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BackgroundHealth systems have recently started to activate patient-facing application programming interfaces (APIs) to facilitate patient access to health data and other interactions. ObjectiveThis study sought to ascertain health systems’ understanding, strategies, governance, and organizational infrastructure around patient-facing APIs, as well as their business drivers and barriers, to facilitate national learning, policy, and progress toward adoption. MethodsWe performed a content analysis of semistructured interviews with a convenience sample of 10 health systems known to be leading adopters of health technology, having either implemented or planning to implement patient-facing APIs. ResultsOf the 10 health systems, eight had operational patient-facing APIs, with organizational strategy driven most by federal policy, the emergence of Health Records on iPhone, and feelings of ethical obligation. The two priority use cases identified were enablement of a patient’s longitudinal health record and digital interactions with the health system. The themes most frequently cited as barriers to the increased use of patient-facing APIs were security concerns, an immature app ecosystem that does not currently offer superior functionality compared with widely adopted electronic health record (EHR)–tethered portals, a lack of business drivers, EHR vendor hesitation toward data sharing, and immature technology and standards. ConclusionsOur findings reveal heterogeneity in health system understanding and approaches to the implementation and use of patient-facing APIs. Ongoing study, targeted policy interventions, and sharing of best practices appear necessary to achieve successful national implementation.

Objectives We analyzed trends in adoption of advanced patient engagement and clinical data analytics functionalities among critical access hospitals (CAHs) and non-CAHs to assess how historical gaps have changed. Materials and Methods We used 2014, 2018, and 2023 data from the American Hospital Association Annual Survey IT Supplement to measure differences in adoption rates (ie, the "adoption gap") of patient engagement and clinical data analytics functionalities across CAHs and non-CAHs. We measured changes over time in CAH and non-CAH adoption of 6 "core" clinical data analytics functionalities, 5 "core" patient engagement functionalities, 5 new patient engagement functionalities, and 3 bulk data export use cases. We constructed 2 composite measures for core functionalities and analyzed adoption for other functionalities individually. Results Core functionality adoption increased from 21% of CAHs in 2014 to 56% in 2023 for clinical data analytics and 18% to 49% for patient engagement. The CAH adoption gap in both domains narrowed from 2018 to 2023 (both P  < .01). More than 90% of all hospitals had adopted viewing and downloading electronic data and clinical notes by 2023. The largest CAH adoption gaps in 2023 were for Fast Healthcare Interoperability Resources (FHIR) bulk export use cases (eg, analytics and reporting: 63% of CAHs, 81% of non-CAHs, P  < .001). Discussion Adoption of advanced electronic health record functionalities has increased for CAHs and non-CAHs, and some adoption gaps have been closed since 2018. However, CAHs may continue to struggle with clinical data analytics and FHIR-based functionalities. Conclusion Some crucial patient engagement functionalities have reached near-universal adoption; however, policymakers should consider programs to support CAHs in closing remaining adoption gaps. [ABSTRACT FROM AUTHOR]

Objective To identify impacts of different survey methodologies assessing primary care physicians' (PCPs') experiences with electronic health records (EHRs), we compared three surveys: the 2022 Continuous Certification Questionnaire (CCQ) from the American Board of Family Medicine, the 2022 University of California San Francisco (UCSF) Physician Health IT Survey, and the 2021 National Electronic Health Records Survey (NEHRS). Materials and Methods We evaluated differences between survey pairs using Rao-Scott corrected chi-square tests, which account for weighting. Results CCQ received 3991 responses from PCPs (100% response rate), UCSF received 1375 (3.6% response rate), and NEHRS received 858 (18.2% response rate). Substantial, statistically significant differences in demographics were detected across the surveys. CCQ respondents were younger and more likely to work in a health system; NEHRS respondents were more likely to work in private practice; and UCSF respondents disproportionately practiced in larger academic settings. Many EHR experience indicators were similar between CCQ and NEHRS, but CCQ respondents reported higher documentation burden. Discussion The UCSF approach is unlikely to supply reliable data. Significant demographic differences between CCQ and NEHRS raise response bias concerns, and while there were similarities in some reported EHR experiences, there were important, significant differences. Conclusion Federal EHR policy monitoring and maintenance require reliable data. This test of existing and alternative sources suggest that diversified data sources are necessary to understand physicians' experiences with EHRs and interoperability. Comprehensive surveys administered by specialty boards have the potential to contribute to these efforts, since they are likely to be free of response bias. [ABSTRACT FROM AUTHOR]

