Delivering High-Quality Cancer Care Charting a New Course for a System in Crisis (2013) / Chapter Skim
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6 A Learning Health Care Information Technology System for Cancer
6 A Learning Health Care Information Technology System for Cancer
Pages 235-270
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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)
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, 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)
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. 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.
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) 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)
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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.
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; 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)
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. 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)
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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.
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. 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)
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. 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.
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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)
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Cancer care has a long history of being guided by clinical practice guide lines, 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.
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2. public library of clinical decision support hosted by a neutral and re A spected source, from which health care organizations could download decision support modules, and to which they could upload their observed experience using them.
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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)
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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.
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. 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)
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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)
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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)
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Challenges 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.
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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)
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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.
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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)
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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.
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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.
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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.
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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.
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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 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.
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2013b. Shaping the future of oncology: Envisioning cancer care in 2030.
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2011. Improv ing modern cancer care through information technology.
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2007. Linking electronic medical records to large-scale simulation models: Can we put rapid learning on turbo?
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2010. A foundation for evidence-driven practice: A rapid learning system for cancer care: Workshop summary.
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2012. Enabling health care decisionmaking through clinical decision support and knowledge management.
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2009. The clinical decision support consortium.
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2010. Realizing the full potential of health information technology to improve healthcare for americans: The path for ward.
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2007. Reshaping cancer learning through the use of health information technol ogy.
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2011. The evolution of oncology electronic health records.
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