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BACKGROUND: The pandemic rapidly expanded telemedicine, which has persisted as a widely available primary care modality. The uptake of telemedicine among people with dementia specifically in the primary care setting, who have more complex care needs but also benefit from more accessible primary care, is unknown. OBJECTIVE: Among people with dementia, assess uptake of telemedicine-based primary care in the post-pandemic period and determine associations with key socio-demographic characteristics. DESIGN: Retrospective observational study. SUBJECTS: People with dementia at UCSF and Kaiser Permanente Northern CA (KPNC) with at least one primary care encounter in pre- (3/1/2019-2/29/2020) or post-COVID (3/1/2021-2/28/2022) periods, post-COVID sample: N= 419 individuals (UCSF), N=18,037 (KPNC). MAIN MEASURES: Encounter modality: in-person, video telemedicine, or telephone telemedicine. Focal socio-demographic characteristics: age, limited English proficiency, socioeconomic status, driving distance to clinic, and caregiver at encounter. KEY RESULTS: There was a large increase in telemedicine among people with dementia in the post-pandemic period at both sites. At KPNC, those with only in-person primary care visits shrunk from 60.47% (pre) to 26.95% (post). At UCSF, the change was even greater: 98.99% to 35.08%. Across both sites, the only measure significantly associated with use of telemedicine was greater driving distance from home to clinic. At KPNC, those over age 90 were most likely to use telemedicine while patients with limited English proficiency and those with a caregiver at the encounter used telemedicine at lower levels. The relationships were similar at UCSF but not statistically significant. CONCLUSIONS: Telemedicine use is high for people with dementia in the primary care setting in the post-pandemic period. Those with longer drives to clinic and the oldest patients were most likely to use telemedicine, likely due to challenges traveling to appointments. Still, not all people with dementia used telemedicine equally-particularly those with limited English proficiency.

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.

ObjectivesDespite the proliferation of dashboards that display performance data derived from Qualified Clinical Data Registries (QCDR), the degree to which clinicians and practices engage with such dashboards has not been well described. We aimed to develop a conceptual framework for assessing user engagement with dashboard technology and to demonstrate its application to a rheumatology QCDR.Materials and methodsWe developed the BDC (Breadth-Depth-Context) framework, which included concepts of breadth (derived from dashboard sessions), depth (derived from dashboard actions), and context (derived from practice characteristics). We demonstrated its application via user log data from the American College of Rheumatology's Rheumatology Informatics System for Effectiveness (RISE) registry to define engagement profiles and characterize practice-level factors associated with different profiles.ResultsWe applied the BDC framework to 213 ambulatory practices from the RISE registry in 2020-2021, and classified practices into 4 engagement profiles: not engaged (8%), minimally engaged (39%), moderately engaged (34%), and most engaged (19%). Practices with more patients and with specific electronic health record vendors (eClinicalWorks and eMDs) had a higher likelihood of being in the most engaged group, even after adjusting for other factors.DiscussionWe developed the BDC framework to characterize user engagement with a registry dashboard and demonstrated its use in a specialty QCDR. The application of the BDC framework revealed a wide range of breadth and depth of use and that specific contextual factors were associated with nature of engagement.ConclusionGoing forward, the BDC framework can be used to study engagement with similar dashboards.

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.

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.

While thousands of health systems have begun to implement the Age-Friendly Health Systems 4Ms Framework to improve care for older patients, an important phase of work is achieving consistent adherence to 4Ms care processes. Identifying mechanisms that may lead to higher versus lower adherence serves to guide efforts to achieve consistent, equitable adherence. Drawing from prior literature, we identified three mechanisms that may influence 4Ms adherence. We then conducted a 3-year retrospective, observational study of inpatient encounters (n = 28 833) at UCSF Health System with patients aged 65+. We used least squares regression models to assess associations between hospital encounter-level measures of 4Ms adherence and proxy measures of patient and encounter characteristics for each hypothesized mechanism along with control variables. Encounter-level adherence to the 4Ms was 65.5% (SD = 14.3%). We found support for all three mechanisms. Negative implicit biases were associated with lower adherence for patients who were obese [0.79 percentage points (PP) lower; P

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.

