Warranties for healthcare can be greatly beneficial for cost reductions and improvements in patient satisfaction. Under healthcare warranties, healthcare providers receive a lump sum payment for the entire care episode, which covers a bundle of healthcare services, including treatment decisions during initial hospitalization and subsequent readmissions, as well as disease-monitoring plans composed of periodic follow-ups. Higher treatment intensities and more radical monitoring strategies result in higher medical costs, but high treatment intensities reduce the baseline readmission rates. This study intends to provide a systematic optimization framework for healthcare warranty policies. In this paper, the proposed model allows healthcare providers to determine the optimal combination of treatment decisions and disease-monitoring policies to minimize the total expected healthcare warranty cost over the prespecified period. Given the nature of the disease progression, we introduced a delay time model to simulate the progression of chronic diseases. Based on this, we formulated an accumulated age model to measure the effect of follow-up on the patient’s readmission risk. By means of the proposed model, the optimal treatment intensity and the monitoring policy can be derived. A case study of pediatric type 1 diabetes mellitus is presented to illustrate the applicability of the proposed model. The findings could form the basis of developing effective healthcare warranty policies for patients with chronic diseases.

Implementing follow-up policies under healthcare warranties for chronic disease patients plays a crucial role in reducing the risk of adverse outcomes (AOs) and controlling long-term medical costs. However, the additional cost associated with these services often discourages hospitals from providing them. Background/Objectives: To incentivize participation from both hospitals and patients in follow-up programs, this paper introduces a patient copayment mechanism. We propose a theoretical mathematical modeling framework to investigate the optimal pricing of follow-up policies from both patients’ and hospitals’ perspectives to achieve win–win outcomes. Methods: Using the Cox frailty model, we stratify patients by risk level and model hazard rate functions for three follow-up policies featuring periodic checkups, incorporating the virtual age method. Building on this framework, we employ the Non-Homogeneous Poisson Process to analyze the total expected costs incurred by hospitals and patients across different policies and risk strata. This analysis derives the minimum price acceptable to hospitals for providing follow-up services and the maximum additional cost patients are willing to bear for them. The feasibility and applicability of the proposed model are demonstrated through a case study of pediatric type 1 diabetes mellitus (T1DM). Results: Win–win price intervals for T1DM patients are more achievable for higher-risk individuals. These intervals narrow or widen with the age reduction factor, checkup cost, and AO treatment cost. Hospitals should prioritize higher-risk patients, improve checkup effectiveness, and balance costs of checkups and treatments when optimizing pricing decisions. Conclusions: These insights provide valuable guidance for hospitals in strategically designing follow-up policies tailored to diverse risk cohorts and determining optimal price intervals.