ArticleApplied clinical informatics2021
Using Log Data to Measure Provider EHR Activity at a Cancer Center during Rapid Telemedicine Deployment.
Article in Applied clinical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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.
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Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- Using event logs to observe interactions with electronic health records: an updated scoping review shows increasing use of vendor-derived measures.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Accuracy of Physician Electronic Health Record Usage Analytics using Clinical Test Cases.Applied clinical informatics · 2022Article
- Physician Electronic Health Record Usage as Affected by the COVID-19 Pandemic.Applied clinical informatics · 2022Article
- Measuring and Maximizing Undivided Attention in the Context of Electronic Health Records.Applied clinical informatics · 2022Article
- Consequences of Rapid Telehealth Expansion in Nursing Homes: Promise and Pitfalls.Applied clinical informatics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesAccurate metrics of provider activity within the electronic health record (EHR) are critical to understand workflow efficiency and target optimization initiatives. We utilized newly described, log-based core metrics at a tertiary cancer center during rapid escalation of telemedicine secondary to initial coronavirus disease-2019 (COVID-19) peak onset of social distancing restrictions at our medical center (COVID-19 peak). These metrics evaluate the impact on total EHR time, work outside of work, time on documentation, time on prescriptions, inbox time, teamwork for orders, and undivided attention patients receive during an encounter. Our study aims were to evaluate feasibility of implementing these metrics as an efficient tool to optimize provider workflow and to track impact on workflow to various provider groups, including physicians, advanced practice providers (APPs), and different medical divisions, during times of significant policy change in the treatment landscape.
methodsData compilation and analysis was retrospectively performed in Tableau utilizing user and schedule data obtained from Cerner Millennium PowerChart and our internal scheduling software. We analyzed three distinct time periods: the 3 months prior to the initial COVID-19 peak, the 3 months during peak, and 3 months immediately post-peak.
resultsApplication of early COVID-19 restrictions led to a significant increase of telemedicine encounters from baseline <1% up to 29.2% of all patient encounters. During initial peak period, there was a significant increase in total EHR time, work outside of work, time on documentation, and inbox time for providers. Overall APPs spent significantly more time in the EHR compared with physicians. All of the metrics returned to near baseline after the initial COVID-19 peak in our area.
conclusionOur analysis showed that implementation of these core metrics is both feasible and can provide an accurate representation of provider EHR workflow adjustments during periods of change, while providing a basis for cross-vendor and cross-institutional analysis.
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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.