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

Chapter: 5 Fostering Systems Change to Drive Continuous Learning in Health Care

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Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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.
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5

Fostering Systems Change to Drive Continuous Learning in Health Care

INTRODUCTION

The vision of the Institute of Medicine’s (IOM’s) Roundtable on Evidence-Based Medicine (now the Roundtable on Value & Science-Driven Health Care) is “the development of a learning healthcare system that is designed to generate and apply the best evidence for the collaborative health care choices of each patient and provider; to drive the process of discovery as a natural outgrowth of patient care; and to ensure innovation, quality, safety, and value in health care” (Charter pp. xi–xii). How to realize the vision of continuous learning was the focus of the fourth session of the workshop.

The publication The Learning Healthcare System: Workshop Summary (IOM, 2007), based on an earlier Roundtable workshop, identified several common characteristics of a system with continuous learning, including a culture that emphasizes transparency and learning through continuous feedback loops, care as a seamless team process, best practices that are embedded in system design, information systems that reliably deliver evidence and capture results, and results that are bundled to improve the level of practice and the state of the science. With those characteristics in mind, the contributors in this chapter looked closely at how specific aspects of feedback and performance can be improved in the healthcare organizational culture, in the development of accessible knowledge, in the management of information and technology, and in the organization of information systems.

Steven J. Spear, senior lecturer at Massachusetts Institute of Technology and a Senior Fellow at the Institute for Healthcare Improvement, observed

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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.
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that in many sectors of the service and manufacturing economies a few high-performance organizations seem to be the leaders, and their competitors essentially compete for second place. These pioneers deliver value with less effort and cost, even though they have similar—or identical—tools, customers, suppliers, labor, and regulations. Spear said that these organizational leaders continue to push the envelope through differences in systems management and that the lessons from their success might offer perspectives on value, efficiency, quality, and other areas that are important to producing a learning, team-oriented, patient-centric culture within health care. Based on his close observations of Toyota, Southwest Airlines, Alcoa, and other industry leaders, Spear reported that, in contrast to organizations that address systems anomalies with workarounds, industry leaders carefully analyze adverse events and use them as sentinels for investigation into causes. Spear hypothesized that by adopting similar techniques, healthcare systems may be able to deliver better care to more people at less cost and with less effort—on the order of twice as good for twice as many people at half the cost.

Examining the value of knowledge management, access, and use, Donald E. Detmer, president and chief executive officer (CEO) of the American Medical Informatics Association (AMIA) and professor of medical education at the University of Virginia, argued that improved management of information applied to clinical decision support (CDS) will require structured policies and complementary agendas for informatics education and research. Detmer discussed the CDS Roadmap for National Action developed by the AMIA, which is based on the principles of (1) best knowledge available when needed, (2) high adoption and effective use, and (3) continuous improvement of CDS methods and knowledge. Detmer also discussed the Morningside Initiative, which seeks to share information broadly for CDS. Detmer highlighted the AMIA–Association of Academic Health Centers’ (AAHC’s) current collaboration to develop enhanced informatics curriculums for health professional and continuing education students. He also discussed current developments in CDS policy and infrastructure and identified areas for further investigation and efforts. Looking to the future, he emphasized the importance of determining the appropriate mechanism for integrating personal health records with electronic health records (EHRs).

Stephen J. Swensen, director of quality for the Mayo Clinic and professor of radiology at the Mayo Clinic College of Medicine, said the healthcare industry must address specific elements of technology management in order to drive systems change. He described work in technology management at the Mayo Clinic to develop networks that embody optimal reliability, permit nimble and effective diffusion of best practices, have built-in safety nets, and support optimal organizational learning and communication. Swensen

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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.
×

emphasized that technology management should leverage human capital and should embody a decision-making process whereby decisions are made with an organizational perspective by cross-functional, physician-led teams. Swensen’s discussion encompassed five facets of technology management— policy, appropriateness, reliability, diffusion, and social capital. In ensuring appropriate care, for example, Swensen observed that health systems face the complex task of first making sure that the right policies are in place to encourage medical centers, physicians, and other providers to use technology and deliver the appropriate care in the best setting, and then they must ensure that patients are connected in the most efficient way with the technology assets most appropriate to their needs.

Discussing the link between the organization and management of information systems and the quality and safety of patient care, David C. Classen, a physician at Computer Sciences Corporation, described current approaches to the evaluation of clinical information systems. He detailed a new simulation tool that has been developed and used by healthcare organizations to evaluate the effectiveness of clinical information systems implementations in improving the safety of care for patients. Classen demonstrated how such tools have been used by organizations to learn about the capabilities of their implemented clinical information systems and to assess system shortfalls, and he showed how organizations have used these tools to improve clinical information systems.

CHASING THE RABBIT: WHAT HEALTHCARE ORGANIZATIONS CAN LEARN FROM THE WORLD’S GREATEST ORGANIZATIONS

Steven J. Spear, D.B.A., M.S., M.S., , Massachusetts Institute of Technology, Institute for Healthcare Improvement

In manufacturing, heavy industry, high tech, services, aviation, the military, and elsewhere, a small number of organizations always race to the front of the pack in their sector, leaving everyone else competing for runner-up. Although these organizations use similar science and technology to meet the needs of a similar customer base, are dependent on the same group of suppliers, hire from the same labor pools, and are subject to the same regulations as their competitors, they deliver far more value with much less effort and at lower cost. They gain and sustain leadership by managing the complex systems of work on which they depend in markedly different ways. Healthcare organizations can learn—and have learned—from these exemplars, with outstanding results in efficacy, efficiency, safety, and quality of care.

The proposition considered here is that it is possible to deliver much better care then we currently do, to many more people than we currently

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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.
×

do, and at much less cost and with less effort than is currently the case. The envisioned improvements are not incremental; instead, I am speaking of a product that is twice as good for twice as many people at half the cost. The proposition is not based on hypothesis or conjecture, but is supported by good clinical evidence. This paper begins by examining what needs to be done and in particular, the lessons healthcare organizations can learn from other complex, high-performing organizations in other industries so as to achieve the goal of better care for more people at less cost.

Twenty years ago I was an employee of Congress at one of the congressional agencies. At the time, the competitiveness of U.S. manufacturing with that of Japan was a major concern, focused on the idea that the Japanese were gaining an advantage from what was essentially unfair competition. People sensed that financing arrangements, competition, and domestic and international markets were being manipulated. There were accusations of dumping of various types of goods, steel not the least of these. The notion was that the appropriate response to declining competitiveness on the part of American companies was for Congress, regulators, and the executive branch to act similarly to how they perceived the Japanese to be acting.

A few years later, there was a fundamental shift in what people saw as the causes of competitive differences. Replacing the focus on large macro-national elements was recognition that the differences between the countries were rooted in the differences between companies—that what was being done in companies such as Sony, Toshiba, and Hitachi was fundamentally different from what was happening in their U.S. counterparts and that what was taking place at Toyota and Honda was fundamentally different from what was going on at General Motors (GM), Chrysler, and Ford.

This realization was good news because it meant that the solution to the problem did not depend on consensus among the Majority Leader of the Senate, the President, and the Speaker of the House on the source of the problem or the solution. This good news, however, meant that U.S. companies bore a great responsibility, and that managers of individual companies and of business units within those companies had enormous influence on the outcome of their organizations’ efforts.

