Guiding Principles to Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Health Care.

Importance: Health care algorithms are used for diagnosis, treatment, prognosis, risk stratification, and allocation of resources. Bias in the development and use of algorithms can lead to worse outcomes for racial and ethnic minoritized groups and other historically marginalized populations such as individuals with lower income. Objective: To provide a conceptual framework and guiding principles for mitigating and preventing bias in health care algorithms to promote health and health care equity. Evidence Review: The Agency for Healthcare Research and Quality and the National Institute for Minority Health and Health Disparities convened a diverse panel of experts to review evidence, hear from stakeholders, and receive community feedback. Findings: The panel developed a conceptual framework to apply guiding principles across an algorithm's life cycle, centering health and health care equity for patients and communities as the goal, within the wider context of structural racism and discrimination. Multiple stakeholders can mitigate and prevent bias at each phase of the algorithm life cycle, including problem formulation (phase 1); data selection, assessment, and management (phase 2); algorithm development, training, and validation (phase 3); deployment and integration of algorithms in intended settings (phase 4); and algorithm monitoring, maintenance, updating, or deimplementation (phase 5). Five principles should guide these efforts: (1) promote health and health care equity during all phases of the health care algorithm life cycle; (2) ensure health care algorithms and their use are transparent and explainable; (3) authentically engage patients and communities during all phases of the health care algorithm life cycle and earn trustworthiness; (4) explicitly identify health care algorithmic fairness issues and trade-offs; and (5) establish accountability for equity and fairness in outcomes from health care algorithms. Conclusions and Relevance: Multiple stakeholders must partner to create systems, processes, regulations, incentives, standards, and policies to mitigate and prevent algorithmic bias. Reforms should implement guiding principles that support promotion of health and health care equity in all phases of the algorithm life cycle as well as transparency and explainability, authentic community engagement and ethical partnerships, explicit identification of fairness issues and trade-offs, and accountability for equity and fairness.

Medicare Data Linkages for Conducting Patient-Centered Outcomes Research on Economic Outcomes.

BACKGROUND: Medicare patients and other stakeholders often make health care decisions that have economic consequences. Research on economic variables that patients have identified as important is referred to as patient-centered outcomes research (PCOR) and can generate evidence that informs decision-making. Medicare fee-for-service (FFS) claims are widely used for research and are a potentially valuable resource for studying some economic variables, particularly when linked to other datasets. OBJECTIVE: The aim of this study was to identify and assess the characteristics of federally funded administrative and survey data sources that can be linked to Medicare claims for conducting PCOR on some economic outcomes. RESEARCH DESIGN: A targeted internet search was conducted to identify a list of relevant data sources. A technical panel and key informant interviews were used for guidance and feedback. RESULTS: We identified 12 survey and 6 administrative sources of linked data for Medicare FFS beneficiaries. A majority provide longitudinal data and are updated annually. All linked sources provide some data on social determinants of health and health equity-related factors. Fifteen sources capture direct medical costs (beyond Medicare FFS payments); 5 capture indirect costs (eg, lost wages from absenteeism), and 7 capture direct nonmedical costs (eg, transportation). CONCLUSIONS: Linking Medicare FFS claims data to other federally funded data sources can facilitate research on some economic outcomes for PCOR. However, few sources capture direct nonmedical or indirect costs. Expanding linkages to include additional data sources, and reducing barriers to existing data sources, remain important objectives for increasing high-quality, patient-centered economic research.

Implementation and evaluation of an electronic health record-integrated app for postpartum monitoring of hypertensive disorders of pregnancy using patient-contributed data collection.

Remote monitoring of women experiencing hypertensive disorders of pregnancy (HDP) can provide timely life-saving data, particularly if these data are integrated into existing patient and clinical workflows. This pilot intervention of a smartphone application (app) for postpartum monitoring of hypertensive disorders integrates patient-contributed data into electronic health records (EHRs) to support monitoring and clinical decision-making. Results from the evaluation of the pilot highlight the resources needed when implementing the app, challenges for integrating an app into the EHR, and the usability and utility of the HDP monitoring app for patient and clinician users. The implementation team's key observations included the importance of a local clinical champion, more robust patient involvement and support for the remote patient monitoring program, an impetus for EHR developers to adopt data integration standards, and a need to expand the capabilities of the standards to support interventions using patient-contributed data.

