Evidence map›Paper›PMID 30730293›Full record

ArticleJMIR medical informatics2019

Use of Electronic Health Record Access and Audit Logs to Identify Physician Actions Following Noninterruptive Alert Opening: Descriptive Study.

Azraa Amroze, Terry S Field, Hassan Fouayzi, Devi Sundaresan, Laura Burns, Lawrence Garber, Rajani S Sadasivam, Kathleen M Mazor, Jerry H Gurwitz, Sarah L Cutrona

Abstract read
In one paragraph

Article in JMIR medical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–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

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  12. Predicting next-day discharge via electronic health record access logs.Journal of the American Medical Informatics Association : JAMIA · 2021
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  15. Measures of electronic health record use in outpatient settings across vendors.Journal of the American Medical Informatics Association : JAMIA · 2021
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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

10 authors.

Azraa AmrozeMeyers Primary Care Institute, Worcester, MA, United States.ORCID http://orcid.org/0000-0002-0604-6363
Terry S FieldMeyers Primary Care Institute, Worcester, MA, United States.ORCID http://orcid.org/0000-0002-4990-8357
Hassan FouayziMeyers Primary Care Institute, Worcester, MA, United States.ORCID http://orcid.org/0000-0002-4624-0320
Devi SundaresanMeyers Primary Care Institute, Worcester, MA, United States.ORCID http://orcid.org/0000-0003-3597-5548
Laura BurnsUniversity of Massachusetts Memorial Health Care, Worcester, MA, United States.ORCID http://orcid.org/0000-0002-4161-053X
Lawrence GarberMeyers Primary Care Institute, Worcester, MA, United States.ORCID http://orcid.org/0000-0003-1500-5421
Rajani S SadasivamUniversity of Massachusetts Medical School, Worcester, MA, United States.ORCID http://orcid.org/0000-0001-8406-6207
Kathleen M MazorMeyers Primary Care Institute, Worcester, MA, United States.ORCID http://orcid.org/0000-0002-9491-9872
Jerry H GurwitzMeyers Primary Care Institute, Worcester, MA, United States.ORCID http://orcid.org/0000-0003-1630-8213
Sarah L CutronaMeyers Primary Care Institute, Worcester, MA, United States.ORCID http://orcid.org/0000-0002-4795-8377

Funding

Developing Smokers for Smoker (S4S): A Collective Intelligence tailoring systemK07CA172677 · NCI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI SADASIVAM, RAJANI · 2013 to 2017
$702k
AHRQ HHS R21 HS023661NCI NIH HHS K07 CA172677
6 · The paper itself

Abstract

backgroundElectronic 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.

objectiveThis 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.

methodsWe 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.

resultsWe 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.

conclusionsEHR 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.

Indexed as

electronic health recordshealth care communicationhealth information technologyhealth services research

Identifiers

PMID30730293
PMCPMC6383113

What OpenQuestion holds

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LicenceCC BY
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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.