To link this discussion to health care, let us start with a statement of the problem: too few people have access, the costs are too high, and so on. Much of the discussion among politicians focuses on whether more resources should be committed to the system. But if we pursue the parallels with the manufacturing sector, that may not be the answer. I am not going to argue against spending altogether. Certain changes are needed in terms of how information is reported and how coverage is provided for those who are least able to care for themselves. There are also separate issues of transfer of wealth and caring for the least well in our society.

Continuing the focus on the delivery of care, the experience from

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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.
×

manufacturing suggests that presidents of hospitals, presidents of systems, managers of hospitals, and deliverers of care—from small practices to very large organizations such as the Office of Veterans Affairs—can have an enormous influence on outcomes. That is good news because it means that improving the quality of health care will not depend on a confluence of interest and perspective among three people, but can arise from the efforts of thousands or tens of thousands of people who work together to move the system in the right direction.

Returning to the perception of the competition between the United States and Japan, originally the perception was that the key competition was between Tokyo and the Diet (Japan’s legislative body) and Washington and Congress. In a sense, the idea was that somehow the Japanese had unleveled the playing field, that they were not playing by the same rules or were playing by the same rules but cheating. But this perception changed, and people started to recognize that the playing field was in fact quite level.

Consider that to compete today, industries must compete in every region around the world. When they do so, they compete head to head with all of their competitors, so they cannot lock up markets, regions, or customers. For example, many towns have the equivalent of Boston’s “Auto Mile,” where one can walk into a Buick dealer, and if that dealer does not have what one wants, one can visit the Chrysler dealer next door and, if necessary, move on to the Ford dealer, the Toyota dealer, and so forth—all literally within walking distance. Given this phenomenon, major auto companies cannot lock up customers. How, then, can they gain a competitive advantage?

If a monopolistic relationship with one’s customers is impossible, a company might try to lock up its suppliers. That cannot be done with automobiles, however, and, generally speaking, all automobile manufacturers are subject to the same regulations and innate market preferences. Customers are paying the same price for a gallon of gasoline whether they put that gas into a Ford or a Toyota or a Chrysler. The playing field is extraordinarily level. And when the playing field is level, this parity of rules can be expected to lead to a parity of outcomes. When everything is the same in terms of customers, suppliers, labor pools, and so forth, people can be expected to gain and lose leadership, gain and lose profits, in a very fluid, dynamic situation.

In the automobile industry, Ford, Chrysler, GM, Volkswagen, and their competitors do indeed fluctuate between very hot and very cold years. They are engaged in intense competition, but they are all competing for second place. In first place is Toyota, which has experienced extraordinary profitability and growth in market share and revenue. By other measures, such as market capitalization vs. profitability, there is an enormous disproportion

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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.
×

between Toyota and U.S. companies. Not only has Toyota grown, but if one looks at the ratios, the market expects it to continue to grow at a sustained rate over many years.

One might think Toyota is an anomaly and decide to look at another playing field—say, commercial aviation. To make an apples-to-apples comparison, players in this field fly the same planes out of the same airports, hire from the same labor pool, pay the same price for jet fuel, and are subject to the same regulations. By and large, the playing field is level with parity of contest and parity of outcomes. Accordingly, in this competitive environment, Delta, Northwest, Continental, United, and American regularly have good and bad years, and they regularly gain and lose share. It turns out, though, that this is a competition for second place because there is Southwest, with some 35 years of profitable growth, year after year. Even when things go bad at Southwest, they go bad on a much smaller scale than at other companies. For example, when Southwest and American failed similar Federal Aviation Administration inspections, Southwest paid a fine and kept flying, while American essentially shut down for a week.

In terms of parity of playing field and parity of outcome, one can see such anomalous patterns in industry after industry—automobile manufacturing, the aluminum and steel industries, commercial aviation, government services, and on and on. One begins to realize that there are not just anomalies, but a population of anomalous outcomes. When one examines what the leaders—Toyota, Southwest, Alcoa, the Navy’s nuclear reactor program—have in common, one finds that they have solved a problem that plagues every industry—complexity.

For any product or service, the number of elements necessary to make it function is far greater than it was 5, 10, or 20 years ago. The number of interdependencies and interconnections among those elements is far greater than ever before. The basic problem with a complex system is that, at some point, once there are enough elements and connections and interdependences, it is nearly impossible to understand the structure of the system and to understand or predict its behavior perfectly. This is where the divide begins between the companies or organizations that are in first place and those that are stuck competing for second place.

Two fundamental differences in behavior have direct application to health care, which, of course, is a complex system of work to deliver care to patients. The first is that those who are competing tend to organize themselves functionally around specialty silos, whereas those who are highly successful tend to place tremendous emphasis on building functional technical skill because they need it to compete, but this skill is in service of the process by which they deliver value to customers or patients or users. The difference is between a functional view and a functional view plus service of process and system.

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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 second difference is that those who are less successful put a lot of effort into the design of systems. I am not going to try to diminish that effort. But once a complex system is running and operating, its behavior is going to be somewhat unpredictable because of the problem, noted earlier, of the inevitable limits on understanding the system and the way its parts interrelate. Those who focus on designing systems tolerate things going contrary to expectations, as is inevitable with a complex system, and they dismiss this as the inevitable noise of what they do.

In contrast, when highly successful companies design and start operating systems, they do not dismiss such chatter as noise. They do not live in a world of signal and noise, where the signal is what they wanted to get and the noise is something contrary to that. Instead, they live in a world of signal and signal. The expected signals are things that happen that confirm what they believed about the system’s structure and behavior; the chatter or noise points them to the things they did not understand.

A key difference between the highly successful organizations and the others is that the others tolerate, encourage, and depend on an environment where fighting fires, working around problems, coping, and otherwise making do is how work is accomplished. The problem with that approach for the people who work in those organizations is it means that every day they know they are going to go to work and fail to some degree.

Another basic problem with complex systems is that sometimes these little failures come together in idiosyncratic fashion. Not only are there the normal daily annoyances of doing work in a flawed system, but sometimes these things combine catastrophically. In contrast, those who are very good at dealing with complexity will design a system, but when they operate it, they place tremendous emphasis on identifying things that go contrary to expectations. Those signals tell them where they have to invest in building more knowledge. When they see that something has gone wrong, they are quick to deal with the problem because they know that the time to address problems is when they are still hot. Think, for example, of doctors rushing to a patient who is crashing or detectives getting to a crime scene while the evidence is still fresh. It is in dealing with problems while they are still hot that new knowledge can be generated about how the system behaves. When that knowledge is gained locally by an individual, great effort can be made to ensure that this knowledge is shared with everyone else involved.

As an example, the nuclear navy has modeled very well the behavior of constant dynamic discovery, of creating a high-velocity organization. The navy thinks about it in terms of an operator sitting down to run a nuclear reactor on board a submarine. The person may be just 22 years old, perhaps just graduated from Annapolis with a year’s training in the Nuclear Reactor Program. This person is not running the reactor as if he or she has had just a year’s experience, but as if he or she has had the 5,700 reactor-

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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.
×

years of experience the navy has accumulated over the past 50 years of running reactors on board warships.

