healthcare system, with everyone learning from his or her own practice base.

The Nature of the Problem

This section begins with a case study (fictitious name, but based on a real situation) that illustrates the nature of the problem:

Terry Adams, author of a best-selling management book, was a 62-yearold business school professor with a history of disabling low back pain. He had experienced six prior flare-ups in the past decade. When he had an episode of back pain, he had excruciating pain that made him unable to function for days or weeks at a time. Over the years, Professor Adams had received episodic treatments by different clinicians in different practices and had concluded that nothing would work to prevent the problem. He stated: “No one knows what causes my flare-ups, treatments have not worked—except some reduced the pain in the short term. If there is such a thing as best-in-the-world care for people like me, none of the doctors or clinicians that I have seen seemed to know it! They usually respond to my questions about what works best with a phrase like, ‘Well, Terry, in my experience…. Blah blah blah.’ Where’s the evidence? What actually works best for people like me? Moreover, it appears that the doctors and practitioners do not talk to each other, do not look at my past treatments, and do not know what treatments I have gotten, nor have they reviewed the results of the numerous x-rays and CT scans that I have had over the years. Every time I have a new and severe back problem, we start all over from scratch… history, physical, x-rays, CT scans, with no one learning anything from my earlier treatments and apparently no good research to know what treatment is likely to work best for a person with my condition. When I put on my business school hat and think about costs, I would guess that I have cost my Blue Cross plan about $55,000 on ED [emer-

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

gency department] visits, office visits, medications, physical therapy, and spine surgery, not to mention that my time lost from work might run about 120 days in the past 10 years, which would conservatively add another $120,000 in indirect costs associated with lost productivity.

This case represents a common situation: the presence of disconnected, partial, non-patient-centric data and information on the patient’s health status and how it has evolved over time, plus limited information on prior healthcare experiences and the associated treatments and outcomes. This state of affairs is bad for patient care, bad for practice-based learning and improvement (a core competency of today’s physician), and bad for clinical research and health professional education.

The case demonstrates some of the complexities and impediments associated with generating clinical information and knowledge for improvement, research, and learning. This is all too often the current state of affairs in the world of healthcare information systems. In general, (1) data do not follow the patient over time; (2) data are not turned into information to guide treatment, even though both the evidence base and information about the patient’s personal preferences and values at the point of care and in the flow of care offer important guidance on treatment decisions; (3) data are not turned into information to make it possible to learn from every patient for retrospective or prospective research; (4) data systems inside organizations often are not integrated or interoperable across organizations; and (5) data entry often is not standardized, making it difficult to ascertain the diagnoses, the comorbidities, the severity, the diagnostic tests ordered, and the treatments prescribed, and to track the health outcomes and costs over time that are associated with the inputs (patient factors) and processes (treatment factors) of care. For all of these reasons, the healthcare system suffers a variety of information problems:

  • inadequate information for high-quality, patient-centric clinical care;
  • inadequate information with which to understand and improve the process of care;
  • limited quality and cost measures to support public reporting on quality and value; and
  • inadequate information for patient-based outcomes research.

If we cannot understand patients within our systems of care, how are we going to improve? Perhaps these problems can be overcome by designing data-rich, patient-centric, feed-forward information environments with real-time feedback using a novel approach that is described below. The challenge to be overcome is depicted in Figure 3-3. The feed-forward data

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

images

FIGURE 3-3 Feed-forward data challenge.
NOTE: AMC = academic medical center
SOURCE: Eugene C. Nelson and Trustees of Dartmouth College.

 

challenge is to keep the data connected to the individual patient and to the population of patients as they travel through the healthcare system. For example, during an illness patients receive services from different sites, such as primary care, specialty care, home health care, a community hospital, or an academic medical center. The objective is to turn an individual’s data into useful information that can guide intelligent action and to aggregate this patient-level information to show quantifiable results within the clinical microsystem, the healthcare macrosystem, and the community.

The Fundamental Idea

The fundamental idea is to embed feed-forward information systems— with real-time feedback—into the flow of clinical care in frontline “clinical microsystems,” meaning the places where patients, families, and caregivers meet—the places where care is delivered and where outcomes and costs are produced (Nelson et al., 2007a). The terms “feed forward” and “feedback” are described below:

  • The term “feed forward” refers to designing an information system to collect patient data in real time as care is delivered. The data collection occurs from the first visit, and the data move with the patient as personal information. The data are always available and displayed in a useful format as the patient’s healthcare experiences continue. In such a system of care, patients and providers can understand what they need to know, and patients are more likely to receive the right care at the right place at the right time, every time, based on accurate information and their own preferences.
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
  • The term “feedback” refers to designing an information system at the level of the individual patient to accumulate these historical data in order to form subpopulations of patients, and it also refers to displaying patient and physician data for the prospective management of individual patients who are in the care system. Feedback is also necessary for the evaluation, management, and improvement of individual patient care. The information can then be rolled up to better understand populations of patients cared for by clinical programs. Furthermore, at no additional cost, the information provides a database that contributes to basic, translational, outcomes, and evaluative research and to health professional education (promoting practice-based learning and improvement as well as systems-based practice). This real-time feedback system closes the loop, with an active improvement process being part of a patient-centered, integrated clinical practice.

