Information technology (IT) is a key requirement for implementing the components of the committee’s conceptual framework for a high-quality cancer care delivery system. Health IT1 has an important role to play in improving the quality of cancer care delivery, patient health, cancer research, quality measurement, and performance improvement. In the committee’s diagram of its conceptual framework (see Figure S-2), IT supports patient-clinician interactions by providing patients and clinicians with the information and tools necessary to make well-informed medical decisions. Health IT plays a critical role in developing the evidence base from research (e.g., clinical trials and comparative effectiveness studies) and capturing data from real-world settings that researchers can then analyze to generate new knowledge. Further, health systems can use health IT to collect and report quality metrics data and to facilitate the implementation of performance improvement initiatives, and it allows payers to identify and reward high-quality care.
The role of health IT has been transformed and greatly expanded since the publication of the Institute of Medicine’s (IOM’s) 1999 report on the quality of cancer care, which discussed a limited role for health IT in collecting quality metrics data (IOM and NRC, 1999). Several more recent IOM reports have emphasized the potential for health IT to improve the
1 The Institute of Medicine has defined health IT as a broad range of products. “It encompasses a technical system of computers and software that operates in the context of a larger sociotechnical system—a collection of hardware and software working in concert within an organization that includes people, processes, and technology” (IOM, 2011b, p. 2).
quality of care. In Crossing the Quality Chasm, the IOM recommended “a renewed national commitment to building an information infrastructure to support health care delivery, consumer health, quality measurement and improvement, public accountability, clinical and health services research, and clinical education” (IOM, 2001, p. 17). In Best Care at Lower Cost: The Path to Continuously Learning Health Care in America (hereinafter referred to as the Best Care consensus report), the IOM concluded that advances in health IT could improve many features of the health care system, including patient-clinician communication, clinical decision support, capturing the patient experience, population surveillance, planning and evaluation, and the generation of knowledge (IOM, 2012a).
A number of other organizations have also elaborated on the important role of health IT in care delivery and research, citing improvements in patient-centeredness, health outcomes, cost savings, safety, public health monitoring, and the conduct of clinical trials (AHRQ, 2012; Hillestad et al., 2005; Kellermann and Jones, 2013; PCAST, 2010; RAND Health, 2005). The American Society of Clinical Oncology (ASCO) envisions that by 2030 health IT will be the major mechanism for collecting, analyzing, and learning from “big data” in order to drive change in the delivery of care (ASCO, 2013b).
Several national events have pushed the health care sector toward the adoption of health IT. In his 2004 State of the Union Address, President George W. Bush announced the national goal of “wider use of electronic records and other health information technology, to help control costs and reduce dangerous medical errors” (Bush, 2004, p. 344). He followed this announcement with an Executive Order establishing the Office of the National Coordinator for Health Information Technology (ONC), which is charged with overseeing a nationwide effort to create an IT-enabled health care system (ONC, 2013a). The Health Information Technology for Economic and Clinical Health (HITECH) Act of 20092 mandated the continuation of ONC and provided billions of dollars in incentives for clinicians and hospitals to adopt electronic health records (EHRs).
Many of the anticipated gains in the quality of care from health IT, however, have been slow to materialize. A National Research Council report found that the “nation faces a health care information technology chasm that is analogous to the quality chasm highlighted by the IOM over the past decade” (NRC, 2009, p. 5). Clinicians’ and hospitals’ adoption of health IT has been slow, despite the incentives created by the HITECH Act (Kellermann and Jones, 2013), and the EHRs that clinicians use lag behind technological advances in other fields (Mandl and Kohane, 2012).
2 Title XIII of the American Recovery and Reinvestment Act of 2009, Public Law 111:5, 111th Cong., 1st sess. (February. 17, 2009).
Patients have also failed to take full advantage of the benefits of health IT in managing their care (Yamin et al., 2011).
In organizations that have implemented health IT, clinicians have sometimes resisted investing the time and effort necessary to master the use of the technology. Originally designed for billing and coding purposes, health IT systems have not been integrated efficiently into clinical care, do not facilitate the coordination of care, and the need to customize local systems has created a situation where health IT systems cannot communicate with each other (Bitton et al., 2012; Campbell et al., 2009; Cimino, 2013; Kellermann and Jones, 2013; Mandl and Kohane, 2012; McDonnell et al., 2010; Yasnoff et al., 2013). Many of these systems are inflexible and thus are unable to adapt to the changing needs of a modern health care system (NRC, 2009). In addition, the promised cost savings from implementing health IT have not been fully realized (Kellermann and Jones, 2013). These problems are especially challenging in cancer care, which involves a complex disease, multiple clinicians, and complex treatment decisions (see Chapter 1 for further discussion of the unique characteristics of cancer care).
This chapter presents the committee’s vision for a learning health care system that uses IT to improve the quality of cancer care. The chapter focuses on components of IT that support a learning health care system. Other topics relevant to the use of IT in improving the quality of cancer care are discussed elsewhere in this report. Patient and clinicians’ use of web-based information and decision aids is discussed in Chapter 3 and telemedicine is discussed in Chapter 4; a more general discussion of health IT is outside the scope of this report.
The first section of this chapter provides a description of the committee’s vision and outlines how health IT can meet the needs of all of the stakeholders discussed throughout this report, including patients, clinicians, researchers, quality metrics developers, and payers. Subsequent sections describe the challenges to creating a health IT system that meets stakeholders’ needs, as well as potential paths to implementation. Much of the evidence base for this chapter is derived from a large body of previous work conducted by the IOM on a learning health care system, including several workshop summaries produced by the Roundtable on Value & Science-Driven Health Care and the National Cancer Policy Forum, as well as the recent Best Care consensus report (IOM, 2007, 2011a, 2012a,b). In addition, the committee conducted a literature search, from 1999 to the present, for articles relating to health IT in cancer care.3 It also solicited
3 The literature search was conducted by Amy McLeod, Administrative Fellow, The University of Texas MD Anderson Cancer Center.
input from several professionals knowledgeable about health IT.4 The committee’s recommendation on health IT addresses the identified gaps.
The committee’s vision for health IT in a high-quality cancer care system calls for a learning health care IT system. The concept of a learning health care system gained prominence in 2007 (Eddy, 2007; Etheredge, 2007; Kupersmith et al., 2007; Liang, 2007; Lumpkin, 2007; Neumann, 2007; Pawlson, 2007; Perlin and Kupersmith, 2007; Platt, 2007; Slutsky, 2007; Stewart et al., 2007; Tunis et al., 2007; Wallace, 2007). The IOM subsequently explored the development and application of a learning health care system for improving the quality of care (IOM, 2007, 2010, 2012a,b). A learning health care system can be described as a system that:
Uses advances in IT to continuously and automatically collect and compile from clinical practice, disease registries, clinical trials, and other sources of information, the evidence needed to deliver the best, most up-to-date care that is personalized for each patient. That evidence is made available as rapidly as possible to users of a [learning health care system], which include patients, physicians, academic institutions, hospitals, insurers, and public health agencies. A [learning health care system] ensures that this data-rich system learns routinely and iteratively by analyzing captured data, generating evidence, and implementing new insights into subsequent care. (IOM, 2010, p. 7 [adapted from Etheredge, 2007])
Thus, a learning health care system uses IT to “learn” by collecting data on care outcomes and cost in a systematic manner, analyzing the captured data both retrospectively and through prospective studies, implementing the knowledge gained from these analyses into clinical practice, evaluating outcomes of the changes in care, and generating new hypotheses to test and implement in clinical care (Abernethy et al., 2010).
