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 the first two outcomes, beneficiaries returning to the same organization had significantly lower utilization compared to those going to different organizations; for the third outcome, those returning had higher utilization. Comparing only those going to different organizations, HIE was not associated with lower levels of repeat imaging or likelihood of admission following hospital discharge. 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]). DISCUSSION: Differing 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.

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.

OBJECTIVE: Evaluate the association between telemedicine intensity and ambulatory physician electronic health record (EHR) use following the COVID-19 pandemic. MATERIALS AND METHODS: This retrospective study included ambulatory physicians in 11 specialties at 2 large academic medical centers (Washington University in St Louis [WashU], University of California San Francisco [UCSF]). EHR use measures, including time-based and frequency-based, were analyzed in the post-COVID-19 period (March 1, 2021, through March 7, 2022). Multivariable regression models with 2-way fixed effects were used to assess the association between telemedicine intensity and EHR use. RESULTS: Fully telemedicine physician-weeks were associated with higher EHR (hours per 8 patient scheduled hours; β = 3.2 at WashU, β = 1.4 at UCSF; P < .001) and documentation time (β = 2.7 at WashU, β = 1.4 at UCSF; P < .001). Several differences in discrete EHR-based tasks were observed: fully telemedicine physician-days were associated with lesser ordering, and there were mixed patterns for information seeking and clinical communication tasks. DISCUSSION: Expanded use of telemedicine was associated with significant changes in physician EHR use post-COVID-19 onset. Increased EHR time may suggest a shift in workload, whereas decreased ordering may suggest constraints in virtual care, such as ability to perform physical examination and the reliance on patient-reported symptoms. Institutional differences usage patterns suggest that telemedicine's impact is context-specific and provides opportunities for understanding how to optimize EHRs to support telemedicine. CONCLUSION: Telemedicine shifts physician EHR. Supporting physicians through optimized EHR tools, tailored workflows, and team-based interventions is essential for sustainable virtual care delivery without exacerbating EHR burden.

OBJECTIVES: To develop indices of US hospital interoperability to capture the current state and assess progress over time. MATERIALS AND METHODS: A Technical Expert Panel (TEP) informed selection of items from the American Hospital Association Health IT Supplement survey, which were aggregated into interoperability concepts (components) and then further combined into indices. Indices were refined through psychometric analysis and additional TEP input. Final indices included a "Core Index" measuring adoption of foundational interoperability capabilities, a "Pathfinder Index" representing adoption of advanced interoperability technologies and auxiliary exchange activities, and a "Friction Index" quantifying barriers. The first 2 indices were scored from 0 (no interoperability) to 100 (full interoperability); the Friction Index was scored 0 (no friction) to 100 (maximum friction). We calculated indices annually from 2021 to 2023, stratifying by hospital characteristics. RESULTS: Items within components created reliable and meaningful measures, and associations between components within indices followed the TEP's expectations. Weighted mean scores for the Core (2023), Pathfinder (2022), and Friction (2023) Indices were 61, 57, and 30, respectively. Hospitals with 500+ beds (large), not designated as critical access, in metropolitan areas, and using market leading electronic health records had statistically significant higher mean scores on all indices. Index values also improved modestly over time. DISCUSSION: Hospitals performed best on the Core Index. Given recent policy and programmatic initiatives, we anticipate continued improvement across all indices. CONCLUSION: Ongoing index tracking can inform policy impact evaluations and highlight persistent interoperability disparities across hospitals.

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.

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.

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.

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.

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.

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.

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.

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.

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.

A discussion and debate on the American Medical Informatics Association's (AMIA) Ethical, Legal, and Social Issues (ELSI) Working Group listserv in 2021 raised important issues related to a forthcoming conference in Texas. Texas had recently enacted a restrictive abortion law and restricted voting rights. Several AMIA members advocated for a boycott of the state and the scheduled conference. The discussion led the AMIA Board of Directors to request that the organization's Ethics Committee provide general guidance for principle-based venue selection. This document recommends overarching principles for the venue selection for future AMIA events and conferences. Discussions by the AMIA Board, the Ethics Committee, and the ELSI Working Group informed these recommendations, and this document on guiding principles was approved by the AMIA Board of Directors in April 2022.

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.

OBJECTIVE: Despite broad electronic health record (EHR) adoption in U.S. hospitals, there is concern that an "advanced use" digital divide exists between critical access hospitals (CAHs) and non-CAHs. We measured EHR adoption and advanced use over time to analyzed changes in the divide. MATERIALS AND METHODS: We used 2008 to 2018 American Hospital Association Information Technology survey data to update national EHR adoption statistics. We stratified EHR adoption by CAH status and measured advanced use for both patient engagement (PE) and clinical data analytics (CDA) domains. We used a linear probability regression for each domain with year-CAH interactions to measure temporal changes in the relationship between CAH status and advanced use. RESULTS: In 2018, 98.3% of hospitals had adopted EHRs; there were no differences by CAH status. A total of 58.7% and 55.6% of hospitals adopted advanced PE and CDA functions, respectively. In both domains, CAHs were less likely to be advanced users: 46.6% demonstrated advanced use for PE and 32.0% for CDA. Since 2015, the advanced use divide has persisted for PE and widened for CDA. DISCUSSION: EHR adoption among hospitals is essentially ubiquitous; however, CAHs still lag behind in advanced use functions critical to improving care quality. This may be rooted in different advanced use needs among CAH patients and lack of access to technical expertise. CONCLUSIONS: The advanced use divide prevents CAH patients from benefitting from a fully digitized healthcare system. To close the widening gap in CDA, policymakers should consider partnering with vendors to develop implementation guides and standards for functions like dashboards and high-risk patient identification algorithms to better support CAH adoption.

OBJECTIVE: To assess the practice- and market-level factors associated with the amount of provider health information exchange (HIE) use. MATERIALS AND METHODS: Provider and practice-level data was drawn from the Meaningful Use Stage 2 Public Use Files from the Centers for Medicare and Medicaid Services, the Physician Compare National Downloadable File, and the Compendium of US Health Systems, among other sources. We analyzed the relationship between provider HIE use and practice and market factors using multivariable linear regression and compared primary care providers (PCPs) to non-PCPs. Provider volume of HIE use is measured as the percentage of referrals sent with electronic summaries of care (eSCR) reported by eligible providers attesting to the Meaningful Use electronic health record (EHR) incentive program in 2016. RESULTS: Providers used HIE in 49% of referrals; PCPs used HIE in fewer referrals (43%) than non-PCPs (57%). Provider use of products from EHR vendors was negatively related to HIE use, while use of Athenahealth and Greenway Health products were positively related to HIE use. Providers treating, on average, older patients and greater proportions of patients with diabetes used HIE for more referrals. Health system membership, market concentration, and state HIE consent policy were unrelated to provider HIE use. DISCUSSION: HIE use during referrals is low among office-based providers with the capability for exchange, especially PCPs. Practice-level factors were more commonly associated with greater levels of HIE use than market-level factors. CONCLUSION: This furthers the understanding that market forces, like competition, may be related to HIE adoption decisions but are less important for use once adoption has occurred.