Figure 3-4 illustrates the feed-forward and feedback concepts in the context of a single clinical microsystem. In general, a patient enters a clinical microsystem and receives an orientation to that particular system.

images

FIGURE 3-4 Feed-forward and feedback in the context of a general clinical microsystem.
SOURCE: Eugene C. Nelson and Trustees of Dartmouth College.

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

Then an initial health assessment is conducted, which leads to a plan of care based on that patient’s health status, needs, and preferences. Many patients enter a system with appropriate indications for consideration of a diagnostic or therapeutic intervention, but appropriateness does not mean a patient prefers or wants the intervention. In “either–or” clinical situations, an approach must be used that is consistent with both best evidence and patients’ preferences and values. This approach can be facilitated by feeding forward patient-based data on demographics, family history, clinical status, functional status, and expectations for desired health outcomes based on an individual patient’s values and preferences, while healthcare costs are captured as important information for use in considering both the efficacy and efficiency of care. The clinician completes the assessment based on the patient’s medical history, a physical examination, and diagnostic tests, all of which contribute to a patient-centric plan of care. The patient care plan will include a blend of services—preventive, acute, chronic, and palliative—based on the patient’s current needs and preferences and on the success of the care plan at producing desired outcomes efficiently. These measures work best when collected longitudinally as part of normal clinical practice. They often include the patient’s clinical status, functional status, and perceptions of the care received relative to the patient’s needs, in addition to tracking other measures of direct and indirect costs of care for a given episode of illness. This information can then be used in a feedback mode to evaluate care for populations of patients and to improve care in specific clinical settings, and it can be incorporated in a database for research and education.

Of course, many patients with challenging and costly healthcare problems receive care from more than one clinical microsystem as the illness episode evolves. For example, a person who suffers an acute myocardial infarction (AMI) may receive care in a number of frontline microsystems, such as an ED, a coronary catheterization laboratory, a coronary care unit, and a cardiac step-down unit. This patient may receive follow-up care from a cardiac rehabilitation program, a cardiologist, and a primary care physician. Like Professor Adams in the above case study, the person may have concomitant conditions (e.g., back pain) with their own episodes. If one wishes to evaluate the success of care provided to a particular AMI patient—or for a population of patients who have comparable coronary events, such as ST3-elevated myocardial infarctions—one will need to follow the changes in health outcomes (clinical, functional, patient perceptions) and costs as the outcomes evolve over time (e.g., at 30 days, 3

 

images

3 The ST segment is the part of an electrocardiogram immediately following the QRS complex and merging into the T wave.

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

images

FIGURE 3-5 Data challenges: embed, feed forward, generate, and cascade.
SOURCE: Eugene C. Nelson and Trustees of Dartmouth College.

 

months, 6 months, 12 months after AMI) and be cognizant of concomitant illnesses and adjust for their impact.

Figure 3-5 illustrates this common illness episode situation in the context of a multilevel healthcare system serving a community. The AMI patient is moving “horizontally” through frontline clinical microsystems over time. The collection of microsystems that contribute to the care of the AMI patient can be viewed as a cardiovascular mesosystem, which is often part of a larger healthcare system (i.e., a macrosystem), such as a community hospital or academic medical center. This common situation poses several daunting challenges to the design of health information systems that contribute to patient care, research, and education while delivering the best possible results in the most efficient manner. The data challenges can be summarized by the phrase “embed, feed forward, generate, and cascade.” Again referring to Figure 3-5, which portrays the healthcare system by blending “horizontally linked clinical microsystems” with “vertically organized healthcare delivery systems,” we can see that there are three fundamental challenges to the design of high-utility healthcare information systems:

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
  • Design the information system to feed forward and to cascade patient-level data to work at different levels of the healthcare system—micro, meso, macro, community, and region.
  • Embed the evidence base and decision support—for patients and caregivers—in the flow of clinical care to enable the right care, consistent with patient preferences, to be delivered at the right place and at the right time, every time.
  • Generate accurate data from the care process to be used for clinical program improvement, biomedical research, health professional education, and transparent public reporting on health outcomes and costs of care.

