Evidence map›Paper›PMID 34380167›Full record

ArticleApplied clinical informatics2021

Measuring Electronic Health Record Use in the Pediatric ICU Using Audit-Logs and Screen Recordings.

Amrita Sinha, Lindsay A Stevens, Felice Su, Natalie M Pageler, Daniel S Tawfik

Abstract read
In one paragraph

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 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
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

18 citing papers in PubMed.

  1. Article
  2. Mortality risk prediction from dense PICU data from patients with suspected infection: data-derived physiological trajectories outperform expert assessments when temporal resolution is high.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2026
    Article
  3. Observational
  4. Article
  5. Article
  6. Article
  7. Review
  8. Consistency is key: documentation distribution and efficiency in primary care.Journal of the American Medical Informatics Association : JAMIA · 2024
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Using electronic health record audit log data for research: insights from early efforts.Journal of the American Medical Informatics Association : JAMIA · 2022
    Article
  14. Article
  15. Review
  16. Article
  17. Predicting next-day discharge via electronic health record access logs.Journal of the American Medical Informatics Association : JAMIA · 2021
    Article
  18. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Amrita SinhaDivision of Pediatric Critical Care Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States.
Lindsay A StevensDivision of General Pediatrics, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States.
Felice SuDivision of Pediatric Critical Care Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States.
Natalie M PagelerDivision of Pediatric Critical Care Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States.
Daniel S TawfikDivision of Pediatric Critical Care Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States.

Funding

Development and Validation of a Prediction Model to Address Physician BurnoutK08HS027837 · AHRQ · STANFORD UNIVERSITY · PI TAWFIK, DANIEL · 2020 to 2024
$762k
AHRQ HHS K08 HS027837
6 · The paper itself

Abstract

backgroundTime spent in the electronic health record (EHR) has been identified as an important unit of measure for health care provider clinical activity. The lack of validation of audit-log based inpatient EHR time may have resulted in underuse of this data in studies focusing on inpatient patient outcomes, provider efficiency, provider satisfaction, etc. This has also led to a dearth of clinically relevant EHR usage metrics consistent with inpatient provider clinical activity.

objectiveThe aim of our study was to validate audit-log based EHR times using observed EHR-times extracted from screen recordings of EHR usage in the inpatient setting.

methodsThis study was conducted in a 36-bed pediatric intensive care unit (PICU) at Lucile Packard Children's Hospital Stanford between June 11 and July 14, 2020. Attending physicians, fellow physicians, hospitalists, and advanced practice providers with ≥0.5 full-time equivalent (FTE) for the prior four consecutive weeks and at least one EHR session recording were included in the study. Citrix session recording player was used to retrospectively review EHR session recordings that were captured as the provider interacted with the EHR.

resultsEHR use patterns varied by provider type. Audit-log based total EHR time correlated strongly with both observed total EHR time (

conclusionOur study found inpatient audit-log based EHR time to correlate strongly with observed EHR time among pediatric critical care providers. These findings support the use of audit-log based EHR-time as a surrogate measure for inpatient provider EHR use, providing an opportunity for researchers and other stakeholders to leverage EHR audit-log data in measuring clinical activity and tracking outcomes of workflow improvement efforts longitudinally and across provider groups.

Indexed as

Electronic Health RecordsPhysiciansChildHumansIntensive Care Units, PediatricRetrospective StudiesWorkflow

Identifiers

PMID34380167
PMCPMC8357459

What OpenQuestion holds

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Registered trials

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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.