Journal of the American Medical Informatics Association, doi: 10.1093/jamia/ocab024 The author name “Julia Adler-Milstein” was incorrectly given as “Julia Adler-Milstien”. This has been corrected online.

In 2017, 43.9% of US physicians reported symptoms of burnout. Poor electronic health record (EHR) usability and time-consuming data entry contribute to burnout. However, less is known about how modifiable dimensions of EHR use relate to burnout and how these associations vary by medical specialty. Using the KLAS Arch Collaborative's large-scale nationwide physician (MD/DO) data, we used ordinal logistic regression to analyze associations between self-reported burnout and after-hours charting and organizational EHR support. We examined how these relationships differ by medical specialty, adjusting for confounders. Physicians reporting ≤ 5 hours weekly of after-hours charting were twice as likely to report lower burnout scores compared to those charting ≥6 hours (aOR: 2.43, 95% CI: 2.30, 2.57). Physicians who agree that their organization has done a great job with EHR implementation, training, and support (aOR: 2.14, 95% CI: 2.01, 2.28) were also twice as likely to report lower scores on the burnout survey question compared to those who disagree. Efforts to reduce after-hours charting and improve organizational EHR support could help address physician burnout.

OBJECTIVE: Recent policy making aims to prevent health systems, lectronic health record (EHR) vendors, and others from blocking the electronic sharing of patient data necessary for clinical care. We sought to assess the prevalence of information blocking prior to enforcement of these rules. MATERIALS AND METHODS: We conducted a national survey of health information exchange organizations (HIEs) to measure the prevalence of information blocking behaviors observed by these third-party entities. Eighty-nine of 106 HIEs (84%) meeting the inclusion criteria responded. RESULTS: The majority (55%) of HIEs reported that EHR vendors at least sometimes engage in information blocking, while 30% of HIEs reported the same for health systems. The most common type of information blocking behavior EHR vendors engaged in was setting unreasonably high prices, which 42% of HIEs reported routinely observing. The most common type of information blocking behavior health systems engaged in was refusing to share information, which 14% of HIEs reported routinely observing. Reported levels of vendor information blocking was correlated with regional competition among vendors and information blocking was concentrated in some geographic regions. DISCUSSION: Our findings are consistent with early reports, revealing persistently high levels of information blocking and important variation by actor, type of behavior, and geography. These trends reflect the observations and experiences of HIEs and their potential biases. Nevertheless, these data serve as a baseline against which to measure the impact of new regulations and to inform policy makers about the most common types of information blocking behaviors. CONCLUSION: Enforcement aimed at reducing information blocking should consider variation in prevalence and how to most effectively target efforts.

OBJECTIVE: To characterize variation in clinical documentation production patterns, how this variation relates to individual resident behavior preferences, and how these choices relate to work hours. MATERIALS AND METHODS: We used unsupervised machine learning with clinical note metadata for 1265 progress notes written for 279 patient encounters by 50 first-year residents on the Hospital Medicine service in 2018 to uncover distinct note-level and user-level production patterns. We examined average and 95% confidence intervals of median user daily work hours measured from audit log data for each user-level production pattern. RESULTS: Our analysis revealed 10 distinct note-level and 5 distinct user-level production patterns (user styles). Note production patterns varied in when writing occurred and in how dispersed writing was through the day. User styles varied in which note production pattern(s) dominated. We observed suggestive trends in work hours for different user styles: residents who preferred producing notes in dispersed sessions had higher median daily hours worked while residents who preferred producing notes in the morning or in a single uninterrupted session had lower median daily hours worked. DISCUSSION: These relationships suggest that note writing behaviors should be further investigated to understand what practices could be targeted to reduce documentation burden and derivative outcomes such as resident work hour violations. CONCLUSION: Clinical note documentation is a time-consuming activity for physicians; we identify substantial variation in how first-year residents choose to do this work and suggestive trends between user preferences and work hours.

Dear JAMIA editors and readers: We appreciate Shachak’s letter regarding our article entitled “A Call for Social Informatics.”1 In it, he argues that social informatics is a poor term to describe a field dedicated to the application of information technologies to capture and apply social data in conjunction with health data in order to advance health.2 We continue to believe that it is a useful term, however, and value this opportunity to share our reasoning. In selecting the term social informatics, we were aware of the existence of a field of social informatics, which focuses on the study of the interaction of various aspects of information and communication technologies with institutional and cultural contexts.3 However, this use of the term social informatics is rarely found in the biomedical informatics literature. In a PubMed search for the term social informatics, we identified 67 publications (not including our article) with the term. We were able to retrieve 64 of these articles; only 6 used the term in the article itself. In the remaining 58, the term appeared as part of the author affiliation or institutional description (eg, from a School or Department of Social Informatics). We believe that this offers strong evidence that the term is not in widespread use in the biomedical informatics literature.

OBJECTIVE: Electronic Health Records (EHRs) increasingly include designated fields to capture social determinants of health (SDOH). We developed measures to characterize their use, and use of other SDOH data types, to optimize SDOH data integration. MATERIALS AND METHODS: We developed 3 measures that accommodate different EHR data types on an encounter or patient-year basis. We implemented these measures-documented during encounter (DDE) captures documentation occurring during the encounter; documented by discharge (DBD) includes DDE plus documentation occurring any time prior to admission; and reviewed during encounter (RDE) captures whether anyone reviewed documented data-for the newly available structured SDOH fields and 4 other comparator SDOH data types (problem list, inpatient nursing question, social history free text, and social work notes) on a hospital encounter basis (with patient-year metrics in the Supplementary Appendix). Our sample included all patients (n = 27 127) with at least one hospitalization at UCSF Health (a large, urban, tertiary medical center) over a 1-year period. RESULTS: We observed substantial variation in the use of different SDOH EHR data types. Notably, social history question fields (newly added at study period start) were rarely used (DDE: 0.03% of encounters, DBD: 0.26%, RDE: 0.03%). Free-text patient social history fields had higher use (DDE: 12.1%, DBD: 49.0%, RDE: 14.4%). DISCUSSION: Our measures of real-world SDOH data use can guide current efforts to capture and leverage these data. For our institution, measures revealed substantial variation across data types, suggesting the need to engage in efforts such as EHR-user education and targeted workflow integration. CONCLUSION: Measures revealed opportunities to optimize SDOH data documentation and review.

