Modeling Outpatient Visits Using Poisson and Negative Binomial Regression at Sarkies Aisyiyah Kudus Hospital

Nur Hakimah; Ade Ima Afifa Himayati; Findasari Findasari

doi:10.69693/jesa.v3i2.50

Authors

Nur Hakimah Universitas Muhammadiyah Kudus
Ade Ima Afifa Himayati Universitas Muhammadiyah Kudus
Findasari Findasari Universitas Muhammadiyah Kudus

DOI:

https://doi.org/10.69693/jesa.v3i2.50

Keywords:

Outpatient visits, Poisson regression, Negative binomial regression, Overdispersion, Count data

Abstract

Outpatient visits are an important indicator of healthcare service utilization and play a significant role in hospital service planning and resource management. This study aims to model the number of outpatient visits at Sarkies Aisyiyah Kudus Hospital using Poisson and Negative Binomial regression and to identify the factors influencing outpatient visits. This study used secondary data consisting of 41 daily observations from November 1 to December 12, 2025. The response variable was the daily number of outpatient visits, while the predictor variables were the number of BPJS patients, the number of operating clinics, and the number of doctors on duty. Data analysis was performed using RStudio. The results showed that all three predictor variables had a positive relationship with outpatient visits in the Poisson regression model. However, the overdispersion test produced a dispersion value of 10.04, indicating that the Poisson assumption of equidispersion was not satisfied. The Negative Binomial regression model showed that the number of BPJS patients and the number of clinics had a positive and significant effect on outpatient visits, while the number of doctors had a positive but non-significant effect. The Negative Binomial model provided a better fit, with an AIC of 435.58, compared with 673.48 for the Poisson model. Therefore, the Negative Binomial regression model was selected as the more appropriate model for outpatient visit data at Sarkies Aisyiyah Kudus Hospital.

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