Evidence map›Paper›PMID 29049512›Full record

ArticleJAMA ophthalmology2017

Time Requirements for Electronic Health Record Use in an Academic Ophthalmology Center.

Sarah Read-Brown, Michelle R Hribar, Leah G Reznick, Lorinna H Lombardi, Mansi Parikh, Winston D Chamberlain, Steven T Bailey, Jessica B Wallace, Thomas R Yackel, Michael F Chiang

Open access · bronzeAbstract read
In one paragraph

Article in JAMA ophthalmology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 2 pooled it
23.1field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

48 citing papers in PubMed, 2 syntheses or guidelines pooled it, 85 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Observational
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  12. National survey of user-reported usability of electronic medical record software in ophthalmology in Germany.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2023
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors at 1 institution in 1 country.

Sarah Read-BrownDepartment of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland.
Michelle R HribarDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland.
Leah G ReznickDepartment of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland.
Lorinna H LombardiDepartment of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland.
Mansi ParikhDepartment of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland.
Winston D ChamberlainDepartment of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland.
Steven T BaileyDepartment of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland.
Jessica B WallaceDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland.
Thomas R YackelDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland.
Michael F ChiangDepartment of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland.
Oregon Health & Science University · US

Funding

Modeling and Optimization of Clinical Processes Using EHR DataR00LM012238 · NLM · OREGON HEALTH & SCIENCE UNIVERSITY · PI HRIBAR, MICHELLE · 2017 to 2019
$672k
Modeling and Optimization of Clinical Processes Using EHR DataK99LM012238 · NLM · OREGON HEALTH & SCIENCE UNIVERSITY · PI HRIBAR, MICHELLE · 2015 to 2016
$178k
NLM NIH HHS K99 LM012238NLM NIH HHS R00 LM012238
6 · The paper itself

Abstract

Importance: Electronic health record (EHR) systems have transformed the practice of medicine. However, physicians have raised concerns that EHR time requirements have negatively affected their productivity. Meanwhile, evolving approaches toward physician reimbursement will require additional documentation to measure quality and cost of care. To date, little quantitative analysis has rigorously studied these topics. Objective: To examine ophthalmologist time requirements for EHR use. Design, Setting, and Participants: A single-center cohort study was conducted between September 1, 2013, and December 31, 2016, among 27 stable departmental ophthalmologists (defined as attending ophthalmologists who worked at the study institution for ≥6 months before and after the study period). Ophthalmologists who did not have a standard clinical practice or who did not use the EHR were excluded. Exposures: Time stamps from the medical record and EHR audit log were analyzed to measure the length of time required by ophthalmologists for EHR use. Ophthalmologists underwent manual time-motion observation to measure the length of time spent directly with patients on the following 3 activities: EHR use, conversation, and examination. Main Outcomes and Measures: The study outcomes were time spent by ophthalmologists directly with patients on EHR use, conversation, and examination as well as total time required by ophthalmologists for EHR use. Results: Among the 27 ophthalmologists in this study (10 women and 17 men; mean [SD] age, 47.3 [10.7] years [median, 44; range, 34-73 years]) the mean (SD) total ophthalmologist examination time was 11.2 (6.3) minutes per patient, of which 3.0 (1.8) minutes (27% of the examination time) were spent on EHR use, 4.7 (4.2) minutes (42%) on conversation, and 3.5 (2.3) minutes (31%) on examination. Mean (SD) total ophthalmologist time spent using the EHR was 10.8 (5.0) minutes per encounter (range, 5.8-28.6 minutes). The typical ophthalmologist spent 3.7 hours using the EHR for a full day of clinic: 2.1 hours during examinations and 1.6 hours outside the clinic session. Linear mixed effects models showed a positive association between EHR use and billing level and a negative association between EHR use per encounter and clinic volume. Each additional encounter per clinic was associated with a decrease of 1.7 minutes (95% CI, -4.3 to 1.0) of EHR use time per encounter for ophthalmologists with high mean billing levels (adjusted R2 = 0.42; P = .01). Conclusions and Relevance: Ophthalmologists have limited time with patients during office visits, and EHR use requires a substantial portion of that time. There is variability in EHR use patterns among ophthalmologists.

Indexed as

Academic Medical CentersAdultAgedEfficiency, OrganizationalElectronic Health RecordsFemaleHumansMaleMiddle AgedOffice VisitsOphthalmologistsOphthalmologyOregonRetrospective StudiesTime Factors

Identifiers

PMID29049512
PMCPMC5710390
OpenAlexW2766982135

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.