OBJECTIVE: To measure US hospitals' adoption of electronic health record (EHR) functions that support care for older adults, focusing on structured documentation of the 4Ms (What Matters, Medication, Mentation, and Mobility) and electronic health information exchange/communication with patients, caregivers, and long-term care providers. MATERIALS AND METHODS: In an online survey of a national, random sample of 797 US acute-care hospitals in 2018-2019, 479 (60.1%) responded. We calculated nationally representative measures of the percentages of hospitals with EHRs that include structured documentation of the 4Ms and exchange/communications functions. RESULTS: Structured EHR documentation of the 4Ms was fully implemented in at least 1 unit in 64.0% of hospitals and across all units in 41.5% of hospitals. Of the 4Ms, structured documentation was the highest for medications (91.3% in at least 1 unit) and the lowest for mentation (70.3% in at least 1 unit). All exchange/communication functions had been implemented in at least 1 unit in 16.2% of facilities and across all units in 7.6% of hospitals. Less than half of the hospitals had an EHR portal for long-term care facilities to access hospital information (45.4% in at least 1 unit), sent information electronically to long-term care facilities (44.6%), and had training for adults/caregivers on the patient portal (32.1%). DISCUSSION: Despite significant national investment in EHRs, hospital EHRs do not yet include key documentation, exchange, and communication functions needed to support evidence-based care for the older adults who comprise the majority of the inpatient population. Additional policy efforts are likely needed to promote the expansion of EHR capabilities into these high-value domains. CONCLUSIONS: US acute-care hospital EHRs are lacking key functions that support care for older adults.

Crowdsourced ratings have driven increased performance transparency between consumers and suppliers. While many industries have benefitted from such transparency, crowdsourced ratings have struggled to scale in the healthcare domain. In theory, interoperability services offer an ideal setting for crowdsourced ratings: costs are high, performance is variable, and information asymmetries between provider organizations (customers) and vendors offering interoperability solutions exist. Via a Cooperative Agreement between the Office of the National Coordinator for Health Information Technology and University of California, San Francisco, we developed InteropSelect, a public website that allows crowdsourced ratings of interoperability service purchases. While we garnered broad engagement during the development process, the site failed to attract sufficient reviewers, which is fundamental to the success of crowdsourcing. Additional challenges included the lack of service commoditization that resulted in a complex rating form and lack of market dynamics that facilitated vendor engagement. Our lessons cast doubt on whether crowdsourcing and similar performance transparency efforts under the 21st Century Cures Act will succeed.

OBJECTIVE: While there has been a substantial increase in health information exchange, levels of outside records use by frontline providers are low. We assessed whether integration between outside data and local data results in increased viewing of outside records, overall and by encounter, provider, and patient type. MATERIALS AND METHODS: Using data from UCSF Health, we measured change in outside record views after integrating the list of local (UCSF) and outside (other health systems on Epic [Epic Systems, Verona, WI]) encounters on the Chart Review tab. Previously, providers only viewed records from outside encounters on a separate tab. We used an interrupted time series design (with outside record viewing event counts aggregated to the week level) to measure changes in the level and trend over a 1-year period. RESULTS: There was a large increase in the level of outside record views of 22 920 per week (P < .001). The change in trend went from a weekly increase of 116 (P < .05) to a decrease of 402 (P = .08), reflecting a small effect decay. There were increases in the level of views for all provider and encounter types: attendings (n = 3675), residents (n = 3277), and nurses (n = 914); and inpatient (n = 1676), emergency (n = 487), and outpatient (n = 7228) (P < .001 for all). Results persisted when adjusted for total encounter volume. DISCUSSION: While outside records were readily available before the encounter integration, the simple step of clicking on a separate tab appears to have depressed use. CONCLUSIONS: User interface designs that comingle local and outside data result in higher levels of viewing and should be more broadly pursued.

OBJECTIVES: The study sought to determine whether objective measures of electronic health record (EHR) use-related to time, volume of work, and proficiency-are associated with either or both components of clinician burnout: exhaustion and cynicism. MATERIALS AND METHODS: We combined Maslach Burnout Inventory survey measures (94% response rate; 122 of 130 clinicians) with objective, vendor-defined EHR use measures from log files (time after hours on clinic days; time on nonclinic days; message volume; composite measures of efficiency and proficiency). Data were collected in early 2018 from all primary care clinics of a large, urban, academic medical center. Multivariate regression models measured the association between each burnout component and each EHR use measure. RESULTS: One-third (34%) of clinicians had high cynicism and 51% had high emotional exhaustion. Clinicians in the top 2 quartiles of EHR time after hours on scheduled clinic days (those above the sample median of 68 minutes per clinical full-time equivalent per week) had 4.78 (95% confidence interval [CI], 1.1-20.1; P = .04) and 12.52 (95% CI, 2.6-61; P = .002) greater odds of high exhaustion. Clinicians in the top quartile of message volume (>307 messages per clinical full-time equivalent per week) had 6.17 greater odds of high exhaustion (95% CI, 1.1-41; P = .04). No measures were associated with high cynicism. DISCUSSION: EHRs have been cited as a contributor to clinician burnout, and self-reported data suggest a relationship between EHR use and burnout. As organizations increasingly rely on objective, vendor-defined EHR measures to design and evaluate interventions to reduce burnout, our findings point to the measures that should be targeted. CONCLUSIONS: Two specific EHR use measures were associated with exhaustion.

We sought to identify barriers to hospital reporting of electronic surveillance data to local, state, and federal public health agencies and the impact on areas projected to be overwhelmed by the COVID-19 pandemic. Using 2018 American Hospital Association data, we identified barriers to surveillance data reporting and combined this with data on the projected impact of the COVID-19 pandemic on hospital capacity at the hospital referral region level. Our results find the most common barrier was public health agencies lacked the capacity to electronically receive data, with 41.2% of all hospitals reporting it. We also identified 31 hospital referral regions in the top quartile of projected bed capacity needed for COVID-19 patients in which over half of hospitals in the area reported that the relevant public health agency was unable to receive electronic data. Public health agencies' inability to receive electronic data is the most prominent hospital-reported barrier to effective syndromic surveillance. This reflects the policy commitment of investing in information technology for hospitals without a concomitant investment in IT infrastructure for state and local public health agencies.

