Building Data Capacity for Patient-Centered Outcomes Research Priorities for the Next Decade (2022) / Chapter Skim
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Appendix C: Building Data Capacity for Patient-Centered Outcomes Research: Interim Report 2Data Standards, Methods, and Policy
Appendix C: Building Data Capacity for Patient-Centered Outcomes Research: Interim Report 2Data Standards, Methods, and Policy
Pages 149-236
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Appendix C Building Data Capacity for Patient-Centered Outcomes Research: Interim Report 2– Data Standards, Methods, and Policy (Full text of the committee's second interim report released on October 27, 2021.) 1 1 https://www.nap.edu/catalog/26298/building-data-capacity-for-patient-centered-outcomes research-interim-report.
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APPENDIX C 151 Committee on Building Data Capacity for Patient-Centered Outcomes Research: An Agenda for 2021 to 2030 Committee on National Statistics Division of Behavioral and Social Sciences and Education Board on Health Care Services Health and Medicine Division Computer Science and Telecommunications Board Division on Engineering and Physical Sciences A Consensus Study Report of
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. Building Data Capacity for Patient-Centered Outcomes Research: Interim Report 2 -- Data Standards, Methods, and Policy.
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APPENDIX C 153 The National Academy of Sciences was established in 1863 by an Act of Congress, signed by President Lincoln, as a private, nongovernmental institution to advise the nation on issues related to science and technology. Members are elected by their peers for outstanding contributions to research.
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154 BUILDING DATA CAPACITY FOR PCOR Consensus Study Reports published by the National Academies of Sciences, Engineering, and Medicine document the evidence-based consensus on the study's statement of task by an authoring committee of experts. Reports typi cally include findings, conclusions, and recommendations based on information gathered by the committee and the committee's deliberations.
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BRIDGES, The Ohio State University JULIE BYNUM, University of Michigan ANGELA DOBES, IBD Plexus, Crohn's & Colitis Foundation DEBORAH ESTRIN, Cornell Tech OLUWADAMILOLA FAYANJU, University of Pennsylvania CONSTANTINE GATSONIS, Brown University ROBERT GOERGE, Chapin Hall, University of Chicago GEORGE HRIPCSAK, Columbia University LISA IEZZONI, Massachusetts General Hospital S CLAIBORNE JOHNSTON, The University of Texas at Austin MIGUEL MARINO, Oregon Health & Science University ELIZABETH McGLYNN, Kaiser Permanente DAVID MELTZER, University of Chicago PAUL TANG, Stanford University and Palo Alto Medical Foundation KRISZTINA MARTON, Study Director CRYSTAL BELL, Associate Program Officer RUTH COOPER, Associate Program Officer MARY GHITELMAN, Senior Program Assistant BRIAN HARRIS-KOJETIN, Director, Committee on National Statistics SHARYL NASS, Director, Board on Health Care Services JON EISENBERG, Director, Computer Science and Telecommunications Board SAUL RIVAS, National Academy of Medicine Fellow, University of Texas Rio Grande Valley v
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COUPER, University of Michigan JANET M CURRIE, Princeton University DIANA FARRELL, JPMorgan Chase Institute ROBERT GOERGE, Chapin Hall at the University of Chicago ERICA L
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DeVOE, Oregon Health & Science University R ADAMS DUDLEY, University of Minnesota RICHARD G
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158 BUILDING DATA CAPACITY FOR PCOR COMPUTER SCIENCE AND TELECOMMUNICATIONS BOARD LAURA HAAS (Chair) , University of Massachusetts Amherst DAVID CULLER, University of California, Berkeley ERIC HORVITZ, Microsoft Corporation CHARLES ISBELL, Georgia Institute of Technology BETH MYNATT, Georgia Institute of Technology CRAIG PARTRIDGE, Colorado State University DANIELA RUS, Massachusetts Institute of Technology FRED B
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Hubbard, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania; Sue Jinks-Robertson, Department of Molecular Genetics and Microbiology, Duke University; Harold Lehman, Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine; Vincent X Liu, Division of Research, Kaiser Permanente; Keith Marsolo, Population Health Sciences, Duke University School of Medicine; Emily O'Brien, Department of Population Health Sciences, Duke Clinical Research Institute, Duke University School of Medicine; and Jerome Reiter, Depart ment of Statistical Science, Duke University.
