Evidence map›Paper›PMID 42285047›Full record

ArticleJMIR nursing2026

Association Between Electronic Health Record-Based Nursing Workload and Turnover: Retrospective Cohort Study.

Linlin Xia, Daphne Lew, Lindsay Tessmer, Elise Eiden, Sunny Lou, Thomas Kannampallil

Abstract read
In one paragraph

Article in JMIR nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Linlin XiaDivision of Computational and Data Sciences, Washington University in St. Louis, St. Louis, MO, United States.ORCID 0000-0003-0391-5241
Daphne LewInstitute for Informatics, Data Science and Biostatistics, Washington University School of Medicine in St Louis, St Louis, MO, United States.ORCID 0000-0001-5433-2367
Lindsay TessmerBarnes-Jewish College, St. Louis, MO, United States.ORCID 0009-0000-8282-5644
Elise EidenDepartment of Anesthesiology, Washington University School of Medicine in St. Louis, Washington University in St. Louis, 660 South Euclid Avenue, Campus Box 8054, St. Louis, MO, United States, 1 314-273-7801.ORCID 0009-0001-4068-7631
Sunny LouDepartment of Anesthesiology, Washington University School of Medicine in St. Louis, Washington University in St. Louis, 660 South Euclid Avenue, Campus Box 8054, St. Louis, MO, United States, 1 314-273-7801.ORCID 0000-0002-4215-605X
Thomas KannampallilDivision of Computational and Data Sciences, Washington University in St. Louis, St. Louis, MO, United States.ORCID 0000-0003-4119-4836

Funding

Intelligent Clinical Decision Support for Perioperative Blood ManagementK23HL166880 · NHLBI · WASHINGTON UNIVERSITY · PI Sunny S. Lou · 2024 to 2026
$521k
NHLBI NIH HHS K23 HL166880
6 · The paper itself

Abstract

Background: Nurse turnover remains a major challenge for health systems, yet objective, scalable measures of workload that predict turnover are limited. Electronic health record (EHR) audit logs offer a potential data source to quantify nursing work patterns. Objective: This study aimed to evaluate the association between EHR-derived measures of nursing workload and turnover among inpatient nurses. Methods: We analyzed work-related activities of staff nurses from medical and surgical inpatient units at a large academic medical center from January 1, 2022, to December 31, 2022. Data included deidentified demographics (age, sex, years since licensure, and service group), shift characteristics (number of shifts worked, proportion of night shifts, timing, and location), and measures of work activities derived from EHR audit logs. Audit logs were used to develop measures related to nurse workload, including (1) nurse activities (information review, medication administration, alert management, navigation, documentation, and communication), (2) patient load (based on the number of unique patient charts accessed), and (3) cognitive load (based on the number of patient switches). For nurses who left, we excluded the 6 weeks immediately preceding termination (washout period) and measured workload during the preceding 6-week period; for those who remained, a random 6-week working period was selected. Associations between workload measures and turnover were assessed using mixed-effects logistic regression, adjusting for demographics and shift-related characteristics. Results: Among 432 nurses (n=363, 84% female; median age 27, IQR 23-36 years), contributing 6812 shifts and approximately 13 million audit log actions, 84 (19%) left the institution in 2022. A higher proportion of medication administration actions was associated with higher odds of turnover (odds ratio [OR] 2.20, 95% CI 1.36-3.54), whereas greater alert engagement (OR 0.48, 95% CI 0.32-0.72) and more years since licensure (OR 0.57, 95% CI 0.38-0.84) were associated with lower odds of turnover. Conclusions: EHR-derived workload measures, particularly greater medication administration burden and lower alert engagement, were independently associated with the risk of nurse turnover. Patterns of EHR use may help identify nurses at higher risk of leaving an institution and can potentially inform targeted workforce retention strategies.

Indexed as

Electronic Health RecordsPersonnel TurnoverWorkloadAdultCohort StudiesFemaleHumansMaleRetrospective Studiesclinical informaticselectronic health recordsnurse turnovernursing workloadworkforce retention

Identifiers

PMID42285047
PMCPMC13263017

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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.