Objective Understand public comfort with the use of different data types for predictive models Materials and Methods We analyzed data from a national survey of US adults (n  = 1436) fielded from November to December 2021. For three categories of data (identified using factor analysis), we use descriptive statistics to capture comfort level. Results Public comfort with data use for prediction is low. For 13 of 15 data types, most respondents were uncomfortable with that data being used for prediction. In factor analysis, 15 types of data grouped into three categories based on public comfort: (1) personal characteristic data, (2) health-related data, and (3) sensitive data. Mean comfort was highest for health-related data (2.45, SD 0.84, range 1-4), followed by personal characteristic data (2.36, SD 0.94), and sensitive data (1.88, SD 0.77). Across these categories, we observe a statistically significant positive relationship between trust in health systems' use of patient information and comfort with data use for prediction. Discussion Although public trust is recognized as important for the sustainable expansion of predictive tools, current policy does not reflect public concerns. Low comfort with data use for prediction should be addressed in order to prevent potential negative impacts on trust in healthcare. Conclusion Our results provide empirical evidence on public perspectives, which are important for shaping the use of predictive models. Findings demonstrate a need for realignment of policy around the sensitivity of non-clinical data categories. [ABSTRACT FROM AUTHOR]

Objectives This study sought to capture current digital health company experiences integrating with electronic health records (EHRs), given new federally regulated standards-based application programming interface (API) policies. Materials and methods We developed and fielded a survey among companies that develop solutions enabling human interaction with an EHR API. The survey was developed by the University of California San Francisco in collaboration with the Office of the National Coordinator for Health Information Technology, the California Health Care Foundation, and ScaleHealth. The instrument contained questions pertaining to experiences with API integrations, barriers faced during API integrations, and API-relevant policy efforts. Results About 73% of companies reported current or previous use of a standards-based EHR API in production. About 57% of respondents indicated using both standards-based and proprietary APIs to integrate with an EHR, and 24% worked about equally with both APIs. Most companies reported use of the Fast Healthcare Interoperability Resources standard. Companies reported that standards-based APIs required on average less burden than proprietary APIs to establish and maintain. However, companies face barriers to adopting standards-based APIs, including high fees, lack of realistic clinical testing data, and lack of data elements of interest or value. Discussion The industry is moving toward the use of standardized APIs to streamline data exchange, with a majority of digital health companies using standards-based APIs to integrate with EHRs. However, barriers persist. Conclusion A large portion of digital health companies use standards-based APIs to interoperate with EHRs. Continuing to improve the resources for digital health companies to find, test, connect, and use these APIs "without special effort" will be crucial to ensure future technology robustness and durability. [ABSTRACT FROM AUTHOR]

Preparing patients for posthospital care may improve readmission risk. Alternative payment models (APMs) incent hospitals to reduce readmissions by tying payment to outcomes. The impact of APMs on preparation for discharge is not well understood. We assessed whether patient-reported preparation for posthospital care was associated with reduced readmissions, and whether APM participation was associated with improved preparation for posthospital care. We used mixed-effects regression on retrospective (2013–2017) observational data for 2685 U.S. hospitals. We measured patient-reported preparation for posthospital care using the 3-Item Care Transition Measure and readmission using 30-day all-cause risk-adjusted readmissions from Hospital Compare. Participation in accountable care organizations (ACOs), Medical Homes, and Medicare's Bundled Payments for Care Improvement program was obtained from Medicare, the American Hospital Association's Annual Survey, and Leavitt Partner's ACO database. We found that APMs are not associated with improved preparation for posthospital care, even though it was associated with reduced readmissions (Marginal Effect: −0.012 percentage points). This may be because hospitals are not investing in patient engagement. This study has limited insight into causality and reduced generalizability among smaller, rural, and non-teaching hospitals. [ABSTRACT FROM AUTHOR]

