6

A Learning Health Care Information Technology System for Cancer

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

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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).



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6 A Learning Health Care Information Technology System for Cancer I nformation 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 clini- cians with the information and tools necessary to make well-informed medical decisions. Health IT plays a critical role in developing the evi- dence base from research (e.g., clinical trials and comparative effective- ness 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  TheInstitute of Medicine has defined health IT as a broad range of products. “It encom- passes 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). 235

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236 DELIVERING HIGH-QUALITY CANCER CARE 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 re- search, 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 sup- port, 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 impor- tant 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).

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A LEARNING HEALTH CARE INFORMATION TECHNOLOGY SYSTEM 237 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 pur- poses, health IT systems have not been integrated efficiently into clinical care, do not facilitate the coordination of care, and the need to custom- ize 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 commit- tee’s vision and outlines how health IT can meet the needs of all of the stakeholders discussed throughout this report, including patients, clini- cians, 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 previ- ous work conducted by the IOM on a learning health care system, includ- ing 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 Uni- versity of Texas MD Anderson Cancer Center.

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238 DELIVERING HIGH-QUALITY CANCER CARE input from several professionals knowledgeable about health IT.4 The committee’s recommendation on health IT addresses the identified gaps. The Vision 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 learn- ing 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 sub- sequently 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 com- pile 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, hospi- tals, 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 collect- ing 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  JohnFrenzel, 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.

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A LEARNING HEALTH CARE INFORMATION TECHNOLOGY SYSTEM 239 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 transla- tion 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 re- imbursement 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. TABLE 6-1  Characteristics of a 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. Digital capture of the care experience – A learning health care system captures the care experience on digital platforms for real-time generation and application of knowledge for care improvement. Patient-Clinician Partnership 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 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. Full transparency – A learning health care system systematically monitors the safety, quality, processes, prices, costs, and outcomes of care, and makes information available for care improvement, informed choices, and decision making by clinicians, patients, and their families. Culture 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. Supportive system competencies – In a learning health care system, complex care operations and processes are constantly refined through ongoing team training and skill building, systems analysis and information development, and creation of the feedback loops for continuous learning and system improvement. SOURCE: IOM, 2012a, p. 138.

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240 DELIVERING HIGH-QUALITY CANCER CARE 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 physi- cians 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 en- sure 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 organiza- tions (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 sup- port 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 com- ment 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 cap- ture important information on new cancer diagnoses, including the in- cidence 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. popula- tion 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-

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A LEARNING HEALTH CARE INFORMATION TECHNOLOGY SYSTEM 241 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 sup- ported by a robust infrastructure for clinical trials on cancer; namely, the NCI National Clinical Trials Network (NCI, 2013d). Data from these tri- als 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 cre- ate 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 creat- ing 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). Ef- forts to implement key components of a learning health care system are discussed in the next sections of this chapter. Patient Needs A learning health care system facilitates patient engagement. As dis- cussed 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-

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242 DELIVERING HIGH-QUALITY CANCER CARE TABLE 6-2  Examples of Efforts to Develop Learning Health Care Systems Organization Description 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 In 2002, Kaiser Permanente contracted with Epic Systems HealthConnect 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.

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A LEARNING HEALTH CARE INFORMATION TECHNOLOGY SYSTEM 243 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 informa- tion 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. Re- minder systems triggered from data in patients’ EHRs can lead to patients’ improved adherence to treatment protocols and screening recommenda- tions (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 infor- mation, 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 ad- dition, 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 schedul- ing. Patients would be able to email or message their clinicians in real time, have their questions answered, their EHR updated with any perti- nent 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 electroni- cally (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 commu- nications, and the current reimbursement system’s failure to reward these services (Feeny, 2013; Wallwiener et al., 2009). Incentivizing clinicians to

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244 DELIVERING HIGH-QUALITY CANCER CARE quickly respond to patients through an electronic system will require new models of team-based cancer care (see Chapter 4) and reimbursement (see Chapter 8). A learning health care system would also provide patients with edu- cational material and decision aids at key times during their course of treatment. Currently, clinicians may provide patients with overwhelm- ing 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 ef- fects, advance care planning, and anticipated cost of care in a time-sen- sitive manner. In addition, as discussed below in more detail, patients would benefit from a learning health care system’s ability to improve the coordina- tion 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 deci- sion 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 treat- ment 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