I started looking at the anomalies in manufacturing first at Toyota and then at Alcoa. I also got involved in the Pittsburgh Regional Health Initiative. Our first effort in Pittsburgh was to look at medication administration. This is a process problem because it involves doctors making diagnoses and writing prescriptions, prescriptions going to the pharmacy, orders being filled and delivered, nurses providing medication, and so on. It turned out there was a problem with how orders were being transmitted and delivered.

People in the pharmacy and in nursing came up with a solution to the problem that would save a tremendous amount of nursing time and reduce and nearly eliminate any chance of error that would result in giving the wrong medication to a patient—a particularly serious problem in a transplant case. They tried this solution in a low-cost way, then tested it again through a variety of pilots and realized it was a great idea. They wanted to institutionalize it and make their learning valuable to the organization, so they tried to find the person who owned the bridge between nursing and pharmacy. They knew this was not someone in nursing or in pharmacy or in their particular domain, so they started looking elsewhere in the organization. It was not the charge nurse or the person who played a similar role in the pharmacy, and it turned out it was not even the president of the hospital who owned the bridge between the two because the hospital was part of a larger system. Eventually they found that the first person who had formal authority over the bridge between this pharmacy and this nursing unit in a much larger system was the CEO of the hospital. Everything else was managed through functional silos and disciplines—orthopedics, obstetrics, and so on; nursing separate from medicine, medicine from surgery. Consider how difficult it is to institutionalize all the micro-changes necessary so an organization has on a daily basis a homeostatic self-correcting, self-improving dynamic. In that case it was impossible.

To return to my original proposition, it is possible to deliver much better care to many more people with much less cost and effort than is currently the case. We need not wait for the President, the Majority Leader, and the Speaker of the House to come to some kind of agreement. What we do need is for people who are responsible for systems and organizations to understand that although managing functions is necessary, it is not sufficient. They need to manage processes—not just pharmacy and nursing, but also medication administration, and not in a static fashion whereby one designs a process and hopes it will run well, but in a dynamic fashion so that chatter is not treated as inevitable noise, but as an indication of where one needs to improve.

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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.
×

KNOWLEDGE MANAGEMENT FOR CLINICAL CARE

Donald E. Detmer, M.D., M.A., University of Virginia, Charlottesville

Knowledge management for CDS requires a policy framework as well as an education and research agenda. In its CDS Roadmap for National Action, the American Medical Informatics Association (AMIA) recommended a structure with the following three pillars: (1) best knowledge available when needed, (2) high adoption and effective use, and (3) continuous improvement of CDS methods and knowledge. The roadmap led to the Morningside Initiative, which aims to develop a Knowledge Management Repository for CDS through a public–private partnership. The goal is to create a shared repository of executable knowledge for CDS that will be broadly available. The hope is that these and related initiatives funded recently by the Agency for Healthcare Research and Quality (AHRQ) will eventually result in a sustainable infrastructure.

Educational initiatives and relevant informatics research are needed. The AMIA, in collaboration with the AAHC Affiliate Roundtable, will collaborate to create a two-stage, integrated, multimodular informatics curriculum for all students studying to become health professionals. The initial course will be appropriate for students entering professional education, and the second is to be pursued just before students begin professional practice. These initiatives, combined with AMIA’s 10 × 10 program for those in practice, will help address basic professional educational needs, especially in applied clinical informatics. Finally, there are major informatics research issues that need attention. One critical research and development area, for instance, concerns patients’ use of their own EHRs for chronic illness management in collaboration with their clinicians via secure Web portals.

The AMIA is clearly interested in trying to foster change for purposes of improving both health and healthcare delivery. In particular, we are challenged to integrate the carbon dimensions with the silicone dimensions—that is, to bring informatics to bear on the problem. Today, we lack the right policy infrastructure to accomplish this integration. This paper looks briefly at some relevant IOM work and a project that the AMIA carried out for the Office of the National Coordinator on Clinical Decision Support, and then offers some ideas about what a national roadmap for knowledge management should look like.

The 1991 IOM study Computer-Based Patient Record: An Essential Technology for Health Care (reissued in 1997) (IOM, 1997) identified EHRs as an essential technology for health care. It is fascinating that this remains the case 17 years later, yet one would not think so based on the usage of EHRs in the United States today. While the 1991 report emphasized

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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 importance of EHRs for quality improvement and for clinical decision support, these dimensions of EHRs were not really obvious at the time in terms of widely held national policy perspectives.

Whereas the 1991 report addressed essential skills needed in the workforce, the IOM’s 2001 Crossing the Quality Chasm report firmly tied policy to how EHRs and EHR systems and communications technologies should be used to move from a costly, inefficient, and highly variable system to a system that is equitable, safe, patient centered, efficient, effective, and timely (IOM, 2001).

Another crucial IOM effort that has not received as much attention as it deserves is the Health Professions Education Summit. That meeting and the ensuing report, entitled Health Professions Education: A Bridge to Quality 2003, addressed many relevant dimensions of health care, including the need for aligned reimbursement incentives and regulatory requirements, robust information infrastructure, widespread use of evidence-based medicine, and a workforce skilled in evidence-based medicine, information technology (IT), and process improvement (IOM, 2003).

From the perspective of a policy background, a national roadmap for knowledge management with decision support is clearly lacking. In fact, although many developed economies around the world have put in place a good basic information infrastructure, decision support remains immature. Even Denmark has a long way to go.

The AMIA developed the CDS Roadmap for National Action between 2005 and 2007 with the support of many groups and individuals (AMIA, 2006). Some findings have just recently been approved by the American Health Information Community (AHIC) as a guide for U.S. policy in this domain. Essentially, the roadmap was intended to create a blueprint for coordinated nationwide action to ensure that usable and effective CDS will be widely used by clinicians and patients. The challenge was seen as developing decision support that is equally usable by patients and their clinicians to improve health care. Three pillars were envisioned as the foundation of the model:

A system must continually develop the best knowledge available and make that knowledge available at the point and time it is needed. Knowledge must be both current, “right” to the best standards of the day (ideally both generally and locally), and accessible. There needs to be high adoption and effective use—performance is key to this. Methods must be improved continuously in addition to the knowledge base.

With these three pillars in mind, a coherent structure will enable progress. The following objectives are crucial: develop practical, standard formats for representing CDS knowledge and interventions; establish standard approaches for collecting, organizing, and distributing CDS; address policy,

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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.
×

legal, and financial barriers, and create additional support and enablers; compile and disseminate best practices for usability and implementation; develop methods for collecting, learning from, and sharing national experience with CDS; and use EHR data systematically to advance knowledge.

The roadmap recommends a series of activities to improve the development, implementation, and use of CDS. It identifies work products. Objectives include organizing and facilitating the creation of a convening coordinating body and establishing a CDS technical assistance center. (Whether this should be one center or a cluster is not clear, but the goal is to establish consensus groups to answer key questions that arise on roadmap development.) We wish to assemble a best-practice synthesis, conduct training and education, and develop prototypes. Many pilot demonstrations are recommended, including supporting and facilitating related nationwide initiatives, developing practical standard formats to share knowledge and interventions, and collecting and disseminating best practices for usability and implementation.1

Looking at current policy and national structure, one can see that the roadmap has had an impact. AHRQ is supporting some activities through its national resource center and Centers for Education and Research on Therapeutics grants, including knowledge management CDS grants. There have been presentations to the National Committee on Vital and Health Statistics, and the Morningside Initiative begun by the Telemedicine and Advanced Technology Research Center (TATRC) is now gaining some institutional linkage to the AMIA.