There are several distinguishing characteristics of a learning health care system. Foremost, clinical practice and clinical research would be intimately linked. The flow of information would not be linear from clinical research to clinical practice; it would be circular, with information from clinical practice feeding back to clinical researchers in order to generate new knowledge and
4 John Frenzel, Chief Medical Information Officer, The University of Texas MD Anderson Cancer Center; Daniel R. Masys, Affiliate Professor, Biomedical and Health Informatics, University of Washington; Stephen Palmer, Director, Office of e-Health Coordination, Texas Health and Human Services Commission; Adam Schickedanz, IOM Fellow and Pediatrics Resident, University of California, San Francisco; and Peter Yu, Director of Cancer Research, Palo Alto Medical Foundation.
hypotheses for testing. The process of developing new knowledge would be built directly into the health care delivery system. A learning health care system would be designed to expect and accommodate a continuous process for updating what constitutes best evidence and clinical practices.
To support this ongoing process, a learning health care system would facilitate the collection and analysis of big datasets, including genomics data and other complex biomarkers. It would promote the rapid translation of evidence into clinical practice via clinical decision support for clinicians. In addition, a learning health care system would provide tools that engage and empower patients in making decisions about their own care. The achievement of these aims would require payers to create reimbursement incentives that support a system of learning and a health care system that adopts a culture of learning (IOM, 2007, 2010, 2012a). Table 6-1 summarizes fundamental characteristics of the ideal learning health care system.
Science and Informatics
Real-time access to knowledge – A learning health care system continuously and reliably captures, curates, and delivers the best available evidence to guide, support, tailor, and improve clinical decision making and care safety and quality.
Engaged, empowered patients – A learning health care system is anchored in patient needs and perspectives, and promotes the inclusion of patients, families, and other caregivers as vital members of the continuously learning care team.
Incentives aligned for value – In a learning health care system, incentives are actively aligned to encourage continuous improvement, identify and reduce waste, and reward high-value care.
Leadership-instilled culture of learning – A learning health care system is stewarded by leadership committed to a culture of teamwork, collaboration, and adaptability in support of continuous learning as a core aim.
SOURCE: IOM, 2012a, p. 138.
Many elements which are essential to a learning health care system are already in place for cancer care (Abernethy et al., 2010; IOM, 2010). As mentioned above, the HITECH Act created new incentives for physicians and hospitals to adopt EHRs, which are “real-time patient-centered records … [that] contain information about a patient’s medical history, diagnoses, medications, immunization dates, allergies, radiology images, and lab and test results” (ONC, 2013d). The Centers for Medicare & Medicaid Services (CMS) is developing meaningful use standards to ensure that EHRs are not just digital versions of paper medical charts that statically record information, but rather, information systems that support clinical decision making, advance clinical processes and workflow, and facilitate data capture and sharing between clinicians and health organizations (ONC, 2013e; Yu, 2011).
Each meaningful use stage requires more demanding standards for EHR use: collecting and using data (Stage 1); using health IT to improve and coordinate care (Stage 2); and capitalizing on clinical decision support and data collection to improve health outcomes (Stage 3) (ONC, 2013e). Stage 1 of meaningful use has been fully implemented by the clinicians and hospitals participating in the CMS program. Clinicians and hospitals will have to comply with Stage 2 starting in 2014, and the comment period for Stage 3 has ended, with Stage 3 standards scheduled to be implemented in 2016. In response to these standards, many academic and community cancer centers are implementing EHR systems that will ultimately enable them to collect data in real-time on every patient.
There are numerous other potential sources of data for a learning health care system in cancer. These include cancer registries, which capture important information on new cancer diagnoses, including the incidence and types of cancer, the anatomic location, stage at diagnosis, planned first course of treatment, and outcome of treatment and clinical management. This information is somewhat limited (i.e., registries only capture a narrow range of health outcomes, initial treatments, and a small segment of the cancer patient population), but could be broadened through a learning health care system. Some of the major cancer registries in the United States include (1) the National Cancer Institute’s (NCI’s) Surveillance, Epidemiology, and End Results program, which captures cancer incidence and survival data from 28 percent of the U.S. population using data provided by high-quality state and local cancer registries; (2) the Centers for Disease Control and Prevention’s (CDC’s) National Program of Cancer Registries, which supports statewide, population-based cancer registries from 45 states and the District of Columbia, Puerto Rico, and the U.S. Pacific Island jurisdictions, and covers 96 percent of the population; and (3) the Commission on Cancer’s (CoC’s) National Can-
cer Database, which aggregates cancer registry data from approximately 1,500 CoC-accredited institutions (ACoS, 2013; CDC, 2012; NCI, 2013c).
A learning health care system for cancer care would also be supported by a robust infrastructure for clinical trials on cancer; namely, the NCI National Clinical Trials Network (NCI, 2013d). Data from these trials could feed into a learning health care system to provide insights into new and existing cancer treatments. In addition, many biorepositories for cancer are linked with clinical data, genetic data, and environmental data, which could generate new knowledge in a learning health care system (Etheredge, 2013).
A learning health care system for cancer care, as envisioned by the committee, does not yet exist. There are, however, many ongoing efforts to develop prototypes and small-scale learning health care systems that will help demonstrate that the committee’s vision is feasible. Table 6-2 provides a description of several ongoing efforts to develop this type of system: CancerLinQ and the Sentinel Initiative are national efforts to create a learning health care system, and Kaiser Permanente’s HealthConnect is an example of a learning health care system within an integrated health care organization.
A number of other integrated health care organizations are also creating learning health care systems, including the Department of Veterans Affairs, Intermountain Healthcare, and Group Health (Greene et al., 2012; Starr, 2013; VA, 2013). The Patient-Centered Outcomes Research Institute (PCORI) is investing $68 million to support the development of a National Patient-Centered Clinical Research Network (PCORI, 2013a; Selby et al., 2013). In addition, the Center for Learning Health Care at Duke University is an academic initiative facilitating continuous learning (DCRI, 2013). Efforts to implement key components of a learning health care system are discussed in the next sections of this chapter.