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A LEARNING HEALTH CARE INFORMATION TECHNOLOGY SYSTEM 245 systematic review of clinical decision support systems and identified the following list of characteristics as important in making these systems suc- cessful 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 clini- cians (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 pro- vides 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 pre- ventive 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 infrastruc- ture,” analogous to the Federal Aviation Administration’s course guid- ance 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 Corpora- tion, one of the major EHR vendors, has a medical oncology module that provides information on diagnostic staging, treatment options, che- motherapy dosing schedules, and personalized treatment planning (KP, 2011a). A number of cancer centers are also working with IBM to train the

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260 DELIVERING HIGH-QUALITY CANCER CARE 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 develop- ment of health IT through its caBIG initiative (Cancer Biomedical Infor- matics 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 interoper- ability 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 tech- nology, expanding without clear objectives, lacking flexibility, utilizing an unsustainable business model, and lacking independent scientific over- sight (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 Work- ing 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.6 Thus, HHS, including ONC and the NCI, should support the development and integration of a learning health care IT system for cancer. This sup- port 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 pay- ers 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 automati- cally feed into the learning health care system. Ultimately, sharing clinical 6  Personal communication, D. Masys, University of Washington, August 9, 2012.

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A LEARNING HEALTH CARE INFORMATION TECHNOLOGY SYSTEM 261 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. Summary and Recommendations The committee’s conceptual framework for a high-quality cancer care delivery system calls for implementation of a learning health care IT sys- tem: 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 cap- turing data from real-world care settings that researchers can then analyze to generate new knowledge. Further, it is used to collect and report qual- ity 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.

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262 DELIVERING HIGH-QUALITY CANCER CARE Recommendation 7: A Learning Health Care Information Technol- ogy 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: •  rofessional organizations should design and implement the P digital infrastructure and analytics necessary to enable continu- ous learning in cancer care. •  he U.S. Department of Health and Human Services should T support the development and integration of a learning health care IT system for cancer. •  he Centers for Medicare & Medicaid Services and other pay- T ers should create incentives for clinicians to participate in this learning health care system for cancer, as it develops. References Abernethy, A. P., J. E. Herndon, 2nd, J. L. Wheeler, J. M. Day, L. Hood, M. Patwardhan, H. Shaw, and H. K. Lyerly. 2009. Feasibility and acceptability to patients of a longitudinal system for evaluating cancer-related symptoms and quality of life: Pilot study of an e/tablet data-collection system in academic oncology. Journal of Pain and Symptom Management 37(6):1027-1038. Abernethy, A. P., L. M. Etheredge, P. A. Ganz, P. Wallace, R. R. German, C. Neti, P. B. Bach, and S. B. Murphy. 2010. Rapid-learning system for cancer care. Journal of Clinical Oncol- ogy 28(27):4268-4274. ACoS (American College of Surgeons). 2013. National Cancer Data Base. http://www.facs. org/cancer/ncdb/index.html (accessed March 4, 2013). AHRQ (Agency for Healthcare Research and Quality). 2012. Enabling patient-centered care through health information technology. http://effectivehealthcare.ahrq.gov/index.cfm/ search-for-guides-reviews-and-reports/?productid=1158&pageaction=displayproduct (accessed February 20, 2013). ASCO (American Society of Clinical Oncology). 2012. Starting down the path toward electronic QOPI: ASCO and U.S. Oncology collaborate in quality measurement. http://www.ascopost. com/issues/march-1-2012/starting-down-the-path-toward-electronic-qopi-asco-and- us%C2%A0oncology-collaborate-in-quality-measurement.aspx (accessed February 21, 2013). ———. 2013a. CancerLinQ: Building a transformation in cancer care. http://www.asco.org/ ASCOv2/Practice+%26+Guidelines/Quality+Care/CancerLinQ+-+Building+a+ Transformation+in+Cancer+Care (accessed March 7, 2013). ———. 2013b. Shaping the future of oncology: Envisioning cancer care in 2030. http://www. asco.org/ASCOv2/Department%20Content/Communications/Downloads/Shaping Future-lowres.pdf (accessed February 19, 2013).

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