The Department of Health and Human Services (HHS) AHIC meeting on April 22, 2008, approved recommendations of the ad hoc CDS workgroup; AHIC considers the AMIA’s CDS Roadmap for National Action to be a foundational document. At the meeting, three priorities were identified: (1) drive measurable progress toward priority performance goals for healthcare quality improvement, (2) explore options to establish or leverage a public–private entity to facilitate collaboration across CDS development and deployment, and (3) accelerate CDS development and adoption through federal programs and collaborations. All activities relate to seeking measurable progress through quality improvement. Another recommendation was that by October 31, 2008, HHS and relevant partners should have explored options for establishing or leveraging a public–private entity (e.g., AHIC 2.0) to convene public and private organizations and stakeholders for the purpose of promoting effective CDS development and adoption and addressing gaps in CDS capabilities through planning, facilitation, and coordination of activities across diverse constituencies.

 

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1 More CDS Roadmap Information is available at www.amia.org/inside/initiatives/cds/ (accessed September 20, 2010).

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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 2008, the federal government created a collaboratory for CDS. Its goal is to coordinate internal activities across AHRQ, the HHS Personalized Healthcare Initiative, and the Office of the National Coordinator. Its role is to scan and then try to leverage what is happening across the government in these areas. At the same time, as mentioned above, the Morningside Initiative has been supported by TATRC and others. (Current Morningside collaborators include the AMIA, Arizona State University, the Henry Ford Health System, the Veterans Health Administration–Office of Information, the Department of Defense, Kaiser Permanente, Partners Healthcare System, TATRC, and Intermountain Healthcare.) The goal of creating the collaboratory was to explore possibilities for developing a national knowledge management repository for CDS so that CDS information can be available in computer-executable language and thereby shared and made broadly available.

It is too early to say whether or how the above efforts will relate to AHIC 2.0, but there are signs of progress. At the same time, regulations need to be monitored to ensure we do not backslide, intentionally or not. Regulations relating to the Food and Drug Administration guidance document on CDS bear watching, as do efforts to change the Health Insurance Portability and Accountability Act in light of developments in personal health records.

This domain presents an educational challenge. Again referring to the health professions education Bridge to Quality 2003 report, the AMIA has sought to address the challenge that report highlighted (IOM, 2003). Through its Academic Strategic Leadership Council, the AMIA is undertaking an activity with the Affiliate Roundtable of the AAHC, plus a few representatives from other organizations, whose aim is to create a common multidisciplinary approach to entry-level education for all health professional students on knowledge management CDS, as well as a second course that would be taken prior to entering professional practice. Furthermore, AMIA’s 10 × 10 program aims to train 10,000 healthcare professionals to serve as local informatics leaders and champions by 2010, particularly in the area of applied clinical informatics, on which much of knowledge management focuses. Thus far, more than 1,000 people have graduated from the program.

Clearly research is highly important as well. Informatics is an emerging discipline. The AMIA recently conducted a survey of the top research issues for informaticians, and the results showed this order of importance: interoperability, workflow, quality and patient safety, decision support, and information filtering and aggregation. The emphasis on interoperability is probably no surprise, but more interesting perhaps is the attention to workflow and process design.

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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.
×

Regarding decision support, its impact has been relatively limited to date. Understanding of how to develop, maintain, and integrate centralized decision support resources is insufficient. Context awareness of decision support technology needs to be improved. Ultimately, this technology needs to reach the patient through secure Web portal integration, and patients need to be encouraged to work on monitoring and managing their own health care.

With respect to research information and filtering, we need to be able to better manage electronic medical literature, summarize information from clinical literature, summarize patient medical history from large volumes of data, mine data to identify patterns, and present information in the context of individual patients.

Looking to the future, in terms of people issues, we need to focus on the organization and management of complex adaptive systems and to improve policies and procedures that will provide better access to patient data. With regard to technology, our focus should be on data repositories and scaling of research methods and standards. We need human- and machine-readable protocols and results. There are also hybrid issues to be addressed.

In the United States in the near future, I think the issue will come down to where AHIC V2 (or A2) is going. Obviously, technology continues to advance in such areas as genomics, handheld computing, and so forth, and these advances are likely to shape the way the future unfolds.

One current issue in this country is the need to ensure that our basic investment in EHRs goes beyond results reporting and record keeping to encompass the really important value-added dimension of decision support. Decision support will provide the major leverage for quality and efficiency. A key question is how the personal health record and the EHR can be integrated so they inform one another. This is why the AMIA strongly supports patients’ access to their EHRs via secure Web portals, along with patients’ ability to comment on the findings shown.

Mario Andretti has said, “If everything is under control, you are going too slow.” With this in mind, I would recommend that everyone look regularly at Daniel Masys’s Annual Reviews of Informatics (Masys, 2007), as well as Russ Altman’s Annual Review of Translational Bioinformatics.2 Finally, I would stress that leadership from the National Academies in the area of knowledge management for CDS is crucial.

 

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2 See http://rbaltman.wordpress.com/ (accessed September 20, 2010).

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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.
×

TECHNOLOGY MANAGEMENT

Stephen J. Swensen, M.D., M.M.M., F.A.C.R., and James Dilling, Mayo Clinic

Technology management is an important issue for the healthcare business sector. Approximately 50 percent of cost growth in health care over the past 40 years has been the result of technology innovation (CBO, 2008). Technology growth takes many forms, including the development of new pharmaceuticals, devices, and services.

Technology management has many facets, five of which are addressed in this paper:

  1. policy,
  2. appropriateness,
  3. reliability,
  4. diffusion, and
  5. social capital.

Policy

Public policy and health insurance programs are powerful drivers of technology management. Choices about what is incentivized and paid for play a central role in determining what is performed and prescribed. Public policy and health insurance programs are among the reasons we have such high expenditures related to technology today.

For example, American healthcare policy is driven largely by fee for service. Most of our healthcare system pays for more exams, which in turn drive technology use. We do not pay for value (outcomes, safety, or service divided by cost over time). We pay the same to an endoscopic practice that has an accuracy rate of 90 percent as we do to a practice that has an accuracy rate of 60 percent. We pay the same to two practices even if one has a complication rate twice that of the other. We pay for use, not value.

So from a societal perspective, policy and programs that encourage self-referral and overuse achieve exactly the result one would expect, even if this was not the intent. U.S. policy and programs are dominant forces in technology purchase and management. In fact, when physicians own their own imaging equipment, the tendency is to order more exams and to charge more for poorer quality (Hillman et al., 1990, 1992, 1995). We do not pay for superior outcomes in diabetic patient care—we pay for visits, drugs, scans, and procedures.

 

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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.
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Appropriateness

For optimal technology use, a thoughtfully engineered healthcare system must ensure that a patient receives no more and no less than the right care; that is, a patient must receive appropriate care. For instance, some estimate that 30 to 40 percent of imaging procedures in the United States are unnecessary (Thrall, 2004; Tosczak, 2004). General Electric has calculated that poor quality costs that company $127 million per year, $60 million of which is attributed to radiology overuse (de Brantes, 2003).

