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., note templates and dictation services) exist to ease documentation burden, but little evidence exists regarding how physicians use these tools in combination and the degree to which these strategies correlate with reduced time spent on documentation. OBJECTIVE: To characterize EHR note composition strategies, how these strategies differ in time spent on notes and the EHR, and their distribution across specialty types. DESIGN: Secondary analysis of physician-level measures of note composition and EHR use derived from Epic Systems' Signal data warehouse. We used k-means clustering to identify documentation strategies, and ordinary least squares regression to analyze the relationship between documentation strategies and physician time spent in the EHR, on notes, and outside scheduled hours. PARTICIPANTS: A total of 215,207 US-based ambulatory physicians using the Epic EHR between September 2020 and May 2021. MAIN MEASURES: Percent of note text derived from each of five documentation tools: SmartTools, copy/paste, manual text, NoteWriter, and voice recognition and transcription; average total and after-hours EHR time per visit; average time on notes per visit. KEY RESULTS: Six distinct note composition strategies emerged in cluster analyses. The most common strategy was predominant SmartTools use (n=89,718). In adjusted analyses, physicians using primarily transcription and dictation (n=15,928) spent less time on notes than physicians with predominant Smart Tool use. (b=-1.30, 95% CI=-1.62, -0.99, p<0.001; average 4.8 min per visit), while those using mostly copy/paste (n=23,426) spent more time on notes (b=2.38, 95% CI=1.92, 2.84, p<0.001; average 13.1 min per visit). CONCLUSIONS: Physicians' note composition strategies have implications for both time in notes and after-hours EHR use, suggesting that how physicians use EHR-based documentation tools can be a key lever for institutions investing in EHR tools and training to reduce documentation time and alleviate EHR-associated burden.

Early Changes in Billing and Notes After Evaluation and Management Guideline Change.

BACKGROUND: The American Medical Association updated guidance in 2021 for frequently used billing codes for outpatient evaluation and management (E/M) visits. The intent was to account for provider time outside of face-to-face encounters and to reduce onerous documentation requirements. OBJECTIVE: To analyze E/M visit use, documentation length, and time spent in the electronic health record (EHR) before and after the guideline change. DESIGN: Observational, retrospective, pre-post study. SETTING: U.S.-based ambulatory practices using the Epic Systems EHR. PARTICIPANTS: 303 547 advanced practice providers and physicians across 389 organizations. MEASUREMENTS: Data from September 2020 through April 2021 containing weekly provider-level E/M code and EHR use metadata were extracted from the Epic Signal database. We descriptively analyzed overall and specialty-specific changes in E/M visit use, note length, and time spent in the EHR before and after the new guidelines using provider-level paired t tests. RESULTS: Following the new guidelines, level 3 visits decreased by 2.41 percentage points (95% CI, -2.48 to -2.34 percentage points) to 38.5% of all E/M visits, a 5.9% relative decrease from fall 2020. Level 4 visits increased by 0.89 percentage points (CI, 0.82 to 0.96 percentage points) to 40.9% of E/M visits, a 2.2% relative increase. Level 5 visits (the highest acuity level) increased by 1.85 percentage points (CI, 1.81 to 1.89 percentage points) to 10.1% of E/M visits, a 22.6% relative increase. These changes varied by specialty. We found no meaningful changes in measures of note length or time spent in the EHR. LIMITATION: The Epic ambulatory client base may underrepresent smaller and independent practices. CONCLUSION: Immediate changes in E/M coding contrast with null findings for changes in both note length and EHR time. Provider organizations are positioned to respond more rapidly to billing process changes than to changes in care delivery and associated EHR use behaviors. Fully realizing the intended benefits of this guideline change will require more time, facilitation, and scaling of best practices that more directly address EHR documentation practices and associated burden. PRIMARY FUNDING SOURCE: None.

Examining Training Motivations Among Public Health Workers.