Prediction tools driven by artificial intelligence (AI) and machine learning are becoming increasingly integrated into health care delivery in the United States. However, organizational approaches to the governance of AI tools are highly varied. There is growing recognition of the need for evidence on best governance practices and multilayered oversight that could provide appropriate guardrails at the organizational and federal levels to address the unique dimensions of AI prediction tools. We sought to qualitatively characterize salient dimensions of AI-enabled predictive model governance at U.S. academic medical centers (AMCs). We analyzed how AMCs in the United States currently govern predictive models and consider the implications. A total of 17 individuals from 13 AMCs across the country participated in interviews. Half-hour to 1-hour interviews were conducted via Zoom from October 2022 to January 2023. The interview guide focused on the capacity, governance, regulation, and evaluation of AI-driven predictive models. Analysis of interview data was inductive. The research team wrote memos throughout the process of interviewing and analysis. We identified three governance phenotypes: welldefined, emerging, and interpersonal. In the well-defined governance phenotype, health systems have explicit, comprehensive procedures for the review and evaluation of AI and predictive models. In the emerging governance phenotype, systems are in the process of adjusting or adapting previously established approaches for clinical decision support or electronic health records (EHR) to govern AI. In health systems using interpersonal or individual-driven governance approaches, an individual is tasked with making decisions about model implementation without consistent evaluation requirements. We found that the influence of EHR vendors is an important consideration for those tasked with governance at AMCs, given concerns about regulatory gaps and the need for model evaluation. Even well-resourced AMCs are struggling to effectively identify, manage, and mitigate the myriad potential problems and pitfalls related to the implementation of predictive AI tools. The range of governance structures that we identified indicates a need for additional guidance, both regulatory and otherwise, for health systems as prediction and AI proliferate. Rather than concentrating responsibility for governance within organizations, multiple levels of governance that include the industry and regulators would better promote quality care and patient safety. This sort of structure would also provide desired guidance and support to the individuals tasked with governing these tools. [ABSTRACT FROM AUTHOR]

Background: To support implementation of the 4Ms framework and more rigorous evidence of 4Ms impact, we translated Institute for Healthcare Improvement's (IHI's) recommended 4Ms routine care practices into electronic health record‐based, encounter‐level adherence measures and then implemented measures at a large academic medical center. Methods: We started with the 19 care practices in IHI's 4Ms implementation guide and developed encounter‐level adherence measures using structured EHR data. We also developed overall 4Ms‐level and M‐level composite measures. Next, we operationalized measures at UCSF Health—an academic medical center that has implemented the 4Ms using the IHI guide. We identified UCSF Health patients who should have received 4Ms care during their inpatient admission (19,335 individuals 65 years and older with an admission between January 1, 2019 and December 31, 2021), then implemented the individual measures and composite measures (all at the encounter level) using Epic EHR data. We focused on 4Ms inpatient care processes, but similar approaches can be followed for ambulatory, post‐acute, and other settings. Results: We developed 18 EHR‐based measures that captured all IHI care practices, 16 of which could be implemented using UCSF Health EHR data. For example, the EHR‐based measure for the Medication care practice "deprescribe high risk medications" was measured using EHR data as "Patient had no previously existing prescriptions for high‐risk medications OR patient had ≥1 previously existing prescriptions for high‐risk medications deprescribed during the encounter," and 29.5% of UCSF Health encounters met this measure. For composite measures, on average, UCSF Health encounters had 61.1% adherence to the 4Ms (SD = 14.4%), with the lowest average adherence to What Matters (50.9%; SD = 44.3%) and the highest for Mentation (68.4%; SD = 13.4%). Conclusions: It is feasible to construct encounter‐level measures of 4Ms adherence using EHR data and derive insights to guide ongoing implementation efforts. Future efforts should refine measures based on assessments of reliability and validity. [ABSTRACT FROM AUTHOR]