Technology must also be managed after acquisition. Here is an important role for a common outpatient/inpatient EHR (and the instant peer review and communication it affords), standard order sets, decision support, and clinical prediction rules. These resources can address the issues of misuse and underuse of technology. Overuse, in part, results from a systems issue due to financial conflict of interest (i.e., nonsalaried physicians working in a production model). The lack of integrated practice models also has led many technology-intensive areas to be viewed as service areas (e.g., labs, radiology, gastroenterology). When the primary or specialty physician orders a test, it is nearly always completed, regardless of whether it is necessary and whether it is even the appropriate test given the patient’s condition. Through greater communication and integration, these support areas can consult with and educate the ordering physicians on the most appropriate tests or modalities.

Appropriate use of technology can be driven by standard evidence-based work manifested by best-practice order sets and decision support. A logical place to start is where we have the most solid evidence supporting optimal care. One example is clinical prediction rules. Using the Canadian computed tomography (CT) head rule, neurosurgical intervention is still optimized with a sensitivity of 100 percent, yet CT imaging for minor head trauma is reduced by more than a third (Smits et al., 2005). High-value technology management requires methodically identifying the right patient and selectively rendering the right care.

The current public environment reinforced by third-party payment for health care has led to high patient expectations regarding the use of technology, in particular imaging and pharmaceuticals. More is typically seen as better. The constant barrage of pharmaceutical and imaging advertisements on television and in print leads patients to pressure physicians toward increased use of higher-cost—although not necessarily more effective—diagnostics and treatment. The ramifications may be manifest in the variability in the cost of care during the last 2 years of life, which varies by a factor of more than 2 from one region of the country to another (Wennberg et al., 2007a, 2007b).

 

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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.
×

Reliability

Rational incentives from a healthcare system that rewards appropriate use are necessary, but insufficient. If there is no market force that rewards reliability in the context of appropriate use, we will fall short of optimal technology management and high reliability. If an operation is appropriate but injures the patient, we fall short. If magnetic resonance imaging of the spine was appropriate but misinterpreted, we fall short.

Technology management involves managing technology in terms of not only volume, but also reliability (e.g., accuracy, safety). The median accuracy for mammography interpretation in the United States is 66 percent. To increase the median accuracy by 5 points to 71 percent, the bottom 30 percent of radiologists—approximately 6,000 in number—would need to be excluded from practice or improve their performance (Beam et al., 2003). The system today rewards only volume. The practitioner with a 90 percent accuracy rate is paid the same as one with a 40 percent accuracy rate.

If healthcare providers, including residents and fellows, are placed in environments where we know their rate of medical errors will increase, we are falling short of optimal technology management. If we fail to train providers to work in teams where we know their reliability and safety will be enhanced, we are falling short of optimal technology management. Forty percent of residents report making serious medical errors (Mizrahi, 1984). A healthcare provider who has been up for 24 hours is as impaired as someone with a blood alcohol content of 0.08 percent, legally drunk in many states (Dawson and Reid, 1997). We have designed many of our systems for suboptimal technology use.

Simulation is a discipline that has been applied by other industries, including commercial aviation, for a long time. It is now being embraced by medicine. At our institution, we have a simulation center in which we have more than 5,000 learner experiences each year. We expect all medical students to have simulation center competency before their internship year. Before residents and fellows start their jobs at the Mayo Clinic, they must demonstrate competency in an online safety module. Before a central line is placed by any resident or fellow, he or she must first demonstrate competency in a simulated environment with a cross-functional team. Optimal management of technology must include attention not just to its appropriateness, but also to the reliability of its use.

Diffusion

Effective and efficient technology management to support high-reliability patient care requires a nimble and effective diffusion of best practices as well as safety nets, both within an organization and nation-

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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.
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wide. Unfortunately, to this point diffusion of best practices for technology use has proved slow and inconsistent (Ting et al., 2008; Wennberg et al., 2007a, 2007b).

Social engineering is an important dimension of high reliability and is requisite for optimal technology management. For an organization to know what its people know, there must be fluid communication and diffusion of best practices and lessons from adverse events, and safety nets must be put in place to prevent harm to patients from technology in the inevitably imperfect hands of even the most competent, conscientious healthcare providers. Many institutions aspire to become learning organizations. Our design employs 100-day enterprise teams in which colleagues work collaboratively across the 5 states of our organization. A chief event officer has responsibility for actively diffusing the lessons from significant adverse events involving harm and for ensuring that each of our 22 hospitals has a safety net in place that takes into account the lessons and systems of the other 21 hospitals. Our social engineering includes face-to-face meetings, committees, and the expectation that colleagues at each site will collaborate and incorporate best practices into their own organization (Leach and Philibert, 2006).

An important catalyst for diffusion is transparency. Our efforts at transparency include an enterprise-quality dashboard that displays outcomes, safety, and service using common definitions and processes (Swensen and Cortese, 2008).

Technology itself can play an important role in technology management. IT may serve important roles in optimizing the appropriate use of technology. It may be designed to fill expected knowledge gaps at the point of care. One example is push technology for providers who “don’t know what they don’t know,” with concise recommended care and expert contact information. We have developed an enterprise learning system that, for selected conditions, can help close the knowledge gap. For instance, a patient with an electrocardiogram indicating long-QT syndrome may receive a variety of treatments based on the particular practitioner and that practitioner’s knowledge gap. Today, whenever long-QT syndrome is identified on an outpatient electrocardiogram, a semiurgent notification is sent to the point of care with a link to our enterprise learning system, where instructions for the appropriate care, including antibiotic risks, are delivered with a closed-loop feedback auditable system.

There are also situations in which practitioners know that they lack expertise in treating a condition. In such cases, instead of push technology, practitioners can be directed to a central knowledge repository to learn how best to treat that condition. Today, a knowledge repository could be a textbook, a phone call to a colleague, or the Internet browser for an online search. We are developing a technology called Ask Mayo Expert that makes

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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 the agreed-upon standard best practice, salient risks, and references, along with frequently asked questions and appropriate medical specialty contact information. This is a step toward the most appropriate and reliable use of technology, as well as toward high-reliability patient care.

IT will become an even more important tool as its use expands in health care. It will allow for better analysis of practice patterns and improved research on the most effective approaches, and it will ultimately serve as a critical mechanism for effectively implementing the best practices among the front-line staff caring for patients.

Social Capital

Key to a comprehensive technology management strategy and integral to high-reliability patient care is a conscious investment in social capital (i.e., the active connections among colleagues caring for patients). Social capital investments move an organization from a collection of individuals toward an agile, coherent collective mind.

Optimal organizational learning requires fluent communication of three types: (1) intrateam, (2) interteam and intrasite, and (3) interteam and intersite. The networks must be purposely engineered and nurtured; they must engage research, administrative, and education colleagues. Several aspects of social engineering are worthy of exploration: transparency, teamwork training, horizontal infrastructure, and cross-functional, team-based simulation training. A fundamental tactic in this cultural transformation is the training of health care’s youngest learners, medical and nursing students and residents, together on cross-functional teams.

An integrated medical practice with organized care coordination is an ecosystem well suited to learning. An integrated practice offers a community in which the interests of medical staff, medical school, and hospital leadership are not competing but aligned. It is a structure in which inpatient–outpatient care is seen as a continuum. The hospital is viewed not as a centerpiece, but often as the safety net for insufficient chronic and preventive outpatient care.