A learning health care system facilitates patient engagement. As discussed in Chapter 3, the committee’s conceptual framework envisions a high-quality cancer care system that actively engages patients in their care and supports them in making informed medical decisions that are consistent with their needs, values, and preferences.
Several characteristics of a learning health care system are important to patient engagement, including patients’ online access to their EHRs, clinicians’ notes, care plans, and relevant clinical information about their conditions (Walker et al., 2011). The system would allow patients to self-report their health status, side effects of treatment, and other experiences as they happen (Cheng et al., 2011). Many mobile devices, such as smart-
|CancerLinQ||CancerLinQ is the American Society of Clinical Oncology’s (ASCO’s) initiative to create a learning health care system for oncology practices. It will curate and analyze data from electronic health records (EHRs), clinical trials, and clinical practice guidelines. It is in the early stages of development. A demo of the program was presented at the ASCO Quality Symposium in 2012 using data from breast cancer patients.|
|Kaiser Permanente’s HealthConnect||In 2002, Kaiser Permanente contracted with Epic Systems Corporation to create and implement HealthConnect. This is an integrated EHR system that stores information from multiple systems within Kaiser Permanente and presents a longitudinal patient record. The information captured includes demographics, progress notes, active/historical problems, medication records, vital signs, medical history, immunization, preventive health milestones, lab data, and radiology reports. It is designed to allow clinicians to easily document patient encounters, diagnoses and procedures, and clinical notes. It also allows patients and clinicians to electronically message each other. MyHealthManager gives patients the opportunity to see and access their health record. It supports the clinical workforce by providing decision support, capturing quality metrics data, informing clinicians of their concordance with clinical practice guidelines, and including a robust search method of previous treatments and outcomes. HealthConnect encompasses an advanced clinical decision support system for oncology, including 230 standardized protocols for the major adult cancers as well as alerts when patients are eligible for clinical trials. The EHR system captures the goals of therapy and also monitors for potential medication errors and drug interactions.|
|Sentinel Initiative||The Food and Drug Administration announced the Sentinel Initiative in 2008. The goal of this system is to monitor patient safety in the United States. Initially, this program will rely on EHR and administrative data that medical practices, hospitals, delivery systems, health plans, and insurance agencies routinely collect to monitor safety. Eventually, it may also use data from disease registries, vital statistics registries, and repositories of genomics data. The Mini-Sentinel pilot is up and running. It includes 17 data partners and encompasses data from nearly 100 million people. Participating organizations use a distributed data network that allows them to retain their data and provide the centralized network with a standardized data summary.|
SOURCES: ASCO, 2013a; FDA, 2011, 2013; KP, 2011a; Platt et al., 2009; Wallace, 2007.
phones and tablets, could assist with this monitoring process and send patients reminders to take their medications at the correct time or report information to their clinicians (Cheng et al., 2011; West, 2012). The result of these self-reports would be captured in the patients’ EHRs, which the cancer care team would monitor. If any of the patient-reported information warrants special attention by the cancer care team, the team would get an electronic notice to follow up with the patient, thus reducing the likelihood of patients needlessly suffering from adverse events or severe symptoms.
The benefits of these elements are supported by the evidence. Reminder systems triggered from data in patients’ EHRs can lead to patients’ improved adherence to treatment protocols and screening recommendations (Din et al., 2005; Nease et al., 2008; Sequist et al., 2009; Shea et al., 1996). In a study where patients were invited to read their clinicians’ notes, patients accessed their EHRs regularly and reported that this was a positive experience; the clinicians reported this had a minimal impact on their workflow (Delbanco et al. 2012).
Moreover, studies show that clinicians value patient-reported information, patients are willing to self-report their symptoms, and collecting patient-reported outcomes leads to patients who are more satisfied with their care as well as improvements in symptom management and patients’ overall quality of life (Abernethy et al., 2009; Basch and Abernethy, 2011; Basch et al., 2005, 2007; Detmar et al., 2002a,b; Greenhalgh and Meadows, 1999; Snyder et al., 2010; Taenzer et al., 2000; Velikova et al., 2004). In addition, patients are more likely to accurately report sensitive information, such as answering sexuality-related questions, in an electronic reporting system than during live encounters with their cancer care team (Dupont et al., 2009).
A learning health care system would also facilitate patient-clinician communication through electronic messaging and appointment scheduling. Patients would be able to email or message their clinicians in real time, have their questions answered, their EHR updated with any pertinent information, and schedule follow-up office visits. Patients value this feature because it can save them time and visits to their clinicians’ offices, and has the potential to improve care (Chen et al., 2009; Din et al., 2005).
At Group Health Cooperative of Puget Sound, for example, about two-thirds of the patients communicate with their care team electronically (Cohn, 2013). Unfortunately, clinicians in many health care systems have been slow to adopt electronic communication due to the challenges of incorporating patient-reported outcomes into the delivery system, the time it takes busy clinicians to review and respond to electronic communications, and the current reimbursement system’s failure to reward these services (Feeny, 2013; Wallwiener et al., 2009). Incentivizing clinicians to
A learning health care system would also provide patients with educational material and decision aids at key times during their course of treatment. Currently, clinicians may provide patients with overwhelming amounts of information about their treatment without sensitivity to when a patient will actually need critical information. Smart use of patient portals within a learning health care system would push information and decision aids to patients at specific times (e.g., when patients schedule certain types of appointments) and provide patients with information about their prognosis, treatment options, treatment effects and side effects, advance care planning, and anticipated cost of care in a time-sensitive manner.
In addition, as discussed below in more detail, patients would benefit from a learning health care system’s ability to improve the coordination of care, enhance researchers’ and clinicians’ ability to generate new knowledge to inform clinical practice, and facilitate the process of making quality metrics transparent and publicly available.
Clinical Workforce Needs
The committee’s conceptual framework envisions an adequately staffed, trained, and coordinated workforce for cancer care (see Chapter 4). This includes competent, trusted, interprofessional cancer care teams that are aligned with patients’ needs, values, and preferences, and that provide care coordinated with patients’ primary/geriatrics and specialist care teams. A learning health care system can make this vision a reality by improving the workforce’s knowledge of clinical research and best care practices, and by promoting care coordination.
An integral element of a learning health care system is clinical decision support, which can be defined as a system that provides clinicians with “person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care” (ONC, 2013b). Decision support is important in clinical practice because the amount of new evidence clinical researchers are generating each year “exceed(s) the bounds of unaided human cognition” (Masys, 2002, p. 36).