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160 BUILDING DATA CAPACITY FOR PCOR x ACKNOWLEDGMENTS making certain that an independent examination of this report was car ried out in accordance with the standards of the National Academies and that all review comments were carefully considered. Responsibility for the final content rests entirely with the authoring committee and the National Academies.
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APPENDIX C 161 Contents Summary 1 1 Introduction 7 2 Patient-Centered Outcomes Research Data Standards 17 3 Methods for Patient-Centered Outcomes Research 33 4 Data Policies and Other Data Infrastructure Considerations 45 Appendixes A Biographical Sketches of Committee Members 55 B Workshop Agenda 61 C Biographical Sketches of Workshop Speakers 65 xi
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APPENDIX C 163 Boxes and Figures BOXES 1-1 Key Data Infrastructure Functionalities in the Existing Strategic Framework for Patient-Centered Outcomes Research, 12 1-2 Building Blocks of the Patient-Centered Outcomes Research Data Infrastructure, 13 1-3 Statement of Task for the Overall Study, 15 2-1 A Perspective on Data Required for Patient-Centered Outcomes Research and Standards to Consider, 23 FIGURES 1-1 Patient-Centered Outcomes Research Trust Fund: Three streams of work and funding, 9 1-2 ASPE's strategic framework for the patient-centered outcomes research data infrastructure, 11 2-1 Clinical language engineering workbench: Key functionalities in the natural language processing machine learning process, 27 3-1 A patient timeline view of data, 34 xiii
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As part of its information-gathering activities, the committee organized three workshops to collect input from stakeholders on the PCOR data infrastructure, which includes a variety of types of data, such as clinical data, research data, administrative data from payer records, and patient provided data. This report, the second in a series of three interim reports, summarizes the discussion and committee conclusions from the second workshop, which focused on data standards, methods, and policies that could make the PCOR data infrastructure more useful in the years ahead.
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CONCLUSION 2-2: The Office of the Assistant Secretary for Planning and Evaluation could add significant value in the area of standards for patient-centered outcomes research by • continuing to promote the development of a data infrastructure and an implementation strategy that facilitates the use of standards and access to the data; • convening stakeholder meetings to enhance communication and work toward developing a common language for standards; 1 https://www.nap.edu/catalog/26297/building-data-capacity-for-patient-centered-outcomes research-interim-report.
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CONCLUSION 3-1: The ability to adopt a longitudinal, compre hensive perspective of an individual's journey could open new oppor tunities for patient-centered outcomes research. The shift could be facilitated by focusing on efforts to • simplify integration of data across the research data ecosystem; • address challenges posed by the limitations associated with health identifiers; • incorporate person-generated data into health data systems; and • leverage real-world data to expand the timeline view of a person's health-related experiences.
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CONCLUSION 3-2: Observing scientific best practices, including those of transparency and ethical use of data, is essential to generate trust in patient-centered outcomes research among all stakeholders, including the public and researchers. This is important both for observational data and for emerging data sources and methods.
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APPENDIX C 169 SUMMARY 5 1996, are outdated and would benefit from a critical review and updating to facilitate PCOR while preventing misuses of the data. CONCLUSION 4-2: This is an opportune time to revisit and update the legislation and rules governing data privacy and the sharing of data for research.
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. The PCOR data infrastructure provides decision makers with objective, scientific evidence on the effectiveness of treatments, services, and other interventions used in health care.
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of the Public Health Service Act instructed the Secretary of HHS to: … provide for the coordination of relevant Federal health programs to build data capacity for comparative clinical effectiveness research, including the development and use of clinical registries and health outcomes research networks, in order to develop and maintain a comprehensive, interoperable data network to collect, link, and analyze data on outcomes and effective ness from multiple sources including electronic health records.2 1 https://www.ssa.gov/OP_Home/ssact/title11/1181.htm. 2 https://aspe.hhs.gov/collaborations-committees-advisory-groups/os-pcortf/about-os-pcortf.