BACKGROUND: Few hospitals have built surveillance for diagnostic errors into usual care or used comparative quantitative and qualitative data to understand their diagnostic processes and implement interventions designed to reduce these errors. OBJECTIVES: To build surveillance for diagnostic errors into usual care, benchmark diagnostic performance across sites, pilot test interventions, and evaluate the programs impact on diagnostic error rates. METHODS AND ANALYSIS: Achieving diagnostic excellence through prevention and teamwork (ADEPT) is a multicenter, real-world quality and safety program utilizing interrupted time-series techniques to evaluate outcomes. Study subjects will be a randomly sampled population of medical patients hospitalized at 16 US hospitals who died, were transferred to intensive care, or had a rapid response during the hospitalization. Surveillance for diagnostic errors will occur on 10 events per month per site using a previously established two-person adjudication process. Concurrent reviews of patients who had a qualifying event in the previous week will allow for surveys of clinicians to better understand contributors to diagnostic error, or conversely, examples of diagnostic excellence, which cannot be gleaned from medical record review alone. With guidance from national experts in quality and safety, sites will report and benchmark diagnostic error rates, share lessons regarding underlying causes, and design, implement, and pilot test interventions using both Safety I and Safety II approaches aimed at patients, providers, and health systems. Safety II approaches will focus on cases where diagnostic error did not occur, applying theories of how people and systems are able to succeed under varying conditions. The primary outcome will be the number of diagnostic errors per patient, using segmented multivariable regression to evaluate change in y-intercept and change in slope after initiation of the program. ETHICS AND DISSEMINATION: The study has been approved by the University of California, San Francisco Institutional Review Board (IRB), which is serving as the single IRB. Intervention toolkits and study findings will be disseminated through partners including Vizient, The Joint Commission, and Press-Ganey, and through national meetings, scientific journals, and publications aimed at the general public.

OBJECTIVES: In response to evidence linking social risk factors and adverse health outcomes, new incentives have emerged for hospitals to screen for adverse social determinants of health (SDOH). However, little information is available about the current state of social risk-related care practices among childrens hospitals. To address outstanding knowledge gaps, we sought to describe social risk-related care practices among a national sample of childrens hospitals. METHODS: We analyzed responses to the 2020 American Hospital Association Annual Survey. Among childrens hospitals, we calculated the prevalence of screening for social needs, strategies to address social risks/needs, partnerships with community-based organizations to address social risks/needs at the individual and community level, and rates of impact assessments of how social risk-related interventions affect outcomes. We also used χ2 tests to compare results by hospital characteristics. We weighted results to adjust for nonresponse. RESULTS: The sample included 82 childrens hospitals. A total of 79.6% screened for and 96.0% had strategies to address at least 1 social risk factor, although rates varied by SDOH domain. Childrens hospitals more commonly partnered with community-based organizations to address patient-level social risks than to participate in community-level initiatives. A total of 39.2% of hospitals assessed SDOH intervention effectiveness. Differences in social risk-related care practices commonly varied by hospital ownership and Medicaid population but not by region. CONCLUSIONS: We found wide variability in social risk-related care practices among childrens hospitals based on the risk domain and hospital characteristics. Findings can be used to monitor whether social risk-related care practices change in the setting of new incentives.

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.

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.

BackgroundIncreasing electronic health information exchange (HIE) between provider organizations is a top policy priority that has been pursued by establishing varied types of networks.ObjectivesTo measure electronic connectivity enabled by these networks, including community, electronic health record vendor, and national HIE networks, across US hospitals weighted by the volume of shared patients and identify characteristics that predict connectivity.Research designCross-sectional analysis of 1721 hospitals comprising 16,344 hospital pairs and 6,492,232 shared patients from 2018 CareSet Labs HOP data and national hospital surveys.SubjectsPairs of US acute care hospitals that delivered care to 11 or more of the same fee-for-service Medicare beneficiaries in 2018.MeasuresWhether a patient was treated by a pair of hospitals connected through participation in the same HIE network ("connected hospitals") or not connected because the hospitals participated in different networks, only 1 participated, or both did not participate.ResultsSixty-four percent of shared patients were treated by connected hospitals. Of the remaining shared patients, 14% were treated by hospital pairs that participated in different HIE networks, 21% by pairs in which only 1 hospital participated in an HIE network, and 2% by pairs in which neither participated. Patients treated by pairs with at least 1 for-profit hospital, and by pairs located in competitive markets, were less likely to be treated by connected hospitals.ConclusionsWhile the majority of shared patients received care from connected hospitals, remaining gaps could be filled by connecting HIE networks to each other and by incentivizing certain types of hospitals that may not participate because of competitive concerns.

