Primary care physicians’ electronic health record proficiency and efficiency behaviors and time interacting with electronic health records: a quantile regression analysis

Abstract

Objective This study aimed to understand the association between primary care physician (PCP) proficiency with the electronic health record (EHR) system and time spent interacting with the EHR. Materials and Methods We examined the use of EHR proficiency tools among PCPs at one large academic health system using EHR-derived measures of clinician EHR proficiency and efficiency. Our main predictors were the use of EHR proficiency tools and our outcomes focused on 4 measures assessing time spent in the EHR: (1) total time spent interacting with the EHR, (2) time spent outside scheduled clinical hours, (3) time spent documenting, and (4) time spent on inbox management. We conducted multivariable quantile regression models with fixed effects for physician-level factors and time in order to identify factors that were independently associated with time spent in the EHR. Results Across 441 primary care physicians, we found mixed associations between certain EHR proficiency behaviors and time spent in the EHR. Across EHR activities studied, QuickActions, SmartPhrases, and documentation length were positively associated with increased time spent in the EHR. Models also showed a greater amount of help from team members in note writing was associated with less time spent in the EHR and documenting. Discussion Examining the prevalence of EHR proficiency behaviors may suggest targeted areas for initial and ongoing EHR training. Although documentation behaviors are key areas for training, team-based models for documentation and inbox management require further study. Conclusions A nuanced association exists between physician EHR proficiency and time spent in the EHR.

Using event logs to observe interactions with electronic health records: an updated scoping review shows increasing use of vendor-derived measures

“…Reviewed articles mentioned several limitations of EHR log research ( Supplementary eTable 4 ). Three of the most frequently mentioned limitations echo those observed in the prior review of audit log research 11 : EHR logs do not provide a full view of clinical activity which can involve physical and digital interactions outside the EHR (22 articles), 3 , 26 , 34 , 43 , 55 , 57 , 58 , 61 , 65 , 68 , 73 , 74 , 84–86 , 89 , 91 , 92 , 95 , 103 , 109 , 112 qualitative methods are needed to better understand the context and motivation for observed work (15 articles), 32 , 46 , 53 , 62 , 64 , 68 , 72 , 80 , 85 , 91 , 96 , 97 , 101 , 115 , 118 and logs may not contain enough detail to observe complex workflows (13 articles). 4 , 26 , 32 , 43 , 51 , 56 , 59 , 69 , 75 , 81 , 89 , 90 ,…”

“…Of the 37 articles that reported durations of EHR use for specific activities such as inbox management or chart review, 2–4 , 7 , 19 , 24–32 , 35–38 , 40–48 , 51 , 55–58 , 62 , 70 , 71 , 79 , 109 16 described how actions were mapped to activities, either by providing a list of actions mapped to each activity, or by stating that the EHR vendor had done the mapping. 2–4 , 7 , 25 , 27 , 29 , 32 , 40 , 43 , 44 , 57 , 58 , 62 , 70 , 71 There was no difference in reporting between vendor-measure or investigator-measure studies (67% vs 39% of relevant articles, P = .370).…”

“…Looking at specific measures of EHR use reported in each study reveals additional differences, particularly regarding durations of EHR use ( Figure 4 ). Reported time-based measures included total time in the EHR (49 articles), 3 , 4 , 19 , 24–33 , 35–49 , 51–59 , 61 , 66 , 69–73 , 77 , 79 , 95 , 98 time in notes (32 articles), 3 , 4 , 7 , 19 , 24–32 , 35–38 , 40–46 , 48 , 51 , 55–58 , 79 , 109 time in inbox (23 articles), 2–4 , 19 , 24 , 25 , 27–29 , 35–38 , 42 , 43 , 45 , 47 , 48 , 51 , 61 , 62 , 70 , 71 time in chart review (23 articles), 3 , 4 , 24–27 , 29 , 31 , 32 , 35 , 36 , 40 , 42 , …”

“… 4 , 6 , 19 , 25 , 27 , 29 , 32 , 34 , 36 , 39 , 40 , 42 , 48 , 49 , 51 , 53 , 55 , 66 , 69–71 An overlapping set of 22 articles, including 9 that also reported time period-based measures, reported at least 1 measure based on clinician schedules. 4 , 6 , 19 , 27 , 28 , 30 , 34 , 37 , 38 , 40 , 41 , 43 , 44 , 47–50 , 67 , 69 , 72 , 77 , 119 These included time outside scheduled hours on days with appointments, time on days without appointments, and time after the patient checked out. These schedule-based measures differed in whether they (1) measured active EHR use or all time logged into the EHR, (2) included mid-day meetings or breaks in scheduled hours, or (3) included the 30 or 60 min before and after the first and last appointment of the day in scheduled hours.…”

“…The most frequent denominator was days (31 articles) including days in a reporting period, days with a scheduled appointment/shift, weekdays, or weekend days/holidays. 2–4 , 24 , 26 , 27 , 31 , 34–36 , 38 , 40 , 41 , 43–46 , 48–50 , 52 , 54 , 55 , 59 , 62 , 66 , 69–71 , 79 , 95 The next most popular denominators were number of appointments (12 articles), 24 , 25 , 29 , 30 , 32 , 35 , 38 , 40 , 42 , 44 , 72 , 77 and patients (9 articles). 26 , 31 , 39 , 41 , 51 , 53 , 56 , 57 , 98 Eleven other denominators were also used including EHR time per hour of clinic, per 8 h of clinic, per clinic session, per shift, per week, per month, per quarter, per year, per procedure, per note, and per residency.…”