Dataset Information

Structured clinical documentation in the electronic medical record to improve quality and to support practice-based research in epilepsy.

ABSTRACT: OBJECTIVE:Using the electronic medical record (EMR) to capture structured clinical data at the point of care would be a practical way to support quality improvement and practice-based research in epilepsy. METHODS:We describe our stepwise process for building structured clinical documentation support tools in the EMR that define best practices in epilepsy, and we describe how we incorporated these toolkits into our clinical workflow. RESULTS:These tools write notes and capture hundreds of fields of data including several score tests: Generalized Anxiety Disorder-7 items, Neurological Disorders Depression Inventory for Epilepsy, Epworth Sleepiness Scale, Quality of Life in Epilepsy-10 items, Montreal Cognitive Assessment/Short Test of Mental Status, and Medical Research Council Prognostic Index. The tools summarize brain imaging, blood laboratory, and electroencephalography results, and document neuromodulation treatments. The tools provide Best Practices Advisories and other clinical decision support when appropriate. The tools prompt enrollment in a DNA biobanking study. We have thus far enrolled 231 patients for initial visits and are starting our first annual follow-up visits and provide a brief description of our cohort. SIGNIFICANCE:We are sharing these EMR tools and captured data with other epilepsy clinics as part of a Neurology Practice Based Research Network, and are using the tools to conduct pragmatic trials using subgroup-based adaptive designs.

SUBMITTER: Narayanan J

PROVIDER: S-EPMC5245120 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Structured clinical documentation in the electronic medical record to improve quality and to support practice-based research in epilepsy.

Narayanan Jaishree J Dobrin Sofia S Choi Janet J Rubin Susan S Pham Anna A Patel Vimal V Frigerio Roberta R Maurer Darryck D Gupta Payal P Link Lourdes L Walters Shaun S Wang Chi C Ji Yuan Y Maraganore Demetrius M DM

Epilepsia 20161119 1

<h4>Objective</h4>Using the electronic medical record (EMR) to capture structured clinical data at the point of care would be a practical way to support quality improvement and practice-based research in epilepsy.<h4>Methods</h4>We describe our stepwise process for building structured clinical documentation support tools in the EMR that define best practices in epilepsy, and we describe how we incorporated these toolkits into our clinical workflow.<h4>Results</h4>These tools write notes and capt ...[more]

PMID: 27864833

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Project description:Background:Optimization 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. Methods:A 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. Results:There 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). Conclusions:The 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 Evidence:Level III. See Instructions for Authors for a complete description of levels of evidence.

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:BackgroundThe electronic medical record (EMR) contains a rich source of information that could be harnessed for epidemic surveillance. We asked if structured EMR data could be coupled with computerized processing of free-text clinical entries to enhance detection of acute respiratory infections (ARI).MethodologyA manual review of EMR records related to 15,377 outpatient visits uncovered 280 reference cases of ARI. We used logistic regression with backward elimination to determine which among candidate structured EMR parameters (diagnostic codes, vital signs and orders for tests, imaging and medications) contributed to the detection of those reference cases. We also developed a computerized free-text search to identify clinical notes documenting at least two non-negated ARI symptoms. We then used heuristics to build case-detection algorithms that best combined the retained structured EMR parameters with the results of the text analysis.Principal findingsAn adjusted grouping of diagnostic codes identified reference ARI patients with a sensitivity of 79%, a specificity of 96% and a positive predictive value (PPV) of 32%. Of the 21 additional structured clinical parameters considered, two contributed significantly to ARI detection: new prescriptions for cough remedies and elevations in body temperature to at least 38°C. Together with the diagnostic codes, these parameters increased detection sensitivity to 87%, but specificity and PPV declined to 95% and 25%, respectively. Adding text analysis increased sensitivity to 99%, but PPV dropped further to 14%. Algorithms that required satisfying both a query of structured EMR parameters as well as text analysis disclosed PPVs of 52-68% and retained sensitivities of 69-73%.ConclusionStructured EMR parameters and free-text analyses can be combined into algorithms that can detect ARI cases with new levels of sensitivity or precision. These results highlight potential paths by which repurposed EMR information could facilitate the discovery of epidemics before they cause mass casualties.

Dataset Information

Structured clinical documentation in the electronic medical record to improve quality and to support practice-based research in epilepsy.

Publications

Structured clinical documentation in the electronic medical record to improve quality and to support practice-based research in epilepsy.

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