Faculty Bibliography

OBJECTIVES/OBJECTIVE:Some of the most pressing health problems are found in rural America. However, the surveillance needed to track and prevent disease in these regions is lacking. Our objective was to perform a comprehensive health survey of a single rural county to assess the validity of using emergency claims data to estimate rural disease prevalence at a sub-county level. DESIGN/METHODS:We performed a cross-sectional study of chronic disease prevalence estimates using emergency department (ED) claims data versus mailed health surveys designed to capture a substantial proportion of residents in New York's rural Sullivan County. SETTING/METHODS:Sullivan County, a rural county ranked second-to-last for health outcomes in New York State. PARTICIPANTS/METHODS:Adult residents of Sullivan County aged 25 years and older who responded to the health survey in 2017-2018 or had at least one ED visit in 2011-2015. OUTCOME MEASURES/METHODS:We compared age and gender-adjusted prevalence of hypertension, hyperlipidaemia, diabetes, cancer, asthma and chronic obstructive pulmonary disease/emphysema among nine sub-county areas. RESULTS:Our county-wide mailed survey obtained 6675 completed responses for a response rate of 30.4%. This sample represented more than 12% of the estimated 53 020 adults in Sullivan County. Using emergency claims data, we identified 34 576 adults from Sullivan County who visited an ED at least once during 2011-2015. At a sub-county level, prevalence estimates from mailed surveys and emergency claims data correlated especially well for diabetes (r=0.90) and asthma (r=0.85). Other conditions were not well correlated (range: 0.23-0.46). Using emergency claims data, we created more geographically detailed maps of disease prevalence using geocoded addresses. CONCLUSIONS:For select conditions, emergency claims data may be useful for tracking disease prevalence in rural areas and providing more geographically detailed estimates. For rural regions lacking robust health surveillance, emergency claims data can inform how to geographically target efforts to prevent chronic disease.

INTRODUCTION/BACKGROUND:Although screening for diabetes is recommended at age 45, some populations may be at greater risk at earlier ages. Our objective was to quantify age disparities among patients with type 2 diabetes in New York City. METHODS:Using all-payer hospital claims data for New York City, we performed a cross-sectional analysis of patients with type 2 diabetes identified from emergency department visits during the 5-year period 2011-2015. We estimated type 2 diabetes prevalence at each year of life, the age distribution of patients stratified by decade, and the average age of patients by sex, race/ethnicity, and geographic location. RESULTS:We identified 576,306 unique patients with type 2 diabetes. These patients represented more than half of all people with type 2 diabetes in New York City. Patients in racial/ethnic minority groups were on average 5.5 to 8.4 years younger than non-Hispanic white patients. At age 45, type 2 diabetes prevalence was 10.9% among non-Hispanic black patients and 5.2% among non-Hispanic white patients. In our geospatial analyses, patients with type 2 diabetes were on average 6 years younger in hotspots of diabetes-related emergency department use and inpatient hospitalizations. The average age of patients with type 2 diabetes was also 1 to 2 years younger in hotspots of microvascular diabetic complications. CONCLUSION/CONCLUSIONS:We identified profound age disparities among patients with type 2 diabetes in racial/ethnic minority groups and in neighborhoods with poor health outcomes. The younger age of these patients may be due to earlier onset of diabetes and/or earlier death from diabetic complications. Our findings demonstrate the need for geographically targeted interventions that promote earlier diagnosis and better glycemic control.