CONTEXT: As public health needs and priorities evolve, maintaining a trained public health workforce is critical to the success of public health efforts. Researchers have examined training needs in various contexts and subpopulations, but a nationally representative study of what motivates public health workers to seek out training has yet to be conducted. By understanding these motivations, public health agencies and policy makers can appeal to worker motivations in both training programs and organizational incentives. OBJECTIVE: The purpose of this article was to describe overall training motivations and identify patterns of training motivations among public health workers. This study also explored whether or not training needs differ across prevalent motivational patterns. DESIGN AND PARTICIPANTS: Using data from the 2017 Public Health Workforce Interests and Needs Survey (PH WINS), the study used latent class analysis (LCA) to identify motivational patterns and logistic regression to analyze associations with training needs. RESULTS: The most prominent motivation to seek training was personal growth (82.7% of respondents). LCA identified 4 motivational classes of public health workers: those motivated by organizational pressure and requirements (31.8%), those motivated indiscriminately by all factors (28.4%), those motivated primarily by personal growth (21.7%), and those motivated by organizational accommodations and supports (18.2%). Motivational class was not associated with indicating training needs in any of 8 training domains, nor was it associated with indicating any training need in any domain. CONCLUSIONS: Public health agencies should consider the different motivational classes present in the public health workforce. In particular, motivational classes that represent organizational choices suggest that public health agencies should both motivate workers with organizational requirements and pressure from managers and offer institutional support via paid travel and covered time for training.

Secondary Use of Clinical Data to Enable Data-Driven Translational Science with Trustworthy Access Management.

University of Missouri (MU) Health Care produces a large amount of digitized clinical data that can be used in clinical and translational research for cohort identification, retrospective data analysis, feasibility study, and hypothesis generation. In this article, the implementation of an integrated clinical research data repository is discussed. We developed trustworthy access-management protocol for providing access to both clinically relevant data and protected health information. As of September 2014, the database contains approximately 400,000 patients and 82 million observations; and is growing daily. The system will facilitate the secondary use of electronic health record (EHR) data at MU to promote data-driven clinical and translational research, in turn enabling better healthcare through research.

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. 778 (74%) patients had at least one C-CDA document present from either source. Among these patients, two-thirds had information in only one source. All documents contained demographics, but less than half of patients had data in clinical data domains. Moreover, data across HIE networks were not concordant. Results suggest that HIE networks have different, likely complementary, data available for the same patient, suggesting the need for better integration and deduplication for national HIE efforts.

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. Documentation composition measures include note length and percentage use of templated text or copy/paste. Efficiency is measured as the percent of visits with same-day note completion. STUDY DESIGN: All analyses are cross-sectional. Across-organization differences in EHR use and documentation composition are presented via 90th-to-10th percentile ratios of means and SDs. Multilevel modeling with post-estimation variance partitioning assesses the extent of an organizational signature-the proportion of variation in our measures attributable to organizations (versus specialty and individual behaviors). We measured organizational internal consistency for each measure via organization-level SD, which we grouped into quartiles for regression. Association between internally consistent (i.e., low SD) organizational EHR use and physician-level efficiency was assessed with multi-variable OLS models. DATA COLLECTION: Extraction from Epic's Signal platform used for measuring provider EHR efficiency. PRINCIPAL FINDINGS: EHR time per visit for physicians at a 90th percentile organization is 1.94 times the average EHR time at a 10th percentile organization. There is little evidence, on average, of an organizational signature. However, physicians in organizations with high internal consistency in EHR use demonstrate increased efficiency. Physicians in organizations with the highest internal consistency (top quartile) have a 3.77 percentage point higher same-day visit closure rates compared with peers in bottom quartile organizations (95% confidence interval: 0.0142-0.0612). CONCLUSIONS: Results suggest unrealized opportunities for organizations and policymakers to support consistency in how physicians engage in EHR-supported work.

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. We determined each primary care clinician's (PCC) modal documentation pattern and analyzed how consistency and deviations were related to documentation efficiency. RESULTS: We identified 8 distinct daily documentation patterns; the 3 most common patterns accounted for 80.6% of PCC-days and differed primarily in average volume of documentation time (78.1 minutes per day; 35.4 minutes per day; 144.6 minutes per day); associations with note efficiency were mixed. PCCs with >80% of days attributable to a single pattern demonstrated significantly more efficient documentation than PCCs with lower consistency; for high-consistency PCCs, days that deviated from their usual patterns were associated with less efficient documentation. DISCUSSION: We found substantial variation in efficiency across daily documentation patterns, suggesting that PCC-level factors like EHR facility and consistency may be more important than when documentation occurs. There were substantial efficiency returns to consistency, and deviations from consistent patterns were costly. CONCLUSION: Organizational leaders aiming to reduce documentation burden should pay specific attention to the ability for PCCs to execute consistent documentation patterns day-to-day.

