Journal Article

Nudging New York: adaptive models and the limits of behavioral interventions to reduce no-shows and health inequalities

Ruggeri, Kai, Verra, Sanne, Yee, Susan, Steinbeck, Viktoria, Folke, Tomas, Benzerga, Amel, Büttner, Clara, Chaiyachati, Krisda

2020

Overview

Background Missed healthcare appointments (no-shows) are costly and operationally inefficient for health systems. No-show rates are particularly high for vulnerable populations, even though these populations often require additional care. Few studies on no-show behavior or potential interventions exist specifically for Federally Qualified Health Centers (FQHCs), which care for over 24 million disadvantaged individuals in the United States. The purpose of this study is to identify predictors of no-show behavior and to analyze the effects of a reminder intervention in urban FQHCs in order to design effective policy solutions to a protracted issue in healthcare. Methods This is a retrospective observational study using electronic medical record data from 11 facilities belonging to a New York City-based FQHC network between June 2017 to April 2018. This data includes 53,149 visits for 41,495 unique patients. Seven hierarchical generalized linear models and generalized additive models were used to predict no-shows, and multiple regression models evaluated the effectiveness of a reminder. All analyses were conducted in R. Results The strongest predictor of no-show rates in FQHCs is whether or not patients are assigned to empaneled providers (z = − 91.45, p  < 10 − 10 ), followed by lead time for appointments (z = 23.87, p < 10 − 10 ). These effects were fairly stable across facilities. The reminder had minimal effects on no-show rates overall (No show rate before: 41.6%, after: 42.1%). For individuals with appointments before and after the reminder, there was a small decrease in no-shows of 2%. Conclusions The limited effects of the reminder intervention suggest the need for more personalized behavioral interventions to reduce no-shows. We recommend that these begin with increasing the use of empaneled providers for preventive care appointments and reducing the lag time between setting the appointment and the actual date of the appointment, at least for individuals with a high rate of no-show. By complementing these with low-intensity, low-cost behavioral interventions, we would expect greater impacts for improved access to care, contributing to the well-being of vulnerable populations.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Accuracy / Analysis / Appointments and Schedules / At risk populations / Behavior modification / Economic inequality / Electronic Health Records / Female / FQHC / Generalized linear models / Health Administration / Health care access / Health facilities / Health Facilities - statistics & numerical data / Health inequality / Health Informatics / Health services / Health Status Disparities / Healthcare / Humans / Indigent care / Intervention / Linear Models / Low income groups / Low-income populations / Male / Medical appointments / Medical records / Medical research / Medicine / Medicine & Public Health / New York City / No-shows / Nursing Research / Organization / Patient compliance / Patient Compliance - psychology / Patient Compliance - statistics & numerical data / Patients / Population / Preventive medicine / Public Health / Reminder Systems / Research Article / Retrospective Studies / structure and delivery of healthcare / Vulnerable Populations / Well being