Research suggests that clinical decision support can influence treatment selection and the ordering of tests, prevent medication errors, and ensure the safe dosage of drugs (Kralj et al., 2003; Neilson et al., 2004; Potts et al., 2004; Schedlbauer et al., 2009). It can also be used to guide clinicians’ decisions about molecularly targeted medicine (Pulley et al., 2012). The Agency for Healthcare Research and Quality conducted a
systematic review of clinical decision support systems and identified the following list of characteristics as important in making these systems successful at improving care:
• Automatic provision of decision support as part of the clinician’s workflow
• Provision of decision support at the time and location of decision making
• Provision of a recommendation, not just an assessment
• Integration with the charting or order entry system to support workflow
• Promotion of action rather than inaction
• Elimination of the need for additional clinical data entry
• Justification of decision support via research evidence
• Local clinician involvement in development
• Provision of decision support results to patients, as well as clinicians (Lobach et al., 2012)
Clinical decision support is particularly important in cancer care due to the complexity of the disease, the diverse treatment options available, and the enormous body of research relevant to clinical care. Clinicians working in cancer would benefit from clinical decision support that provides guidance on the specific options for therapeutic interventions and diagnostic tests, flags potential patient safety concerns (e.g., drug-drug interactions at time of prescribing), and identifies patients who need preventive services or who are at risk for certain adverse side effects.
Because much of the research on clinical decision support has been conducted in areas of health care outside of cancer, additional research needs to be conducted to identify the most effective design features and timing of clinical decision support for the workforce providing cancer care (Clauser et al., 2011; Pearce and Trumble, 2006). In addition, the content of the clinical decision support should be kept current and continually updated with the results of new clinical trials and observational studies. Masys has argued that a learning health care system should meet this requirement by including a “national cancer course guidance infrastructure,” analogous to the Federal Aviation Administration’s course guidance database (see Box 6-1).
Many EHR vendors are seeking to include clinical decision support for cancer care in their products. For example, Epic Systems Corporation, one of the major EHR vendors, has a medical oncology module that provides information on diagnostic staging, treatment options, chemotherapy dosing schedules, and personalized treatment planning (KP, 2011a). A number of cancer centers are also working with IBM to train the
Efforts to improve the consistency and safety of health care have drawn on the experience and process of other high-risk industries, and parallels between commercial aviation and health care have been cited since the first of the Institute of Medicine Quality Chasm reports, To Err Is Human, was published in 1999 (IOM, 1999). One of the most dramatic transformations in aviation has been the supplementation of paper charts and narrative text for critical aspects of flight with an electronic course guidance infrastructure. The U.S. Federal Aviation Administration maintains a series of continuously updated databases of system routes, safe approach paths, destinations, and topographic coordinate data, which is available for downloading by users and commercial developers of navigation systems and autopilots (FAA, 2013). When downloaded onto plug-in media, these data give each aircraft a set of “evidence-based” guidance that, when linked to realtime global positioning system data and other forms of radio navigation, enable autopilot-equipped aircraft to fly complex route patterns, departures, and arrivals with precise, second-by-second automated course monitoring and guidance.
These data have transformed the task of piloting an aircraft from one of eye-hand coordination and physical manipulation of controls into a task of selecting a destination, choosing an appropriate route, entering that plan into the systems that control the aircraft’s vertical and lateral movement, and then monitoring whether the flight is proceeding according to the plan. Pilots retain the legal responsibility for the safe conduct of all of the events from takeoff to touchdown, and frequently encounter circumstances that require the plan to be revised as the journey progresses. But the actual flight path taken does not require their minute-by-minute, hands-on movement of the flight controls, and the hundreds of individual control inputs needed in the correct sequence are part of the electronic interaction between the database’s representation of the ideal course and the actual course being flown. From the pilot’s perspective, this electronic infrastructure dramatically reduces the burden of reading, remembering, and translating a flight plan into physical actions in a safety-critical environment.
Cancer care has a long history of being guided by clinical practice guidelines, wherein diagnostic and therapeutic protocols include dozens of carefully sequenced clinical observations and interventions that require an orchestrated team effort; that effort commonly requires the members of the team to process human-readable documents and manually translate them into a time-sensitive, patient-specific plan. Thus, cancer care is well positioned to take advantage of guidance technologies analogous to those used in aviation. The infrastructure for implementing patient-specific clinical decision support exists and is operational at
a small number of leading health centers in the United States. To achieve broad implementation and the benefits of a learning health care system at a national scale, additional research, development, deployment, and evaluation are needed in the following areas:
1. Standards for clinical decision support “modules” that encode the recognition logic (as represented in data recorded in electronic health record [EHR] systems) of the clinical condition for which evidence-based guidance is available. This specification for recognizing when the guidance applies would be packaged together electronically with the educational information to be displayed to clinicians, patients, and families when a decision needs to be made. That information would include the actionable options available, and the specification for the sequence of events that constitute the plan actually chosen (e.g., the computer-interpretable schema of a multi-agent chemotherapy regimen and its monitoring parameters), along with the downstream parameters that would constitute evidence of a successful or unsuccessful health outcome.
2. A public library of clinical decision support hosted by a neutral and respected source, from which health care organizations could download decision support modules, and to which they could upload their observed experience using them. Although federal entities such as the U.S. National Library of Medicine would be potential clearinghouses for the health care course guidance data, a community-based Wikipedia-like resource hosted by a not-for-profit entity is a feasible alternative. The Agency for Healthcare Research and Quality’s Clinical Decision Support Consortium (Middleton, 2009), for example, might serve in a dissemination and data exchange role.
3. Standards and software tools for importing electronic guidance data into the decision support components of EHR systems, along with easy-to-use visualization and editing tools that would enable local practice committees to understand, modify, and implement organization-wide guidance for care.
4. Standards and software tools for collecting data on the organizational experience of using the decision support modules, the subsequent health outcomes of individual cases where the guidance was accepted, along with outcomes where the guidance was given, but not implemented by providers, and methods for uploading that aggregate within-organization experience back to the Public Library of decision support. Within the Public Library, those experiences of organizations using the same decision support infrastructure would be pooled together.
SOURCE: Personal communication, D. Masys, University of Washington, August 9, 2012.
Watson Computer to help clinicians with complex diagnostic and treatment decisions in oncology (Cohn, 2013; Kohn, 2012). This is the same computer that went on Jeopardy! and beat several human champions. IBM sold the technology supporting the Watson Computer to WellPoint Inc. and Citigroup Inc., and these groups expect it to generate revenue by 2015 (Jinks, 2013).
A learning health care system would also support clinicians’ decision making in circumstances where there is little to no evidence about the benefits and harms of various treatment options. For example, Hoffman and Podgurski (2011, p. 425) proposed using health IT to enable “personalized comparisons of treatment effectiveness.” In their framework, a clinician would be able to search the deidentified EHRs of a cohort of patients who are clinically similar to a patient in question for potential treatments and health outcomes. This feature would enable clinicians to use previous patients’ experiences in the health care system to guide future care. Frankovich and colleagues operationalized this concept using EHRs from Stanford University to identify the best way to treat a 13-year-old girl with systemic lupus erythematous (Frankovich et al., 2011). For that case, clinicians conducted a search of other EHRs in less than 4 hours and developed a treatment plan. In a learning health care system, this type of search would become regular practice.