Technology management should be approached in a manner that leverages and creates social capital. Decisions should be made with an organizational perspective by cross-functional, physician-led teams. Allocations should be evidence driven, peer reviewed, and based on merit from the patients’ perspective. Individual departments should be advocates and technology experts, not decision makers (because in most organizations the different departments have a financial conflict of interest).

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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

To achieve the goals of technology management and highly reliable patient care, the healthcare industry must foster systems change that leads to continuous learning. Whether the opportunity is a pharmaceutical device or a service, five perspectives need to be addressed to ensure the best outcome:

  1. Policy—Pay for value, not volume.
  2. Appropriateness—There are three opportunities for improvement: over-, under-, and mis-utilization.
  3. Reliability—Appropriate matching of patient needs with technology is futile if there is an inaccurate diagnosis or a complication.
  4. Diffusion—The disciplined spread of best practices must be actively managed.
  5. Social capital—Active interpersonal connections facilitate best use of technology across an organization and the industry.

A LEARNING SYSTEM FOR IMPLEMENTATION OF ELECTRONIC HEALTH RECORDS

David C. Classen, M.D., M.S., Jane B. Metzger, and Emily Welebob, R.N., M.S., Computer Sciences Corporation, University of Utah School of Medicine

Over the past decade, many hospitals and ambulatory care sites have implemented EHR systems to improve the quality and safety of patient care. Yet recent studies reveal that, despite considerable investment in these systems, many organizations have thus far made only limited use of their most powerful capabilities to improve the quality and safety of care (Crosson et al., 2007; Nebeker et al., 2005; Simon et al., 2007; Walsh et al., 2008). This paper reviews current approaches to evaluating the contributions of EHR systems to improving clinical performance and describes a new simulation tool that is designed to help organizations evaluate the effectiveness of currently implemented EHR capabilities in meeting quality and safety goals. The problem of the underuse of EHR capabilities exists in all types of care settings, and the simulation tool can assist in several of these settings. However, this discussion is focused in particular on the hospital environment, where the simulation tool is first being applied. The paper also describes how hospitals have applied the knowledge gained from use of this simulation tool.

The EHR for the hospital is a set of interrelated clinical applications. The process of implementing the EHR involves adding new IT support in

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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.
×

increments over many years in a series of projects. One promise of EHR systems is that they can improve the safety of medication use in the hospital, both at the point at which medications are ordered and when they are administered. The unsafe use of medications is not the only safety problem in the healthcare system, but it is certainly one of the most significant contributors to preventable adverse events. Hence much research on causes, consequences, and ways to avoid incidents of unsafe care has been focused in this area (Bates et al., 1995, 1998; Classen et al., 1991, 1992a, 1992b, 1997; Evans et al., 1994; Leape et al., 1995).

Achieving a reliably safer way to manage medications in the hospital is a major challenge that involves policies, processes, procedures, IT, and a transformational level of change in all of these areas. Furthermore, the changes must be made in concert. Engineers tend to think in terms of process, and this is one way to approach the issue of medication management. From a process perspective, medication management is multidisciplinary and highly complex. Interestingly enough, in most hospitals medication management is carried out largely with manual processes once the medications leave the pharmacy. Even if the process of providing medications goes well, every step of the manual processes must include redundancies (e.g., verification of medication order transcription each shift, double sign-off and signatures on intraveneous pump settings and high-risk medications) and must be monitored prospectively because there are so many opportunities for things to go wrong. Attempts to improve medication management must be undertaken with great care and attention to the many process details to avoid inadvertently introducing new opportunities for errors (Ash et al., 2004).

Many studies have shown that the use of medications in the hospital is very risky for patients (Adams et al., 2008; Bates et al., 1995; Kaushal et al., 2001). Efforts to improve the safety of the medication process should initially be focused on the errors that harm patients rather than on those that do not, even though the latter are far more numerous. When medication-related adverse events are subjected to root-cause analysis, nearly 60 percent are found to originate during the prescribing and transcription steps (Leape et al., 1995).

This finding explains the priority placed on computerized physician order entry (CPOE), which can provide a significant additional safety net during physician ordering and eliminate the need for transcription. CPOE software comes with a set of decision support tools that each hospital can use (Metzger and Turisco, 2001). However, doing so involves instituting new accountabilities and processes for using these tools, and the extent of use of CPOE software varies considerably among hospitals. Hence studies of the impacts of CPOE on patient safety also show variable and often disappointing results (Bates, 1998; Classen et al., 1997; Han et al., 2005; Kilbridge et al., 2001; Koppel et al., 2005).

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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.
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One study of a CPOE system at Brigham and Women’s Hospital in Boston showed a significant decrease in medication errors but a significantly smaller decrease in actual harm to patients (Bates, 1998). A study at another hospital found a much larger decrease in adverse drug events with CPOE (Evans et al., 1998). A study in a pediatric hospital showed that the introduction of CPOE had reduced nonintercepted, serious medication errors by 7 percent, but there was no change in the rate of injuries that resulted from errors. As these studies show, having CPOE in place and operational does not improve safety uniformly, nor does it ensure that the potential contributions to medication safety are being well leveraged.

Given the drive for improvements in medication safety, the limited number of ways to evaluate this aspect of CPOE and other modules of the inpatient EHR, as shown in Table 5-1, is somewhat surprising. One approach that first became available in 2007 is certifying inpatient EHR vendor products on the shelf (Metzger et al., 2007). This approach provides a “seal of approval” showing buyers that the software product incorporates certain essential capabilities, including many related to CDS. Another approach to evaluating these EHR systems occurs in certain pay-for-performance initiatives, which use simple questionnaires to gather information about structural measures—use of IT and certain capabilities—although these initiatives are limited and somewhat embryonic.

Other high-level approaches to evaluating EHR systems are included in the CPOE standards from the National Quality Forum and the Leapfrog Group, which address a limited number of issues related to how the systems are used. The Leapfrog Group’s standard requires physicians and other licensed prescribers to enter more than 75 percent of medication orders electronically, and it also requires that CDS be capable of intercepting at least 50 percent of common, avoidable adverse drug events (Kilbridge et al., 2006b). Until very recently, hospitals self-certified the status of CPOE use as part of the Leapfrog annual survey (Metzger et al., 2008).

Only two of the available evaluation methods can provide hospitals with feedback concerning how well CPOE capabilities are being used to improve medication safety. System use monitoring, although essential for managing CDS, provides insight only into the CDS tools in use, not into gaps in the coverage of common, preventable medication adverse events. Furthermore, the necessary reports are not easily obtained from all of the CPOE products in the marketplace. Because of time and cost, evaluation studies are infrequent and, even in hospitals with a research capacity, focus on limited areas of impact. Evaluation studies also often take years to complete. The simulation tool developed in support of the Leapfrog CPOE standard fills this void.