We appreciate the productive discussion about our article, Barriers to Hospital Electronic Public Health Reporting and Implications for the COVID-19 Pandemic, which serves to advance efforts to strengthen information sharing between hospitals and public health agencies. The letter by Staes et al raises important considerations and contributes to a useful dialogue regarding the current state and barriers to hospital–public health agency electronic data sharing as well as opportunities to increase knowledge of extant public health capabilities and foster more comprehensive utilization of those capabilities. We agree that the current level of interoperability between hospitals and public health agencies is not at an ideal level, even under normal circumstances, and critical infrastructure gaps have been laid bare as a result of the COVID-19 pandemic.1 Our findings should not be interpreted as evidence of fault or sole responsibility on either the hospital or public health side for barriers that impede effective information sharing. Indeed, we are careful to focus our discussion on the likely roots of current challenges, which we trace to federal incentive programs that have focused almost entirely on health care delivery organizations, rather than the public health agencies and other community partners that play a critical role in emergency preparedness, disease monitoring, and efforts to improve population health. Instead, we believe the value in our findings comes in prompting better coordination between public health and clinical partners to address current shortcomings and ensure that robust electronic data exchange is meeting the needs for both clinical and public health organizations. The first step towards this coordination is better understanding of how the other side views the issues. We suspect that, when citing barriers to public health receipt of data, hospitals are not referring specifically to the pure technical capability (which the letter indicates exists at a broad level). As with any interoperability effort, functional interoperability requires the technological capability to send and receive data alongside the nontechnical factors such as data governance, incentives to share electronically, a clear onboarding and testing process, and more. Surveys like the AHA IT Supplement shed some light on where the sticky points may lie but isn’t able to home in on and separate one from another. Given these constraints, the key insight from our study is that 1 side of the exchange—namely, the Chief Information Officers or Chief Medical Information Officers who typically respond to the AHA IT Supplement—perceive some aspect of public health agency ability to receive data as a barrier to effective electronic exchange. Regardless of whether that barrier is technical in nature or related to a socio-technical process such as data governance, public health agencies should be aware that nearly 40% of potential exchange partner hospitals view their ability to receive data electronically as a barrier to effective exchange. Awareness of this perceived barrier—especially if it is inconsistent with barriers perceived by public health agencies—is a critical first step towards resolving outstanding issues and clarifying any misunderstandings. We suggest that 1 possible strategy going forward is for public health agencies and hospitals to publicly list their electronic exchange partners, similar to how health information exchange organizations publicly list participants.2 This may help both clinical and public health organizations better understand who is successfully sharing data, enable both parties to engage in peer learning and best practice dissemination, serve as an accountability mechanism for all parties, and allow researchers to differentiate between stated ability to send and receive data electronically and actual connectivity in practice. Secondarily, national surveys of public health agency informatics infrastructure and capabilities should seek to capture more detailed data than they have historically, which has thus far prevented insight into such basic questions as regional variation in capabilities, much less the geographic or proportional scope of connectivity for a given public health agency.3,4 We applaud public health agencies’ hard work on building electronic case reporting capabilities through platforms such as AIMS. However, it’s important to note that the AIMS service is primarily facilitating data exchange between public health laboratories and public health agencies, not from hospitals or other clinical exchange partners. This underscores the complex nature of interoperability for public health surveillance, which frequently involves local and state agencies establishing and maintaining bidirectional interoperability with multiple exchange partners of many types. Most importantly, we wholeheartedly agree with Staes et al that increasing support for public health agencies to build a more robust informatics infrastructure is a critical policy goal to ensure accurate, reliable data exchange.

OBJECTIVE: The study sought to determine which patient characteristics are associated with the use of patient-facing digital health tools in the United States. MATERIALS AND METHODS: We conducted a literature review of studies of patient-facing digital health tools that objectively evaluated use (eg, system/platform data representing frequency of use) by patient characteristics (eg, age, race or ethnicity, income, digital literacy). We included any type of patient-facing digital health tool except patient portals. We reran results using the subset of studies identified as having robust methodology to detect differences in patient characteristics. RESULTS: We included 29 studies; 13 had robust methodology. Most studies examined smartphone apps and text messaging programs for chronic disease management and evaluated only 1-3 patient characteristics, primarily age and gender. Overall, the majority of studies found no association between patient characteristics and use. Among the subset with robust methodology, white race and poor health status appeared to be associated with higher use. DISCUSSION: Given the substantial investment in digital health tools, it is surprising how little is known about the types of patients who use them. Strategies that engage diverse populations in digital health tool use appear to be needed. CONCLUSION: Few studies evaluate objective measures of digital health tool use by patient characteristics, and those that do include a narrow range of characteristics. Evidence suggests that resources and need drive use.

As evidence of the associations between social factors and health outcomes continues to mount, capturing and acting on social determinants of health (SDOH) in clinical settings has never been more relevant. Many professional medical organizations have endorsed screening for SDOH, and the U.S. Office of the National Coordinator for Health Information Technology has recommended increased capacity of health information technology to integrate and support use of SDOH data in clinical settings. As these efforts begin their translation to practice, a new subfield of health informatics is emerging, focused on the application of information technologies to capture and apply social data in conjunction with health data to advance individual and population health. Developing this dedicated subfield of informatics-which we term social informatics-is important to drive research that informs how to approach the unique data, interoperability, execution, and ethical challenges involved in integrating social and medical care.

Electronic health record (EHR) log data have shown promise in measuring physician time spent on clinical activities, contributing to deeper understanding and further optimization of the clinical environment. In this article, we propose 7 core measures of EHR use that reflect multiple dimensions of practice efficiency: total EHR time, work outside of work, time on documentation, time on prescriptions, inbox time, teamwork for orders, and an aspirational measure for the amount of undivided attention patients receive from their physicians during an encounter, undivided attention. We also illustrate sample use cases for these measures for multiple stakeholders. Finally, standardization of EHR log data measure specifications, as outlined here, will foster cross-study synthesis and comparative research.

