Multicenter Analysis of Electronic Health Record Use among Ophthalmologists

Baxter, Sally L.; Gali, Helena E.; Mehta, Mitul C.; Rudkin, Scott; Bartlett, John; Brandt, James D.; Sun, Catherine Q.; Millen, Marlene; Longhurst, Chris

doi:10.1016/j.ophtha.2020.06.007

Cited by 7 publications

(14 citation statements)

References 4 publications

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“… 75 , 93 , 94 , 113 Only one of the experimental studies was a randomized controlled trial. 75 Across both observational and experimental studies, 41 articles compared EHR use across different groups of users including comparisons by specialty (14 studies), 4 , 7 , 25 , 29 , 31 , 36 , 42 , 48 , 51 , 58 , 59 , 85 , 105 , 115 clinical role (12), 24 , 26 , 33 , 37 , 46 , 65 , 76 , 95 , 99 , 106 , 108 , 115 gender (8), 19 , 34 , 47 , 49 , 66 , 68 , 70 , 106 year in residency (8), 31 , 32 , 35 , 39 , 52 , 56 , 57 , 95 organization (3), 24 , 44 , 75 and country (1). 27 Vendor-measure studies were more likely than investigator-measure studies to make such comparisons of EHR use by user group (65% vs 25...…”

Section: Resultsmentioning

confidence: 99%

“…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 , …”

Section: Resultsmentioning

confidence: 99%

“… 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.…”

Section: Resultsmentioning

confidence: 99%

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

Rule

Melnick

Apathy

2022

Journal of the American Medical Informatics Association

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Objective The aim of this article is to compare the aims, measures, methods, limitations, and scope of studies that employ vendor-derived and investigator-derived measures of electronic health record (EHR) use, and to assess measure consistency across studies. Materials and Methods We searched PubMed for articles published between July 2019 and December 2021 that employed measures of EHR use derived from EHR event logs. We coded the aims, measures, methods, limitations, and scope of each article and compared articles employing vendor-derived and investigator-derived measures. Results One hundred and two articles met inclusion criteria; 40 employed vendor-derived measures, 61 employed investigator-derived measures, and 1 employed both. Studies employing vendor-derived measures were more likely than those employing investigator-derived measures to observe EHR use only in ambulatory settings (83% vs 48%, P = .002) and only by physicians or advanced practice providers (100% vs 54% of studies, P < .001). Studies employing vendor-derived measures were also more likely to measure durations of EHR use (P < .001 for 6 different activities), but definitions of measures such as time outside scheduled hours varied widely. Eight articles reported measure validation. The reported limitations of vendor-derived measures included measure transparency and availability for certain clinical settings and roles. Discussion Vendor-derived measures are increasingly used to study EHR use, but only by certain clinical roles. Although poorly validated and variously defined, both vendor- and investigator-derived measures of EHR time are widely reported. Conclusion The number of studies using event logs to observe EHR use continues to grow, but with inconsistent measure definitions and significant differences between studies that employ vendor-derived and investigator-derived measures.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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

Rule

Melnick

Apathy

2022

Journal of the American Medical Informatics Association

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“…Developing standardized terminology for EHR logs is ongoing. In the near term, matched measures on the same vendor platform can enable cross-institution research, 12 , 20 a valuable first step towards more generalizable evidence. Over the long term, standardization will not only require consensus on appropriate measure definitions, but also interorganizational collaboration and ongoing maintenance.…”

Section: Discussionmentioning

confidence: 99%

Measures of electronic health record use in outpatient settings across vendors

Baxter

Apathy

Cross

et al. 2020

Journal of the American Medical Informatics Association

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Electronic health record (EHR) log data capture clinical workflows and are a rich source of information to understand variation in practice patterns. Variation in how EHRs are used to document and support care delivery is associated with clinical and operational outcomes, including measures of provider well-being and burnout. Standardized measures that describe EHR use would facilitate generalizability and cross-institution, cross-vendor research. Here, we describe the current state of outpatient EHR use measures offered by various EHR vendors, guided by our prior conceptual work that proposed seven core measures to describe EHR use. We evaluate these measures and other reporting options provided by vendors for maturity and similarity to previously proposed standardized measures. Working toward improved standardization of EHR use measures can enable and accelerate high-impact research on physician burnout and job satisfaction as well as organizational efficiency and patient health.

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“…The time burden of EHR documentation has been a well-studied contributor to physician burnout and decreased face-to-face communication with patients. 37 However, EHR tools and predictive models can be used to optimize documentation and practice efficiency. 38 Prior studies have shown that although predictive modeling of EHRs has advanced rapidly, research describing actual implementation of these predictive models in clinical settings is lacking.…”

Section: Discussionmentioning

confidence: 99%