Objectives National attention has focused on increasing clinicians' responsiveness to the social determinants of health, for example, food security. A key step toward designing responsive interventions includes ensuring that information about patients' social circumstances is captured in the electronic health record (EHR). While prior work has assessed levels of EHR "social risk" documentation, the extent to which documentation represents the true prevalence of social risk is unknown. While no gold standard exists to definitively characterize social risks in clinical populations, here we used the best available proxy: social risks reported by patient survey. Materials and Methods We compared survey results to respondents' EHR social risk documentation (clinical free-text notes and International Statistical Classification of Diseases and Related Health Problems [ICD-10] codes). Results Surveys indicated much higher rates of social risk (8.2%-40.9%) than found in structured (0%-2.0%) or unstructured (0%-0.2%) documentation. Discussion Ideally, new care standards that include incentives to screen for social risk will increase the use of documentation tools and clinical teams' awareness of and interventions related to social adversity, while balancing potential screening and documentation burden on clinicians and patients. Conclusion EHR documentation of social risk factors currently underestimates their prevalence. [ABSTRACT FROM AUTHOR]

Introduction Research on how people interact with electronic health records (EHRs) increasingly involves the analysis of metadata on EHR use. These metadata can be recorded unobtrusively and capture EHR use at a scale unattainable through direct observation or self-reports. However, there is substantial variation in how metadata on EHR use are recorded, analyzed and described, limiting understanding, replication, and synthesis across studies. Recommendations In this perspective, we provide guidance to those working with EHR use metadata by describing 4 common types, how they are recorded, and how they can be aggregated into higher-level measures of EHR use. We also describe guidelines for reporting analyses of EHR use metadata—or measures of EHR use derived from them—to foster clarity, standardization, and reproducibility in this emerging and critical area of research. [ABSTRACT FROM AUTHOR]

This study assesses US trends in e-visit billing using national all-payer claims. [ABSTRACT FROM AUTHOR]

OBJECTIVES: To understand patient perceptions of specific applications of predictive models in health care. STUDY DESIGN: Original, cross-sectional national survey. METHODS: We conducted a national online survey of US adults with the National Opinion Research Center from November to December 2021. Measures of internal consistency were used to identify how patients differentiate between clinical and administrative predictive models. Multivariable logistic regressions were used to identify relationships between comfort with various types of predictive models and patient demographics, perceptions of privacy protections, and experiences in the health care system. RESULTS: A total of 1541 respondents completed the survey. After excluding observations with missing data for the variables of interest, the final analytic sample was 1488. We found that patients differentiate between clinical and administrative predictive models. Comfort with prediction of bill payment and missed appointments was especially low (21.6% and 36.6%, respectively). Comfort was higher with clinical predictive models, such as predicting stroke in an emergency (55.8%). Experiences of discrimination were significant negative predictors of comfort with administrative predictive models. Health system transparency around privacy policies was a significant positive predictor of comfort with both clinical and administrative predictive models. CONCLUSIONS: Patients are more comfortable with clinical applications of predictive models than administrative ones. Privacy protections and transparency about how health care systems protect patient data may facilitate patient comfort with these technologies. However, larger inequities and negative experiences in health care remain important for how patients perceive administrative applications of prediction. [ABSTRACT FROM AUTHOR]

