Evidence map›Paper›PMID 31750912›Full record

SynthesisJournal of the American Medical Informatics Association : JAMIA2020

Using electronic health record audit logs to study clinical activity: a systematic review of aims, measures, and methods.

Adam Rule, Michael F Chiang, Michelle R Hribar

Abstract readSystematic Review
In one paragraph

Synthesis in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers.

0numbers the graph read from it
0cells of the map it votes in
80citing 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

80 citing papers in PubMed.

  1. Trial
  2. Article
  3. Observational
  4. Article
  5. Article
  6. Article
  7. Article
  8. Observational
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Observational
  15. Article
  16. Review
  17. Article
  18. Review
  19. Article
  20. Article

20 more citing papers are in PubMed but not listed here.

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

3 authors.

Adam RuleDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA.
Michael F ChiangDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA.
Michelle R HribarDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA.

Funding

TRAINING PROGRAM IN HEALTH INFORMATICST15LM007088 · NLM · OREGON HEALTH & SCIENCE UNIVERSITY · PI WILLIAM R HERSH, Vishnu Mohan · 1992 to 2026
$23.4M
Proteomics CoreP30EY010572 · NEI · OREGON HEALTH & SCIENCE UNIVERSITY · PI Kate E Keller · 1995 to 2026
$19.4M
Modeling and Optimization of Clinical Processes Using EHR DataR00LM012238 · NLM · OREGON HEALTH & SCIENCE UNIVERSITY · PI HRIBAR, MICHELLE · 2017 to 2019
$672k
NEI NIH HHS P30 EY010572NLM NIH HHS R00 LM012238NLM NIH HHS T15 LM007088
6 · The paper itself

Abstract

objectiveTo systematically review published literature and identify consistency and variation in the aims, measures, and methods of studies using electronic health record (EHR) audit logs to observe clinical activities. MATERIALS AND

methodsIn July 2019, we searched PubMed for articles using EHR audit logs to study clinical activities. We coded and clustered the aims, measures, and methods of each article into recurring categories. We likewise extracted and summarized the methods used to validate measures derived from audit logs and limitations discussed of using audit logs for research.

resultsEighty-five articles met inclusion criteria. Study aims included examining EHR use, care team dynamics, and clinical workflows. Studies employed 6 key audit log measures: counts of actions captured by audit logs (eg, problem list viewed), counts of higher-level activities imputed by researchers (eg, chart review), activity durations, activity sequences, activity clusters, and EHR user networks. Methods used to preprocess audit logs varied, including how authors filtered extraneous actions, mapped actions to higher-level activities, and interpreted repeated actions or gaps in activity. Nineteen studies validated results (22%), but only 9 (11%) through direct observation, demonstrating varying levels of measure accuracy. DISCUSSION: While originally designed to aid access control, EHR audit logs have been used to observe diverse clinical activities. However, most studies lack sufficient discussion of measure definition, calculation, and validation to support replication, comparison, and cross-study synthesis.

conclusionEHR audit logs have potential to scale observational research but the complexity of audit log measures necessitates greater methodological transparency and validated standards.

Indexed as

Electronic Health RecordsManagement AuditTask Performance and AnalysisWorkflowHealth Services ResearchHumansaudit logselectronic health recordssystematic reviewusabilityworkflow

Identifiers

PMID31750912
PMCPMC7025338

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.