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. Objectives: To analyze how EHR documentation time and visit volume change following the adoption of team-based documentation approaches. Design, Setting, and Participants: This national longitudinal cohort study analyzed physician-week EHR metadata from September 2020 through April 2021. A 2-way fixed-effects difference-in-differences regression approach was used to analyze changes in the main outcomes after team-based documentation support adoption. Event study regression models were used to examine variation in changes over time and stratified models to analyze the moderating role of support intensity. The sample included US ambulatory physicians using the EHR. Data were analyzed between October 2022 and September 2023. Exposure: Team-based documentation support, defined as new onset and consistent use of coauthored documentation with another clinical team member. Main Outcomes and Measures: The main outcomes included weekly visit volume, EHR documentation time, total EHR time, and EHR time outside clinic hours. Results: Of 18 265 physicians, 1024 physicians adopted team-based documentation support, with 17 241 comparison physicians who did not adopt such support. The sample included 57.2% primary care physicians, 31.6% medical specialists, and 11.2% surgical specialists; 40.0% practiced in academic settings and 18.4% in outpatient safety-net settings. For adopter physicians, visit volume increased by 6.0% (2.5 visits/wk [95% CI, 1.9-3.0]; P < .001), and documentation time decreased by 9.1% (23.3 min/wk [95% CI, -30.3 to -16.2]; P < .001). Following a 20-week postadoption learning period, visits per week increased by 10.8% and documentation time decreased by 16.2%. Only high-intensity adopters (>40% of note text authored by others) realized reductions in documentation time, both for the full postadoption period (-53.9 min/wk [95% CI, -65.3 to -42.4]; 21.0% decrease; P < .001) and following the learning period (-72.2 min/wk; 28.1% decrease). Low adopters saw no meaningful change in EHR time but realized a similar increase in visit volume. Conclusions and Relevance: In this national longitudinal cohort study, physicians who adopted team-based documentation experienced increased visit volume and reduced documentation and EHR time, especially after a learning period.

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. METHODS: We used cross-sectional data from the National Electronic Health Records Survey to measure VBP program participation and our outcomes of reported electronic health record (EHR) documentation burden. We used ordinary least squares regression models adjusting for physician and practice characteristics to estimate the relationship between participation in any VBP program and EHR burden outcomes. We also estimated the relationship between participation in 6 distinct VBP programs and our outcomes to decompose the aggregate relationship into program-specific estimates. RESULTS: In adjusted analyses, participation in any VBP program was associated with 10.5% greater probability of reporting more than 1 hour per day of after-hours documentation time (P = .01), which corresponded to an estimated additional 11 minutes per day (P = .03). Program-specific estimates illustrated that ACO participation drove the aggregate relationship, with ACO participants reporting greater after-hours documentation time (18 additional minutes per day; P < .001), more difficulty documenting (30.6% more likely; P < .001), and more inappropriateness of time spent documenting (21.7% more likely; P < .001). CONCLUSIONS: Office-based physicians participating in ACOs report greater documentation burden across several measures; the same is not true for other VBP programs. Although many ACOs relax documentation requirements for reimbursement, documentation for quality reporting and risk adjustment may lead to a net increase in burden, especially for physicians exposed to numerous programs and payers.

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. MATERIALS AND METHODS: We combined EHR active use data capturing physicians' hourly documentation work with survey data capturing documentation preferences and burnout. Our sample included 318 ambulatory physicians at MedStar Health. We conducted a mediation analysis to estimate if and how preferences mediated the relationship between after-hours documentation time and burnout. Our primary outcome was physician-reported burnout. We measured preferences for after-hours documentation work via a novel survey instrument (Burden Scenarios Assessment). We measured after-hours documentation time in the EHR as the total active time respondents spent documenting between 7 pm and 3 am. RESULTS: Physician preferences varied, with completing clinical documentation after clinic hours while at home the scenario rated most burdensome (52.8% of physicians), followed by dealing with prior authorization (49.5% of physicians). In mediation analyses, preferences partially mediated the relationship between after-hours documentation time and burnout. DISCUSSION: Physician preferences regarding EHR-based work play an important role in the relationship between after-hours documentation time and burnout. CONCLUSION: Studies of EHR work and burnout should incorporate preferences, and operational leaders should assess preferences to better target interventions aimed at EHR-based contributors to burnout.

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. 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.