The boundaries of academic fields morph over time and, over the past decade, there has been a notable convergence of the fields of health informatics and health services research. Both fields are notable in their interdisciplinary nature and they are certainly more distinct than overlapping. But the overlap between them is now evident and offers an opportunity to reflect not only on its nature but also on why it has emerged as well as the implications. Health services research has many definitions, all of which reflect the breadth of the field; according to the Agency for Healthcare Research and Quality, health services research “examines how people get access to health care, how much care costs, and what happens to patients as a result of this care. The main goals of health services research are to identify the most effective ways to organize, manage, finance, and deliver high quality care; reduce medical errors; and improve patient safety.” A close relative of health services research is health policy research, and the 2 are often invoked synonymously. Where do we see evidence of the convergence between health services research and health informatics? It appears in all major domains of academic work: (1) journals, (2) conferences, (3) departmental structures, and (4) education or training programs. If you search the major health services and health policy journals, you will undoubtedly find significant growth in the number of articles that examine health information technology (IT) tools and their associated implications for cost, quality, or access or attempt to evaluate the impact of health IT-related policies. Similarly, the Journal of the American Medical Informatics Association (JAMIA) has featured a growing number of studies that tackle these same topics, and the group of Associate Editors now includes several members who are grounded in both fields. The primary health services research conference—the Academy Health Annual Research Meeting—added a technology-focused track and, while the American Medical Informatics Association Annual Meeting has not created an explicit designation, health services research and policy oriented work is well represented. There is also a growing number of health services departments (often within Schools of Public Health or Medical Schools that are called some variant of Health Policy and Management) that have divisions with a health informatics focus. For example, the Department of Healthcare Policy and Research at Weill Cornell Medical College has a Health Informatics Division. Finally, new training programs at this intersection are flourishing, such as University of Michigan’s master in health informatics degree that is a joint program between the School of Public Health and School of Information. Why have these 2 fields converged over the past decade? The primary driver was the passage of the 2009 Health Information Technology for Economic and Clinical Health Act that created a new set of associated policies and regulations ripe for study by those with an interest in policy, policy making, and policy impact. Perhaps more importantly, it resulted in the widespread adoption of electronic health records (EHRs) and related tools that have profound implications for the core focal areas of health services research: cost, quality, and access. Those interested in healthcare disparities now have opportunities to study the emergence of a digital divide in access to and use of patient portals. Those interested in patient safety now have opportunities to study the new safety risks associated with clinician EHR use and EHR design or usability issues. Those interested in payment and incentives now have opportunities to study their impact on willingness of healthcare providers to share data electronically. These are just 3 of countless examples of emergent research questions that are highly timely and impactful for researchers with interests in both health services research and health informatics. While such opportunities are exciting and have undoubtedly led to the expansion of both fields, there are also stumbling blocks. As someone who regularly reviews for the journals and conferences in both fields, the most common one is failing to appreciate and address the complexity inherent in both fields. For example, many JAMIA papers will feature a measure of EHR adoption but fail to acknowledge the many ways to measure EHR adoption and the strengths or limitations of each and then justify their selected measure. An extension of this is the broader view of health IT as a simple tool and a technological determinist perspective where it is assumed that a tool will be adopted and used as designed and intended. Not every study can fully address all these issues, but failing to acknowledge them as factors suggests a naivety that does not result in work that is embraced by the field. Similarly, informatics work will often attempt to be geared to a health services audience without making clear why it has important implications for cost, quality, or access. A new natural language processing method to capture a patient’s social needs will have highly technical dimensions that need to be reported, but equally important to the field is understanding how the output of the method will be used to better address social needs. A second common stumbling block is understanding the current state of the field and ensuring that the contribution of the work is innovative. In my role as Associate Editor at JAMIA, I often see articles from health services researchers that address a question for which the informatics field has largely moved on. While it is difficult to make definitive statements about what is and is not innovative, these articles most commonly focus on describing differences in hospitals or ambulatory providers that have or have not adopted EHRs, participated or not participated in Meaningful Use, or otherwise use basic demographics to describe adoption trends. Similarly, on the patient-facing side, many studies characterize patients that are and are not using patient portals, secure messaging, or other common types of IT, which is ground well covered. There are also many studies that attempt to tie IT adoption to widely available process and outcome measures, usually cross-sectionally. Again, these relationships have been well studied and the innovative questions now focus on explaining variation in IT-driven outcomes. Studies that describe the implementation experience at a single institution (including surveys of provider attitudes or perceptions pre- and postimplementation) are a final category of studies that are often submitted but rarely offer findings that are sufficiently novel to advance the field. The hard part of interdisciplinary work is mastering multiple disciplines, and the bar is even higher when working at the intersection of 2 interdisciplinary fields. However, both fields are also strong proponents of team science, which means that there is only upside to adding a collaborator who brings deeper expertise in one field or the other. Ultimately, the convergence between the fields of health informatics and health services research is critical to ensure that the substantial investment in health IT over the past decade maximizes impact on health. The common foundation of both fields is health—and even if you are working on the most technical aspects of health informatics, it is likely because you are driven by a motivation to help people live healthier lives. If we use health as the starting point—whether health outcomes are the direct focus of the research examining the impact of health informatics or a factor that lives far downstream of a health informatics study—acknowledging that tie, and articulating how the focal informatics effort has the potential to improve health will ensure that the 2 fields journey forward most productively.

OBJECTIVE: Although patient generated health data (PGHD) has stimulated excitement about its potential to increase patient engagement and to offer clinicians new insights into patient health status, we know little about these efforts at scale and whether they align with patient preferences. This study sought to characterize provider-led PGHD approaches, assess whether they aligned with patient preferences, and identify challenges to scale and impact. MATERIALS AND METHODS: We interviewed leaders from a geographically diverse set of health systems (n = 6), leaders from large electronic health record vendors (n = 3), and leaders from vendors providing PGHD solutions to health systems (n = 3). Next, we interviewed patients with 1 or more chronic conditions (n = 10), half of whom had PGHD experience. We conducted content analysis to characterize health system PGHD approaches, assess alignment with patient preferences, and identify challenges. RESULTS: In this study, 3 primary approaches were identified, and each was designed to support collection of a different type of PGHD: 1) health history, 2) validated questionnaires and surveys, and 3) biometric and health activity. Whereas patient preferences aligned with health system approaches, patients raised concerns about data security and the value of reporting. Health systems cited challenges related to lack of reimbursement, data quality, and clinical usefulness of PGHD. DISCUSSION: Despite a federal policy focus on PGHD, it is not yet being pursued at scale. Whereas many barriers contribute to this narrow pursuit, uncertainty around the value of PGHD, from both patients and providers, is a primary inhibitor. CONCLUSION: Our results reveal a fairly narrow set of approaches to PGHD currently pursued by health systems at scale.