The core assumption is that in the design of high-utility EHRs it is not enough to have standardized nomenclature for the essential elements of care (tests, diagnoses, procedures, medications, and so forth). One must also have patient-centric, feed-forward, and feedback information systems to manage patients, improve processes, and serve as a research database for learning how to reliably produce better health outcomes, higher quality, and better value. Without this information, the EHR is not patient-centric, nor does it exemplify a learning healthcare system. The key term here is “patient-centric,” which requires

  • measurement of health status and outcomes that are consistent with the IOM’s definition of health,
  • the ability to follow patients over time as they move in and out of different parts of the healthcare system and to enable aggregation of data at different levels of the system (micro, meso, macro, community, and region), and
  • use of patient reports as well as clinician reports of health status and health-related data in a consistent manner.

The term “health” is often used without an agreed-upon definition, but it is important to define exactly what the term means if one intends to design a “health” information system. The IOM has defined health in this way: “Health is a state of well-being and the capability to function in the face of changing circumstances. Health is a positive concept emphasizing social and personal resources as well as physical capabilities” (IOM, 1997). Improving health is a shared responsibility of healthcare providers, public health officials, and a variety of actors in the community who can contribute to the well-being of individuals and populations.

If one wishes to measure, study, and improve the outcomes and costs of care, it is also important to have an agreed-upon framework for defining

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

images

FIGURE 3-6 Value compass framework for measuring outcomes and costs of care and demonstrating the need for patient- and clinician-reported data.
SOURCE: Eugene C. Nelson and Trustees of Dartmouth College.

 

what is meant by outcomes and costs. One useful paradigm for defining and measuring outcomes and costs is the clinical value compass, which is shown in Figure 3-6 (Nelson et al., 1996, 2007b). The value compass approach suggests that the quality of patient care outcomes can be measured by focusing on three domains—clinical, functional, and satisfaction against need—whereas the costs of care can be captured in a fourth domain, which is measured by determining the direct costs of providing care to patients and the indirect social costs patients incur by being ill or injured and receiving care. Consequently, one way to measure the value of patient care is to assess quality in relationship to costs over time. A careful examination of the data required to measure quality in relationship to costs reveals that some areas can best be measured on the basis of clinician-reported data, some on the basis of patient-reported data, and some on the basis of billing data. These “best sources” are specified in Figure 3-6.

To summarize, the fundamental idea is that if we wish to have an information system that can generate clinical information and knowledge and that can create the conditions necessary to build a learning healthcare system, we will need to design feed-forward and feedback information systems that can

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
  • be embedded in the flow of patient care and can enhance patient care, research, and education and capture patient- and clinician-reported data in a standardized way;
  • aggregate data horizontally to capture outcomes of patients and populations over time, and aggregate data vertically to portray quality and cost outcomes at different levels of the system—micro, meso, macro, community, and region; and
  • be responsive to the IOM’s definition of health, which emphasizes the functional health status of the individual and reflects the social need to increase the value of care by providing better-quality results in a more efficient manner.

The next section offers a case study to demonstrate that these demanding requirements for designing this kind of information system can be met in the real world of health care.

A Case Study

To explore the fundamental idea presented above, this section presents a case study involving the Dartmouth Spine Center, the collaborative National Spine Network, and a unique randomized controlled trial sponsored by the National Institutes of Health (NIH)—along with a simultaneous observational and preference-based cohort study—that involved evaluating the effectiveness of alternative methods for treating the most common spinal problems. The case study started when James Weinstein came to Dartmouth in 1996 to lead the orthopedic surgery program. He is an orthopedic surgeon with interests in basic research on pain, outcomes research, and patient-centered, shared decision-making research (Weinstein et al., 2000).

Upon coming to Dartmouth, Weinstein had the opportunity to start an innovative interdisciplinary program for back and neck care and to design it from the ground up—such a program had not existed at Dartmouth, and even today, 12 years later, still may not exist anywhere else. Part of the plan for what would come to be called the Dartmouth Spine Center was to build a real-time, feed-forward information environment, using the clinical value compass framework, that would actively contribute to better patient care, better research, and a better learning environment. This information environment was built for primary and subspecialty care, all delivered and integrated within the same home, addressing a multidimensional set of clinical problems with an interdisciplinary, patient-centered approach and incorporating patients’ values and preferences.

