Dataset Information

Electronic Medical Record Context Signatures Improve Diagnostic Classification Using Medical Image Computing.

ABSTRACT: Composite models that combine medical imaging with electronic medical records (EMR) improve predictive power when compared to traditional models that use imaging alone. The digitization of EMR provides potential access to a wealth of medical information, but presents new challenges in algorithm design and inference. Previous studies, such as Phenome Wide Association Study (PheWAS), have shown that EMR data can be used to investigate the relationship between genotypes and clinical conditions. Here, we introduce Phenome-Disease Association Study to extend the statistical capabilities of the PheWAS software through a custom Python package, which creates diagnostic EMR signatures to capture system-wide co-morbidities for a disease population within a given time interval. We investigate the effect of integrating these EMR signatures with radiological data to improve diagnostic classification in disease domains known to have confounding factors because of variable and complex clinical presentation. Specifically, we focus on two studies: First, a study of four major optic nerve related conditions; and second, a study of diabetes. Addition of EMR signature vectors to radiologically derived structural metrics improves the area under the curve (AUC) for diagnostic classification using elastic net regression, for diseases of the optic nerve. For glaucoma, the AUC improves from 0.71 to 0.83, for intrinsic optic nerve disease it increases from 0.72 to 0.91, for optic nerve edema it increases from 0.95 to 0.96, and for thyroid eye disease from 0.79 to 0.89. The EMR signatures recapitulate known comorbidities with diabetes, such as abnormal glucose, but do not significantly modulate image-derived features. In summary, EMR signatures present a scalable and readily applicable.

SUBMITTER: Chaganti S

PROVIDER: S-EPMC6844192 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Electronic Medical Record Context Signatures Improve Diagnostic Classification Using Medical Image Computing.

Chaganti Shikha S Mawn Louise A LA Kang Hakmook H Egan Josephine J Resnick Susan M SM Beason-Held Lori L LL Landman Bennett A BA Lasko Thomas A TA

IEEE journal of biomedical and health informatics 20181228 5

Composite models that combine medical imaging with electronic medical records (EMR) improve predictive power when compared to traditional models that use imaging alone. The digitization of EMR provides potential access to a wealth of medical information, but presents new challenges in algorithm design and inference. Previous studies, such as Phenome Wide Association Study (PheWAS), have shown that EMR data can be used to investigate the relationship between genotypes and clinical conditions. Her ...[more]

PMID: 30602428

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Project description:Background Only 50% of eligible atrial fibrillation ( AF ) patients receive anticoagulation ( AC ). Feasibility and effectiveness of electronic medical record (EMR)-based interventions to profile and raise provider AC percentage is poorly understood. The SUPPORT-AF (Supporting Use of AC Through Provider Profiling of Oral AC Therapy for AF) study aims to improve rates of adherence to AC guidelines by developing and delivering supportive tools based on the EMR to providers treating patients with AF. Methods and Results We emailed cardiologists and community-based primary care providers affiliated with our institution reports of their AC percentage relative to peers. We also sent an electronic medical record-based message to these providers the day before an appointment with an atrial fibrillation patient who was eligible but not receiving AC . The electronic medical record message asked the provider to discuss AC with the patient if he or she deemed it appropriate. To assess feasibility, we tracked provider review of our correspondence. We also tracked the change in AC for intervention providers relative to alternate primary care providers not receiving our intervention. We identified 3786, 1054, and 566 patients cared for by 49 cardiology providers, 90 community-based primary care providers, and 88 control providers, respectively. At baseline, the percentage of AC was 71.3%, 63.5%, and 58.3% for these 3 respective groups. Intervention providers reviewed our e-mails and electronic medical record messages 45% and 96% of the time, respectively. For providers responding, patient refusal was the most common reason for patients not being on AC (21%) followed by high bleeding risk (19%). At follow-up 10 weeks later, change in AC was no different for either cardiology or community-based primary care providers relative to controls (0.2% lower and 0.01% higher, respectively). Conclusions Our intervention profiling AC was feasible, but not sufficient to increase AC in our population.

Project description:BackgroundOptimization of the electronic medical record (EMR) is essential to support the clinician and to improve the quality and efficiency of patient care. The present report describes the development and implementation of a standardized template that is embedded in the EMR and is focused on a comprehensive physical examination during the evaluation of pediatric supracondylar humeral fractures. We compared the completeness of physical examinations as well as the timing of detection and documentation of neurovascular injuries before and after implementation of the template. We hypothesized that the use of a template would increase the completeness of examinations and would lead to earlier documentation of neurovascular injuries.MethodsA multidisciplinary quality-improvement task force was created to address neurovascular documentation practices for patients who underwent operative treatment of supracondylar humeral fractures. Following a series of formative and process evaluations, a standardized EMR template was implemented. Neurovascular examination documentation practices that were in use before (pre-template group, n = 224) and after (template group, n = 300) the implementation of the template were compared. Logistic regression analyses of the 2 groups were used to compare the likelihood of a complete neurovascular examination and the timing of neurovascular injury identification.ResultsThere was significant improvement in the documentation of the vascular (odds ratio [OR], 70.7; 95% confidence interval [CI], 39.5 to 126.6; p < 0.0001), motor (OR, 17.6; 95% CI, 9.5 to 32.7; p < 0.0001), and sensory (OR, 23.9; 95% CI, 12.9 to 44.4; p < 0.0001) examinations in the template group. Neurological injuries were more likely to be identified preoperatively in the template group compared with the pre-template group (OR, 6.8; 95% CI, 1.7 to 27.1; p = 0.0067).ConclusionsThe incorporation of a standardized template in the EMR improved the completeness and timing of documentation of neurological injury. Standardized EMR templates developed by a clinically driven multidisciplinary task force have the potential to improve the quality of clinical documentation and to ease communication among providers.Level of evidenceLevel III. See Instructions for Authors for a complete description of levels of evidence.

Dataset Information

Electronic Medical Record Context Signatures Improve Diagnostic Classification Using Medical Image Computing.

Publications

Electronic Medical Record Context Signatures Improve Diagnostic Classification Using Medical Image Computing.

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