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.

The rapid adoption of electronic health records (EHRs) has created extensive repositories of digitized data that can be used to inform improvements in care delivery, processes, and patient outcomes. While the clinical data captured in EHRs are widely used for such efforts, EHRs also capture audit log data that reflect how users interact with the EHR to deliver care. Automatically collected audit log data provide a unique opportunity for new insights into EHR user behavior and decision-making processes. Here, we provide an overview of audit log data and examples that could be used to improve oncology care and outcomes in four domains: diagnostic reasoning and consumption, care team collaboration and communication, patient outcomes and experience, and provider burnout/fatigue. This data source could identify gaps in performance and care, physician uptake of EHR features that enhance decision-making, and integration of data trends for oncology. Ensuring researchers and oncologists are familiar with the data's potential and developing the data engineering capacity to utilize this rich data source, will expand the breadth of research to improve cancer care.

ObjectiveTo identify organizational complementarities of adoption and use of electronic health records (EHRs) and assess what organizational strategies were associated with more advanced EHR use.Data sourcesPrimary survey data of US hospitals combined with secondary data from the American Hospital Association Annual Survey and IT Supplement.Study designIn this cross-sectional study, we describe hospital organizational practices around EHR adoption and use and identify how these practices coalesce into distinct strategies. We then assess the association between those organizational strategies and adoption of advanced EHR functions.Data collectionPrimary data collection consisted of surveys sent to 797 US acute care hospitals in 2018-2019, with 451 complete respondents.Principal findingsThere was significant variation in hospital organizational practices for EHR adoption and use. Factor analysis identified practices in three domains: leadership engagement, human capital, and systems integration. Hospitals in the top quartile of the leadership engagement factor were 14 percentage points more likely to have adopted patient engagement EHR functions (P = 0.01) while hospitals in the top quartile of human capital were 14 percentage points less likely to have adopted these functions (P = 0.02). Hospitals in the top quartile of systems integration were 12 percentage points more likely to have adopted patient engagement functions (P = 0.02) and 14 percentage points more likely to have adopted EHR data analytics functions (P = 0.02).ConclusionsOur findings suggest that specific organizational strategies are associated with more advanced EHR adoption. Hospital leaders interested in realizing more value from their EHR investment may find it useful to know that there is an association between adoption of more advanced EHR functions, and engaging senior leadership as well as building connectivity between clinical and administrative systems.

BackgroundThere is growing recognition that health care providers are embedded in networks formed by the movement of patients between providers. However, the structure of such networks and its impact on health care are poorly understood.PurposeWe examined the level of dispersion of patient-sharing networks across U.S. hospitals and its association with three measures of care delivered by hospitals that were likely to relate to coordination.Methodology/approachWe used data derived from 2016 Medicare Fee-for-Service claims to measure the volume of patients that hospitals treated in common. We then calculated a measure of dispersion for each hospital based on how those patients were concentrated in outside hospitals. Using this measure, we created multivariate regression models to estimate the relationship between network dispersion, Medicare spending per beneficiary, readmission rates, and emergency department (ED) throughput rates.ResultsIn multivariate analysis, we found that hospitals with more dispersed networks (those with many low-volume patient-sharing relationships) had higher spending but not greater readmission rates or slower ED throughput. Among hospitals with fewer resources, greater dispersion related to greater readmission rates and slower ED throughput. Holding an individual hospital's dispersion constant, the level of dispersion of other hospitals in the hospital's network was also related to these outcomes.ConclusionDispersed interhospital networks pose a challenge to coordination for patients who are treated at multiple hospitals. These findings indicate that the patient-sharing network structure may be an overlooked factor that shapes how health care organizations deliver care.Practice implicationsHospital leaders and hospital-based clinicians should consider how the structure of relationships with other hospitals influences the coordination of patient care. Effective management of this broad network may lead to important strategic partnerships.