In addition to guiding clinical decisions, a learning health care system would facilitate a coordinated cancer care workforce (Bitton et al., 2012; Forti et al., 2005; Galligioni et al., 2009). The use of health IT to coordinate care is particularly important for cancer because of the diverse professional teams providing care and the multiple transitions in care between primary care/geriatrics care teams, the cancer care team, and other specialist care teams. A learning health care system would provide individual members of the cancer care team with a mechanism for easily sharing information with each other, as well as with the primary care/ geriatrics care team.
As cancer care becomes increasingly based on clinical practice guidelines, nonphysician professionals will likely play a larger role in routine cancer care. For example, ASCO envisions nurse practitioners and physician assistants using clinical decision support embedded in a learning health care system to deliver the majority of cancer care in the future. The oncologist’s role would evolve to focus on managing the care teams, overseeing the development of care plans, collaborating with primary care/geriatrics care teams, and overseeing complex cases (ASCO, 2013b). Such a change in the provision of cancer care would address the projected workforce shortages (see Chapter 4) and would require a heightened level of coordination between the team of professionals providing the care. A learning health care system would support this shift by enabling
improved communication, assigning tasks, and monitoring and updating patients’ care plans.
A learning health care system would also enhance clinicians’ abilities to recruit patients to clinical trials. As noted in Chapter 5, very few adults with cancer participate in clinical trials and the individuals who do participate are often unrepresentative of the broader population with the disease. A computerized notification system that identifies trials for potentially eligible patients would improve this situation. For example, Kaiser Permanente has embedded alerts into its EHR system that notify clinicians and patients of potentially relevant trials (KP, 2011a). The challenges to creating an effective clinical trial notification system include keeping the list of potential trials current, using consistent terminology for categorizing trials (e.g., “stage IV” vs. “metastatic”), and including the location of the trials (Monaco et al., 2005).
A learning health care system would support the clinical workforce by enhancing communication between clinicians and insurance companies. One estimate found that the average U.S. physician spends 3 hours each week interacting with insurers (Casalino et al., 2009). IBM’s Watson, for example, includes a button that allows clinicians to send a treatment proposal to an insurance company for rapid reimbursement approval (Cohn, 2013). The Patient Protection and Affordable Care Act5 supports electronic communication between payers and clinicians by requiring uniform standards and operating rules for electronic transactions (CMS, 2013).
Finally, learning health care system would also monitor and capture data from clinical encounters, provide clinicians with a report on the concordance of their care with clinical practice guidelines, and inform clinicians about how their performance compares to that of their peers. As discussed in more detail below in the section on Challenges, extracting and analyzing data in a learning health care system is an incredibly complex process and will likely require advances in IT, natural language processing, and analytics in order to become reality.
Cancer Research Needs
In Chapter 5, the committee acknowledges the role that health IT could play in improving the evidence base for high-quality cancer care. A learning health care system would allow researchers to conduct powerful new types of observational studies by utilizing all of the data captured during real-world clinical encounters and integrating it with data cap-
5 Patient Protection and Affordable Care Act, Public Law 111-148, 111th Congress, 2nd Sess. (March 23, 2010).
tured from other sources (e.g., cancer registries, clinical trials, administrative claims databases).
Most datasets currently available for observational studies are small and at risk of bias. The larger databases are narrow in scope (e.g., administrative databases and adverse event reporting systems) and cannot be used to answer broad clinical questions. A learning health care system would address these shortcomings by pooling data from multiple sources to create a very large database (or a number of integrated databases) that would include a diverse population in terms of gender, geography, ethnicity, age, educational level, socioeconomics, and disease/health characteristics. Such a database would provide an enormous quantity of data about older adults and individuals with comorbidities from real-life clinical encounters that researchers would be able to analyze. For example, researchers have used the Department of Veterans Affairs’ National Surgical Quality Improvement Program database to pool an enormous numbers of patients (+300,000) to examine the effects of perioperative anemia and polycthemia on postoperative outcomes in older veterans (Wu et al., 2007). It would also capture data on the off-label use of cancer drugs and facilitate the Food and Drug Administration’s surveillance of drugs on the market that were granted accelerated approval (Abernethy et al., 2010).
To reach its full potential for research, a learning health care system would need to enable researchers to link patient-level data across databases and time, collect data relevant to the quality of cancer care (e.g., functional status, comorbidities), and allow patients to enter information into their EHR about their symptoms. This type of observational research has many advantages over clinical trials because it can be conducted quickly, is less expensive, and analyzes real-world clinical practice.
In addition, a learning health care system would facilitate genomic research by providing researchers with the large numbers of patients necessary to understand the biological complexity of cancer. As noted in Chapter 2, there has been a trend in cancer treatment toward molecular targeted interventions, particularly because collecting molecular data on individual patients has become less expensive and clinicians’ understanding of molecular medicine has rapidly increased. A learning health care system would allow researchers to identify patients for clinical trials who have the relevant molecular markers. Researchers would also be able to augment clinical trial data by using EHRs to gather additional patient characteristics and fill in missing clinical details.
For example, in the United Kingdom, the Patient Pathway Manager integrates patient data from EHRs with research data. Researchers are then able to correlate demographic and clinical information (e.g., age, diagnosis, staging, treatment, time of treatment) with study data. The system protects patient privacy by providing different levels of access
to patient data for authorized clinical staff and researchers (Newsham et al., 2011). Similarly, there are a number of large biorepositories that link individual genetic data to EHRs, such as Kaiser Permanente’s biobank, the Department of Veterans Affairs’ Million Veteran Bank, The National Human Genome Research Institute’s Electronic Medical Records and Genomics (eMERGE) Network, and the United Kingdom’s National Biobank (KP, 2011b; Kupersmith and O’Leary, 2012; McCarty et al., 2011; Wellcome Trust, 2013).
Quality Metrics Development Needs
The committee’s conceptual framework for high-quality cancer care requires a system that will measure and assess progress in improving the delivery of cancer care, publicly report that information, and develop innovative strategies for performance improvement (see Chapter 7). A learning health care system, that collects, analyzes, and reports on quality data in real-time, is essential for achieving this goal. It would facilitate the capture of clinical and patient-reported data in EHRs, allowing researchers to measure both the proficiency of care and patients’ experiences with care. It would also allow the translation of meaningful quality metrics data back to the point of care to inform clinicians about their performance and to foster improvement. Through such a process, the cancer care team would learn about the concordance of their care with clinical practice guidelines and how their care compares to the care provided by their colleagues. Providing this information to the cancer care team could, in and of itself, drive improved care through clinicians’ desire for self-improvement and assurance that they are providing comparable or better care than their colleagues (Lamb et al., 2013). In addition, a learning health care system would offer the necessary infrastructure for transparently reporting quality metrics in a way that meets the needs of clinicians, patients, and payers. These changes require that a learning health care system go beyond simply documenting care processes and that clinicians apply any knowledge gained to improve the quality of care.
