No-Shows in Adult Urology Outpatient Clinics: Economic and Operational Implications.

INTRODUCTION: We analyzed trends and explored implications of no-show rates in adult urology from provider related characteristics at an academic program. METHODS: No-show rates were determined from electronic health records of appointments in adult urology at Duke University Medical Center and affiliated clinics between January 2014 and December 2016. t-Test, Wilcoxon rank sum and ANOVA were employed. RESULTS: Of 72,571 total appointments 13,219 (18.2%) were no-shows. The no-show rates per provider related characteristic were provider type (physician 22.1% vs advanced primary provider 34.0%), visit category (new 26.9% vs return 25.6% vs procedure 17.5%), faculty status (assistant 22.9% vs associate 21.9% vs professor 21.4%) and specialty (oncology 26.7% vs reconstructive 22.9% vs stones 25.4%). Average lead times of advanced primary practitioners and physicians were 47 and 62 days, respectively. There was a statistically significant difference in mean no-show rates by provider type (p <0.01) and new patient by provider type (p <0.01). However, there was no statistical difference in mean rates by specialty, faculty status, provider bump history, provider based visit types and average lead time. The potential loss in revenue from outpatient no-shows is at least $429,810 annually. CONCLUSIONS: Provider type and new patient visits by provider type have statistically different no-show rates. Missed appointments are costly and affect clinical efficiency, access to care and potentially patient outcomes. Given the shift toward value based care and future workforce changes, further investigations are needed to determine interventions to help reduce no-show rates. Models to predict and adjust clinics should be developed and deployed.

Designing risk prediction models for ambulatory no-shows across different specialties and clinics.

Objective: As available data increases, so does the opportunity to develop risk scores on more refined patient populations. In this paper we assessed the ability to derive a risk score for a patient no-showing to a clinic visit. Methods: Using data from 2 264 235 outpatient appointments we assessed the performance of models built across 14 different specialties and 55 clinics. We used regularized logistic regression models to fit and assess models built on the health system, specialty, and clinic levels. We evaluated fits based on their discrimination and calibration. Results: Overall, the results suggest that a relatively robust risk score for patient no-shows could be derived with an average C-statistic of 0.83 across clinic level models and strong calibration. Moreover, the clinic specific models, even with lower training set sizes, often performed better than the more general models. Examination of the individual models showed that risk factors had different degrees of predictability across the different specialties. Implementation of optimal modeling strategies would lead to capturing an additional 4819 no-shows per-year. Conclusion: Overall, this work highlights both the opportunity for and the importance of leveraging the available electronic health record data to develop more refined risk models.

Assessing electronic health record phenotypes against gold-standard diagnostic criteria for diabetes mellitus.

OBJECTIVE: We assessed the sensitivity and specificity of 8 electronic health record (EHR)-based phenotypes for diabetes mellitus against gold-standard American Diabetes Association (ADA) diagnostic criteria via chart review by clinical experts. MATERIALS AND METHODS: We identified EHR-based diabetes phenotype definitions that were developed for various purposes by a variety of users, including academic medical centers, Medicare, the New York City Health Department, and pharmacy benefit managers. We applied these definitions to a sample of 173 503 patients with records in the Duke Health System Enterprise Data Warehouse and at least 1 visit over a 5-year period (2007-2011). Of these patients, 22 679 (13%) met the criteria of 1 or more of the selected diabetes phenotype definitions. A statistically balanced sample of these patients was selected for chart review by clinical experts to determine the presence or absence of type 2 diabetes in the sample. RESULTS: The sensitivity (62-94%) and specificity (95-99%) of EHR-based type 2 diabetes phenotypes (compared with the gold standard ADA criteria via chart review) varied depending on the component criteria and timing of observations and measurements. DISCUSSION AND CONCLUSIONS: Researchers using EHR-based phenotype definitions should clearly specify the characteristics that comprise the definition, variations of ADA criteria, and how different phenotype definitions and components impact the patient populations retrieved and the intended application. Careful attention to phenotype definitions is critical if the promise of leveraging EHR data to improve individual and population health is to be fulfilled.

Assessing the distribution of safe routes to school program funds, 2005-2012.

BACKGROUND: The federal Safe Routes to School (SRTS) program was launched in 2005 to increase the safety of, and participation in, walking and biking to school. PURPOSE: This study assesses how SRTS funds were allocated to public and private schools and communities and whether there were demographic or locational differences between schools that benefited from SRTS funding and those that did not receive SRTS awards. METHODS: The study analyzes all SRTS projects awarded between 2005 and 2012 (N=5532) by using descriptive statistics to profile SRTS funding amounts and purposes, and to compare demographic and neighborhood characteristics of schools with and without SRTS programs. Analysis was conducted in 2013. RESULTS: The average SRTS award was $158,930 and most funding was spent on infrastructure (62.8%) or combined infrastructure and non-infrastructure (23.5%) projects. Schools benefiting from the SRTS program served higher proportions of Latino students and were more likely to be in higher-density areas. Few differences existed in neighborhood demographics, particularly educational attainment, work-trip commute mode, and median household income. CONCLUSIONS: Schools benefiting from the SRTS program are more urban and have higher Latino populations but are otherwise comparable to U.S. public schools. This suggests that disadvantaged areas have had access to the SRTS program.