Characterization of Electronic Health Record Use Outside Scheduled Clinic Hours Among Primary Care Pediatricians: Retrospective Descriptive Task Analysis of Electronic Health Record Access Log Data
Bibliographic record
Abstract
BACKGROUND: Many of the benefits of electronic health records (EHRs) have not been achieved at expected levels because of a variety of unintended negative consequences such as documentation burden. Previous studies have characterized EHR use during and outside work hours, with many reporting that physicians spend considerable time on documentation-related tasks. These studies characterized EHR use during and outside work hours using clock time versus actual physician clinic schedules to define the outside work time. OBJECTIVE: This study aimed to characterize EHR work outside scheduled clinic hours among primary care pediatricians using a retrospective descriptive task analysis of EHR access log data and actual physician clinic schedules to define work time. METHODS: We conducted a retrospective, exploratory, descriptive task analysis of EHR access log data from primary care pediatricians in September 2019 at a large Midwestern pediatric health center to quantify and identify actions completed outside scheduled clinic hours. Mixed-effects statistical modeling was used to investigate the effects of age, sex, clinical full-time equivalent status, and EHR work during scheduled clinic hours on the use of EHRs outside scheduled clinic hours. RESULTS: Primary care pediatricians (n=56) in this study generated 1,523,872 access log data points (across 1069 physician workdays) and spent an average of 4.4 (SD 2.0) hours and 0.8 (SD 0.8) hours per physician per workday engaged in EHRs during and outside scheduled clinic hours, respectively. Approximately three-quarters of the time working in EHR during or outside scheduled clinic hours was spent reviewing data and reports. Mixed-effects regression revealed no associations of age, sex, or clinical full-time equivalent status with EHR use during or outside scheduled clinic hours. CONCLUSIONS: For every hour primary care pediatricians spent engaged with the EHR during scheduled clinic hours, they spent approximately 10 minutes interacting with the EHR outside scheduled clinic hours. Most of their time (during and outside scheduled clinic hours) was spent reviewing data, records, and other information in EHR.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".