In planning the Spine Center, it was decided that the mission would be patient-centric: to get people back to work and back to play, one back at

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

a time. The vision for the information system was to implement the feed-forward idea in real time, embedding standardized methods for patient- and clinician-based reporting into the flow of care. Every time a patient was seen at the center, a database would accumulate information for (1) achieving better shared decision making by both patients and clinicians; (2) accomplishing better care planning to match needs, preferences, and the evidence base to treatments selected; (3) monitoring the effect of care on individual patients using standard metrics; (4) improving the center’s ability to track outcomes and to use this information for improving care; (5) contributing to the National Spine Network, a comparative database involving more than 25 clinical programs across the country; and (6) building the infrastructure to conduct leading-edge prospective and retrospective research, such as the Spine Patient Outcomes Research Trial (Weinstein et al., 2006, 2007a).

Figure 3-4 shows what a feed-forward/feedback system might look like in general, while Figure 3-7 shows how such a system was designed to work in the Spine Center. When patients come to the Spine Center, they complete a computerized survey before seeing a clinician or clinical team, and their health status and expectations are recorded. That information feeds into the assessment. The clinician, or clinical team, adds information on the severity of disease, on the patient’s diagnoses, and on the tests and treatments being ordered. This information contributes to a care plan that matches health status and patient preferences with the relevant evidence base and contributes to the patient’s making informed decisions in cooperation with the clinician (Weinstein et al., 2007b). Patients are then assigned to different customized tracks depending on their health needs and their willingness to adhere to (or select) a particular treatment approach consistent with their preferences and values. They are followed over time as they come back to the center and update their health status information and their perceptions of the benefits of their treatment compared with their expectations. The clinician continues to use and update the standardized, fixed-field information. Charge data are extracted from billing records and added to the information system so the patient and clinical team can see, in a quantifiable and measurable way, how the patient’s health outcomes are changing over time in response to treatments and how this change is influencing the costs associated with care. This same information contributes to the National Spine Network’s comparative database, used to assess the Spine Center’s performance in contrast to that of its peers, and it offers a database for program improvement and for research.

In practice, patients complete their health survey either when they visit the Spine Center or on the Internet before traveling to the center. The survey is analyzed instantly, and it becomes the first page of the patient’s medical record so that when the patient sees the clinician, they are literally on the

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

same page. The patient and practitioners can view the patient’s clinical and functioning status and outcomes the patient hopes to experience. This information is used to promote shared and informed decision making, which leads to a plan of care for the patient. The one-page summary includes such essential information as patient history, symptoms, the patient’s perceived options for treatment and desired health benefits, and clinical and functioning status. This information is updated over time and is available for each visit, making it possible to compare visits over time. Figures 3-7 through 3-8 illustrate the process and the one-page summary report.

The Dartmouth Spine Center feed-forward information system has been running and evolving for more than a decade. With research grant support from the NIH, a similar data system was exported to 13 other medical centers in 11 states across the country to gather data for randomized controlled trials and for observational cohorts concerning back surgery; the data have resulted in numerous articles in leading clinical journals (Weinstein et al., 2007a). In addition, the feed-forward system has been adapted for several other clinical programs at Dartmouth–Hitchcock Medical Center, including breast cancer, general internal medicine, plastic surgery, bone marrow transplant, and cardiovascular care.

The Spine Center case provides a proof of principle for the patient-

images

FIGURE 3-7 Spine Center process for a feed-forward and feedback information system.
SOURCE: Eugene C. Nelson and Trustees of Dartmouth College.

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

centric, feed-forward collaborative idea. The data system supports individualized, patient-centered care. Clinicians are now able to inform patients about their chances of success and the likelihood of complications for nonoperative vs. surgical treatment options based on research on people like them. Data are used for program evaluation and improvement as well as for comparative benchmarking. The data system contributes to the infrastructure for interdisciplinary research programs—from bench to bedside to outcomes experienced by patients. It is being used for retrospective and prospective research. Quality and cost data are published on the Dartmouth–Hitchcock website (DHMC, 2008) for transparent public reporting on important populations of patients. This initiative has helped the organization become an accountable healthcare system (Nelson et al., 2005).

One interesting footnote to the Spine Center case study is that Terry Adams, the Dartmouth business school professor mentioned earlier, had the experience of going to the Spine Center soon after it opened its doors. He did not know that the Spine Center had been designed based on his own research concerning how the world’s best-in-class service organizations worked to bring quality and value to customers at the point of service, but he was moved to write a letter to the local newspaper about the wonderful care he had just received from the center. He praised the center for using innovative information technology to focus on the patient’s individual and unique health state, to elicit the patient’s expectations for care outcomes and explore all treatment options, to help patients make wise treatment decisions based on medical evidence and personal preferences, and to work smoothly with a full interdisciplinary team without having to go from clinic to clinic and experience frustrating waits and delays.