OBJECTIVE: There are increasing efforts to capture psychosocial information in outpatient care in order to enhance health equity. To advance clinical decision support systems (CDSS), this study investigated which psychosocial information clinicians value, who values it, and when and how clinicians use this information for clinical decision-making in outpatient type 2 diabetes care. MATERIALS AND METHODS: This mixed methods study involved physician interviews (n = 17) and a survey of physicians, nurse practitioners (NPs), and diabetes educators (n = 198). We used the grounded theory approach to analyze interview data and descriptive statistics and tests of difference by clinician type for survey data. RESULTS: Participants viewed financial strain, mental health status, and life stressors as most important. NPs and diabetes educators perceived psychosocial information to be more important, and used it significantly more often for 1 decision, than did physicians. While some clinicians always used psychosocial information, others did so when patients were not doing well. Physicians used psychosocial information to judge patient capabilities, understanding, and needs; this informed assessment of the risks and the feasibility of options and patient needs. These assessments influenced 4 key clinical decisions. DISCUSSION: Triggers for psychosocially informed CDSS should include psychosocial screening results, new or newly diagnosed patients, and changes in patient status. CDSS should support cost-sensitive medication prescribing, and psychosocially based assessment of hypoglycemia risk. Electronic health records should capture rationales for care that do not conform to guidelines for panel management. NPs and diabetes educators are key stakeholders in psychosocially informed CDSS. CONCLUSION: Findings highlight opportunities for psychosocially informed CDSS-a vital next step for improving health equity.

Objective: Hospitals that routinely share patients are those that most critically need to engage in electronic health information exchange (HIE) with each other to ensure clinical information is available to inform treatment decisions. We surveyed pairs of hospitals in a nationwide sample to describe whether and how hospitals within each hospital referral region (HRR) that have the highest shared patient (HSP) volume engaged in HIE with each other. Methods: We used Medicare's Physician Shared Patient Patterns data to identify hospital pairs with the highest shared patient volume in each hospital referral region. We surveyed a purposeful sample of pairs and then calculated descriptive statistics to compare: (1) HIE with the HSP hospital vs HIE with other hospitals, and (2) HIE with the HSP hospital versus federal measures of HIE engagement that are not partner-specific. Results: We received responses from 25.5% of contacted hospitals and 33.5% of contacted pairs, allowing us to examine information sharing among 68 hospitals in 63 pairs. 23% of respondents reported worse information sharing with their HSP hospital than with other hospitals while 17% indicated better sharing with their HSP hospital and 48% indicated no difference. Our HSP-specific measures of HIE differed from federal measures of HIE engagement: while 97% of respondents are classified as routinely sending information electronically in federal measures, in our data only 63% did so with their HSP hospital. Conclusions: Despite increased HIE engagement, our descriptive results indicate that HIE is not developing in a way that facilitates information exchange where it might benefit the most patients. New policy efforts, particularly those emerging from the 21st Century Cures Act, need to explicitly pursue strategies that ensure that HSP providers engage in exchange with each other.

Objective: The federal electronic health record (EHR) certification process was intended to ensure a baseline level of system quality and the ability to support meaningful use criteria. We sought to assess whether there was variation across EHR vendors in the degree to which hospitals using products from those vendors were able to achieve high levels of performance on meaningful use criteria. Materials and Methods: We created a cross-sectional national hospital sample from the Office of the National Coordinator for Health Information Technology EHR Products Used for Meaningful Use Attestation public use file and the Centers for Medicare & Medicaid Services Medicare EHR Incentive Program Eligible Hospitals public use file. We used regression models to assess the relationship between vendor and hospital performance on 6 Stage 2 Meaningful Use criteria, controlling for hospital characteristics. We also calculated how much variation in performance is explained by vendor choice. Results: We found significant associations between specific vendor and level of hospital performance for all 6 meaningful use criteria. Epic was associated with significantly higher performance on 5 of the 6 criteria; relationships for other vendors were mixed, with some associated with significantly worse performance on multiple criteria. EHR vendor choice accounted for between 7% and 34% of performance variation across the 6 criteria. Discussion: A nontrivial proportion of variation in hospital meaningful use performance is explained by vendor choice, and certain vendors are more often associated with better meaningful use performance than others. Our results suggest that policy-makers should improve the certification process by including more "real-world" scenario testing and provider feedback or ratings to reduce this variation. Hospitals can use these results to guide interactions with vendors. Conclusion: Vendor choice accounts for a meaningful proportion of variation in hospital meaningful use performance, and specific vendors are consistently associated with higher or lower performance across criteria.

Objective: Ensuring the ability to exchange patient information among disparate electronic health records systems is a top priority and a domain of substantial public investment across countries. However, we know little about the extent to which current capabilities meet the needs of frontline clinicians. Materials and Methods: We conducted in-person, semistructured interviews with emergency care physicians and nurses in select hospitals in Canada, Denmark, Finland, Germany, and the USA. We characterized the state of health information exchange (HIE) by country and used thematic analysis to identify the perceived benefits of access to complete past medical history (PMH), the conditions under which PMH is sought, and the challenges to accessing and using HIE capabilities. Results: HIE approaches, and the information electronically accessible to clinicians, differed by country. Benefits of access to PMH included safer care, reduced patient length of stay, and fewer lab and imaging orders. Conditions under which PMH was sought included moderate-acuity patients, patients with chronic conditions, and instances where accessing PMH was convenient. Challenges to HIE access and use included difficulty knowing where information is located, delay in receiving information, and difficulty finding information within documents. Discussion: Even with different HIE approaches across countries, all clinicians reported shortcomings in their country's approach. Notably, challenges were similar and shaped the conditions under which PMH was sought. Conclusion: As countries continue to pursue broad-based HIE, they appear to be facing similar challenges in realizing HIE value and therefore have an opportunity to learn from one another.

While great progress has been made in digitizing the US health care system, today's health information technology (IT) infrastructure remains largely a collection of systems that are not designed to support a transition to value-based care. In addition, the pursuit of value-based care, in which we deliver better care with better outcomes at lower cost, places new demands on the health care system that our IT infrastructure needs to be able to support. Provider organizations pursuing new models of health care delivery and payment are finding that their electronic systems lack the capabilities needed to succeed. The result is a chasm between the current health IT ecosystem and the health IT ecosystem that is desperately needed.In this paper, we identify a set of focal goals and associated near-term achievable actions that are critical to pursue in order to enable the health IT ecosystem to meet the acute needs of modern health care delivery. These ideas emerged from discussions that occurred during the 2015 American Medical Informatics Association Policy Invitational Meeting. To illustrate the chasm and motivate our recommendations, we created a vignette from the multistakeholder perspectives of a patient, his provider, and researchers/innovators. It describes an idealized scenario in which each stakeholder's needs are supported by an integrated health IT environment. We identify the gaps preventing such a reality today and present associated policy recommendations that serve as a blueprint for critical actions that would enable us to cross the current health IT chasm by leveraging systems and information to routinely deliver high-value care.

