Accuracy of Physician Electronic Health Record Usage Analytics using Clinical Test Cases.
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SUBMITTER: Lo B
PROVIDER: S-EPMC9534596 | biostudies-literature | 2022 Oct
REPOSITORIES: biostudies-literature
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Accuracy of Physician Electronic Health Record Usage Analytics using Clinical Test Cases.
Lo Brian B Sequeira Lydia L Strudwick Gillian G Jankowicz Damian D Almilaji Khaled K Karunaithas Anjchuca A Hang Dennis D Tajirian Tania T
Applied clinical informatics 20221005 5
Usage log data are an important data source for characterizing the potential burden related to use of the electronic health record (EHR) system. However, the utility of this data source has been hindered by concerns related to the real-world validity and accuracy of the data. While time-motion studies have historically been used to address this concern, the restrictions caused by the pandemic have made it difficult to carry out these studies in-person. In this regard, we introduce a practical ap ...[more]
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Project description:ObjectivesTo create test collections for evaluating clinical information retrieval (IR) systems and advancing clinical IR research.Materials and methodsElectronic health record (EHR) data, including structured and free-text data, from 45 000 patients who are a part of the Mayo Clinic Biobank cohort was retrieved from the clinical data warehouse. The clinical IR system indexed a total of 42 million free-text EHR documents. The search queries consisted of 56 topics developed through a collaboration between Mayo Clinic and Oregon Health & Science University. We described the creation of test collections, including a to-be-evaluated document pool using five retrieval models, and human assessment guidelines. We analyzed the relevance judgment results in terms of human agreement and time spent, and results of three levels of relevance, and reported performance of five retrieval models.ResultsThe two judges had a moderate overall agreement with a Kappa value of 0.49, spent a consistent amount of time judging the relevance, and were able to identify easy and difficult topics. The conventional retrieval model performed best on most topics while a concept-based retrieval model had better performance on the topics requiring conceptual level retrieval.DiscussionIR can provide an alternate approach to leveraging clinical narratives for patient information discovery as it is less dependent on semantics. Our study showed the feasibility of test collections along with a few challenges.ConclusionThe conventional test collections for evaluating the IR system show potential for successfully evaluating clinical IR systems with a few challenges to be investigated.
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Project description:BackgroundCollaboration is vital within health care institutions, and it allows for the effective use of collective health care worker (HCW) expertise. Human-computer interactions involving electronic health records (EHRs) have become pervasive and act as an avenue for quantifying these collaborations using statistical and network analysis methods.ObjectiveWe aimed to measure HCW collaboration and its characteristics by analyzing concurrent EHR usage.MethodsBy extracting concurrent EHR usage events from audit log data, we defined concurrent sessions. For each HCW, we established a metric called concurrent intensity, which was the proportion of EHR activities in concurrent sessions over all EHR activities. Statistical models were used to test the differences in the concurrent intensity between HCWs. For each patient visit, starting from admission to discharge, we measured concurrent EHR usage across all HCWs, which we called temporal patterns. Again, we applied statistical models to test the differences in temporal patterns of the admission, discharge, and intermediate days of hospital stay between weekdays and weekends. Network analysis was leveraged to measure collaborative relationships among HCWs. We surveyed experts to determine if they could distinguish collaborative relationships between high and low likelihood categories derived from concurrent EHR usage. Clustering was used to aggregate concurrent activities to describe concurrent sessions. We gathered 4 months of EHR audit log data from a large academic medical center's neonatal intensive care unit (NICU) to validate the effectiveness of our framework.ResultsThere was a significant difference (P<.001) in the concurrent intensity (proportion of concurrent activities: ranging from mean 0.07, 95% CI 0.06-0.08, to mean 0.36, 95% CI 0.18-0.54; proportion of time spent on concurrent activities: ranging from mean 0.32, 95% CI 0.20-0.44, to mean 0.76, 95% CI 0.51-1.00) between the top 13 HCW specialties who had the largest amount of time spent in EHRs. Temporal patterns between weekday and weekend periods were significantly different on admission (number of concurrent intervals per hour: 11.60 vs 0.54; P<.001) and discharge days (4.72 vs 1.54; P<.001), but not during intermediate days of hospital stay. Neonatal nurses, fellows, frontline providers, neonatologists, consultants, respiratory therapists, and ancillary and support staff had collaborative relationships. NICU professionals could distinguish high likelihood collaborative relationships from low ones at significant rates (3.54, 95% CI 3.31-4.37 vs 2.64, 95% CI 2.46-3.29; P<.001). We identified 50 clusters of concurrent activities. Over 87% of concurrent sessions could be described by a single cluster, with the remaining 13% of sessions comprising multiple clusters.ConclusionsLeveraging concurrent EHR usage workflow through audit logs to analyze HCW collaboration may improve our understanding of collaborative patient care. HCW collaboration using EHRs could potentially influence the quality of patient care, discharge timeliness, and clinician workload, stress, or burnout.
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Project description:BackgroundPhysician burnout has a direct impact on the delivery of high-quality health care, with health information technology tools such as electronic health records (EHRs) adding to the burden of practice inefficiencies.ObjectiveThe aim of this study was to determine the extent of burnout among physicians and learners (residents and fellows); identify significant EHR-related contributors of physician burnout; and explore the differences between physicians and learners with regard to EHR-related factors such as time spent in EHR, documentation styles, proficiency, training, and perceived usefulness. In addition, the study aimed to address gaps in the EHR-related burnout research methodologies by determining physicians' patterns of EHR use through usage logs.MethodsThis study used a cross-sectional survey methodology and a review of administrative data for back-end log measures of survey respondents' EHR use, which was conducted at a large Canadian academic mental health hospital. Chi-square and Fisher exact tests were used to examine the association of EHR-related factors with general physician burnout. The survey was sent out to 474 individuals between May and June 2019, including physicians (n=407), residents (n=53), and fellows (n=14), and we measured physician burnout and perceptions of EHR stressors (along with demographic and practice characteristics).ResultsOur survey included 208 respondents, including physicians (n=176) and learners (n=32). The response rate was 43.2% for physicians (full-time: 156/208, 75.0%; part-time: 20/199, 10.1%), and 48% (32/67) for learners. A total of 25.6% (45/176) of practicing physicians and 19% (6/32) of learners reported having one or more symptoms of burnout, and 74.5% (155/208) of all respondents who reported burnout symptoms identified the EHR as a contributor. Lower satisfaction and higher frustration with the EHRs were significantly associated with perceptions of EHR contributing toward burnout. Physicians' and learners' experiences with the EHR, gathered through open-ended survey responses, identified challenges around the intuitiveness and usability of the technology as well as workflow issues. Metrics gathered from back-end usage logs demonstrated a 13.6-min overestimation in time spent on EHRs per patient and a 5.63-hour overestimation of after-hours EHR time, when compared with self-reported survey data.ConclusionsThis study suggests that the use of EHRs is a perceived contributor to physician burnout. There should be a focus on combating physician burnout by reducing the unnecessary administrative burdens of EHRs through efficient implementation of systems and effective postimplementation strategies.
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