Use of Electronic Health Record Access and Audit Logs to Identify Physician Actions Following Noninterruptive Alert Opening: Descriptive Study
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Abstract
BACKGROUND: Electronic health record (EHR) access and audit logs record behaviors of providers as they navigate the EHR. These data can be used to better understand provider responses to EHR-based clinical decision support (CDS), shedding light on whether and why CDS is effective. OBJECTIVE: This study aimed to determine the feasibility of using EHR access and audit logs to track primary care physicians' (PCPs') opening of and response to noninterruptive alerts delivered to EHR InBaskets. METHODS: We conducted a descriptive study to assess the use of EHR log data to track provider behavior. We analyzed data recorded following opening of 799 noninterruptive alerts sent to 75 PCPs' InBaskets through a prior randomized controlled trial. Three types of alerts highlighted new medication concerns for older patients' posthospital discharge: information only (n=593), medication recommendations (n=37), and test recommendations (n=169). We sought log data to identify the person opening the alert and the timing and type of PCPs' follow-up EHR actions (immediate vs by the end of the following day). We performed multivariate analyses examining associations between alert type, patient characteristics, provider characteristics, and contextual factors and likelihood of immediate or subsequent PCP action (general, medication-specific, or laboratory-specific actions). We describe challenges and strategies for log data use. RESULTS: We successfully identified the required data in EHR access and audit logs. More than three-quarters of alerts (78.5%, 627/799) were opened by the PCP to whom they were directed, allowing us to assess immediate PCP action; of these, 208 alerts were followed by immediate action. Expanding on our analyses to include alerts opened by staff or covering physicians, we found that an additional 330 of the 799 alerts demonstrated PCP action by the end of the following day. The remaining 261 alerts showed no PCP action. Compared to information-only alerts, the odds ratio (OR) of immediate action was 4.03 (95% CI 1.67-9.72) for medication-recommendation and 2.14 (95% CI 1.38-3.32) for test-recommendation alerts. Compared to information-only alerts, ORs of medication-specific action by end of the following day were significantly greater for medication recommendations (5.59; 95% CI 2.42-12.94) and test recommendations (1.71; 95% CI 1.09-2.68). We found a similar pattern for OR of laboratory-specific action. We encountered 2 main challenges: (1) Capturing a historical snapshot of EHR status (number of InBasket messages at time of alert delivery) required incorporation of data generated many months prior with longitudinal follow-up. (2) Accurately interpreting data elements required iterative work by a physician/data manager team taking action within the EHR and then examining audit logs to identify corresponding documentation. CONCLUSIONS: EHR log data could inform future efforts and provide valuable information during development and refinement of CDS interventions. To address challenges, use of these data should be planned before implementing an EHR-based study.
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Studies using raw audit logs fit into three broad categories: manual mapping, identification of networks, application of automatic methods.
“Studies using raw audit logs fit into three broad categories:”
From Electronic health record activity changes around new decision support implementation: monitoring using audit logs and topic modeling (2025) · cites this paper as [37]
The Methods and Results describe manually identifying and interpreting EHR audit-log entries with physician–data manager collaboration, which fits the claim’s manual-mapping category. The paper does not classify studies using raw audit logs into three broad categories or establish the other two categories. No page numbers are available in our copy. (LLM, conf=0.76)
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Cites
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“Using EHR audit logs to measure providers’ response to a specific EHR tool is challenging, typically involving manual mapping of low-level actions recorded in the log files to EHR use activities [39,50,53].”
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Self-citation
- Authors
- Amroze, A., Field, T. S., Fouayzi, H., Sundaresan, D., Burns, L., Garber, L., Sadasivam, R. S., Mazor, K. M., Gurwitz, J. H., & Cutrona, S. L.
- Affiliations
- University of Massachusetts Chan Medical School (US)
- UMass Memorial Medical Center (US)
- Corresponding author
- Amroze, Azraa
- ORCID
- Venue
- JMIR Medical Informatics
- Publisher
- JMIR Publications Inc.
- Published
- 2019-02-07
- Volume
- 7
- Issue
- 1
- Pages
- e12650
- ISSN
- 2291-9694 (ISSN-L)
- PMC
- PMC6383113
- OpenAlex
- W2911135505
- Cited by
- 25
- References
- 5
- Citations by year
- 2025: 3 · 2023: 5 · 2022: 4 · 2021: 6 · 2020: 5 · 2019: 2
- FWCI
- 4.64
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Medical Informatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- The Journal of the American Medical Association (JAMA) Network (American Medical Association) (submittedVersion, cc-by)
- Funders
- U.S. Department of Veterans Affairs
- National Institutes of Health
- Agency for Healthcare Research and Quality (R21HS023661)
- National Cancer Institute (K07CA172677)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
- Keywords
- electronic health records, audit logs, clinical decision support, primary care physicians, drug recommendation, elderly patients
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