Few EHR systems, however, currently capture quality metrics data reliably. Much of the information that would feed into those metrics is unstructured within clinicians’ notes (Jha, 2011). Advances in natural language processing could address this problem by enabling computers to analyze the context of words and phrases within clinicians’ notes, making the information for quality metrics available electronically (Murff et al., 2011). EHR systems could also lead to clinical data having more standardized content and structure for use in assessing quality metrics.
In addition, Section 601(b) of the Taxpayer Relief Act of 2012 could increase the volume of data collected for quality metrics. This provision
creates an incentive for clinicians to submit more data on the quality of care to existing disease registries (including cancer registries).
As discussed in Chapter 7, a major challenge to the collection of quality metrics is that stakeholders in cancer care do not agree about which metrics should be collected. Very little information exists about what outcome measures are important to patients in their decision-making processes. Plus, outcomes that are important to patients may not always be the same as those that are important to clinicians. The complexity of the disease, the diverse treatment options available, and their variability in the potential complications and outcomes of care further complicates the identification of appropriate data to capture. Nevertheless, it is important that the learning health care IT system capture information about the committee’s components for a high-quality cancer care delivery system (i.e., the delivery of patient-centered communication and shared decision making, team-based care, evidence-based care, and accessible and affordable care).
Several quality metrics reporting systems currently use health IT. The CoC’s Rapid Quality Reporting System Project is a Web-based quality metrics tool that provides hospital-level data on adherence to National Quality Forum–endorsed quality of cancer care measures for breast and colorectal cancers (CoC, 2013). Similarly, ASCO is redesigning its Quality Oncology Practice Initiative (QOPI) to utilize advances in health IT. Through a pilot program with U.S. Oncology, ASCO concluded that EHRs could be used to automatically collect and report data to QOPI rather than relying on manual chart abstraction and retrospective analyses of data reported by clinicians. However, this would require adapting many of QOPI’s quality metrics to utilize data that clinicians are capturing in their EHRs (ASCO, 2012). The University of Kentucky also recently developed a model system that enables EHRs to report cancer cases directly to the state’s cancer registry in real time (Perry, 2012). Similarly, the CDC is working to automate EHR reporting to cancer registries across the United States (CDC, 2013).
The committee’s conceptual framework states that payers should align reimbursement to reward delivery models that are patient centered and provide high-value care based on measured health outcomes. A learning health care system would make the true cost of cancer care delivery more transparent by systematically collecting data on utilization, patient out-of-pocket costs, reimbursement, and costs to the health care system. It would also integrate this data with quality and outcomes of care data, information which is important for patients, their families, and clinicians
in making informed medical decisions (see discussion in Chapter 3). A learning health care system would inform payers’ pricing for bundled payments and other reimbursement reforms currently being piloted for cancer (see Chapter 8). In addition, the system’s ability to capture quality metrics data would allow payers to identify and reward high-performing clinicians and health care organizations.
There are implementation challenges, technical challenges, and ethical oversight challenges to achieving the committee’s vision for a learning health care system for cancer care. Each of these challenges is explored below.
The Best Care consensus report recognized that clinicians’ concerns about the impact of a learning health care system on their workflow could be a major challenge to implementation (IOM, 2012a). It noted that time pressures, stresses, and inefficiencies in the practice of medicine limit clinicians’ ability to focus on new initiatives, including the creation of a learning health care system. The sheer number of quality improvement initiatives being implemented by various stakeholders in the health care system can be overwhelming. Thus, initiatives that focus on only incremental improvements to the health care system and add to a clinician’s daily workload are unlikely to succeed. The success of a learning health care system will depend on major changes in the environment, context, and systems in which clinicians practice so that they are motivated to participate in this new system of learning and quality improvement. Currently, health IT is often hard to use, does not integrate well with existing workflows, and adds to the time it takes to see patients and to record clinical data (Campbell et al., 2009; Hesse et al., 2010; McDonnell et al., 2010).
For a learning health care system to work, all of the stakeholders involved will need to change their culture to one that values continuous learning. Some clinicians are likely to be resistant to switching from a paper-based system to an electronic system of recording and accessing their patients’ data. Additionally, organizations from multiple sectors of the cancer community might be resistant to sharing their data. Likewise, clinicians and the institutions for which they work may not want to share their data because they could lose their competitive advantage, which is gained from the knowledge they generate during their own provision of care. Researchers, too, are often focused on individual achievement and publication rather than on collaborating and sharing data. Similarly,
developers of new drugs and devices are likely to be protective of their intellectual property, and EHR vendors have a disincentive to develop interoperable systems that would allow the learning health care system to integrate their data because they do not want to lose market share of their products. Patients may be concerned about the privacy and security of their data in an electronic system and may not want or have the capacity to use IT to communicate with their clinicians and other sectors of the health care system (Kean et al., 2012).
The cost of implementing a learning health care system is also prohibitive. It is very expensive for a health care organization to implement sophisticated EHR systems that have the capacity to feed into a learning health care system. The costs of implementation include software and IT infrastructure costs, as well as considerable personnel and training costs. Health IT experts need to customize the health IT systems for the local environments. In addition, health care organizations need to spend time and money to train the users of the health IT system in best practices. Clinicians and health care organizations often pay the costs of implementing health IT systems, yet it is the payers and patients who benefit from the expected gains in quality and efficiency of care. Thus, there is a disconnect between the parties who pay to implement health IT and the parties who benefit the most from its implementation (Hillestad et al., 2005).
The recent increase in clinicians’ and hospitals’ adoption of EHRs suggests that meaningful use has been effective at offsetting some of these costs. In 2012, the proportion of office-based physicians who used EHR systems was 72 percent, up from 48 percent in 2009. Sixty-six percent of office-based physicians reported that they planned to apply, or already had applied, for meaningful use incentives, and 27 percent of these physicians had computerized systems that met the requirements for Stage 1 of meaningful use (Hsiao and Hing, 2012). However, organizations in many care settings, such as long-term acute care hospitals and rehabilitation hospitals, are excluded from the HITECH Act and are not adopting health IT at major rates (Wolf et al., 2012). In addition, the meaningful use incentives are temporary. Clinicians and hospitals will eventually be penalized through lower reimbursement rates for failing to adopt EHRs that meet the requirements for meaningful use.
The technology currently exists for many of the applications within a learning health care system; however, many technological challenges will need to be addressed to achieve its full potential. Interoperability is one area that will need to be addressed. In a learning health care system, organizations need to be able to transfer information from one entity to another in
a way that is timely, accurate, secure, and transparent (Abernethy et al., 2010). This includes EHR systems communicating with each other, as well as EHRs communicating with other critical databases (e.g., Medicare databases and cancer registries). Conversely, health care organizations have routinely adopted health IT systems customized to local institutional needs, which are unable to communicate with other organizations.