Discussion: A Solution, Limitations, and Conclusions

This final section of the paper proposes a solution to the challenge cited at the beginning of the paper, describes some of the limitations associated with this solution, and offers concluding remarks.

The Challenge and a Solution

If the aim is to build an information environment capable of generating clinical information and knowledge that can promote a learning healthcare system, we believe an essential part of the solution—although clearly not the full solution—is to intentionally develop what we call “patient-centric, professionally organized, feed-forward collaboratories.” A few brief descriptions of the key terms in this phrase follow:

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

images

images

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

images

FIGURE 3-8 Patient summary report with longitudinal data: Dartmouth Spine Center.
NOTE: MCS = mental component scale, ODI = oswestry disability index, PCS = physical component scale.
SOURCE: James N. Weinstein and Trustees of Dartmouth College and Dynamic Clinical Systems, Inc.

 

  • Patient-centric—The individual patient’s health status, health risks, decisions based on preferences and values, perceptions of good care and good outcomes, and costs of care are at the forefront of all that is done (IOM, 2001).
  • Professionally organized—The healthcare professionals who serve patients are expected to be responsible for the design of patient-centric delivery systems and the supporting information systems that enable them to partner with patients in delivering patient-centric care.
  • Feed forward—Keeping patients and their data together over time requires a well-designed information system that enables key information and data to move with the patient through the healthcare
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

    system over time to promote quality, safety, efficiency, and the best and safest match of services to patient health needs at any point in time and at any place in the system.

•   Collaboratories—The term denotes a method for organizing virtual organizations in a complex world that combines the idea of collaboration across physically distinct settings and the idea of a scientific laboratory. The purpose is to form a community of practice that can build shared information repositories for use in advancing science and improving practice (Schneiderman, 2008).

What we are proposing, therefore, is to thoughtfully design and test innovative collaboratories that have all of the key features embedded in the Spine Center case. Some of the key characteristics of healthcare collaboratories would be

  • patient-centric and focused on relevant dimensions of health outcomes for any given population of patients;
  • professionally organized to fit into the flow of health care for the purpose of improving care while contributing to research and education;
  • based on feed-forward methods to follow patients over time as their healthcare experience evolves and to better match patients’ changing health status with an evidence-based preference-sensitive plan of care; and
  • dependent on feedback methods to track health risks, health status, diagnoses, and treatments associated with health outcomes and costs and to analyze results at multiple levels of the system (patient, micro, meso, macro, community, and region).

This type of population-specific, feed-forward collaboratory could advance goals on three major fronts:

  • Health care—Provide better care for patients by matching wants, needs, and health status with desired, effective, and efficient treatments.
  • Health research—Provide data for observational and prospective research on the causes of disease and disability and on the effectiveness of alternative methods for treating disease and disability.
  • Health professional education—Create better learning environments that are information rich, patient focused, outcomes driven, and engaged in advancing healthcare science as part of regular work.
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

The idea of patient-centric, feed-forward collaboratories is innovative, but not new. The best examples we know of today are the Dartmouth Spine Center and the National Spine Network (Weinstein et al., 2000), as well as the Karolinska Institute and the Swedish Rheumatoid Arthritis Registry. However, there are other research networks and communities of practice that have some collaboratory features, including the Cystic Fibrosis Foundation and cystic fibrosis centers in the United States; the Vermont Oxford Project and neonatal intensive care units in North America and Europe; the Autism Program at Geisinger Health System; the Northern New England Cardiovascular Group and cardiovascular programs in Maine, New Hampshire, and Vermont; and the Clinical Program Model at Intermountain Health Care (James and Lazar, 2007).

 

Limitations

Any effort to work with professional organizations and health systems to develop and evaluate feed-forward collaboratories will have to recognize the current reality and some of the challenges and limitations this reality imposes. A few of these are listed below:

  • Vision—Only a few models of collaboratories in health care are available, and these are not well known.
  • Rewards—Limited incentives and resources exist to establish collaboratories (at least in a non-Clinical Translational Science Award [CTSA] world).
  • Health Insurance Portability and Accountability Act and security— Following patients over time and across settings requires careful attention to privacy and security issues.
  • Measurement—Only a limited number of patient-based “gold standard” metrics exist for gathering both generic and condition-specific information.
  • Standardization—Resistance exists among many clinicians to using standard, fixed-field data entry, and there are concerns about wasting time and doing work that is not value added.
  • Patient role—It is a new role for the patient to act as a primary reporter of key information using standard approaches. Exercising this role will require changes in patients’ expectations and an understanding that their information-providing task is essential for their own care as well as for improving care and advancing science.