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. Data analysis was performed from March 2022 to April 2023. Main Outcomes and Measures: Appointment scheduling patterns, percentage of new and established and level of evaluation and management (E/M) visits, and EHR use metrics per day and week. Results: The sample consisted of 217 924 clinicians across 389 organizations, including 174 939 physicians and 42 985 APPs. Although primary care physicians were more likely than APPs to have more than 3 days per week with appointments (50 921 physicians [79.5%] vs 17 095 APPs [77.9%]), this trend was reversed for medical (38 645 physicians [64.8%] vs 8124 APPs [74.0%]) and surgical (24 155 physicians [47.1%] vs 5198 APPs [51.7%]) specialties. Medical and surgical specialty physicians saw 6.7 and 7.4 percentage points, respectively, more new patient visits than did their APP counterparts, whereas primary care physicians saw 2.8 percentage points fewer new patient visits than did APPs. Physicians saw a greater percentage of level 4 or 5 visits across all specialties. Medical and surgical physicians spent 34.3 and 45.8 fewer minutes per day, respectively, using the EHR than did APPs in their specialties, whereas primary care physicians spent 17.7 minutes per day more. These differences translated to primary care physicians spending 96.3 minutes more per week using the EHR than APPs, whereas medical and surgical physicians spent 149.9 and 140.7 fewer minutes, respectively, than did their APP counterparts. Conclusions and Relevance: This cross-sectional, national study of clinicians found significant differences in visit and EHR patterns for physicians compared with APPs across specialty types. By underscoring the different current usage of physicians vs APPs across specialty types, this study helps place into context the work and visit patterns of physicians compared with APPs and serves as a foundation for evaluations of clinical outcomes and quality.

I had not time to make it shorter: an exploratory analysis of how physicians reduce note length and time in notes.

OBJECTIVE: We analyze observed reductions in physician note length and documentation time, 2 contributors to electronic health record (EHR) burden and burnout. MATERIALS AND METHODS: We used EHR metadata from January to May, 2021 for 130 079 ambulatory physician Epic users. We identified cohorts of physicians who decreased note length and/or documentation time and analyzed changes in their note composition. RESULTS: 37 857 physicians decreased either note length (n = 15 647), time in notes (n = 15 417), or both (n = 6793). Note length decreases were primarily attributable to reductions in copy/paste text (average relative change of -18.9%) and templated text (-17.2%). Note time decreases were primarily attributable to reductions in manual text (-27.3%) and increases in note content from other care team members (+21.1%). DISCUSSION: Organizations must consider priorities and tradeoffs in the distinct approaches needed to address different contributors to EHR burden. CONCLUSION: Future research should explore scalable burden-reduction initiatives responsive to both note bloat and documentation time.

Documentation dynamics: Note composition, burden, and physician efficiency.

OBJECTIVE: To analyze how physician clinical note length and composition relate to electronic health record (EHR)-based measures of burden and efficiency that have been tied to burnout. DATA SOURCES AND STUDY SETTING: Secondary EHR use metadata capturing physician-level measures from 203,728 US-based ambulatory physicians using the Epic Systems EHR between September 2020 and May 2021. STUDY DESIGN: In this cross-sectional study, we analyzed physician clinical note length and note composition (e.g., content from manual or templated text). Our primary outcomes were three time-based measures of EHR burden (time writing EHR notes, time in the EHR after-hours, and EHR time on unscheduled days), and one measure of efficiency (percent of visits closed in the same day). We used multivariate regression to estimate the relationship between our outcomes and note length and composition. DATA EXTRACTION: Physician-week measures of EHR usage were extracted from Epic's Signal platform used for measuring provider EHR efficiency. We calculated physician-level averages for our measures of interest and assigned physicians to overall note length deciles and note composition deciles from six sources, including templated text, manual text, and copy/paste text. PRINCIPAL FINDINGS: Physicians in the top decile of note length demonstrated greater burden and lower efficiency than the median physician, spending 39% more time in the EHR after hours (p < 0.001) and closing 5.6 percentage points fewer visits on the same day (p < 0.001). Copy/paste demonstrated a similar dose/response relationship, with top-decile copy/paste users closing 6.8 percentage points fewer visits on the same day (p < 0.001) and spending more time in the EHR after hours and on days off (both p < 0.001). Templated text (e.g., Epic's SmartTools) demonstrated a non-linear relationship with burden and efficiency, with very low and very high levels of use associated with increased EHR burden and decreased efficiency. CONCLUSIONS: "Efficiency tools" like copy/paste and templated text meant to reduce documentation burden and increase provider efficiency may have limited efficacy.

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. We find that patients admitted to nursing homes more specialized in PAC have lower hospital readmissions and mortality, longer nursing home stays, and higher Medicare spending for the episode of care, suggesting that specialization improves patient outcomes but at higher costs.