The exponential growth of telemedicine in ambulatory care triggered by the COVID-19 public health emergency has undoubtedly impacted the quality of care and patient safety. In particular, the increased adoption of remote care has impacted communication, care teams, and patient engagement, which are key factors that impact patient safety in ambulatory care. In this perspective, we draw on a scoping review of the literature, our own clinical experiences, and conversations with patient safety experts to describe how changes in communication, care teams, and patient engagement have impacted two high priority areas in ambulatory safety: diagnostic errors and medication safety. We then provide recommendations for research funders, researchers, healthcare systems, policy makers, and healthcare payors for how to improve patient safety in telemedicine based on what is currently known as well as next steps for how to advance understanding of the safety implications of telemedicine utilization.

BackgroundThe inability to achieve high COVID-19 vaccination rates can continue to have serious harm to our communities. Vaccine hesitancy is a major barrier towards high vaccination rates. We evaluated the relationship between COVID-19 vaccine uptake and vaccine hesitancy, and then examined whether community factors were associated with COVID-19 vaccine uptake and hesitancy.MethodsWe constructed and evaluated a cross-sectional, county-level dataset that included the levels of vaccination uptake and vaccine hesitancy, and population characteristics based on those included in the CDC's Social Vulnerability Index.ResultsAcross 3142 US counties, vaccine hesitancy was significantly and negatively correlated with vaccine uptake rates (r = -0.06, P value

ObjectiveTo assess longitudinal primary care organization participation patterns in large-scale reform programs and identify organizational characteristics associated with multiprogram participation.Data sourcesSecondary data analysis of national program participation data over an eight-year period (2009-2016).Study designWe conducted a retrospective, observational study by creating a unique set of data linkages (including Medicare and Medicaid Meaningful Use and Medicare Shared Savings Program Accountable Care Organization (MSSP ACO) participation from CMS, Patient-Centered Medical Home (PCMH) participation from the National Committee for Quality Assurance, and organizational characteristics) to measure longitudinal participation and identify what types of organizations participate in one or more of these reform programs. We used multivariate models to identify organizational characteristics that differentiate those that participate in none, one, or two-to-three programs.Data extraction methodsWe used Medicare claims to identify organizations that delivered primary care services (n = 56 ,287) and then linked organizations to program participation data and characteristics.Principal findingsNo program achieved more than 50% participation across the 56,287 organizations in a given year, and participation levels flattened or decreased in later years. 36% of organizations did not participate in any program over the eight-year study period; 50% participated in one; 13% in two; and 1% in all three. 14.31% of organizations participated in five or more years of Meaningful Use while 3.84% of organizations participated in five years of the MSSP ACO Program and 0.64% participated in at least five years of PCMH. Larger organizations, those with younger providers, those with more primary care providers, and those with larger Medicare patient panels were more likely to participate in more programs.Conclusions and relevancePrimary care transformation via use of voluntary programs, each with their own participation requirements and approach to incentives, has failed to broadly engage primary care organizations. Those that have chosen to participate in multiple programs are likely those already providing high-quality care.

ObjectiveElectronic 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 methodsWe 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.ResultsWe 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%).DiscussionOur 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.ConclusionMeasures revealed opportunities to optimize SDOH data documentation and review.

Patient-generated health data (PGHD) have not achieved widespread clinical adoption. However, the COVID-induced shift to telemedicine may have created opportunities for PGHD as surrogates for vital signs collected in person. We assessed whether this shift was associated with greater ambulatory care PGHD use. We conducted an interrupted time series analysis of physician enrollment in, and patient-initiated vital sign transmission of non-COVID-associated PGHD through, a national PGHD platform (Validic). Ten health systems, 4695 physicians, and 51 320 patients were included. We found a significant increase in physician enrollment (slope change of 0.86/week, P = .02). Platform application programming interface calls continued their pre-COVID upward trend, despite large reductions in overall encounters. These findings suggest significantly greater pandemic-associated clinical demand for PGHD, and patient supply disproportionate to encounter rates. Increasing clinical use and ongoing efforts to reduce barriers, could help seize current adoption momentum to realize PGHDs potential value.