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As the coordinating agency for the data infrastructure investment port folio across HHS agencies, ASPE guides the PCOR data infrastructure's strategic framework and vision, sets funding priorities, and coordinates interagency workgroups. ASPE's work is assisted by a Leadership Council for the PCOR Trust Fund, which includes representatives from other HHS agencies, including the Administration for Children and Families, the Administration for Community Living, the Assistant Secretary for Prepared ness and Response, AHRQ, the Centers for Disease Control and Prevention (CDC)
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Examples of primary data collected as part of research studies include data from clinical trials and national health surveys. Other ex amples of data sources include Medicare or Medicaid claims data; quality or outcomes data collected by health care providers for the purposes of improving health care value; FDA data on the safety of medications and medical devices; and CDC data on births and deaths provided by state public health authorities.
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APPENDIX C FIGURE 1-2 ASPE's strategic framework for the patient-centered outcomes research data infrastructure. 11 SOURCE: Workshop presentation by ASPE, May 3, 2021.
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Researchers will be able to capture the range of variables influencing health outcomes and link clinical and other types of data (e.g., other clinical data, claims data, participant-provided information, and environmental data) required for research regardless of where the participant goes.
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ISSUES FOR THE COMMITTEE ASPE asked the National Academies of Sciences, Engineering, and Medicine to appoint a consensus study committee and identify issues criti cal to building data capacity for PCOR and for generating new evidence to inform health care decisions. The input provided by the committee will contribute to ASPE's strategic planning for their work related to the data infrastructure over the next decade.
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The third workshop discussed research and data collaborations. This report summarizes the discussion and committee conclusions from the second workshop, which focused on data standards, methods, and policies that could make the PCOR data infrastructure more useful.
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Appendix B shows the agenda for the workshop, which was held on May 24, 2021. The committee's goal for this event was to bring together researchers and policy experts to • Identify data standards and methods that can make the PCOR data infrastructure more useful for research and other data needs.
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, using a public input form available on the National Academies website. OVERVIEW OF THE REPORT This report is organized around the three main sessions of the work shop: Chapter 2 discuses data standards, Chapter 3 is centered on research methods, and Chapter 4 describes discussions focused on data policies and related infrastructure considerations.
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The brief overview of the input received from the presenters is followed by the committee's conclusions. • What data standards could make the PCOR data infrastructure more useful for research and other data needs?
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John Halamka, Mayo Clinic, provided some context for the session by describing three ways of thinking of data standards. First, there is a need for standards for presenting content from data sources such as electronic health records or other administrative records.
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Grannis noted that the patient identity strategy in the United States is evolving based on a recognition that matching patient records from differ ent sources is one of the few remaining large holes in the electronic health data infrastructure. For this reason, Congress charged the Office of the National Coordinator for Health Information Technology with writing a report focused on effective matching methods.
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As an example of building on evidence based research to develop standards, Grannis mentioned a 2019 paper that showed that standardizing address and last name significantly improves matching accuracy.2 This research led to a bipartisan Senate bill calling to address standardization, and work is now in progress on developing a universal standard. Evelyn Gallego, EMI Advisors, discussed her work on the Gravity Project, which focuses on developing consensus-driven data standards to support use and exchange of SDOH within the health care sectors and between the health care sector and other sectors, including research.
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• Unnecessary medicalization of SDOH.3 Gallego discussed two of these areas in detail: standardization and data sharing. The Gravity Project was launched in 2019 with the goal of developing data standards for domains that Gallego described as grounded in a 2014 National Academies report.4 The domains include items such as education, elder abuse, environment, financial insecurity, food deserts, food insecu rity, homelessness, housing instability, inadequate housing, interpersonal violence, material hardship, neighborhood safety, racism, social isolation, stress, transportation insecurity, unemployment, and veteran status.
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This framework emphasizes the value of these data for secondary use by public and private payers, social service providers, public health entities, and researchers. Rachel Richesson, University of Michigan, discussed the concept of a learning health care system, where research influences practice and practice influences research.
