Event Title

Session 10: Healthcare - No Show Predictive Model: A Bayesian Approach

Presenter Information

Robert Menzie, Sanford Health

Location

University Student Union: Pheasant Room 253 A/B

Start Date

12-2-2018 3:30 PM

End Date

12-2-2018 5:00 PM

Description

Patients not showing up to their appointments is a detriment to both the patient and the health care system. As health care systems transition from fee-for-service programs to value-based program, clinical visits (especially primary care) will become the gateway to improving overall patient health outcomes. In order to ensure patients are receiving the appropriate treatment and maintaining a healthy lifestyle they must be completing their scheduled visits. The main goal of the model is to predict patient no-show probabilities with the intent of taking the model one step further by linking it to actionable data points and decisions. The model employs the use of a logistic regression and Bayesian update approach. The regression is devised of patient demographical, behavioral and diagnosis characteristics, as well as visit logistics. The logistic regression creates a priori probability based on requisite factors. Then due to the highly behavioral impetus of missing appointments, a Bayesian update is applied to the priori probability to obtain a final, posterior probability. The Bayesian application to this model significantly contributes to the patient’s probability and details the importance behind patient-level interventions. The output of the model has a high level of accuracy that allows clinics not only to see which patients have a high risk of not showing up, but also the factors that physicians may be able to remedy down the road. The model was built using a standard 10-fold cross-validation. The test set was then ran through the model and used to determine the weighting for the Bayesian update. Lastly the data was validated using the remaining 10%, which resulted in an AUC of .927. Combining the accuracy of this model with the prescriptive ability of the factors, can allow for a significant reduction of no-shows, not only by enhancing appointment logistics (calls, overbooking, etc.) but also by improving patients’ lifestyle.

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Feb 12th, 3:30 PM Feb 12th, 5:00 PM

Session 10: Healthcare - No Show Predictive Model: A Bayesian Approach

University Student Union: Pheasant Room 253 A/B

Patients not showing up to their appointments is a detriment to both the patient and the health care system. As health care systems transition from fee-for-service programs to value-based program, clinical visits (especially primary care) will become the gateway to improving overall patient health outcomes. In order to ensure patients are receiving the appropriate treatment and maintaining a healthy lifestyle they must be completing their scheduled visits. The main goal of the model is to predict patient no-show probabilities with the intent of taking the model one step further by linking it to actionable data points and decisions. The model employs the use of a logistic regression and Bayesian update approach. The regression is devised of patient demographical, behavioral and diagnosis characteristics, as well as visit logistics. The logistic regression creates a priori probability based on requisite factors. Then due to the highly behavioral impetus of missing appointments, a Bayesian update is applied to the priori probability to obtain a final, posterior probability. The Bayesian application to this model significantly contributes to the patient’s probability and details the importance behind patient-level interventions. The output of the model has a high level of accuracy that allows clinics not only to see which patients have a high risk of not showing up, but also the factors that physicians may be able to remedy down the road. The model was built using a standard 10-fold cross-validation. The test set was then ran through the model and used to determine the weighting for the Bayesian update. Lastly the data was validated using the remaining 10%, which resulted in an AUC of .927. Combining the accuracy of this model with the prescriptive ability of the factors, can allow for a significant reduction of no-shows, not only by enhancing appointment logistics (calls, overbooking, etc.) but also by improving patients’ lifestyle.