The Bipartisan Policy Center found that the “level of health information exchange in the U.S. is extremely low” (BPC, 2012, p. 5). The Direct Project has attempted to address this problem by developing standards and documentation to support the transfer of data from one health care institution to another (Direct Project, 2013). Health information exchanges may also help address this obstacle by providing services that enable organizations to share their data (ONC, 2013c). Additional investments will be required to improve interoperability.
In addition, a number of issues with health care data are likely to create technological challenges for a learning health care system, including the ability to efficiently handle the large quantity of data collected, especially in the age of molecularly targeted medicine. In order for data within a learning health care system to improve the quality of cancer care, clinicians, researchers, quality metrics developers, and payers must be able to effectively extract, use, and analyze the data. This will require input and forethought from data scientists who are skilled at organizing and handling large datasets and in developing IT infrastructure that supports these functions. Unfortunately, there are not enough adequately trained data scientists in health care and it can be difficult to identify individuals with the required skills (Davenport and Patil, 2012). Thus, the quantity of data within a learning health care system could become unmanageable, and it may be difficult for the stakeholders in a learning health care system to effectively extract data necessary for improving the quality of cancer care.
The success of a learning health care system will also depend upon the collection of the right data. Much of the current data that clinicians collect do not relate to important aspects of the quality of care. For example, EHR systems often do not capture data on the patients’ experiences with care, patients’ ultimate clinical outcomes, or patients’ transition from primary cancer treatment to survivorship care (IOM and NRC, 2005; Kean et al., 2012). As mentioned above, many stakeholders disagree about which metrics are important in a high-quality cancer care system and little is known about which metrics patients’ value.
The lack of uniformity among data is an additional challenge. Data are often collected in a free-text format rather than a structured format, making the information difficult to aggregate and analyze (Kean et al., 2012). Also, it can be difficult for organizations to share their data across
settings because current health care systems use different vocabularies, definitions, and infrastructures. Despite the many ongoing efforts to standardize data definitions, such as the Systematized Nomenclature of Medicine Clinical Terms (Snowmed CT), researchers, clinicians, and industry often define medical terms differently (e.g., disease classifications, symptoms). In addition, many of the standardized codes are not detailed enough for research purposes, especially for cancer, where the disease can be defined by its molecular characteristics (West, 2011). Data definitions will need to be standardized in a way that recognizes the health care system’s evolving knowledge of diseases and advances in treatment.
Another technological challenge to a learning health care system is the use of appropriate analytic methods. Data captured in a learning health care system may be less accurate and more subject to bias than data collected in clinical trials. Thus, researchers need new analytic methods to adjust and account for these limitations (IOM, 2012a). PCORI is funding methodological research in this area (PCORI, 2013b). For example, it sponsored an IOM workshop on conducting observational studies in a learning health care system to identify analytic methods for improving the validity and reliability of results from such studies (IOM, 2013).
Ethical Oversight Challenges
The major regulations that govern the ethical oversight of a learning health care system in the United States include (1) the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule, which protects the privacy of personally identifiable health information by restricting the types of allowable uses and disclosures of data; (2) the HIPAA Security Rule, which requires health care organizations to securely store any personally identifiable health information that is in electronic format; and (3) the Common Rule, which governs human subject research by requiring institutional review board (IRB) oversight and research participants’ informed consent.
The IOM has concluded that these regulations often create unnecessary barriers to clinical research and do not protect research participants as well as they should (IOM, 2009, 2012a). It recommended streamlining and revising the existing research regulations to improve care, promote the capture of clinical data, and generate knowledge. A number of ethicists have reached similar conclusions and recommended changes to the existing oversight paradigm (Faden et al., 2013; Platt et al., 2013; Selker et al., 2011).
Members of the IOM’s Roundtable on Value & Science-Driven Health Care have proposed exempting many of the activities of a learning health care system from these regulations by classifying the actions as quality
improvement and clinical effectiveness assessments rather than research (Platt et al., 2013; Selker et al., 2011). They argue that the creation of generalizable knowledge is a necessary and routine aspect of health care delivery. The amount of oversight required should be commensurate with the level of risk imposed on the patient by the activity. In quality improvement and effectiveness assessments, the biggest risk to patients is that their data might be misused or inappropriately released. However, patients are unlikely to be exposed to risks that exceed those of usual care. Thus, the authors argue that institutions should designate these activities as a type of continuous improvement reviewed through normal institutional systems and exempt them from research oversight (i.e., they should not be overseen by an IRB and patient consent should not be required).
Similarly, in a recent Hastings Center Report, Faden and colleagues argued that the current regulatory distinction between research and clinical practice is antiquated. They stated that a new ethical foundation should be developed that facilitates both care and research, is likely to benefit patients, and provides oversight that is commensurate with risk and burden (Faden et al., 2013; Kass et al., 2013). They believe that a growing number of health care activities cannot be classified as either research or clinical practice. By definition, learning health care systems are designed to “simultaneously deliver the care patients need while capturing the experience of clinical practice in a systematic way that produces generalizable knowledge to improve care for both present and future patients” (Kass et al., 2013, p. S6).
This proposal has been met with a variety of reactions, ranging from strong support to others finding the approach too radical and arguing for maintaining a distinction between research and clinical care (Grady and Wendler, 2013; Kupersmith, 2013; Largent et al., 2013; Menikoff, 2013; Puglisi, 2013; Selby and Krumholz, 2013). Regardless of which approach is taken, developers of a learning health care system will need to ensure that the system is ethically sound and complies with all relevant regulations.
Although the challenges to creating a learning health care IT system for cancer are formidable, there are many steps that stakeholders can take to move toward the development of such a system. The Best Care consensus report outlines recommendations for establishing the digital infrastructure and data utility necessary for continuous learning (see Box 6-2). It recognizes that the creation of a learning health care system will require an effort on the part of many stakeholders, including health care delivery organizations, clinicians, the U.S. Department of Health and Human Services (HHS), payers, patients, researchers, health IT vendors,
Recommendation 1: The Digital Infrastructure
Improve the capacity to capture clinical, care delivery process, and financial data for better care, system improvement, and the generation of new knowledge. Data generated in the course of care delivery should be digitally collected, compiled, and protected as a reliable and accessible resource for care management, process improvement, public health, and the generation of new knowledge.
Strategies for progress toward this goal:
• Health care delivery organizations and clinicians should fully and effectively employ digital systems that capture patient care experiences reliably and consistently, and implement standards and practices that advance the interoperability of data systems.
• The National Coordinator for Health Information Technology, digital technology developers, and standards organizations should ensure that the digital infrastructure captures and delivers the core data elements and interoperability needed to support better care, system improvement, and the generation of new knowledge.
• Payers, health care delivery organizations, and medical product companies should contribute data to research and analytic consortia to support expanded use of care data to generate new insights.