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.

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.

Clinical workflow is the enactment of a series of steps to perform a clinical activity. The transition from paper to electronic health records (EHRs) over the past decade has been characterized by profound challenges supporting clinical workflow, impeding frontline clinicians' ability to deliver safe, efficient, and effective care. In response, there has been substantial effort to study clinical workflow as well as workarounds-exceptions to routine workflow-in order to identify opportunities for improvement. This article describes predominant methods of studying workflow and workarounds and provides examples of the applications of these methods along with the resulting insights. Challenges to studying workflow and workarounds are described, and recommendations for how to approach such studies are given. Although there is not yet a set of standard approaches, this article helps advance workflow research that ultimately serves to inform how to coevolve the design of EHR systems and organizational decisions about processes, roles, and responsibilities in order to support clinical workflow that more consistently delivers on the potential benefits of a digitized health care system.

The study sought to determine which patient characteristics are associated with the use of patient-facing digital health tools in the United States.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.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.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.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.

The widespread implementation of electronic health records (EHRs) was predicated on hopes that they would rapidly improve care, but initial experiences have been disappointing and thought to be a key part of physician dissatisfaction and burnout. The crisis created by EHR implementation is only in part due to EHRs themselves, and might also be viewed as a crisis that has served to surface longstanding problems in healthcare-ones that if grappled with, will lead to more rapidly effective digital transformation.

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.

ObjectiveWhile 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 methodsUsing 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.ResultsThere was a large increase in the level of outside record views of 22 920 per week (P

ObjectivesThe 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 methodsWe 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.ResultsOne-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.DiscussionEHRs 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.ConclusionsTwo specific EHR use measures were associated with exhaustion.

ObjectiveOne potential benefit of greater electronic health information exchange is a reduction in the effort required for patients to switch between providers. We therefore assessed whether hospital participation in health information organizations (HIOs) led to increased patient sharing.Data sourcesSecondary data from 2010 to 2016.Study designUsing hospital-pair and year fixed effects regression models, we assessed change in patient sharing volume following HIO participation by hospitals and compared the effect by the level of market competition and by hospital size.Data extraction methodsWe used national data on hospital participation in HIOs from the American Hospital Association Information Technology Supplement and data on the volume of Medicare patients shared between pairs of hospitals from 2010 to 2016.Principal findingsThe volume of patients shared between hospitals increased by 2.6 percent when both hospitals participated in a health information organization (P = .008, 95% CI: 0.7%-4.5%). This increase was greater in competitive markets (3.1 percent increase, P = .03 95% CI: 0.3%-5.9%) and between large hospitals (4.3 percent increase, P = .007 95% CI: 1.2%-7.3%). Participation by only one hospital in a pair had no effect on patient sharing.ConclusionsOur results suggest that an important policy goal motivating the investment in health information exchange has been at least partially achieved. However, our results also support hospital concerns about the competitive implications of engaging in health information exchange. HIO participation appears to facilitate patient movement between hospitals, likely by lowering switching costs and resulting in greater competition.

ObjectiveTo assess whether an electronic health record (EHR) portal to enable health information exchange (HIE) between a hospital and three skilled nursing facilities (SNFs) reduced likelihood of patient readmission.Setting/dataSecondary data; all discharges from a large academic medical center to SNFs between July 2013 and March 2017, combined with portal usage records from SNFs with HIE access.DesignWe use difference-in-differences to determine whether portal implementation reduced likelihood of readmission over time for patients discharged to HIE-enabled SNFs, relative to those discharged to nonenabled facilities. Additional descriptive analyses of audit log data characterize portal use within enabled facilities.Data collectionEncounter-level clinical EHR data were merged with EHR audit log data that captured portal usage in the timeframe associated with a patient transition from hospital to SNF.Principal findingsDeclines in likelihood of 30-day readmission were not significantly different for patients in HIE-enabled vs control SNFs (diff-in-diff = 0.022; P = .431). We observe similar null effects with shorter readmission windows. The portal was used for 46 percent of discharges, with significant usage pattern variation within/across facilities.ConclusionsImplementation of a hospital-SNF EHR portal did not reduce readmissions from enabled SNFs. Emergent HIE use cases need to be better defined and leveraged for design and implementation that generates value in the context of postacute transitions.

