ABSTRACT
The current study evaluates changes in access as a result of the MyVA Access program-a system-wide effort to improve patient access in the Veterans Health Administration. Data on 20 different measures were collected, and changes were analyzed using t tests and Chow tests. Additionally, organizational health-how able a system is to create health care practice change-was evaluated for a sample of medical centers (n = 36) via phone interviews and surveys conducted with facility staff and technical assistance providers. An organizational health variable was created and correlated with the access measures. Results showed that, nationally, average wait times for urgent consults, new patient wait times for mental health and specialty care, and slot utilization for primary and specialty care patients improved. Patient satisfaction measures also improved, and patient complaints decreased. Better organizational health was associated with improvements in patient access.
Subject(s)
Health Services Accessibility/organization & administration , Quality Improvement/organization & administration , United States Department of Veterans Affairs/organization & administration , Humans , Organizational Innovation , Patient Satisfaction/statistics & numerical data , Program Evaluation , Surveys and Questionnaires , United States , Waiting ListsABSTRACT
BACKGROUND: Missed appointments reduce the efficiency of the health care system and negatively impact access to care for all patients. Identifying patients at risk for missing an appointment could help health care systems and providers better target interventions to reduce patient no-shows. OBJECTIVES: Our aim was to develop and test a predictive model that identifies patients that have a high probability of missing their outpatient appointments. METHODS: Demographic information, appointment characteristics, and attendance history were drawn from the existing data sets from four Veterans Affairs health care facilities within six separate service areas. Past attendance behavior was modeled using an empirical Markov model based on up to 10 previous appointments. Using logistic regression, we developed 24 unique predictive models. We implemented the models and tested an intervention strategy using live reminder calls placed 24, 48, and 72 hours ahead of time. The pilot study targeted 1,754 high-risk patients, whose probability of missing an appointment was predicted to be at least 0.2. RESULTS: Our results indicate that three variables were consistently related to a patient's no-show probability in all 24 models: past attendance behavior, the age of the appointment, and having multiple appointments scheduled on that day. After the intervention was implemented, the no-show rate in the pilot group was reduced from the expected value of 35% to 12.16% (p value < 0.0001). CONCLUSIONS: The predictive model accurately identified patients who were more likely to miss their appointments. Applying the model in practice enables clinics to apply more intensive intervention measures to high-risk patients.