Federal Data for Conducting Patient-centered Outcomes Research on Economic Outcomes.

BACKGROUND: Patients are increasingly interested in data on the economic burdens and impacts of health care choices; caregivers, employers, and payers are also interested in these costs. Although there have been various federal investments into patient-centered outcomes research (PCOR), an assessment of the coverage and gaps in federally funded data for PCOR economic evaluations has not been produced to date. OBJECTIVES: To classify relevant categories of PCOR economic costs, to assess current federally funded data for coverage of these categories, and to identify gaps for future research and collection. RESEARCH DESIGN: A targeted internet search was conducted to identify a list of relevant outcomes and data sources. The study team assessed data sources for coverage of economic outcomes. A technical panel and key informant interviews were used for evaluation and feedback. RESULTS: Four types of formal health care sector costs, 3 types of informal health care sector costs, and 10 types of non-health care sector costs were identified as relevant for PCOR economic evaluations. Twenty-nine federally funded data sources were identified. Most contained elements on formal costs. Data on informal costs (eg, transportation) were less common, and non-health care sector costs (eg, productivity) were the least common. Most data sources were annual, cross-sectional, nationally representative individual-level surveys. CONCLUSIONS: The existing federal data infrastructure captures many areas of the economic burden of health and health care, but gaps remain. Research from multiple data sources and potential future integrations may offset gaps in individual data sources. Linkages are promising strategies for future research on patient-centered economic outcomes.

Assessment of Changes in Visits and Antibiotic Prescribing During the Agency for Healthcare Research and Quality Safety Program for Improving Antibiotic Use and the COVID-19 Pandemic.

Importance: The Agency for Healthcare Research and Quality (AHRQ) Safety Program for Improving Antibiotic Use aimed to improve antibiotic prescribing in ambulatory care practices by engaging clinicians and staff to incorporate antibiotic stewardship into practice culture, communication, and decision-making. Little is known about implementation of antibiotic stewardship in ambulatory care practices. Objective: To examine changes in visits and antibiotic prescribing during the AHRQ Safety Program. Design, Setting, and Participants: This cohort study evaluated a quality improvement intervention in ambulatory care throughout the US in 389 ambulatory care practices from December 1, 2019, to November 30, 2020. Exposures: The AHRQ Safety Program used webinars, audio presentations, educational tools, and office hours to engage stewardship leaders and clinical staff to address attitudes and cultures that challenge judicious antibiotic prescribing and incorporate best practices for the management of common infections. Main Outcomes and Measures: The primary outcome of the Safety Program was antibiotic prescriptions per 100 acute respiratory infection (ARI) visits. Data on total visits and ARI visits were also collected. The number of visits and prescribing rates from baseline (September 1, 2019) to completion of the program (November 30, 2020) were compared. Results: Of 467 practices enrolled, 389 (83%) completed the Safety Program; of these, 292 (75%) submitted complete data with 6 590 485 visits to 5483 clinicians. Participants included 82 (28%) primary care practices, 103 (35%) urgent care practices, 34 (12%) federally supported practices, 39 (13%) pediatric urgent care practices, 21 (7%) pediatric-only practices, and 14 (5%) other practice types. Visits per practice per month decreased from a mean of 1624 (95% CI, 1317-1931) at baseline to a nadir of 906 (95% CI, 702-1111) early in the COVID-19 pandemic (April 2020), and were 1797 (95% CI, 1510-2084) at the end of the program. Total antibiotic prescribing decreased from 18.2% of visits at baseline to 9.5% at completion of the program (-8.7%; 95% CI, -9.9% to -7.6%). Acute respiratory infection visits per practice per month decreased from baseline (n = 321) to a nadir of 76 early in the pandemic (May 2020) and gradually increased through completion of the program (n = 239). Antibiotic prescribing for ARIs decreased from 39.2% at baseline to 24.7% at completion of the program (-14.5%; 95% CI, -16.8% to -12.2%). Conclusions and Relevance: In this study of US ambulatory practices that participated in the AHRQ Safety Program, significant reductions in the rates of overall and ARI-related antibiotic prescribing were noted, despite normalization of clinic visits by completion of the program. The forthcoming AHRQ Safety Program content may have utility in ambulatory practices across the US.