Introduction/UNASSIGNED:Emergency department (ED) patient care often hinges on the result of a diagnostic test. Frequently there is a lag time between a test result becoming available for review and physician decision-making or disposition based on that result. We implemented a system that electronically alerts ED providers when test results are available for review via a smartphone- and smartwatch-push notification. We hypothesized this would reduce the time from result to clinical decision-making. Methods/UNASSIGNED:We retrospectively assessed the impact of the implementation of a push notification system at three EDs on time-to-disposition or time-to-follow-up order in six clinical scenarios of interest: chest radiograph (CXR) to disposition, basic metabolic panel (BMP) to disposition, urinalysis (UA) to disposition, respiratory pathogen panel (RPP) to disposition, hemoglobin (Hb) to blood transfusion order, and abnormal D-dimer to computed tomography pulmonary angiography (CTPA) order. All ED patients during a one-year period of push-notification availability were included in the study. The primary outcome was median time in each scenario from result availability to either disposition order or defined follow-up order. The secondary outcome was the overall usage rate of the opt-in push notification system by providers. Results/UNASSIGNED:During the study period there were 6115 push notifications from 4183 ED encounters (2.7% of all encounters). Of the six clinical scenarios examined in this study, five were associated with a decrease in median time from test result availability to patient disposition or follow-up order when push notifications were employed: CXR to disposition, 80 minutes (interquartile range [IQR] 32-162 minutes) vs 56 minutes (IQR 18-141 minutes), difference 24 minutes (p<0.01); BMP to disposition, 128 minutes (IQR 62-225 minutes) vs 116 minutes (IQR 33-226 minutes), difference 12 minutes (p<0.01); UA to disposition, 105 minutes (IQR 43-200 minutes) vs 55 minutes (IQR 16-144 minutes), difference 50 minutes (p<0.01); RPP to disposition, 80 minutes (IQR 28-181 minutes) vs 37 minutes (IQR 10-116 minutes), difference 43 minutes (p<0.01); and D-dimer to CTPA, 14 minutes (IQR 6-30 minutes) vs 6 minutes (IQR 2.5-17.5 minutes), difference 8 minutes (p<0.01). The sixth scenario, Hb to blood transfusion (difference 19 minutes, p=0.73), did not meet statistical significance. Conclusion/UNASSIGNED:Implementation of a push notification system for test result availability in the ED was associated with a decrease in lag time between test result and physician decision-making in the examined clinical scenarios. Push notifications were used in only a minority of ED patient encounters.

Background: Current guidelines for diabetes screening start at age 45, but disparities in certain subgroups exist and poor diabetic outcomes are known to cluster in specific neighborhoods. The objective of this study was to quantify disparities in the age distribution of patients with type 2 diabetes by sex, race/ethnicity, and geographic location. We also studied how patient age relates to diabetes-related hospital use and development of diabetic complications.
Method(s): Using all-payer hospital claims data, we performed a cross-sectional analysis of patients with type 2 diabetes. Our study included patients in New York City as identified by geocoded home address. Patients aged 10 to 100 years old were identified as having type 2 diabetes based on diagnosis codes from emergency claims data from 2011-2015. Our main measures included the estimated prevalence of type 2 diabetes at each year of life, the age distribution of patients as stratified by decade, and the comparison of patient age in geographic hotspots of frequent diabetes-related hospital use and diabetic complications.
Result(s): We identified 576,306 unique patients diagnosed with type 2 diabetes, which represented over half of all cases in New York City. Minority subgroups were on average 5.5 to 8.4 years younger than non-Hispanic White patients. Males with type 2 diabetes were 2.6 years younger than females. At 45 years of age, the estimated prevalence of type 2 diabetes was 10.9% among Black patients compared to 5.2% among White patients. In our geospatial analyses, patients with type 2 diabetes were on average 5.9 years younger in hotspots of diabetes-related emergency department use and inpatient hospitalizations. The average age of patients with type 2 diabetes was 1.5 to 2.2 years younger in hotspots of microvascular diabetic complications.
Conclusion(s): We identified profound disparities in the age of patients with type 2 diabetes among minorities and in neighborhoods with poor health outcomes. The younger age of these patients may be due to earlier onset of diabetes and/or earlier death from diabetes-related complications. Our findings demonstrate the need for geographically targeted interventions that promote earlier diagnosis and better glycemic control to reduce disparities in diabetes burden. [Figure Presented] Age Distribution of Patients with Type 2 Diabetes by Race and Ethnicity

