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Proceedings of a Workshop
Pages 1-54

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From page 1...
... Analyses of big data have the potential to elucidate ways in which socioeconomic status, race and ethnicity, and other social determinants of health (SDOH) contribute to cancer incidence and outcomes, and may also identify promising avenues for intervention.
From page 2...
... The workshop featured presentations and discussion by experts in technology, oncology, and SDOH, as well as representatives from government, industry, academia, and health care systems. Workshop speakers examined topics that included the following: • the impact of SDOH on cancer incidence and outcomes; • opportunities to capture and analyze precise and meaningful data on SDOH in oncology; • cultural, technical, and legal challenges in applying big data to address SDOH in cancer; • ethical considerations in big data research and opportunities to improve representation and reduce bias in this research; and • policies, practices, and research priorities to advance progress using big data to improve health and reduce health disparities in oncology.
From page 3...
... (Virnig) Supporting and Improving Research on SDOH •  nsure that improving health and advancing health equity are E core goals of research on SDOH and consider equity in each element of the design and in the conduct of research.
From page 4...
... • E  ncourage participation of diverse populations in clinical research by exploring decentralized clinical trials, alternate modes of data collection, and broader clinical trial eligibility requirements. (Hudis, Khozin, Pérez-Stable)
From page 5...
... • U  se standardized terminologies and data elements for collecting SDOH data, including in electronic health records, to facilitate data aggregation and analyses. (Ash, Fichtenberg, Gomez, Hughes Halbert, Pérez-Stable)
From page 6...
... . Ayanian noted that SDOH affect patient outcomes across the continuum of cancer care, through prevention, early detection, treatment, and survivorship care (Ward et al., 2008)
From page 7...
... PROCEEDINGS OF A WORKSHOP 7 FIGURE 1  Conceptual model of health care disparities. NOTE: SES = socioeconomic status.
From page 8...
... These AIAN individuals are more likely to be diagnosed with advanced colorectal cancer and have worse prognoses compared to white populations, and colorectal cancer mortality is 39 percent higher among AIAN populations compared to white populations (Cueto et al., 2011; Jemal et al., 2004; Perdue et al., 2014)
From page 9...
... that the next big thing to improve quality and reduce costs in health care is to assess social determinants and use that information to improve care in various ways." Fichtenberg added that a National Academies consensus study report on integrating social care into the delivery of health care concluded that "taking the social conditions in which an individual lives, works, and plays into account is critical to improving both primary prevention 8 Health disparities are "preventable differences in the burden of disease, injury, violence, or opportunities to achieve optimal health that are experienced by socially disadvantaged populations" (CDC, 2008)
From page 10...
... Augusto Ochoa, director of the Stanley S Scott Cancer Center and professor of pediatrics at the Louisiana State University Health Sciences Center New Orleans, stressed that clinical trials often underrepresent patients who receive their cancer care in community oncology practices, where the vast majority of people with cancer are treated.
From page 11...
... Ayanian noted that by mining health care data on multiple levels -- including patient, clinician, practice, and health system characteristics -- researchers can more fully assess the quality of care that people receive. Adamson noted that big data research can also help investigators evaluate health care policies and their effects on health equity.
From page 12...
... J Leonard Lichtenfeld, deputy chief medical officer of the American Cancer Society, recommended collecting longitudinal SDOH data along with data on the performance of the economy over time.
From page 13...
... . CancerLinQ: An effort to organize, standardize, clean, structure, and analyze real-world cancer care data extracted from electronic health records (EHRs)
From page 14...
... : NCDB is jointly sponsored by the American College of Surgeons and the American Cancer Society. It com piles clinical oncology data from hospital registries, including more than 1,500 facilities accredited by the Commission on Cancer (CoC)
From page 15...
... : UPDB links local, state, and national health care data in an effort to promote health equity through a highly connected data environment. UPDB has built linkages between major medical, administrative, demographic, and public health databases in the state.
From page 16...
... data. EHR = electronic health record; SDOH = social determinants of health.
From page 17...
... In addition, Karen Basen-Engquist, director of the Center for Energy Balance in Cancer Prevention and Survivorship at The University of Texas MD Anderson Cancer Center, said that combining datasets may also require cooperation across institutions that are reluctant to partner. Scott Ramsey, director of the Hutchinson Institute for Cancer Outcomes Research at the Fred Hutchinson Cancer Research Center, agreed that collaboration among disparate organizations can be difficult, and suggested that a unifying call to action can help spur progress.
From page 18...
... Artificial Intelligence Advances in AI technologies underlie much of the enthusiasm for mining big data for insights related to SDOH and cancer outcomes. Many of those advances involve greater computing power and new machine learning algorithms that enable a computer program to learn patterns and classify novel data.