The Leapfrog CPOE standard has always required proof of the ability of the implemented CPOE to intercept at least 50 percent of the common,

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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.
×

TABLE 5-1 Available Methods for Evaluating the Performance of Computerized Provider Order Entry


Certification of inpatient computerized provider order entry (CPOE) and clinical decision support as part of certification of the inpatient electronic health record (EHR) Commission for Certification of Health Information Technology Information for purchasers concerning necessary capabilities in off-the-shelf products
     
Leapfrog Standards, National Quality Forum (NQF) Safe Practices The Leapfrog Group, NQF Voluntary self-certification of adoption of CPOE, including clinical decision support
     
Structural measures in pay-for-performance programs Various payer-sponsored programs Voluntary self-certification of use of EHRs (sometimes including specific features)
     
System use monitoring EHR-provided reports Provides information about use of order sets and instances of, and responses to, clinical decision support
     
Evaluation study Research study exploring hypotheses about potential impacts of CPOE and other applications that build the inpatient EHR Documents the type and extent of change in hypothesized change areas
     
Measurement of performance Process and outcomes measures concerning inpatient care Provides evidence of the combined effects of improvements in clinical practice and processes, including use of information technology when applicable

 

preventable medication errors that harm patients (The Leapfrog Group, 2008). The simulation tool that makes this possible has been in development since 2001 and recently became available as part of the Leapfrog survey process (Kilbridge et al., 2001, 2006a, 2006b; Metzger et al., 2008). The objective in developing this tool was to provide a credible, remote CPOE evaluation methodology for hospitals to use to assess and self-report the status of CDS tool use. The resulting evaluation tool provides overall scoring for incorporation into the Leapfrog survey results, as well as a status report back to the hospital (Metzger et al., 2008). (Another version of the tool supporting a similar assessment of implemented ambulatory EHRs will be made available at a future date.)

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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 evaluation simulates physician order writing in CPOE, using test patients and a set of test orders. It is a Web-based application, and hospital teams obtain instructions and report results through the Web, as described in Table 5-2. The test addresses ten categories of problem medication orders identified in numerous research studies as the frequent causes of medication-related adverse events and unnecessary costs (duplicate laboratory testing), as shown in Table 5-3. The evaluation also considers nuisance alerting, which is a major barrier to physician adoption. During development, reliability testing, and piloting, the assessment tool has been exercised in approximately 20 hospitals. The experience of two case studies is described in Boxes 5-1 and 5-2.

Thus far, the team has learned four major lessons while developing and testing the CPOE evaluation tool:

  1. Although not all vendor tools are created equal, the use of medication-related decision support depends more on the effort applied to using the tools than on the specific vendor solution. Some hospitals overcome limitations of the CDS toolset with local software customizations.
  2. CDS toolsets in CPOE products now in the marketplace do not address the full range of types of problem medication orders that can lead to patient harm, and many hospitals do not implement the full set of available tools because of usability or manageability issues.
  3. The CDS toolset in CPOE has been applied most aggressively in those hospitals with an advanced, enterprise-wide approach to standards for clinical process and practice, including leadership and significant participation by physicians. In this setting, CDS is directly linked to ongoing quality improvement.
  4. Smaller hospitals that are part of health systems typically benefit from all of the resources and expertise applied to medication safety and CPOE implementation at the health system level and are generally ahead of their peer institutions that undertake these projects on their own.

In every hospital where the evaluation tool has been employed during its long development process, the physician CPOE leaders and other team members have gained knowledge about gaps in CDS coverage of important order categories in addition to confirming what some already knew about CDS usage. The increased insight now available to other hospitals through use of the CPOE simulation tool promises to spur significant progress on the long journey that remains until the full potential of CPOE is realized to help prevent medication-related adverse drug events.

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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.
×

TABLE 5-2 Leapfrog Computerized Provider Order Entry Evaluation Tool Procedure


Steps in the Evaluation Procedure Activities

Register for the computerized provider order entry (CPOE) evaluation

•   Obtain the password used to participate in the Leapfrog survey

•   Sign on to the Web application

•   Enter hospital information

•   Sign up for adult or pediatric evaluation

•   Assemble teams for patient set-up and order entry

•   Ensure that the test system mirrors the production system, or make plans to use the production system

   
Download test patient information (e.g., age, weight, allergies, lab values)

•   When ready to begin set-up for the sample test or full evaluation, sign on to the Web application

•   Print the list of test patients

•   Set up test patients

•   Ensure that patients are “active” (may require nursing unit and bed before orders can be written and signed)

   
Download test orders

•   When ready to begin the sample test or full evaluation, sign on to the Web application

•   Print test orders, instructions, and answer sheets

•   Ensure that the physician performing the evaluation has system authorizations required for order entry in CPOE (may be a test user)

BREAKOUT SESSION: CAPTURING MORE VALUE IN HEALTH CARE

During a breakout session, participants broke into small groups to discuss how to capture more value in health care. They were asked to discuss three issues: (1) how much more value (health returned for dollars invested) could be obtained through the application of systems engineering principles in health care, (2) which one area had the potential for the greatest value to be returned from applying these principles, and (3) which actions could do the most to facilitate the needed changes. The main points of their discussions were reported back to the entire group.

In response to the question of how much value could be obtained from

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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.
×

Steps in the Evaluation Procedure Activities

Enter orders into the CPOE application

•   Enter test orders for specified test patients

•   Sign every test order (or pair of orders)

•   Record the system responses on the answer sheet

•   Discontinue each test order (or pair of orders)

   
Enter and submit results

•   Sign on to the Web application

•   Submit information from the answer sheet as instructed

   
Scoring

•   Use automatic scoring of success in providing decision support to avert common, harmful medication errors for each order category and the evaluation overall

   
Reporting

•   Print or view the feedback report immediately available (scores for each order category)

•   Aggregate the score available for posting along with hospital survey results


SOURCE: Reprinted with permission from Patient Safety & Quality Healthcare. Metzger et al., 2008.

 

the application of systems engineering principles in health care, respondents began by pointing out that the definition of value was problematic. They discussed the fact that value is hard to measure because it is composed of different components that are measured in different ways, including safety, quality and cost. Some groups concluded that value can be construed as a measure with many definitions, and the particular definition used will depend on the stakeholder’s point of view. One group identified the problem of not having a common definition of value among stakeholders as one of the barriers to a patient-centered healthcare system and pointed to the need to align the value space as an interesting point for potential follow-up and additional research. The work of the Commonwealth Commission on High

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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.
×

TABLE 5-3 Medication Order Categories in the Leapfrog Computerized Provider Order Entry Evaluation


Order Category Description Examples

Therapeutic duplication Medication with therapeutic overlap with another new or active order; may be same drug, within drug class, or involve components of combination products Codeine and Tylenol #3
     
Single and cumulative dose limits Medication with a specified dose that exceeds recommended dose ranges or that will result in a cumulative dose that exceeds recommended ranges Ten-fold excess dose of Methotrexate
     
Allergies and cross-allergies Medication for which patient allergy has been documented or allergy to other drug in same category has been documented Penicillin prescribed for patient with documented penicillin allergy
     
Contraindicated route of administration Order specifying a route of administration (e.g., oral, intramuscular, intravenous) not appropriate for the identified medication Tylenol to be administered intravenously
     
Drug–drug and drug–food interactions Medication that results in a known, dangerous interaction when administered in combination with a different medication in a new or existing order for the patient or results in an interaction in combination with a food or food group Digoxin and quinidine
Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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.
×

Order Category Description Examples

Contraindication/dose limits based on patient diagnosis Medication either contraindicated based on patient diagnosis or diagnosis affects appropriate dosing Nonspecific beta blocker in patient with asthma
     
Contraindication dose limits based on patient age and weight Medication either contraindicated for this patient based on age and weight or for which age and weight must be considered in appropriate dosing Adult dose of antibiotic in a newborn
     
Contraindication/dose limits based on laboratory studies Medication either contraindicated for this patient based on laboratory studies or for which relevant laboratory results must be considered in appropriate dosing Normal adult dose regimen of renally eliminated medication in patient with elevated creatinine
     
Contraindication/dose limits based on radiology studies Medication contraindicated for this patient based on interaction with contrast medium in recent or ordered radiology study Medication prescribed known to interact with iodine to be used as contrast medium in ordered head computed tomography exam
     
Corollary Intervention that requires an associated or secondary order to meet the standard of care Prompt to order drug levels when ordering aminoglycoside
     
Cost of care Test that duplicates a service within a time frame in which there are typically minimal benefits from repeating the test Repeat test for digoxin level within 2 hours

SOURCE: Reprinted with permission from Patient Safety & Quality Healthcare. Metzger et. al., 2008.