These challenges suggest the need to develop demonstration programs to evaluate, validate, and refine the feed-forward collaboratory approach.

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

Conclusion

The time may be right for testing the patient-centric, feed-forward collaborative model. Powerful forces at work are creating a climate favorable to the development of collaboratories. These forces include communities of professional practice combining patient care and health research, the funding of research by the NIH through the new CTSA approach, the formation of regional health information organizations across the country, the emergence of new scientific paradigms that recognize complexity and the value of multiple research methods, and demands for better quality and value that are measured and transparent. An excellent example of these forces coming together can be seen in the new National Quality Forum (NQF) framework that is being considered for measuring the outcomes and efficiency of episodes of care. The NQF approach is illustrated in Figure 3-9. It calls for the collection of feed-forward, patient-centric data on populations of at-risk individuals residing in different regions of the country. Then, after the onset of an illness episode, it calls for following people over time to measure critical information, including patient factors for risk adjustment, informed decisions guided by patient preferences, treatment processes, symptoms, physical function, and emotional status. Finally,

images

FIGURE 3-9 Generic episodes of care.
SOURCE: Reprinted with permission from the National Quality Forum (NQF, 2009).

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

at the end of the illness episode, it calls for completing the assessment by measuring mortality, functional status, and costs of care.

The following statement by Fisher (2008) summarizes the value of designing patient-centric, feed-forward healthcare collaboratories:

The same underlying information system is required to improve the evidence base for both biotechnology and care delivery. We need to know:

  • Patient attributes and risks (including biologic markers).
  • Specific, targeted biologic interventions performed.
  • Attributes of the system—delivery methods—where care is provided.
  • Health outcomes and costs.

We could then have a truly learning healthcare system:

 

  • Comparative effectiveness research: Compare biologic interventions, controlling for patient and system attributes.
  • Comparative performance assessment: Compare systems and care delivery methods, controlling for patient and treatment attributes.

The bold aim is to achieve better patient and population health and better healthcare outcomes by applying research and education. Accomplishing this aim will require that our health system become composed of learning healthcare systems. We conclude with four key points. First, the IOM definition of health stresses the functioning and well-being of the individual and requires patient-reported information to measure health status. Second, patient-centric health risks, health status, and health outcomes are an essential component of any comprehensive approach for improving health care and studying health outcomes. Third, it will be essential to design feed-forward information systems to accomplish the tripartite aim of improving healthcare outcomes, advancing biomedical research, and enhancing health professional learning. Fourth, we believe that developing and testing patient-centric, professionally organized collaboratories can help the nation achieve this bold aim.

REFERENCES

Ash, J. S., M. Berg, and E. Coiera. 2004. Some unintended consequences of information technology in health care: The nature of patient care information system-related errors. Journal of the American Medical Information Association 11(2):104–112.

Bates, D. W. 2005. Physicians and ambulatory electronic health records. Health Affairs 24(5): 1180–1189.

Brenner, D. J., and E. J. Hall. 2007. Computed tomography—an increasing source of radiation exposure. New England Journal of Medicine 357(22):2277–2284.

DHMC (Dartmouth Hitchcock Medical Center). 2008. Quality reports. http://www.dhmc.org/qualityreports (accessed June 6, 2008).

Farnsworth, C. 2005. Testimony before the House Ways and Means Subcommittee on Health. U.S. Congress, House of Representatives. March 17.

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

Fisher, E. S. 2008. Learning to deliver better health care: Rigorous study of the most effective ways to deliver care as well as what care works best can result in not only better treatment but also significant cost savings. Issues in Science and Technology 24(3):58–62. Spring 2008.

Gray, R. M., W. R. Newman, and A. M. Reinhardt. 1996. The effect of medical specialization on physicians’ attitudes. Journal of Health and Human Behavior 7(2):128–132.

IOM (Institute of Medicine). 1997. Improving health in the community: A role for performance monitoring. Washington, DC: National Academy Press.

_____. 2000. To err is human: Building a safer health system. Washington, DC: National Academy Press.

_____. 2001. Crossing the quality chasm. Washington, DC: National Academy Press.

_____. 2007. Clinicians and the electronic health record as a learning tool. In The learning healthcare system (pp. 268-274). Washington, DC: The National Academies Press.

_____. 2009a. Evidence-based medicine and the changing nature of health care. In Leadership commitments to improve value in health care: Finding common ground. Washington, DC: The National Academies Press.