OBJECTIVE: While most hospitals have adopted electronic health records (EHRs), we know little about whether hospitals use EHRs in advanced ways that are critical to improving outcomes, and whether hospitals with fewer resources - small, rural, safety-net - are keeping up. MATERIALS AND METHODS: Using 2008-2015 American Hospital Association Information Technology Supplement survey data, we measured "basic" and "comprehensive" EHR adoption among hospitals to provide the latest national numbers. We then used new supplement questions to assess advanced use of EHRs and EHR data for performance measurement and patient engagement functions. To assess a digital "advanced use" divide, we ran logistic regression models to identify hospital characteristics associated with high adoption in each advanced use domain. RESULTS: We found that 80.5% of hospitals adopted at least a basic EHR system, a 5.3 percentage point increase from 2014. Only 37.5% of hospitals adopted at least 8 (of 10) EHR data for performance measurement functions, and 41.7% of hospitals adopted at least 8 (of 10) patient engagement functions. Critical access hospitals were less likely to have adopted at least 8 performance measurement functions (odds ratio [OR] = 0.58; P < .001) and at least 8 patient engagement functions (OR = 0.68; P = 0.02). DISCUSSION: While the Health Information Technology for Economic and Clinical Health Act resulted in widespread hospital EHR adoption, use of advanced EHR functions lags and a digital divide appears to be emerging, with critical-access hospitals in particular lagging behind. This is concerning, because EHR-enabled performance measurement and patient engagement are key contributors to improving hospital performance. CONCLUSION: Hospital EHR adoption is widespread and many hospitals are using EHRs to support performance measurement and patient engagement. However, this is not happening across all hospitals.

BACKGROUND: Provider organizations increasingly have the ability to exchange patient health information electronically. Organizational health information exchange (HIE) policy decisions can impact the extent to which external information is readily available to providers, but this relationship has not been well studied. OBJECTIVE: Our objective was to examine the relationship between electronic exchange of patient health information across organizations and organizational HIE policy decisions. We focused on 2 key decisions: whether to automatically search for information from other organizations and whether to require HIE-specific patient consent. METHODS: We conducted a retrospective time series analysis of the effect of automatic querying and the patient consent requirement on the monthly volume of clinical summaries exchanged. We could not assess degree of use or usefulness of summaries, organizational decision-making processes, or generalizability to other vendors. RESULTS: Between 2013 and 2015, clinical summary exchange volume increased by 1349% across 11 organizations. Nine of the 11 systems were set up to enable auto-querying, and auto-querying was associated with a significant increase in the monthly rate of exchange (P = .006 for change in trend). Seven of the 11 organizations did not require patient consent specifically for HIE, and these organizations experienced a greater increase in volume of exchange over time compared to organizations that required consent. CONCLUSIONS: Automatic querying and limited consent requirements are organizational HIE policy decisions that impact the volume of exchange, and ultimately the information available to providers to support optimal care. Future efforts to ensure effective HIE may need to explicitly address these factors.

OBJECTIVE: To assess whether electronic health information exchange (HIE) is associated with improved emergency department (ED) care processes and utilization through more timely clinician viewing of information from outside organizations. MATERIALS AND METHODS: Our data included 2163 patients seen in the ED of a large academic medical center for whom clinicians requested and viewed outside information from February 14, 2014, to February 13, 2015. Outside information requests w.ere fulfilled via HIE (Epic's Care Everywhere) or fax/scan to the electronic health record (EHR). We used EHR audit data to capture the time between the information request and when a clinician accessed the data. We assessed whether the relationship between method of information return and ED outcomes (length of visit, odds of imaging [computed tomography (CT), magnetic resonance imaging (MRI), radiographs] and hospitalization, and total charges) was mediated by request-to-access time, controlling for patient demographics, case mix, and acuity. RESULTS: In multivariate analysis, there was no direct association between return of information via HIE vs fax/scan and ED outcomes. HIE was associated with faster outside information access (58.5 minutes on average), and faster access was associated with changes in ED care. For each 1-hour reduction in access time, visit length was 52.9 minutes shorter, the likelihood of imaging was lower (by 2.5, 1.6, and 2.4 percentage points for CT, MRI, and radiographs, respectively), the likelihood of admission was 2.4 percentage points lower, and average charges were $1187 lower ( P ≤ .001 for all). CONCLUSION: The relationship between HIE and improved care processes and reduced utilization in the ED is mediated by faster accessing of information from outside organizations.

OBJECTIVE: To pilot benchmark measures of health information and communication technology (ICT) availability and use to facilitate cross-country learning. MATERIALS AND METHODS: A prior Organization for Economic Cooperation and Development-led effort involving 30 countries selected and defined functionality-based measures for availability and use of electronic health records, health information exchange, personal health records, and telehealth. In this pilot, an Organization for Economic Cooperation and Development Working Group compiled results for 38 countries for a subset of measures with broad coverage using new and/or adapted country-specific or multinational surveys and other sources from 2012 to 2015. We also synthesized country learnings to inform future benchmarking. RESULTS: While electronic records are widely used to store and manage patient information at the point of care-all but 2 pilot countries reported use by at least half of primary care physicians; many had rates above 75%-patient information exchange across organizations/settings is less common. Large variations in the availability and use of telehealth and personal health records also exist. DISCUSSION: Pilot participation demonstrated interest in cross-national benchmarking. Using the most comparable measures available to date, it showed substantial diversity in health ICT availability and use in all domains. The project also identified methodological considerations (e.g., structural and health systems issues that can affect measurement) important for future comparisons. CONCLUSION: While health policies and priorities differ, many nations aim to increase access, quality, and/or efficiency of care through effective ICT use. By identifying variations and describing key contextual factors, benchmarking offers the potential to facilitate cross-national learning and accelerate the progress of individual countries.

BACKGROUND: Stage 2 and proposed Stage 3 meaningful use criteria ask providers to support patient care coordination by electronically generating, exchanging, and reconciling key information during patient care transitions. METHODS: A stratified random sample of primary care practices in Michigan (n = 328) that had already met Stage 1 meaningful use criteria was surveyed, in order to identify the anticipated barriers to meeting these criteria as well as the expected impact on patient care coordination from doing so. RESULTS: The top three barriers, as identified by >65% of the primary care providers surveyed, were difficulty sending and receiving patient information electronically, a lack of provider and practice staff time, and the complex workflow changes required. Despite these barriers, primary care providers expressed strong agreement that meeting the proposed Stage 3 care coordination criteria would improve their patients' treatment and ensure they know about their patients' visits to other providers. CONCLUSION: The survey results suggest the need to enhance policy approaches and organizational strategies to address the key barriers identified by providers and practices in order to realize important care coordination benefits.