Application Programming Interfaces in Health Care: Findings from a Current-State Sociotechnical Assessment.

OBJECTIVE: Interest in application programming interfaces (APIs) is increasing as key stakeholders look for technical solutions to interoperability challenges. We explored three thematic areas to assess the current state of API use for data access and exchange in health care: (1) API use cases and standards; (2) challenges and facilitators for read and write capabilities; and (3) outlook for development of write capabilities. METHODS: We employed four methods: (1) literature review; (2) expert interviews with 13 API stakeholders; (3) review of electronic health record (EHR) app galleries; and (4) a technical expert panel. We used an eight-dimension sociotechnical model to organize our findings. RESULTS: The API ecosystem is complicated and cuts across five of the eight sociotechnical model dimensions: (1) app marketplaces support a range of use cases, the majority of which target providers' needs, with far fewer supporting patient access to data; (2) current focus on read APIs with limited use of write APIs; (3) where standards are used, they are largely Fast Healthcare Interoperability Resources (FHIR); (4) FHIR-based APIs support exchange of electronic health information within the common clinical data set; and (5) validating external data and data sources for clinical decision making creates challenges to provider workflows. CONCLUSION: While the use of APIs in health care is increasing rapidly, it is still in the pilot stages. We identified five key issues with implications for the continued advancement of API use: (1) a robust normative FHIR standard; (2) expansion of the common clinical data set to other data elements; (3) enhanced support for write implementation; (4) data provenance rules; and (5) data governance rules. Thus, while APIs are being touted as a solution to interoperability challenges, they remain an emerging technology that is only one piece of a multipronged approach to data access and use.

Application Programming Interfaces (APIs) in Health Care: Findings from a Current-State Assessment.

Interest in application programming interfaces (APIs) as a means to increase health data access and exchange among patients, health care providers, and payers has become an important area for development. In an effort to better understand the various contexts in which APIs can be applied, we explored different use cases. While APIs and our collective understanding of the best ways to implement and use them continue to develop, in the coming years the use of proprietary and standards-based APIs could be key to the sustainability of applied clinical informatics research, as well as associated improvements in patient engagement, clinical decision making, efficiency, quality and safety of the healthcare delivery system.

Incorporating Social Determinants of Health in Electronic Health Records: Qualitative Study of Current Practices Among Top Vendors.

BACKGROUND: Social determinants of health (SDH) are increasingly seen as important to understanding patient health and identifying appropriate interventions to improve health outcomes in what is a complex interplay between health system-, community-, and individual-level factors. OBJECTIVE: The objective of the paper was to investigate the development of electronic health record (EHR) software products that allow health care providers to identify and address patients' SDH in health care settings. METHODS: We conducted interviews with six EHR vendors with large market shares in both ambulatory and inpatient settings. We conducted thematic analysis of the interviews to (1) identify their motivations to develop such software products, (2) describe their products and uses, and (3) identify facilitators and challenges to collection and use of SDH data-through their products or otherwise-either at the point of care or in population health interventions. RESULTS: Our findings indicate that vendor systems and their functionalities are influenced by client demand and initiative, federal initiatives, and the vendors' strategic vision about opportunities in the health care system. Among the small sample of vendors with large market shares, SDH is a new area for growth, and the vendors range in the number and sophistication of their SDH-related products. To enable better data analytics, population health management, and interoperability of SDH data, vendors recognized the need for more standardization of SDH performance measures across various federal and state programs, better mapping of SDH measures to multiple types of codes, and development of more codes for all SDH measures of interest. CONCLUSIONS: Vendors indicate they are actively developing products to facilitate the collection and use of SDH data for their clients and are seeking solutions to data standardization and interoperability challenges through internal product decisions and collaboration with policymakers. Due to a lack of policy standards around SDH data, product-specific decisions may end up being de facto policies given the market shares of particular vendors. However, commercial vendors appear ready to collaboratively discuss policy solutions such as standards or guidelines with each other, health care systems, and government agencies in order to further promote integration of SDH data into the standard of care for all health systems.