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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.
×

BOX 5-1
Use of Simulation Tool to Evaluate Computerized Physician Order Entry: Case Study 1

    The Setting

•   Academic medical center

•   Commercially available computerized provider order entry (CPOE) in use for many years

    Lessons Learned

•   Verified poor results in some areas: drug–lab, drug–disease, dose limits

•   Surprising results in drug–drug and drug–allergy interaction checking

•   Pointed out new areas to pursue: wrong route, corollary orders, duplicate test

    Actions Taken

•   Initiated pharmacy review of preconfigured allergy and drug–drug alerts

•   Planned to reduce redundant drug–drug alerting by building from the ground up

•   Reviewed important food allergies and how to handle

•   Began pharmacy/physician review of circumstances in which corollary orders are important

•   Began work with third-party drug knowledge vendor on content needed for dosing-related messages

•   Plan to incorporate new functions into next big rebuild of CPOE

Performance Healthcare Systems3 was cited as an important reference in defining of policy areas that could affect significant cost savings in the system as a way of approaching increased value. Other groups took a pragmatic approach to the question of how much more value could be obtained and based their estimation on the figures presented during the workshop, which had suggested the existence of up to 50 percent waste in the current system. Based on this, they concluded that it was reasonable to assume that a doubling of value was attainable through the application of systems engineering principles. They went on to identify some of the key changes that would be needed to bring about this increased value. These included a realignment of payment incentives away from volume of services, the institution of a comprehensive EHR and health IT system for greater efficiency and as a

 

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3 For more information, see http://www.commonwealthfund.org/Content/Program-Areas/Commission-on-a-High-Performance-Health-System.aspx.

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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.
×

BOX 5-2
Use of Simulation Tool to Evaluate Computerized Physician Order Entry: Case Study 2

    The Setting

•   A 750-bed academic medical center where computerized provider order entry (CPOE) is in use house-wide

•   Very proud of work and accomplishments in safety and quality

    Lessons Learned

•   Only order category covered was drug–allergy checking

•   Some categories (patient-specific dose checking based on renal status, weight) being done in pharmacy application, but not delivered to physicians

    Actions Taken

•   Evaluated order categories in simulation tool against local experience (pharmacist interventions) to assign priorities for advancing clinical decision support (CDS) in CPOE

•   Launched aggressive effort to advance CDS

 

source of data for continuous learning and improvement, and, finally, better systems integration.

Breakout groups were also asked to identify the area in which the greatest value could be returned. Participants pointed to several areas within the healthcare system that were discussed during the workshop and also to some themes that appeared in several presentations. The major area identified was the use of health IT systems in the form of EHRs and a coordinated system for the transfer of knowledge and communication of best practices, as well as a resource for research and improvement. Participants pointed to these information systems as potential conduits for better systemic coordination and informed decision making as a way to increase value.

The area of health provider education was also cited as one that could yield increased value. Participants pointed to the various workshop presentations that touched on the need for change in the culture of the healthcare system and suggested that modifying the way that caregivers are trained would be one way to initiate these changes. They identified several potential modifications to training, including greater interdisciplinary exposure and more emphasis on the team-based nature of modern health care.

Increasing the use of a collaborative approach among caregivers and between disciplines was identified as another area that should be targeted

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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.
×

for increasing value. Increasing efficiency and efficacy through better integration of systems was also discussed, along with the adoption of practices that translate to use and evaluation as a part of execution.

Groups further identified the area of payment as one with great potential to extract increased value. They suggested that incentives be realigned in order to promote best practices instead of favoring greater volume, which the current fee-for-service compensation system rewards.

In response to the question of what actions could do the most to facilitate the changes needed to capture more value in health care, participants returned to some of the areas and themes mentioned previously and described strategies that could be taken to carry out these actions. Participants noted that the particular approach to reform is itself an important consideration. They suggested that reform start with easy, manageable issues and then progress to broader, more difficult reforms. This two-tiered approach would allow for a demonstration of the potential for improvement within the system, and it would give those orchestrating the reform the opportunity to get greater buy-in from stakeholders. One group described the necessity to be prepared to undergo constant evolution and to not have a predetermined end state.

Several groups mentioned the need to encourage a more collaborative approach to the care process and to involve multidisciplinary groups. Participants mentioned the need to overcome barriers created by the current culture in order to allow for more integrated care; reforming the models of education for healthcare providers would be one way to approach this problem. The need for greater collaboration between process engineers and medical professionals was also mentioned as an area for action in achieving higher value from health care. Groups discussed what steps might be taken to encourage greater interdisciplinary research, including changing the way engineers and health professionals are educated and developing funding mechanisms. Specific suggestions included the creation of a master’s of engineering in engineering and healthcare systems and the establishment of combined interdisciplinary institutes for research and practice.

Changes in the availability, implementation, and application of EHRs and health IT were discussed as ways to better communicate best practices, to allow for better analysis of process and outcomes data that could be fed back and used to improve the system, and to create better continuity of care. One group described the health IT system as the glue that ties everything together and makes it act like a system. In order to achieve connectedness, however, interfaces between technology and users need to be redesigned to allow for ease of use and seamless integration into the care process. Steps in creating a successful health IT system will include using simulation to validate the systems before implementation and inculcating the expectation that systems will improve with use and learning over time.

Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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 data from health IT systems to model and optimize care processes would be a natural application of systems engineering to health care. One of the groups discussed the value of examining existing processes to get a better understanding of what needs to be done, what may be done better, and what may not need to be done at all and then using this evaluation as a basis to reengineer systems.

Several groups shared ideas for specific projects or approaches that could take the field further down the path to greater value. Exploiting the EHR system as a resource for research through data mining was one suggestion. Another was to combine healthcare economics models with process engineering models in order to get a better grasp on measuring value and outlining strategies for further action. One group recommended subjecting healthcare processes in which engineering is particularly experienced, such as resource allocation and queuing prioritization, to more rigorous study through the lens of operations research. Additionally, there was widespread support for an effort to clarify nomenclature between the two fields in order to simplify future collaboration. Development of best practices that incorporate systems engineering principles was discussed, as well as the creation of a web portal for the dissemination of these best practices; this portal could be supervised by a joint IOM/National Academy of Engineering committee or subcontracted to a university. Participants suggested that the financial engineering community should be engaged to design more effective incentives for wellness was suggested. Finally, several groups reiterated the need to better define value in the context of a learning healthcare system and from the perspective of all of the stakeholders involved. This would allow the creation of processes that measure value and make it possible to include value in decision-making processes.

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Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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.
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Suggested Citation:"5 Fostering Systems Change to Drive Continuous Learning in Health Care." 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.
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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.

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