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• Document names • Nursing, physical therapy, Open mHealth mobile occupational health data interoperability therapy, dietary, standard education • Questions/answers • Coordinated care • Person-controlled HL7 – Learning Health • Fidelity Systems care team; Gender Harmony group Patient Goals and HL7 FHIR Preferences, Outcomes, • Profiles, FHIR and Endpoints Accelerator projects • General and condition specific BPM+ Health (Business • Calculated or Process Management for summary data Healthcare) • Clinical/treatment • Clinical pathways, response interventions, use • Patient-reported cases • Patient-delivered Agency for Healthcare Research and Quality • Outcome Measures Framework SOURCE: Workshop presentation by Rachel Richesson, May 24, 2021.
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In this context, the key questions to ask would be (1) What evidence would be useful to improve health policy and health care, which could be reliably generated by the PCOR data infrastructure?
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, data standards to harmonize data structure and enable analytics (e.g., the Obser vational Medical Outcomes Partnership Common Data Model or OMOP CDM6) , and analytics standards to generate and disseminate evidence (e.g., the Health Analytics Data-to-Evidence Suite or HADES)
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looking beyond electronic health records for health data. To illustrate the use of clinical notes, Vydiswaran discussed his work with the Patient-Centered Network of Learning Health Systems (LHSNet)
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This includes standardized clinical natural language tools for processing text so that it is interpreted the same way across multiple sites. The typical data elements in computable phenotypes, Vydiswaran said, are structured components such as ICD-9 and -10 codes, Current Procedural Terminology (CPT)
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Vydiswaran also encouraged looking beyond electronic health records for health data. For example, information on the adverse effects of drugs can increasingly be found on social media.
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She noted that there is a need for better organizing information on existing standards. ASPE could facilitate the sharing of tools and metadata standards and incentivize the reporting of results.
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Participants cautioned against the blunt instrument of regulation, arguing that standards are most likely to be adopted when they bring value, because without a clear purpose and value for the standards, clinician frustration with electronic health records could increase. HHS could play a role in facilitating discussions to prioritize areas where standardization could be most useful and convening activities around topics such as SDOH, where there is a notable lack of standards and a common language.
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CONCLUSION 2-2: The Office of the Assistant Secretary for Planning and Evaluation could add significant value in the area of standards for patient-centered outcomes research by • continuing to promote the development of a data infrastructure and an implementation strategy that facilitates the use of standards and access to the data; • convening stakeholder meetings to enhance communication and work toward developing a common language for standards; • facilitating accessibility to the data and collaborations with existing organizations working in this area; and • leading efforts to catalogue and exemplify data standards and analytic standards. The speakers touched on the need for a broad interpretation of stan dards, to include not only the data but also the methods used to analyze PCOR data.
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196 BUILDING DATA CAPACITY FOR PCOR 32 INTERIM REPORT 2 -- DATA STANDARDS, METHODS, AND POLICY CONCLUSION 2-4: An international perspective is an important consideration for the patient-centered outcomes research data infra structure, and the infrastructure focused on standards specifically would benefit from building on work that happens internationally.
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The brief overview of the input received from the presenters is followed by the committee's conclusions. Speakers in this session were asked to focus on the following questions: • What emerging methods are likely to be most relevant for the PCOR data infrastructure looking forward?
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From the electronic health records, researchers might be able to access test results, the clinician's notes, and perhaps the signal streams from bedside monitors. Outside of the health system, we might get data from wearables, such as a Fitbit or an Apple watch.
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There is also a need for a stronger focus on systems and software, beyond methods development. Sharon-Lise Normand, Harvard University, highlighted data silos as one of the main challenges for PCOR data and PCOR in general.
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Normand pointed at missing data as another challenge for PCOR. While missing data has always posed difficulties for statisticians, it is im portant to understand what this means specifically for electronic health records and to consider solutions for irregularly spaced data.
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Rose discussed her prior work on using data transformation to bring causal conceptual thinking to the matter of fairness in data infrastructure.3 The work focused on payment systems, aiming to reduce disparities in low-income neighborhoods and underprovision of services for chronic conditions, and the idea was to develop a methodology to set policies at desired levels. Rose added that thinking about how to do these types of data transformations is challenging, but this methodology is underleveraged and could benefit data infrastructure.