• Patients should participate in the development of a robust data utility; use new clinical communication tools, such as personal portals, for self-management and care activities; and be involved in building new knowledge, such as through patient-reported outcomes and other knowledge processes.
• The Secretary of Health and Human Services should encourage the development of distributed data research networks and expand the availability of
and other stakeholders. These recommendations continue to be relevant and, if followed, would facilitate the development of a learning health care IT system for cancer.
In addition, there are steps that stakeholders in cancer care should take to facilitate the development of a learning health care IT system for cancer. The committee believes that clinicians, through their professional organizations, should take a lead role in creating a learning health care system for cancer. Having clinicians guide the development process will help ensure that the resulting system is seamlessly integrated into clinical practice so that clinicians can easily participate and contribute patient
departmental health data resources for translation into accessible knowledge that can be used for improving care, lowering costs, and enhancing public health.
• Research funding agencies and organizations, such as the National Institutes of Health, the Agency for Healthcare Research and Quality, the Veterans Health Administration, the Department of Defense, and the Patient-Centered Outcomes Research Institute, should promote research designs and methods that draw naturally on existing care processes and that also support ongoing quality improvement efforts.
Recommendation 2: The Data Utility
Streamline and revise research regulations to improve care, promote the capture of clinical data, and generate knowledge. Regulatory agencies should clarify and improve regulations governing the collection and use of clinical data to ensure patient privacy but also the seamless use of clinical data for better care coordination and management, improved care, and knowledge enhancement.
Strategies for progress toward this goal:
• The Secretary of Health and Human Services should accelerate and expand the review of the Health Insurance Portability and Accountability Act and institutional review board policies with respect to actual or perceived regulatory impediments to the protected use of clinical data, and clarify regulations and their interpretation to support the use of clinical data as a resource for advancing science and care improvement.
• Patient and consumer groups, clinicians, professional specialty societies, health care delivery organizations, voluntary organizations, researchers, and grantmakers should develop strategies and outreach to improve understanding of the benefits and importance of accelerating the use of clinical data to improve care and health outcomes.
SOURCE: IOM, 2012a.
data. Moreover, professional organizations are already taking the lead in developing a learning health care system for cancer through ASCO’s CancerLinQ project. These groups should continue to design and implement the digital infrastructure and analytics necessary to enable continuous learning in cancer care. This process should involve consultation with the other stakeholders discussed in this chapter (patients, researchers, quality metrics developers, and payers) to help ensure that the final product also meets their needs.
As in other countries, the federal government has a role to play in developing a learning health care system for cancer (BCG, 2012). HHS,
because of its role in promoting health in the United States, should take the lead, with ONC and the NCI involved in the development process. ONC, charged with coordinating nationwide efforts to implement and use advances in health IT to improve quality of care (ONC, 2013a), has the technical expertise necessary to contribute to setting standards and developing the IT infrastructure required for this system.
Similarly, the NCI, with its focus on cancer research and training (NCI, 2013b), has demonstrated an interest in supporting the development of health IT through its caBIG initiative (Cancer Biomedical Informatics Grid), which was designed to enable researchers, clinicians, and patients to share data and knowledge through an informatics grid. This program started a dialogue among cancer researchers on the interoperability of clinical and research software tools, developing standards for data exchange and interoperability, and disseminating research tools to the community.
The program was criticized, however, as being too focused on technology, expanding without clear objectives, lacking flexibility, utilizing an unsustainable business model, and lacking independent scientific oversight (IOM, 2012b). The NCI ended this initiative due to these problems, but has continued to support informatics infrastructure development via a new National Cancer Informatics Program and an Informatics Working Group of National Cancer Advisory Board, which is considering the NCI’s future role in developing an IT infrastructure (NCI, 2013a). This Working Group and NCI Director Harold Varmus have expressed the belief that the NCI’s investment in health IT should extend to clinical practice, and not be limited to research as it has been in the past.6Thus, HHS, including ONC and the NCI, should support the development and integration of a learning health care IT system for cancer. This support could be both intellectual and financial.
The committee is concerned that many stakeholders will be reluctant to provide data to the learning health care system. As described above, many clinicians and institutions use their data to achieve a competitive advantage. Thus, the committee recommends that CMS and other payers create incentives for clinicians to participate in this learning health care system for cancer care, as it develops.
These incentives could be structured similar to the meaningful use standards for the adoption of EHRs. Payers could provide cancer care teams with bonus payments for being early participants in a learning health care system and allowing the data in their EHR system to automatically feed into the learning health care system. Ultimately, sharing clinical
6 Personal communication, D. Masys, University of Washington, August 9, 2012.
data will require less cost and effort on the part of the cancer care team because the learning health care system will automate this process. Thus, as in meaningful use, payers could change the incentives into penalties for cancer care teams at a later date if they fail to share their data with this system. The new payment models, discussed in Chapter 8, could also include incentives for clinicians to participate in a learning health care system for cancer.
The committee’s conceptual framework for a high-quality cancer care delivery system calls for implementation of a learning health care IT system: a system that “learns” by collecting data on care outcomes and cost in a systematic manner, analyzing the captured data both retrospectively and through prospective studies, implementing the knowledge gained from these analyses into clinical practice, evaluating the outcomes of the changes in care, and generating new hypotheses to test and implement into clinical care. A learning health care IT system is a key requirement for implementing the components of the committee’s conceptual framework for high-quality cancer care.
In the committee’s conceptual framework (see Figure S-2), a learning health care IT system supports patient-clinician interactions by providing patients and clinicians with the information and tools necessary to make well-informed medical decisions. It plays an integral role in developing the evidence base from research (e.g., clinical trials and CER) and by capturing data from real-world care settings that researchers can then analyze to generate new knowledge. Further, it is used to collect and report quality metrics data, implement performance improvement initiatives, and allow payers to identify and reward high-quality care.
Many of the elements needed to create a learning health care system are already in place for cancer, including EHRs, cancer registries, a robust infrastructure for cancer clinical trials, and biorepositories that are linked with clinical data. Unfortunately, they are incompletely implemented, have functional deficiencies, and are not integrated in a way that creates a true learning health care system. In addition, relevant regulations that govern clinical care and research could pose a challenge to a learning health care system. The learning system will either need to comply with the relevant regulations or, alternatively, the regulations may need to be updated to accommodate such a system.
Recommendation 7: A Learning Health Care Information Technology System for Cancer
Goal: Develop an ethically sound learning health care information technology system for cancer that enables real-time analysis of data from cancer patients in a variety of care settings.
To accomplish this:
• Professional organizations should design and implement the digital infrastructure and analytics necessary to enable continuous learning in cancer care.
• The U.S. Department of Health and Human Services should support the development and integration of a learning health care IT system for cancer.
• The Centers for Medicare & Medicaid Services and other payers should create incentives for clinicians to participate in this learning health care system for cancer, as it develops.
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