Modeling Patient No-Show History and Predicting Future Appointment Behavior at the Veterans Administration's Outpatient Mental Health Clinics: NIRMO-2.

INTRODUCTION: No-shows are detrimental to both patients' health and health care systems. Literature documents no-show rates ranging from 10% in primary care clinics to over 60% in mental health clinics. Our model predicts the probability that a mental health clinic outpatient appointment will not be completed and identifies actionable variables associated with lowering the probability of no-show. MATERIALS AND METHODS: We were granted access to de-identified administrative data from the Veterans Administration Corporate Data Warehouse related to appointments at 13 Veterans Administration Medical Centers. Our modeling data set included 1,206,271 unique appointment records scheduled to occur between January 1, 2013 and February 28, 2017. The training set included 846,668 appointment records scheduled between January 1, 2013 and December 31, 2015. The testing set included 359,603 appointment records scheduled between January 1, 2016 and February 28, 2017. The dependent binary variable was whether the appointment was completed or not. Independent variables were categorized into seven clusters: patient's demographics, appointment characteristics, patient's attendance history, alcohol use screening score, medications and medication possession ratios, prior diagnoses, and past utilization of Veterans Health Administration services. We used a forward stepwise selection, based on the likelihood ratio, to choose the variables in the model. The predictive model was built using the SAS HPLOGISTIC procedure. RESULTS: The best indicator of whether someone will miss an appointment is their historical attendance behavior. The top three variables associated with higher probabilities of a no-show were: the no-show rate over the previous 2 years before the current appointment, the no-show probability derived from the Markov model, and the age of the appointment. The top three variables that decrease the chance of no-showing were: the appointment was a new consult, the appointment was an overbook, and the patient had multiple appointments on the same day. The average of the areas under the receiver operating characteristic curves was 0.7577 for the training dataset, and 0.7513 for the test set. CONCLUSIONS: The National Initiative to Reduce Missed Opportunities-2 confirmed findings that previous patient attendance is one of the key predictors of a future attendance and provides an additional layer of complexity for analyzing the effect of a patient's past behavior on future attendance. The National Initiative to Reduce Missed Opportunities-2 establishes that appointment attendance is related to medication adherence, particularly for medications used for treatment of mood disorders or to block the effects of opioids. However, there is no way to confirm whether a patient is actually taking medications as prescribed. Thus, a low medication possession ratio is an informative, albeit not a perfect, measure. It is our intention to further explore how diagnosis and medications can be better captured and used in predictive modeling of no-shows. Our findings on the effects of different factors on no-show rates can be used to predict individual no-show probabilities, and to identify patients who are high risk for missing appointments. The ability to predict a patient's risk of missing an appointment would allow for both advanced interventions to decrease no-shows and for more efficient scheduling.

MyVA Access: An Evaluation of Changes in Access for a System-Wide Program Implemented in the Veterans Health Administration.

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.

Modeling Patient No-Show History and Predicting Future Outpatient Appointment Behavior in the Veterans Health Administration.

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.

Large-Scale No-Show Patterns and Distributions for Clinic Operational Research.

Patient no-shows for scheduled primary care appointments are common. Unused appointment slots reduce patient quality of care, access to services and provider productivity while increasing loss to follow-up and medical costs. This paper describes patterns of no-show variation by patient age, gender, appointment age, and type of appointment request for six individual service lines in the United States Veterans Health Administration (VHA). This retrospective observational descriptive project examined 25,050,479 VHA appointments contained in individual-level records for eight years (FY07-FY14) for 555,183 patients. Multifactor analysis of variance (ANOVA) was performed, with no-show rate as the dependent variable, and gender, age group, appointment age, new patient status, and service line as factors. The analyses revealed that males had higher no-show rates than females to age 65, at which point males and females exhibited similar rates. The average no-show rates decreased with age until 75-79, whereupon rates increased. As appointment age increased, males and new patients had increasing no-show rates. Younger patients are especially prone to no-show as appointment age increases. These findings provide novel information to healthcare practitioners and management scientists to more accurately characterize no-show and attendance rates and the impact of certain patient factors. Future general population data could determine whether findings from VHA data generalize to others.