PURPOSE:To improve care coordination for complex cancers, it is critical to establish a more nuanced understanding of the types of providers involved. As the number of provider types increases, strategies to support cancer care coordination must adapt to a greater variety of information needs, communication styles, and treatment strategies. METHODS:We categorized providers into 11 types, using National Provider Identifier specialties. Using Medicare claims, we counted the number of unique combinations of provider types billed during preoperative, operative, and postdischarge care for colon cancer surgery and assessed how this count varies across hospitals. The study included 70,567 beneficiaries in fee-for-service Medicare A and B for 6 months before and 60 days after an admission for colectomy for colon cancer between 2008 and 2011. RESULTS:We observed 1,554 preoperative provider-type combinations, 975 operative combinations, and 1,571 postdischarge combinations. The three most common combinations in the preoperative phase were general medicine only, other medical specialists only, and general medicine and other medical specialists. In the operative phase, the three most common combinations were primary surgery, anesthesiology, and pathology; general medicine, other medical specialists, radiology, primary surgery, anesthesiology, and pathology; and other medical specialists, radiology, primary surgery, anesthesiology, and pathology. In the postdischarge phase, the three most common combinations were general medicine, general medicine and other medical specialists, and general medicine and oncology. On average, each hospital had 15 preoperative, 11 operative, and 15 postoperative combinations. High-volume, larger, teaching, urban, and noncritical access hospitals had more combinations in all phases. CONCLUSION:Many provider-type combinations are involved in colon cancer surgery care. Substantial variation exists across hospitals types, suggesting that certain hospitals need additional resources and more flexible infrastructure to coordinate care.

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]

BACKGROUND:Health Information Organizations (HIOs) are third party organizations that facilitate electronic health information exchange (HIE) between providers in a geographic area. Despite benefits from HIE, HIOs have struggled to form and subsequently gain broad provider participation. We sought to assess whether market-level hospital and EHR vendor dynamics are associated with presence and level of hospital participation in HIOs. METHODS:2014 data on 4523 hospitals and their EHR vendors were aggregated to the market level. We used multivariate OLS regression to analyze the relationship between hospital and vendor dynamics and (1) probability of HIO presence and (2) percent of hospitals participating in an HIO. RESULTS:298 of 469 markets (64%) had HIO presence, and in those markets, 47% of hospitals participated in an HIO on average. In multivariate analysis, four characteristics were associated with HIO presence. Markets with more hospitals, markets with more EHR vendors, and markets with an EHR vendor-led HIE approach were more likely to have an HIO. Compared to markets with low hospital competition, markets with high hospital competition had a 25 percentage point lower probability of HIO presence. Two characteristics were associated with level of hospital HIO participation. Markets with more hospitals as well as markets with high vendor competition (compared to low competition) had lower participation. CONCLUSION:Both hospital and EHR vendor dynamics are associated with whether a market has an HIO as well as the level of hospital participation in HIOs.

Failed diffusion of innovations may be linked to an inability to use and apply data, information, and knowledge to change perceptions of current practice and motivate change. Using qualitative and quantitative data from three large-scale health care delivery innovations-accountable care organizations, advanced primary care practice, and EvidenceNOW-we assessed where data-driven innovation is occurring and where challenges lie. We found that implementation of some technological components of innovation (for example, electronic health records) has occurred among health care organizations, but core functions needed to use data to drive innovation are lacking. Deficits include the inability to extract and aggregate data from the records; gaps in sharing data; and challenges in adopting advanced data functions, particularly those related to timely reporting of performance data. The unexpectedly high costs and burden incurred during implementation of the innovations have limited organizations' ability to address these and other deficits. Solutions that could help speed progress in data-driven innovation include facilitating peer-to-peer technical assistance, providing tailored feedback reports to providers from data aggregators, and using practice facilitators skilled in using data technology for quality improvement to help practices transform. Policy efforts that promote these solutions may enable more rapid uptake of and successful participation in innovative delivery system reforms.

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]