From page 19...
... and then allow humans to interpret it." Tang added, "The true working of any technology should be the intersection of the technology with humanity. That produces a better human professional that helps others." David Steiner noted that some research has found that the combination of a pathologist and a machine learning algorithm is more accurate and efficient at diagnosing metastatic breast cancer from images of sentinel lymph node specimens than either approach alone (Liu et al., 2019; Steiner et al., 2019)
From page 20...
... . Osonde Osoba, information scientist at the RAND Corporation and professor at the Pardee RAND Graduate School, reported on various techniques to ensure patient privacy while using large digital datasets for research.12 He 11 Difference-in-difference and interrupted time series analyses are quasi-experimental s ­tatistical techniques to improve causal inference from observational data.
From page 21...
... "They provide barriers but … there is always background information in other datasets, so even if you think your data are deidentified, by linking enough of those background databases, it may be possible to reidentify many of your participants." Given the potential for reidentification, several speakers discussed strategies to protect patient privacy (see the "Implementing Data Policies and Standards" section)
From page 22...
... 22 FIGURE 4  Availability of SDOH indicators. SOURCES: Ayanian presentation, October 28, 2019; Buntin and Ayanian, 2017; NASEM, 2017.
From page 23...
... Several speakers said that additional research is needed to identify best practices for collecting and interpreting SDOH data over time. "Social determinants of health can change over the life course so we need to think carefully about whether we are getting the right measure so that we are understanding the right association," Virnig said.
From page 24...
... , a multi-site research project that integrates several studies focused on address ing the role of social stressors, genetics, and tumor genomics and biology on tumor aggressiveness in prostate cancer. RESPOND will recruit 10,000 African American men with prostate cancer, primarily through cancer registries.
From page 25...
... Robin Yabroff, senior scientific director of health services research at the American Cancer Society, added that it is important to identify research strategies that enable patients to safely access health care and contribute to SDOH research. CHALLENGES Many workshop participants discussed challenges in leveraging big data to assess and address SDOH in oncology.
From page 26...
... Arlene Ash, professor of population and quantitative health sciences and division chief of biostatistics and health services research at the University of Massachusetts Medical School, also described the difficulties of SDOH data collection. She said it is important to build patients' understanding of the value of SDOH data as well as patients' confidence that sensitive data will be handled appropriately.
From page 27...
... She noted that many researchers may incorrectly assume that African American patients would not want to participate in clinical trials due to mistrust, and this assumption could dissuade clinicians from offering the option of a clinical trial. To ensure that diverse participants are included in research, Virnig suggested that researchers should seek to recruit patients from varied clinical settings, and not only from academic medical centers.
From page 28...
... Gomez added, "We need to be mindful of how we're educating and training the folks who are using the data to be responsible data stewards, and also, unfortunately, to think about those people who might be using it for nefarious purposes and how to protect against that." She also pointed out the need to educate clinicians about the importance of collecting SDOH data, because many do not see it as part of their purview. Ahmed Hassoon, research associate in the Division of Cardiovascular and Clinical Epidemiology at Johns Hopkins University, stressed the importance of investments in training the next generation of leaders and scientists, noting that his institution has no clear educational or career path for the inter­ isciplinary research required for applying big data to address SDOH d in oncology.
From page 29...
... She added that the lack of EHR interoperability also complicates the extraction of SDOH data for research purposes. Madigan noted that one initiative to address the lack of standardization in real-world data is the Observational Health Data Sciences and Informatics collaboration (see Box 4)
From page 30...
... The ultimate objective of The Gravity ­ roject P is to facilitate the use of SDOH data in research, payment, risk adjustment, community health improvement, and care coordination. The project is guided by representatives from key federal agencies, expert organizations, insurers, and patient advocacy groups.
From page 31...
... . Ensuring Equity and Quality in AI Technologies Several speakers highlighted the challenge of ensuring that AI technologies used in cancer research and care are high quality and achieve the goal of health equity.
From page 32...
... Rosati noted a recent trend toward implementation of rigorous state-based consumer privacy protection laws, beginning with the California Consumer Privacy Act.17 In addition, she said there is bipartisan support for federal legislation to strengthen consumer privacy protections.18 She added that the fragmented nature of state, federal, and international laws protecting patient privacy and their varying interpretations often create barriers to effective collaboration and data sharing. Virnig said that the Privacy Rule promulgated under the Health Insurance Portability and Accountability Act (HIPAA)
From page 33...
... F •  ational Institutes of Health policies on clinical trials and N regulations regarding Certificates of Confidentiality. •  tate consumer privacy protection laws (e.g., the California S Consumer Privacy Act)
From page 34...
