Current Use And Evaluation Of Artificial Intelligence And Predictive Models In US Hospitals.

Effective evaluation and governance of predictive models used in health care, particularly those driven by artificial intelligence (AI) and machine learning, are needed to ensure that models are fair, appropriate, valid, effective, and safe, or FAVES. We analyzed data from the 2023 American Hospital Association Annual Survey Information Technology Supplement to identify how AI and predictive models are used and evaluated for accuracy and bias in hospitals. Hospitals use AI and predictive models to predict health trajectories or risks for inpatients, identify high-risk outpatients to inform follow-up care, monitor health, recommend treatments, simplify or automate billing procedures, and facilitate scheduling.

Inbox message prioritization and management approaches in primary care.

Objectives: Patient messaging to clinicians has dramatically increased since the pandemic, leading to informatics efforts to categorize incoming messages. We examined how message prioritization (as distinct from categorization) occurs in primary care, and how primary care clinicians managed their inbox workflows.

Materials And Methods: Semi-structured interviews and inbox work observations with 11 primary care clinicians at MedStar Health.

Trending in the right direction: critical access hospitals increased adoption of advanced electronic health record functions from 2018 to 2023.

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.

Electronic Health Record Documentation Burden Crowds Out Health Information Exchange Use By Primary Care Physicians.

Although electronic health record (EHR) documentation burden is known to be associated with reduced clinician well-being and burnout, it may have even worse unintended consequences if documentation work also crowds out other high-value EHR tasks. We examined this possibility by assessing the relationship between documentation burden and a high-value but optional EHR task: the use of health information exchange (HIE) to view patient records from outside organizations. Our study took advantage of an exogenous shock to documentation time: appointment no-shows.

Physician EHR Time and Visit Volume Following Adoption of Team-Based Documentation Support.

Importance: Physicians spend the plurality of active electronic health record (EHR) time on documentation. Excessive documentation limits time spent with patients and is associated with burnout. Organizations need effective strategies to reduce physician documentation burden; however, evidence on team-based documentation (eg, medical scribes) has been limited to small, single-institution studies lacking rigorous estimates of how documentation support changes EHR time and visit volume.

Accounting for taste: preferences mediate the relationship between documentation time and ambulatory physician burnout.

Objectives: Physician burnout in the US has reached crisis levels, with one source identified as extensive after-hours documentation work in the electronic health record (EHR). Evidence has illustrated that physician preferences for after-hours work vary, such that after-hours work may not be universally burdensome. Our objectives were to analyze variation in preferences for after-hours documentation and assess if preferences mediate the relationship between after-hours documentation time and burnout.

Consistency is key: documentation distribution and efficiency in primary care.

Objectives: We analyzed the degree to which daily documentation patterns in primary care varied and whether specific patterns, consistency over time, and deviations from clinicians' usual patterns were associated with note-writing efficiency.

Materials And Methods: We used electronic health record (EHR) active use data from the Oracle Cerner Advance platform capturing hourly active documentation time for 498 physicians and advance practice clinicians (eg, nurse practitioners) for 65 152 clinic days. We used k-means clustering to identify distinct daily patterns of active documentation time and analyzed the relationship between these patterns and active documentation time per note.

Physicians in ACOs report greater documentation burden.

Objectives: First, to analyze the relationship between value-based payment (VBP) program participation and documentation burden among office-based physicians. Second, to analyze the relationship between specific VBP programs (eg, accountable care organizations [ACOs]) and documentation burden.

Study Design: Retrospective analyses of US office-based physicians in 2019 and 2021.

Interoperability in the Wild: Comparison of Real-World Electronic C-CDA Documents from Two Sources.

Although health information exchange (HIE) networks exist in multiple nations, providers still require access multiple sources to obtain medical records. We sought to measure and compare differences in data presence and concordance across regional HIE and EHR vendor-based networks. Using 1,054 randomly selected patients from a large health system in the US, we generated consolidated clinical document architecture (C-CDA) documents from each network.

Guidance for reporting analyses of metadata on electronic health record use.

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.

The health effects of nursing home specialization in post-acute care.

Nursing homes serve both long-term care and post-acute care (PAC) patients, two groups with distinct financing mechanisms and requirements for care. We examine empirically the effect of nursing home specialization in PAC using 2011-2018 data for Medicare patients admitted to nursing homes following a hospital stay. To address patient selection into specialized nursing homes, we use an instrumental variables approach that exploits variation over time in the distance from the patient's residential ZIP code to the closest nursing home with different levels of PAC specialization.

Identifying and Addressing Barriers to Implementing Core Electronic Health Record Use Metrics for Ambulatory Care: Virtual Consensus Conference Proceedings.

Precise, reliable, valid metrics that are cost-effective and require reasonable implementation time and effort are needed to drive electronic health record (EHR) improvements and decrease EHR burden. Differences exist between research and vendor definitions of metrics. PROCESS:  We convened three stakeholder groups (health system informatics leaders, EHR vendor representatives, and researchers) in a virtual workshop series to achieve consensus on barriers, solutions, and next steps to implementing the core EHR use metrics in ambulatory care.

Examining primary care provider experiences with using a clinical decision support tool for pain management.

Objective: To evaluate primary care provider (PCP) experiences using a clinical decision support (CDS) tool over 16 months following a user-centered design process and implementation.

Materials And Methods: We conducted a qualitative evaluation of the Chronic Pain OneSheet (OneSheet), a chronic pain CDS tool. OneSheet provides pain- and opioid-related risks, benefits, and treatment information for patients with chronic pain to PCPs.

The role of organizations in shaping physician use of electronic health records.

Objective: The aim of the study was to (1) characterize organizational differences in primary care physicians' electronic health record (EHR) behavior; (2) assess within-organization consistency in EHR behaviors; and (3) identify whether organizational consistency is associated with physician-level efficiency.

Data Sources: EHR metadata capturing averaged weekly measures of EHR time and documentation composition from 75,124 US primary care physicians across 299 organizations between September 2020 and May 2021 were taken. EHR time measures include active time in orders, chart review, notes, messaging, time spent outside of scheduled hours, and total EHR time.

Comparison of Work Patterns Between Physicians and Advanced Practice Practitioners in Primary Care and Specialty Practice Settings.

Importance: Despite the increasing involvement of advanced practice practitioners (APPs; ie, nurse practitioners and physician assistants) in care delivery across specialties, the work patterns of APPs compared with physicians and how they are integrated into care teams have not been well characterized.

Objective: To characterize differences between physicians and APPs across specialty types related to days with appointments, visit types seen, and time spent using the electronic health record (EHR).

Design, Setting, And Participants: This nationwide, cross-sectional study used EHR data from physicians and APPs (ie, nurse practitioners and physician assistants) at all US institutions that used Epic Systems' EHR between January and May 2021.

Physician Note Composition Patterns and Time on the EHR Across Specialty Types: a National, Cross-sectional Study.

Background: The burden of clinical documentation in electronic health records (EHRs) has been associated with physician burnout. Numerous tools (e.g.