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She emphasized as well the importance of supporting the development of creative new methods for building data infrastructure. In closing, she urged researchers to consider whether their algorithms have a social impact statement.
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For example, instead of creating bespoke sets of required minimum data elements, perhaps the Office of the National Coordinator for Health Information Technology could collaborate with the research community to augment the United States Core Data for Interoperability (USCDI) to include essential data elements for research.
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For example, raw data need to be transformed into variables that can be analyzed. For federal data sets that are available for public use, this could involve creating standardized, validated measures and measure sets to help operationalize and augment the use of the data.
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She said that transparency could be accelerated through incentives, such as tying access to federal data sets or federal funding to the registration of the studies and the publication of the research protocols and results. Lara Mangravite, Sage Bionetworks, focused on the governance com ponent of the data infrastructure, discussing issues related to the gover nance structures used to enable research that typically involves data from multiple sources.
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In summary, Mangravite highlighted the need for integration of data across systems, and the integration of participants into the research life cycle, as two of the areas that need the most attention in terms of the data infrastructure. To integrate participants, her specific suggestions were to focus on enabling richer understanding of lived experiences outside of the medical system, support the alignment of research questions with commu nity needs, and support capacity building for translating research outcomes into action.
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A theme that had been explored in detail in the committee's first workshop and was revisited by the participants in this one was the need to broaden research perspectives from the patient to the person in a broader sense, bringing in additional data on factors that are outside of the health care provider system. Integrating relevant data that go beyond provider databases represents its own challenges, but a timeline view that expands beyond a person's experiences within the health care system would greatly increase our ability to understand, for example, chronic diseases.
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CONCLUSION 3-2: Observing scientific best practices, including those of transparency and ethical use of data, is essential to generate trust in patient-centered outcomes research among all stakeholders, including the public and researchers. This is important both for observational data and for emerging data sources and methods.
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The chapter concludes with the committee's conclusions. • What data policies are likely to be most relevant for the patient centered outcomes research (PCOR)
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Is it for academic research? The uses of the data need to be considered when thinking about policies around data infrastructures.
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He said that it is important to consider not only the implications of research bias, but also social bias. For ex ample, gender information has historically not been considered useful for record linkages, because this information typically has binary values in electronic health records.
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The information available in health records to identify a person for the purposes of record linkages is also constantly changing. While binary gender is captured in virtually all electronic health records, Kho and his colleagues have been noticing an increase in the availability of data on sex assigned at birth and sexual orientation.
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as a policy vehicle to promote scale. USCDI is a set of data elements that health systems must make available through an applica tion programming interface (via their electronic health records)
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In the case of some entities, more than one of these laws might apply. McGraw said that HIPAA has the most impact on PCOR data.
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Initially they will cover the USCDI data elements, but eventually they will cover all electronic health information. The penalties for "blocking" the sharing are up to $1 million per incident for electronic health records vendors and health information exchanges.
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This would address the burden and challenges associated with obtaining written consent. He also wondered whether authorization could be created that would allow specified entities, such as the Patient-Centered Outcomes Research Institute, to securely access PHI in relevant databases, without individual consent.
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Participants discussed the need to revisit HIPAA, which was passed in 1996, before the spread of social media, apps that require broad consent for data sharing, and expansive databases that are publicly available or can be purchased. HIPAA, in its current form, is not focused on privacy, and it 2 Institute of Medicine.
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The workshop made it clear that there are concerns about the laws and rules governing data access and data sharing. HIPAA, in particular, was developed several decades ago, and its approach to setting thresholds for data disclosures makes it outdated.
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is a senior fellow at the HealthPartners Institute and a senior advisor for the Alliance of Community Health Plans. Previously, he served as a senior advisor to the board of directors and the senior management team of HealthPartners, and prior to that, he was HealthPartners' medical director and chief health officer, responsible for quality of care and health and health care improvement.
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She is also chief of breast surgery at Penn Medicine. Previously, she was associate professor of surgery and population health sciences in the Duke University School of Medicine and director of the
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He is a leading authority on the evaluation of diagnostic and screening tests and has made major contributions to the development of methods for medical technology assessment and health ser vices and outcomes research. He is a world leader in methods for applying and synthesizing evidence on diagnostic tests in medicine and is currently developing methods for comparative effectiveness research in diagnosis and prediction and radiomics.