_____. 2009b. Practical frontline challenges to moving beyond the expert-based practice. In Leadership commitments to improve value in health care: Finding common ground. Washington, DC: The National Academies Press.

James, B., and J. Lazar. 2007. Sustaining and extending clinical improvements: A health system’s use of clinical programs to build quality infrastructure. In Practice-based learning and improvement: A clinical improvement action guide, 2nd ed. Joint Commission Resources.

Karsh, B. T. 2004. Beyond usability: Designing effective technology implementation systems to promote patient safety. Quality and Safety in Health Care 13(5):388–394.

Kawamoto, K., C. A. Houlihan, E. A. Balas, and D. F. Lobach. 2005. Improving clinical practice using clinical decision support systems: A systematic review of trials to identify features critical to success. British Medical Journal 330(7494):765.

Lin, G. A., R. A. Dudley, and R. F. Redberg. 2007. Cardiologists’ use of percutaneous coronary interventions for stable coronary artery disease. Archives of Internal Medicine 167(15):1604–1609.

_____. 2008. Why physicians favor use of percutaneous coronary intervention to medical therapy: A focus group study. Journal of General Internal Medicine 23(9):1458–1463.

MedPAC (Medicare Payment Advisory Commission). 2007. MedPAC 2007 report to Congress. Washington, DC: MedPAC

Miller, G. A. 1956. The magical number seven plus or minus two: Some limits on our capacity for processing information. Psychological Review 63(2):81–97.

National Priorities Partnership. 2008. National priorities and goals: Aligning our efforts to transform America’s healthcare. Washington, DC: National Quality Forum.

NQF (National Quality Forum). 2009. Measurement framework: evaluating efficiency across patient-focused episodes of care. Washington, DC: National Quality Forum.

Nelson, E. C., P. B. Batalden, S. K. Plume, and J. J. Mohr. 1996. Improving health care, part 1: The clinical value compass. Joint Commission Journal on Quality Improvement 22(8):243–258.

Nelson, E. C., K. Homa, M. Mastenduno, E. S. Fisher, P. B. Batalden, E. F. Malcolm, T. C. Foster, D. S. Likosky, J. A. Guth, and P. B. Gardent. 2005. Publicly reporting comprehensive quality and cost data: A health care system’s transparency initiative. Joint Commission Journal of Quality Improvement 31(10):573–584.

Nelson, E. C., P. B. Batalden, and M. Godfrey. 2007a. Quality by design: A clinical microsystems approach. San Francisco, CA: Jossey–Bass.

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

Nelson, E. C., P. B. Batalden, and J. Lazar. 2007b. Practice-based learning and improvement: A clinical improvement action guide, 2nd ed. Oakbrook Terrance: Joint Commission Resources, Inc.

Nolte, E., and C. M. McKee. 2008. Measuring the health of nations: Updating an earlier analysis. Health Affairs (Millwood) 27(1):58–71.

Norris, P. 2007. Skeptical patients: Performance, social capital, and culture. In D. A. Shore (ed.), The trust crisis in healthcare: Causes, consequences, and cures (pp. 32-46). New York: Oxford University Press.

Peterson, E. D. 2008. Is information the answer for hypertension control? Archives of Internal Medicine 168(3):259–260.

Pham, H. H. 2007. Care patterns in Medicare and their implications for pay for performance. New England Journal of Medicine 356:1130–1139.

Porter, M., and E. O. Teisberg. 2006. Redefining health care: Creating value-based competition on results. Boston, MA: Harvard Business School Press.

Pronovost, P. J., C. A. Goeschel, and R. M. Wachter. 2008. The wisdom and justice of not paying for “preventable complications.” JAMA 299(18):2197–2199.

Redberg, R. F. 2007. Evidence, appropriateness, and technology assessment in cardiology: A case study of computed tomography. Health Affairs (Millwood) 26(1):86–95.

Rotschild, M., N. Elias, D. Berkowitz, S. Pollak, M. Shinawi, R. Beck, and L. Bentur. 2005. Autoantibodies against bactericidal/permeability-increasing protein (bpi-anca) in cystic fibrosis patients treated with azithromycin. Clinical and Experimental Medicine 5(2):80–85.

Schneiderman, B. 2008. Science 2.0. Science 319:1349–1350.

Shih, A., J. Quinley, T. K. Lee, and C. R. Messina. 2002. Assessing pneumococcal revaccination safety among New York state Medicare beneficiaries. Public Health Reports 117(2):164–173.