... apply to health information generated by covered entities (hospitals, medical practices, and health insurers and their business partners) , but much health information is not covered by HIPAA regulations.
From page 35...
... . Several workshop participants also raised the challenge of protecting patient privacy after identifying information has been removed from health data (i.e., the data have been deidentified)
From page 36...
... POLICIES AND PRACTICES TO ADVANCE PROGRESS Many workshop speakers discussed policies and practices to advance progress in the application of big data to assess and address SDOH in oncology. These policies and practices included the following: • embracing diversity and equity, • promoting stakeholder engagement, • translating research into action, • implementing data policies and standards, • revising payment policies, and • supporting SDOH research.
From page 37...
... ­ ichardson said that R an increased use of cross-disciplinary research teams would bring a diversity of perspectives and training backgrounds to SDOH research, and she called for changing the culture of academia to place greater value on collaboration. Webb Hooper added, "It is important to have a team of stakeholders at the table who have an equal voice … so that it is more of a truly collaborative process." Promoting Diversity in Clinical Trials Workshop participants discussed opportunities to promote diversity in clinical trials.
From page 38...
... The network is a collaboration among academic medical centers and community cancer care practices. Academic centers provide centralized regulatory and data management services, clinical trials software, and support to community oncologists.
From page 39...
... 22 and other strategies to partner with communities in SDOH 22CBPR is "a collaborative approach to research that equitably involves all partners in the research process and recognizes the unique strengths that each brings. CBPR begins with a research topic of importance to the community and has the aim of combining knowledge with action and achieving social change to improve health outcomes and eliminate health disparities" (Community Health Scholars Program, 2002, p.
From page 40...
... BOX 8 Our Voice Abby King, professor of epidemiology and population health and professor of medicine at Stanford University, reported on Our Voice,a a citizen science data collection initiative that engages resi dents to collect environmental health data to promote changes for healthier neighborhoods. The process is facilitated by ­ esearchers, r community organizations, governments, and business groups.
From page 41...
... By linking data from diverse sources -- including EHRs, administrative claim databases, and cancer registries -- this group has created a comprehensive and continuously updated database of approximately 160,000 people with cancer. The collaborative produces periodic reports on clinic-level quality 23 For more information on the Metropolitan Chicago Breast Cancer Task Force, see http://www.chicagobreastcancer.org (accessed April 10, 2020)
From page 42...
... She suggested that SDOH could be included in NCI's list of common data elements for clinical trials. Ayanian said that the National Academies report on accounting for social risk factors in Medicare payment provided recommendations for collecting SDOH data in health care (NASEM, 2017)
From page 43...
... initiative -- led by ASCO, the American Society for Radiation Oncology, FDA, MITRE Corporation, and the Alliance for Clinical Trials in Oncology Foundation -- to identify a core set of structured data elements for ­ oncology EHRs (HL7 FHIR, 2019; mCODE, 2019)
From page 44...
... This may be accomplished through cross-stakeholder efforts like mCODE. Gomez also suggested accreditation requirements or professional guidelines for hospitals and clinical practices, which could further incentivize data standardization and specify which SDOH data should be collected.
From page 45...
... Darien added that although concerns are often expressed about whether self-reported patient data are valid, the accuracy of reporting by clinicians should also be considered. Privacy Protections Several speakers stressed the importance of protecting patient privacy and preventing data misuse, especially with SDOH data.
From page 46...
... Revising Payment Policies Several workshop participants discussed how payment policies may hamper ­ SDOH research. Hudis noted that Medicaid is the only insurance program in the United States not required to provide coverage for clinical trial participation.
From page 47...
... Ayanian stressed that actionable policies to address SDOH should be targeted at every level: nationally, within states, within communities, and within health care systems. Supporting SDOH Research Many workshop participants called for additional research support for SDOH in oncology.
From page 48...
... He said that these collaborations could transform FQHCs not just to deliver primary care but also to provide broad and equitable patient access to oncology research and the advances in patient care that stem from this research. Winn emphasized that it is critical to apply the principle of intersectionality of SDOH when prioritizing research on interventions to promote health equity.
From page 49...
... He also agreed with suggestions to hold health care organizations and insurers accountable for population health. He noted that experimentation with payment and care delivery models could help expand access to high-quality care for vulnerable populations and promote health equity.
From page 50...
... 2008. Closing the gap in a generation: Health equity through action on the social determinants of health.
From page 51...
... 2019. HL7 FHIR Implementation guide: Minimal common oncology data elements (mCODE)
From page 52...
... 2019. Enrollment of racial minorities in clinical trials: Old problem assumes new urgency in the age of immunotherapy.
From page 53...
... RESPOND African American prostate cancer study. http://respondstudy.org (accessed April 10, 2020)


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