OBJECTIVE: To identify factors that impede payer engagement in a health information exchange (HIE), along with organizational and policy strategies that might effectively address the impediments. MATERIALS AND METHODS: Qualitative analysis of semi-structured interviews with leaders from 17 varied payer organizations from across the country (e.g., large, national payers; state Blues plans; local Medicaid managed care plans). RESULTS: We found a large gap between payers' vision of what optimal HIE should be and the current approach to HIE in the United States. Notably, payers sought to be active participants in HIE efforts--both providing claims data and accessing clinical data to support payer HIE use cases. Instead, payers were often asked by HIE efforts only to provide financial support without the option to participate in data exchange, or, when given the option, their data needs were secondary to those of providers. DISCUSSION: Efforts to engage payers in pursuit of more robust and sustainable HIE need to better align their value proposition with payer HIE use cases. This will require addressing provider concerns about payer access to clinical data. Policymakers should focus on creating the conditions for broader payer engagement by removing common obstacles, such as low provider engagement in HIE. CONCLUSION: Despite variation in the extent to which payers engaged with current HIE efforts, there was agreement on the vision of optimal HIE and the facilitators of greater payer engagement. Specific actions by those leading HIE efforts, complemented by policy efforts nationally, could greatly increase payer engagement and enhance HIE sustainability.

OBJECTIVE: To examine whether there is a common sequence of adoption of electronic health record (EHR) functions among US hospitals, identify differences by hospital type, and assess the impact of meaningful use. MATERIALS AND METHODS: Using 2008 American Hospital Association (AHA) Information Technology (IT) Supplement data, we calculate adoption rates of individual EHR functions, along with Loevinger homogeneity (H) coefficients, to assess the sequence of EHR adoption across hospitals. We compare adoption rates and Loevinger H coefficients for hospitals of different types to assess variation in sequencing. We qualitatively assess whether stage 1 meaningful use functions are those adopted early in the sequence. RESULTS: There is a common sequence of EHR adoption across hospitals, with moderate-to-strong homogeneity. Patient demographic and ancillary results functions are consistently adopted first, while physician notes, clinical reminders, and guidelines are adopted last. Small hospitals exhibited greater homogeneity than larger hospitals. Rural hospitals and non-teaching hospitals exhibited greater homogeneity than urban and teaching hospitals. EHR functions emphasized in stage 1 meaningful use are spread throughout the scale. DISCUSSION: Stronger homogeneity among small, rural, and non-teaching hospitals may be driven by greater reliance on vendors and less variation in the types of care they deliver. Stage 1 meaningful use is likely changing how hospitals sequence EHR adoption--in particular, by moving clinical guidelines and medication computerized provider order entry ahead in sequence. CONCLUSIONS: While there is a common sequence underlying adoption of EHR functions, the degree of adherence to the sequence varies by key hospital characteristics. Stage 1 meaningful use likely alters the sequence.

OBJECTIVE: To develop benchmark measures of health information and communication technology (ICT) use to facilitate cross-country comparisons and learning. MATERIALS AND METHODS: The effort is led by the Organisation for Economic Co-operation and Development (OECD). Approaches to definition and measurement within four ICT domains were compared across seven OECD countries in order to identify functionalities in each domain. These informed a set of functionality-based benchmark measures, which were refined in collaboration with representatives from more than 20 OECD and non-OECD countries. We report on progress to date and remaining work to enable countries to begin to collect benchmark data. RESULTS: The four benchmarking domains include provider-centric electronic record, patient-centric electronic record, health information exchange, and tele-health. There was broad agreement on functionalities in the provider-centric electronic record domain (eg, entry of core patient data, decision support), and less agreement in the other three domains in which country representatives worked to select benchmark functionalities. DISCUSSION: Many countries are working to implement ICTs to improve healthcare system performance. Although many countries are looking to others as potential models, the lack of consistent terminology and approach has made cross-national comparisons and learning difficult. CONCLUSIONS: As countries develop and implement strategies to increase the use of ICTs to promote health goals, there is a historic opportunity to enable cross-country learning. To facilitate this learning and reduce the chances that individual countries flounder, a common understanding of health ICT adoption and use is needed. The OECD-led benchmarking process is a crucial step towards achieving this.

In industries outside healthcare, highly skilled employees enable substantial gains in productivity after adoption of information technologies. The authors explore whether the presence of highly skilled, autonomous clinical support staff is associated with higher performance among physicians with electronic health records (EHRs). Using data from a survey of general internists, the authors assessed whether physicians with EHRs were more likely to be top performers on cost and quality if they worked with nurse practitioners or physician assistants. It was found that, among physicians with EHRs, those with highly skilled, autonomous staff were far more likely to be top performing than those without such staff (OR 7.0, 95% CI 1.7 to 34.8, p=0.02). This relationship did not hold among physicians without EHRs (OR 1.0). As we begin a national push towards greater EHR adoption, it is critical to understand why some physicians gain from EHR use and others do not.

OBJECTIVE: Regional Health Information Organizations (RHIOs) will likely play a key role in our nation's effort to catalyze health information exchange. Yet we know little about why some efforts succeed while others fail. We sought to identify factors associated with RHIO viability. DESIGN: Using data from a national survey of RHIOs that we conducted in mid-2008, we examined factors associated with becoming operational and factors associated with financial viability. We used multivariate logistic regression models to identify unique predictors. MEASUREMENTS: We classified RHIOs actively facilitating data exchange as operational and measured financial viability as the percent of operating costs covered by revenue from participants in data exchange (0-24%, 25-74%, 75-100%). Predictors included breadth of participants, breadth of data exchanged, whether the RHIO focused on a specific population, whether RHIO participants had a history of collaborating, and sources of revenue during the planning phase. RESULTS: Exchanging a narrow set of data and involving a broad group of stakeholders were independently associated with a higher likelihood of being operational. Involving hospitals and ambulatory physicians, and securing early funding from participants were associated with a higher likelihood of financial viability, while early grant funding seemed to diminish the likelihood. CONCLUSION: Finding ways to help RHIOs become operational and self-sustaining will bolster the current approach to nationwide health information exchange. Our work suggests that convening a broad coalition of stakeholders to focus on a narrow set of data is an important step in helping RHIOs become operational. Convincing stakeholders to financially commit early in the process may help RHIOs become self-sustaining.