Silber, J. H., S. V. Williams, H. Krakauer, and J. S. Schwartz. 1992. Hospital and patient characteristics associated with death after surgery. A study of adverse occurrence and failure to rescue. Medical Care 30(7):615–629.

Warburton, R. N. 2005. Preliminary outcomes and cost–benefit analysis of a community hospital emergency department screening and referral program for patients aged 75 or more. International Journal of Health Care Quality Assurance Incorporating Leadership in Health Services 18(6–7):474–484.

Weinstein, J. N., P. W. Brown, B. Hanscom, T. Walsh, and E. C. Nelson. 2000. Designing an ambulatory clinical practice for outcomes improvement: From vision to reality. Quality Management in Health Care 8(2):1–20.

Weinstein, J. N., T. D. Tosteson, J. D. Lurie, A. N. Tosteson, B. Hanscom, J. S. Skinner, W. A. Abdu, A. S. Hilibrand, S. D. Boden, and R. A. Deyo. 2006. Surgical vs. nonoperative treatment for lumbar disk herniation: The Spine Patient Outcomes Research Trial (SPORT): A randomized trial. JAMA 296(20):2441–2450.

Weinstein, J. N., T. D. Tosteson, J. D. Lurie, A. N. A. Tosteson, E. Blood, B. Hanscom, H. Herkowitz, F. Cammisa, T. Albert, S. D. Boden, A. Hilibrand, H. Goldberg, S. Berven, H. An, and SPORT Investigators. 2007a. Surgical vs. nonsurgical treatment for lumbar degenerative spondylolisthesis. New England Journal of Medicine 356:2257–2270.

Weinstein, J. N., K. Clay, and T. S. Morgan. 2007b. Informed patient choice: Patient-centered valuing of surgical risks and benefits. Health Affairs 26(3):726–730.

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×

This page intentionally left blank.

Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 117
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 118
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 119
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 120
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 121
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 122
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 123
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 124
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 125
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 126
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 127
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 128
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 129
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 130
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 131
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 132
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 133
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 134
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 135
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 136
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 137
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 138
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 139
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 140
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 141
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 142
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 143
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 144
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 145
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 146
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 147
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 148
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 149
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 150
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 151
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 152
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 153
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 154
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 155
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 156
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 157
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 158
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 159
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 160
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 161
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 162
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 163
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 164
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 165
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 166
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 167
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 168
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 169
Suggested Citation:"3 Healthcare System Complexities, Impediments, and Failures." Institute of Medicine and National Academy of Engineering. 2011. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary. Washington, DC: The National Academies Press. doi: 10.17226/12213.
×
Page 170
Next: 4 Case Studies in Transformation Through Systems Engineering »
Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary Get This Book
×
Buy Paperback | $69.00 Buy Ebook | $54.99
MyNAP members save 10% online.
Login or Register to save!
Download Free PDF

Improving our nation's healthcare system is a challenge which, because of its scale and complexity, requires a creative approach and input from many different fields of expertise. Lessons from engineering have the potential to improve both the efficiency and quality of healthcare delivery. The fundamental notion of a high-performing healthcare system--one that increasingly is more effective, more efficient, safer, and higher quality--is rooted in continuous improvement principles that medicine shares with engineering. As part of its Learning Health System series of workshops, the Institute of Medicine's Roundtable on Value and Science-Driven Health Care and the National Academy of Engineering, hosted a workshop on lessons from systems and operations engineering that could be applied to health care.

Building on previous work done in this area the workshop convened leading engineering practitioners, health professionals, and scholars to explore how the field might learn from and apply systems engineering principles in the design of a learning healthcare system. Engineering a Learning Healthcare System: A Look at the Future: Workshop Summary focuses on current major healthcare system challenges and what the field of engineering has to offer in the redesign of the system toward a learning healthcare system.

  1. ×

    Welcome to OpenBook!

    You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

    Do you want to take a quick tour of the OpenBook's features?

    No Thanks Take a Tour »
  2. ×

    Show this book's table of contents, where you can jump to any chapter by name.

    « Back Next »
  3. ×

    ...or use these buttons to go back to the previous chapter or skip to the next one.

    « Back Next »
  4. ×

    Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

    « Back Next »
  5. ×

    Switch between the Original Pages, where you can read the report as it appeared in print, and Text Pages for the web version, where you can highlight and search the text.

    « Back Next »
  6. ×

    To search the entire text of this book, type in your search term here and press Enter.

    « Back Next »
  7. ×

    Share a link to this book page on your preferred social network or via email.

    « Back Next »
  8. ×

    View our suggested citation for this chapter.

    « Back Next »
  9. ×

    Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

    « Back Next »
Stay Connected!