Faculty Bibliography

OBJECTIVE:Focusing health interventions in places with suboptimal glycemic control can help direct resources to neighborhoods with poor diabetes-related outcomes, but finding these areas can be difficult. Our objective was to use indirect measures versus a gold standard, population-based A1C registry to identify areas of poor glycemic control. RESEARCH DESIGN AND METHODS/METHODS:Census tracts in New York City were characterized by race, ethnicity, income, poverty, education, diabetes-related emergency visits, inpatient hospitalizations, and proportion of adults with diabetes having poor glycemic control, based on A1C >9.0% (75 mmol/mol). Hot spot analyses were then performed, using the Getis-Ord Gi* statistic for all measures. We then calculated the sensitivity, specificity, positive and negative predictive values, and accuracy of using the indirect measures to identify hot spots of poor glycemic control found using the A1C Registry data. RESULTS:Using A1C Registry data, we identified hot spots in 42.8% of 2,085 NYC census tracts analyzed. Hot spots of diabetes-specific inpatient hospitalizations, diabetes-specific emergency visits, and age-adjusted diabetes prevalence estimated from emergency department data, respectively, had 88.9, 89.6, and 89.5% accuracy for identifying the same hot spots of poor glycemic control found using A1C Registry data. No other indirect measure tested had accuracy >80% except for the proportion of minority residents, which was 86.2%. CONCLUSIONS:Compared with demographic and socioeconomic factors, health care utilization measures more accurately identified hot spots of poor glycemic control. In places without a population-based A1C Registry, mapping diabetes-specific health care utilization may provide actionable evidence for targeting health interventions in areas with the highest burden of uncontrolled diabetes.

Given the inequalities in the distribution of disease burden, geographically detailed methods of disease surveillance are needed to identify local hot spots of chronic disease. However, few data sources include the patient-level addresses needed to perform these studies. Given that individual hospitals would have access to this geographically granular data, this study assessed the reliability of estimating chronic disease prevalence using emergency department surveillance at specific hospitals. Neighborhood-level diabetes, hypertension, and asthma prevalence were estimated using emergency claims data from each individual hospital in New York City from 2009-2012. Estimates were compared to prevalence obtained from a traditional health survey. A multivariable analysis also was performed to identify which individual hospitals were more accurate at estimating citywide disease prevalence. Among 52 hospitals, variation was found in the accuracy of disease prevalence estimates using emergency department surveillance. Estimates at some hospitals, such as NYU Langone Medical Center, had strong correlations for all diseases studied (diabetes: 0.81, hypertension: 0.84, and asthma: 0.84). Hospitals with patient populations geographically distributed throughout New York City had better accuracy in estimating citywide disease prevalence. For diabetes and hypertension, hospitals with racial/ethnic patient distributions similar to Census estimates and higher fidelity of diagnosis coding also had more accurate prevalence estimates. This study demonstrated how citywide chronic disease surveillance can be performed using emergency data from specific sentinel hospitals. The findings may provide an alternative means of mapping chronic disease burden by using existing data, which may be critical in regions without resources for geographically detailed health surveillance.

BACKGROUND: Testing for venous thromboembolism (VTE) is associated with cost and risk to patients (e.g. radiation). To assess the appropriateness of imaging utilization at the provider level, it is important to know that provider's diagnostic yield (percentage of tests positive for the diagnostic entity of interest). However, determining diagnostic yield typically requires either time-consuming, manual review of radiology reports or the use of complex and/or proprietary natural language processing software. OBJECTIVES: The objectives of this study were twofold: 1) to develop and implement a simple, user-configurable, and open-source natural language processing tool to classify radiology reports with high accuracy and 2) to use the results of the tool to design a provider-specific VTE imaging dashboard, consisting of both utilization rate and diagnostic yield. METHODS: Two physicians reviewed a training set of 400 lower extremity ultrasound (UTZ) and computed tomography pulmonary angiogram (CTPA) reports to understand the language used in VTE-positive and VTE-negative reports. The insights from this review informed the arguments to the five modifiable parameters of the NLP tool. A validation set of 2,000 studies was then independently classified by the reviewers and by the tool; the classifications were compared and the performance of the tool was calculated. RESULTS: The tool was highly accurate in classifying the presence and absence of VTE for both the UTZ (sensitivity 95.7%; 95% CI 91.5-99.8, specificity 100%; 95% CI 100-100) and CTPA reports (sensitivity 97.1%; 95% CI 94.3-99.9, specificity 98.6%; 95% CI 97.8-99.4). The diagnostic yield was then calculated at the individual provider level and the imaging dashboard was created. CONCLUSIONS: We have created a novel NLP tool designed for users without a background in computer programming, which has been used to classify venous thromboembolism reports with a high degree of accuracy. The tool is open-source and available for download at http://iturrate.com/simpleNLP. Results obtained using this tool can be applied to enhance quality by presenting information about utilization and yield to providers via an imaging dashboard.

Background: Although bedside ultrasound (BUS) competency is required for emergency medicine (EM) residents and BUS is an integral part of EM clinical practice, few opportunities exist for medical students to receive formal BUS instruction. Objectives: We developed a BUS simulation-based curriculum for rotating EM medical students consisting of web-based didactics and a hands-on skills session. We hypothesized that the curriculum would adequately prepare students to perform two common EM procedures: a Focused Assessment for Sonography in Trauma (FAST) exam and placement of ultrasound-guided internal jugular central venous access (IJ CVA). Methods: Forty-five medical students (16 2nd yr, 21 3rd yr, 8 4th yr) on an EM rotation were enrolled. Participants viewed three instructional web-based videos about BUS physics, the FAST exam, and BUS-guided IJ CVA. Subsequently, participants attended a 3-hour hands-on BUS simulation-based training session led by a BUS expert, an EM attending physician with > 7 years of BUS experience and > 3,000 completed BUS scans. After the initial training session, the BUS expert observed participants' FAST exams on a live volunteer, while a trained research assistant evaluated participants' IJ CVA skills on an instructional mannequin. Standardized checklists were used for both assessments. A passing score of 70% on each checklist was chosen prior to study initiation. Results: 89.0% (40/45) of participants passed the FAST and 96.0% (43/45) passed the IJ CVA skills assessments. Participants were successful in obtaining most required FAST views, yielding a right upper quadrant mean score of 90.6%, left upper quadrant score of 88.3%, bladder view score of 97.2%, and lung sliding score of 90.6%, but had the most difficulty with the cardiac view (72.2%). 84% (38/45) of participants placed successful IJ CVA within three attempts, with 64.4% (29/45) achieving success on the first attempt. 91% (41/45) avoided inadvertent puncture of the carotid artery. Conclusion: A standardized c!