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Documentation FreedomObservational2023

After-Hours Documentation ("Pajama Time") and Physician Work–Life Balance

Key Finding

Large multi-system surveys show that physicians spending ≥6 hours per week on after-hours EHR work report significantly higher burnout, whereas those with ≤5 hours have roughly half the odds of burnout, even after adjusting for specialty and workload. However, one smaller time-logging study found no consistent association between objectively measured after-hours EHR time and burnout, highlighting methodological variability.

7 min read3 sources cited
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Executive Summary

After-hours EHR work—often termed "pajama time"—has emerged as a surrogate marker for documentation burden and a contributor to work–life conflict. Analyses of large survey datasets combined with EHR usage logs demonstrate that higher levels of after-hours charting are associated with greater odds of burnout, emotional exhaustion, and intent to reduce clinical FTE. In an Arch Collaborative study, physicians doing ≤5 hours per week of after-hours charting were about twice as likely to report lower burnout compared with those spending ≥6 hours after hours.

At the same time, a prospective study using detailed EHR time logs did not find a consistent relationship between objectively measured after-hours EHR time and burnout, suggesting that individual context, perception of control, and organizational support may moderate the impact of pajama time. Across studies, physicians describe after-hours documentation as eroding family and personal time, contributing to MSK strain, and blurring boundaries between work and home, even when absolute time is modest.

Detailed Research

Methodology

Evidence comprises cross-sectional survey studies with linked EHR usage data, time-motion and log-based analyses, and smaller prospective observational cohorts. Burnout is typically measured using the Maslach Burnout Inventory or validated single-item scales, while after-hours EHR time is quantified either by self-report or audit logs that track activity outside scheduled clinic hours.

Multivariable models examine associations between after-hours EHR time, in-clinic EHR time, organizational support, and burnout while adjusting for specialty, gender, clinical load, and practice setting.

Key Studies

Arch Collaborative Study: After-Hours Charting and Burnout (2021)

  • Design: Large cross-sectional survey with EHR usage data
  • Sample: Thousands of physicians across multiple systems
  • Findings: Physicians reporting ≤5 hours per week of after-hours charting had significantly lower odds of burnout than those with ≥6 hours, independent of specialty and perceived EHR usability. Family medicine showed particularly high burnout levels and sensitivity to after-hours work.
  • Clinical Relevance: Clear threshold effect for after-hours documentation burden

Physician Burnout and Timing of EHR Use (2020)

  • Design: Prospective study linking objective EHR time logs to burnout
  • Sample: Smaller cohort with detailed time tracking
  • Findings: Significant association between in-clinic EHR time and burnout, but not between average after-hours EHR time and burnout, possibly due to low statistical power and relatively low after-hours burden in that setting.
  • Clinical Relevance: Highlights methodological challenges in measuring documentation burden

System-Level Factors and EHR Time (JAMA Network Open, 2023)

  • Design: System-level analysis of EHR time and burnout
  • Sample: Large multi-system data
  • Findings: Greater total time on the EHR, particularly after hours, is associated with emotional exhaustion and higher burnout rates, emphasizing the influence of panel size, staffing, and organizational culture.
  • Clinical Relevance: Organizational factors moderate individual burden

Primary Care EHR Time Study (AMA STEPS Forward, 2024)

  • Design: Analysis of primary care EHR time across clinics
  • Sample: Multiple primary care practices
  • Findings: Median total EHR time of 36.2 minutes per visit, with median pajama time ranging from 1.7 to 13.1 minutes across clinics. Highlighted EHR burden as a major threat to the primary care workforce.
  • Clinical Relevance: Significant variability in pajama time across settings

Clinical Implications

For osteopathic physicians, after-hours documentation competes directly with recovery, family engagement, and professional development, undermining the physician's own structural and emotional health.

Strategies that move documentation into the visit—such as ambient AI scribes, better team support, and optimized templates—can reduce pajama time and potentially improve work–life balance, particularly in DO practices that include time-intensive OMT.

Limitations & Research Gaps

Most studies are observational and cannot establish causality; physicians with burnout may perceive or report after-hours EHR time differently. Measurement methods vary substantially (self-report vs logs, definitions of "after-hours"), complicating comparisons.

There is little osteopathy-specific data on pajama time, and virtually no studies evaluate how AI documentation tools affect after-hours work in DO-heavy clinics that integrate OMT into daily practice.

Osteopathic Perspective

Osteopathic principles emphasize that the physician's body and mind are part of the therapeutic system; chronic after-hours documentation can impair the physician's own structure and function, contributing to MSK strain, fatigue, and reduced capacity for empathic engagement.

Reducing pajama time via thoughtful workflow redesign and selective AI adoption aligns with the osteopathic commitment to rational treatment and the unity of body, mind, and spirit—both for patients and for physicians.

References (3)

  1. Thompson M, Baier Manwell L, Brown R, et al. “Physician Burnout and Timing of Electronic Health Record Use.” Journal of General Internal Medicine, 2020;35:2385-2391. DOI: 10.1007/s11606-020-05912-5
  2. Gardner RL, Cooper E, Haskell J, et al. “Associations of physician burnout with organizational electronic health record support and after-hours charting time.” Journal of the American Medical Informatics Association, 2021;28:960-967. DOI: 10.1093/jamia/ocaa343
  3. Adler-Milstein J, Holmgren AJ, et al. “System-Level Factors and Time Spent on Electronic Health Records.” JAMA Network Open, 2023;6:e2330980. DOI: 10.1001/jamanetworkopen.2023.30980

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