Objective To use more precise measures of which hospitals are electronically connected to determine whether health information exchange (HIE) is associated with lower emergency department (ED)-related utilization. Materials and Methods We combined 2018 Medicare fee-for-service claims to identify beneficiaries with 2 ED encounters within 30 days, and Definitive Healthcare and AHA IT Supplement data to identify hospital participation in HIE networks (HIOs and EHR vendor networks). We determined whether the 2 encounters for the same beneficiary occurred at: the same organization, different organizations connected by HIE, or different organizations not connected by HIE. Outcomes were: (1) whether any repeat imaging occurred during the second ED visit; (2) for beneficiaries with a treat-and-release ED visit followed by a second ED visit, whether they were admitted to the hospital after the second visit; (3) for beneficiaries discharged from the hospital followed by an ED visit, whether they were admitted to the hospital. Results In adjusted mixed effects models, for all outcomes, beneficiaries returning to the same organization had significantly lower utilization compared to those going to different organizations. Comparing only those going to different organizations, HIE was not associated with lower levels of repeat imaging. HIE was associated with lower likelihood of hospital admission following a treat-and-release ED visit (1.83 percentage points [−3.44 to −0.21]) but higher likelihood of admission following hospital discharge (2.78 percentage points [0.48-5.08]). Discussion Lower utilization for beneficiaries returning to the same organization could reflect better access to information or other factors such as aligned incentives. Conclusion HIE is not consistently associated with utilization outcomes reflecting more coordinated care in the ED setting. [ABSTRACT FROM AUTHOR]

Thousands of health systems have adopted the 4 Ms framework, a set of evidence-based practices specific to older adults, as part of the Age-Friendly Health Systems (AFHS) initiative. However, implementation efforts have largely been setting-specific and approaches to achieve continuity of the 4 Ms during care transitions are nascent. Transitions from hospitals to skilled nursing facilities (SNFs) are one type of care transition that would greatly benefit from continuity of 4 Ms practices. Drawing from the authors' insights and 5 exploratory interviews at 3 health systems that implemented the 4 Ms in the inpatient setting, we describe a set of current-state challenges when trying to extend specific inpatient 4 Ms practices (eg, deprescribing of high-risk medications) as well as the nuanced understanding of the individual's clinical trajectory developed during an inpatient stay. We also offer concrete opportunities, such as developing 4 Ms–centric discharge summary templates, to address the challenges. With the large investment in AFHS transformation and associated efforts to implement the 4 Ms framework in all care settings used by older adults, it is critical to raise awareness of the specific obstacles to promoting continuity of successful 4 Ms practices during care transitions and work to overcome them. Our insights from hospital-to-SNF transitions offer a starting point. [ABSTRACT FROM AUTHOR]

Background: Redesigning the healthcare system to consistently provide effective and tailored care to older adults is needed. The 4Ms (What Matters, Mobility, Medication, and Mentation) offer a framework to guide health systems' efforts to deliver Age‐Friendly care. We use an implementation science framework to characterize and assess real‐world implementation experiences with the 4Ms across varied health systems. Methods: With expert input, we selected three health systems that were early adopters of the 4Ms and engaged in different implementation support models through the Institute for Healthcare Improvement. We conducted 29 semi‐structured interviews with diverse stakeholders from each site. Stakeholders ranged from hospital leadership to frontline clinicians. Interviews covered each site's approach to and experiences with implementation, including facilitators and barriers. Interviews were recorded, transcribed, and deductively coded using the Consolidated Framework for Implementation Research. We characterized each site's implementation decisions and then inductively identified overarching themes and subthemes with supporting quotes. Results: Health systems varied in their implementation approach, including the implementation order of each of the 4Ms. We identified three overarching themes: (1) the 4Ms offered a compelling conceptual framework for advancing Age‐Friendly care, but implementation was complex and fragmented; (2) complete and sustained implementation of the 4Ms required multidisciplinary and multilevel leadership and engagement; (3) strategies that facilitate implementation success and support frontline culture change included top‐down communication and infrastructure alongside hands‐on clinical education and support. Common barriers are siloed implementation efforts across settings that impeded synergies and scaling; disengaged physicians; and difficulty implementing What Matters in a meaningful way. Conclusions: Similar to other implementation studies, we identified multifactorial domains impacting 4Ms implementation. To achieve Age‐Friendly transformation, health systems must plan for and attend to multiple phases of implementation while ensuring that the work coheres under a unified vision that spans disciplines and settings. [ABSTRACT FROM AUTHOR]