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His current research is on the clinical information stored in electronic health records. Using data mining techniques, he is developing the methods necessary to support clini cal research and patient safety initiatives.
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She is the lead of Kaiser Permanente & Strategic Partners Patient Outcomes Research To Advance Learning (PORTAL) Network.
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He was formerly chief innovation and technology officer at the Palo Alto Medical Foundation and vice president, chief health transformation officer at IBM Watson Health. He has more than 25 years of executive leadership experience in health information technology within medical groups, health systems, and corporate settings.
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• Discuss what HHS is best positioned to address and support, and how the agency could maximize resources available for the PCOR data infrastructure (representing 4% of the PCOR trust fund) , in the context of the HHS public mission, authorities, programs, and data resources.
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What characteristics of HHS's public mission, programs, or authorities could be leveraged? Moderators: George Hripcsak, Columbia University, and David Meltzer, University of Chicago Speakers: John Halamka, Mayo Clinic Shaun Grannis, Regenstrief Institute Evelyn Gallego, EMI Advisors Rachel Richesson, University of Michigan Patrick Ryan, Janssen Research and Development VG Vinod Vydiswaran, University of Michigan 1:05–1:20 pm Break EDT 1:20–3:00 pm PCOR Methods EDT Discussion questions: • What emerging methods are likely to be most relevant for the PCOR data infrastructure looking forward?
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What characteristics of HHS's public mission, programs, or authorities could be leveraged? Moderators: Deborah Estrin, Cornell Tech and Paul Tang, Palo Alto Medical Foundation and Stanford Clinical Excellence Research Center Speakers: Pamela Riley, Government of the District of Columbia Abel Kho, Northwestern University Julia Adler-Milstein, University of California, San Francisco Deven McGraw, Ciitizen Don Detmer, University of Virginia 4:55-5:00 pm Wrap-up EDT George Isham (Committee Chair)
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She is a leading researcher in health information technology policy, with a specific focus on electronic health records (EHRs) and interoperability.
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Current boards include the Corporation for National Research Initiatives, the American College of Medical Informatics, and the Inter national Academy of Health Sciences Informatics. He helped envision the national health information infrastructures of the United States and Hong Kong, as well as shaped policy for direct electronic communications of health records with patients in the United States and Europe.
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He has served as principal investigator for several regional or national projects including the Office of the National Coordina tor for Health Information Technology–funded Chicago Health IT Regional Extension Center, the Patient-Centered Outcomes Research Institute–funded Chicago Area Patient Centered Outcomes Research Network, and the Agency
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She has more than 15 years of pharmaceutical policy and health economics and outcomes research experiences, including providing evidence-generation advisory services at Avalere Health, working in commercial and medical roles at Genentech and Bristol-Myers Squibb, respectively, and serving on Capitol Hill during the passage of the Affordable Care Act. Lederer received her Ph.D.
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in the Department of Health Care Policy at Harvard Medical School and professor in the Department of Biostatistics at Harvard School of Public Health. Her research focuses on the development of statistical methods for health services and outcomes research, including the evaluation of medical devices, causal inference, provider profiling, evidence synthesis, item response theory, and latent variables analyses.
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She has directed implementation of data standards for a number of multinational multisite clinical research and epidemiological studies, including the National Institutes of Health (NIH) Rare Diseases Clinical Research Network, Type 1 Diabetes TrialNet, and The Environmental Determinants of Diabetes in the Young study, and the national distributed Patient-Centered Outcomes Research Network.
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at Stanford University, associate chief information officer for data science at Stanford Healthcare, and a member of the Biomedical Informatics Grad uate Program as well as the Clinical Informatics Fellowship. His research focuses on combining machine learning and prior knowledge in medical ontologies to enable use cases of the learning health system.
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His research focuses on developing and applying text mining, natural language process ing, and machine-learning methodologies for extracting relevant informa tion from health-related text corpora. This includes medically relevant information from clinical notes and biomedical literature, and studying the information quality and credibility of online health communication (via health forums and tweets)
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