An advanced logistic regression model for forecasting payer revenue in private hospitals: a case study in manado - Mokodaser, Wilsen; Koapaha, Hartiny Pop (Journal of Soft Computing Exploration, 2025)
Manado, the provincial capital, stands as a vital center for healthcare services, where private hospitals compete intensively to attract patients from various economic and social backgrounds. Accurate revenue forecasting for partnered payers is essential for effective management strategies. This study employs a logistic regression model, achieving a notable accuracy of 79.55% in predicting hospital revenue based on payer partnerships. The confusion matrix reveals 21 true negatives (TN), confirming the model accurately identified low-revenue customers, with zero false positives (FP), indicating no misclassification of these individuals. However, 9 false negatives (FN) highlight a critical risk, as high-revenue customers were miscategorized as low revenue, even though 14 true positives (TP) were precisely identified. Based on these insights, hospitals can strategically target 61 payers projected to exceed median revenue, presenting a significant opportunity for income growth. Conversely, the 159 payers identified as below median revenue warrant urgent attention. To enhance engagement and increase revenue from these lower-revenue groups, targeted business strategies such as intensified marketing, personalized service offerings, and promotional discounts are recommended. This research contributes a novel approach to leveraging predictive analytics in healthcare, underscoring the pressing need for hospitals to innovate their operational strategies to optimize revenue in a competitive landscape.