Citizens' access to their online health information is pivotal. Therefore, this study examines citizens' access to their online health information across countries and healthcare settings. The study is based on a survey design targeting the 98 IMIA representatives of the national societies. Results indicate that Test results and Medications are the two types of online information that citizens in most cases have access to. Ten countries provide citizens access to all the different types of information included in the study. That relatively few countries provide citizens access to all the included types of online health information underscores the importance of continuous emphasis on accessibility and research within this field. [ABSTRACT FROM AUTHOR]

Background: Skilled nursing facilities' (SNFs) ability to provide optimal post-acute care depends on effective receipt of information from hospitals ("information continuity"). Little is known about how SNFs perceive information continuity and how it may relate to upstream information sharing processes, organizational context, and downstream outcomes. Purpose: First, this study aims to identify how SNF perceptions of information continuity may be shaped by hospital information sharing practices, including measures of completeness, timeliness, and usability, as well as characteristics of the transitional care environment (i.e., integrated care relationships and/or consistency of information sharing practices across different hospital partners). Second, we analyze which of these characteristics are associated with quality of transitional care (measured by 30-day readmissions). Approach: A cross-sectional analysis of nationally representative SNF survey (N = 212) linked to Medicare claims was performed. Results: SNF perceptions of information continuity are strongly and positively associated with hospital information sharing practices. Adjusting for actual information sharing practices, SNFs that experienced discordance across hospitals reported lower perceptions of continuity (β = −0.73, p =.022); evidence of stronger relationships with a given hospital partner appears to help facilitate resources and communication that helps to close this gap. Perceptions of information continuity, more so than the upstream information sharing processes reported, exhibited a more reliable and significant association with rates of readmissions as an indicator of transitional care quality. Conclusion: SNF perceptions of information continuity are strongly associated with patient outcomes and are reflective of both hospital information sharing practices as well as characteristics of the transitional care environment that can mitigate or amplify the cognitive and administration challenge of their work. Practice Implications: Improving transitional care quality requires that hospitals improve information sharing behaviors but also invest in capacity for learning and process improvement in the SNF environment. [ABSTRACT FROM AUTHOR]

Objective Electronic health records (EHRs) are increasingly used to capture social determinants of health (SDH) data, though there are few published studies of clinicians' engagement with captured data and whether engagement influences health and healthcare utilization. We compared the relative frequency of clinician engagement with discrete SDH data to the frequency of engagement with other common types of medical history information using data from inpatient hospitalizations. Materials and Methods We created measures of data engagement capturing instances of data documentation (data added/updated) or review (review of data that were previously documented) during a hospitalization. We applied these measures to four domains of EHR data, (medical, family, behavioral, and SDH) and explored associations between data engagement and hospital readmission risk. Results SDH data engagement was associated with lower readmission risk. Yet, there were lower levels of SDH data engagement (8.37% of hospitalizations) than medical (12.48%), behavioral (17.77%), and family (14.42%) history data engagement. In hospitalizations where data were available from prior hospitalizations/outpatient encounters, a larger proportion of hospitalizations had SDH data engagement than other domains (72.60%). Discussion The goal of SDH data collection is to drive interventions to reduce social risk. Data on when and how clinical teams engage with SDH data should be used to inform informatics initiatives to address health and healthcare disparities. Conclusion Overall levels of SDH data engagement were lower than those of common medical, behavioral, and family history data, suggesting opportunities to enhance clinician SDH data engagement to support social services referrals and quality measurement efforts. [ABSTRACT FROM AUTHOR]

The article discusses the use of telemedicine in primary care for people with dementia, comparing it to in-person care. The study found that telemedicine was associated with more timely access to care, with patients receiving care approximately 2 days earlier than with in-person visits. While telemedicine did not result in higher subsequent utilization at one health system, another system saw slightly higher levels of ED visits and return in-person primary care visits after telemedicine encounters. Overall, the study suggests that telemedicine is being used appropriately to meet the primary care needs of people with dementia, providing timeliness benefits without unintended consequences. [Extracted from the article]