Background: Emergency department (ED) care decisions often hinge on the result of a diagnostic test. Frequently there is a lag time between a test result becoming available for review, and physician decision-making based on that result. Push notifications to physician smartphones have demonstrated improvement in this lag time in chest pain patients, but have not been studied in other ED patients. We implemented a system by which ED providers can subscribe to electronic alerts when test results are available for review via a smartphone or smartwatch push notification, and hypothesized that this would reduce the time to make clinical decisions. Method(s): This was a retrospective, multicenter, observational study in three emergency departments of an urban health system. We assessed push notification impact on time to disposition or time to follow-up order in six clinical scenarios of interest: chest x-ray (CXR) to disposition, basic metabolic panel (BMP) to disposition, urinalysis (UA) to disposition, respiratory pathogen panel (RPP) to disposition, hemoglobin (Hb) to blood transfusion order, and D-dimer to computed tomography pulmonary angiography (CTPA) order. All adult ED patients during a one-year period of push notification availability were included in the study. The primary outcome was median time from result availability to disposition order or defined follow-up order. Median times with interquartile ranges were determined in each scenario and the Mann Whitney (Wilcoxon) test for unpaired data was used to determine statistical significance. Result(s): During the study period there were 6,115 push notifications from 4,183 eligible ED encounters (2.7% of all ED encounters). All six scenarios studied were associated with a decrease in median time from test result availability to patient disposition, or from test result availability to follow-up order, when push notifications were employed: CXR to disposition (24 minutes, p<0.01), BMP to disposition (12 minutes, p<0.01), UA to disposition (50 minutes, p<0.01), RPP to disposition (43 minutes, p<0.01), D-dimer to CTPA (8 minutes, p<0.01), Hb to blood transfusion (19 minutes, p=0.73). Conclusion(s): Implementation of a push notification system for test result availability in the ED was associated with a decrease in lag time between test result availability and physician decision-making

BACKGROUND:The assessment of clinical guideline adherence for the evaluation of pulmonary embolism (PE) via computed tomography pulmonary angiography (CTPA) currently requires either labor-intensive, retrospective chart review or prospective collection of PE risk scores at the time of CTPA order. The recording of clinical data in a structured manner in the electronic health record (EHR) may make it possible to automate the calculation of a patient's PE risk classification and determine whether the CTPA order was guideline concordant. OBJECTIVES/OBJECTIVE:The objective of this study was to measure the performance of automated, structured-data-only versions of the Wells and revised Geneva risk scores in emergency department encounters during which a CTPA was ordered. The hypothesis was that such an automated method would classify a patient's PE risk with high accuracy compared to manual chart review. METHODS:We developed automated, structured-data-only versions of the Wells and revised Geneva risk scores to classify 212 emergency department (ED) encounters during which a CTPA was performed as "PE Likely" or "PE Unlikely." We then combined these classifications with D-dimer ordering data to assess each encounter as guideline concordant or discordant. The accuracy of these automated classifications and assessments of guideline concordance were determined by comparing them to classifications and concordance based on the complete Wells and revised Geneva scores derived via abstractor manual chart review. RESULTS:The automatically derived Wells and revised Geneva risk classifications were 91.5% and 92% accurate compared to the manually determined classifications, respectively. There was no statistically significant difference between guideline adherence calculated by the automated scores as compared to manual chart review (Wells: 70.8 vs. 75%, p = 0.33 | Revised Geneva: 65.6 vs. 66%, p = 0.92). CONCLUSION/CONCLUSIONS:The Wells and revised Geneva score risk classifications can be approximated with high accuracy using automated extraction of structured EHR data elements in patients who received a CTPA. Combining these automated scores with D-dimer ordering data allows for the automated assessment of clinical guideline adherence for CTPA ordering in the emergency department, without the burden of manual chart review.

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.