This study evaluates the adoption of clinician billing for patient portal messages as e-visits, prompted by significant increases in patient messaging after the onset of the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Objective To determine whether novel measures of contextual factors from multi-site electronic health record (EHR) audit log data can explain variation in clinical process outcomes. Materials and Methods We selected one widely-used process outcome: emergency department (ED)-based team time to deliver tissue plasminogen activator (tPA) to patients with acute ischemic stroke (AIS). We evaluated Epic audit log data (that tracks EHR user-interactions) for 3052 AIS patients aged 18+ who received tPA after presenting to an ED at three Northern California health systems (Stanford Health Care, UCSF Health, and Kaiser Permanente Northern California). Our primary outcome was door-to-needle time (DNT) and we assessed bivariate and multivariate relationships with six audit log-derived measures of treatment team busyness and prior team experience. Results Prior team experience was consistently associated with shorter DNT; teams with greater prior experience specifically on AIS cases had shorter DNT (minutes) across all sites: (Site 1: −94.73, 95% CI: −129.53 to 59.92; Site 2: −80.93, 95% CI: −130.43 to 31.43; Site 3: −42.95, 95% CI: −62.73 to 23.17). Teams with greater prior experience across all types of cases also had shorter DNT at two sites: (Site 1: −6.96, 95% CI: −14.56 to 0.65; Site 2: −19.16, 95% CI: −36.15 to 2.16; Site 3: −11.07, 95% CI: −17.39 to 4.74). Team busyness was not consistently associated with DNT across study sites. Conclusions EHR audit log data offers a novel, scalable approach to measure key contextual factors relevant to clinical process outcomes across multiple sites. Audit log-based measures of team experience were associated with better process outcomes for AIS care, suggesting opportunities to study underlying mechanisms and improve care through deliberate training, team-building, and scheduling to maximize team experience. [ABSTRACT FROM AUTHOR]

On June 24, 2022, the US Supreme Court ended constitutional protections for abortion, resulting in wide variability in access from severe restrictions in many states and fewer restrictions in others. Healthcare institutions capture information about patients' pregnancy and abortion care and, due to interoperability, may share it in ways that expose their providers and patients to social stigma and potential legal jeopardy in states with severe restrictions. In this article, we describe sources of risk to patients and providers that arise from interoperability and specify actions that institutions can take to reduce that risk. Institutions have significant power to define their practices for how and where care is documented, how patients are identified, where data are sent or hosted, and how patients are counseled, and thus should protect patients' privacy and ability to receive medical care that is safe and legal where it is performed. [ABSTRACT FROM AUTHOR]

Electronic health record audit logs capture a time-sequenced record of clinician activities while using the system. Audit log data therefore facilitate unobtrusive measurement at scale of clinical work activities and workflow as well as derivative, behavioral proxies (eg, teamwork). Given its considerable research potential, studies leveraging these data have burgeoned. As the field has matured, the challenges of using the data to answer significant research questions have come into focus. In this Perspective, we draw on our research experiences and insights from the broader audit log literature to advance audit log research. Specifically, we make 2 complementary recommendations that would facilitate substantial progress toward audit log-based measures that are: (1) transparent and validated, (2) standardized to allow for multisite studies, (3) sensitive to meaningful variability, (4) broader in scope to capture key aspects of clinical work including teamwork and coordination, and (5) linked to patient and clinical outcomes. [ABSTRACT FROM AUTHOR]

Healthcare organizations are increasing social determinants of health (SDH) screening and documentation in the electronic health record (EHR). Physicians may use SDH data for medical decision-making and to provide referrals to social care resources. Physicians must be aware of these data to use them, however, and little is known about physicians' awareness of EHR-based SDH documentation or documentation capabilities. We therefore leveraged national physician survey data to measure level of awareness and variation by physician, practice, and EHR characteristics to inform practice- and policy-based efforts to drive medical-social care integration. We identify higher levels of social needs documentation awareness among physicians practicing in community health centers, those participating in payment models with social care initiatives, and those aware of other advanced EHR functionalities. Findings indicate that there are opportunities to improve physician education and training around new EHR-based SDH functionalities. [ABSTRACT FROM AUTHOR]