Two-dimensional warranty has played a significant role in improving product quality and competitiveness. However, two-dimensional basic warranty (BW) and extended warranty (EW) have not yet been combined effectively, and the customized maintenance strategies based on the consumers’ heterogeneity have been quite limited, which result in high maintenance costs. In this paper, the possibility of reducing warranty cost over BW and EW is explored by classifying customers based on their usage rates and then offering them with customized preventive maintenance (PM) strategies. By considering the purchasing ratio of EW contracts, the PM strategies for BW and EW are closely combined by the bi-level programming model. Numerical analysis is given to show the application of the proposed warranty strategy, and the significant findings and sensitivity of the main parameters are analyzed in the end. The findings demonstrate that the customized PM strategies combining BW and EW can effectively reduce warranty costs within the warranty period and provide a scientific guidance for manufacturers to design a more reasonable PM strategy.

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.

In order to solve the shortcomings of the readmission risk prediction and assessment model based on Chinese and Western medicine indicators for patients with unstable angina (UA) in clinical application, a risk prediction model incorporating tradition Chinese medicine (TCM) indicators for the readmission risk of UA patients within one year was constructed by introducing the CatBoost algorithm. First, data preprocessing, imbalance processing, and feature selection were performed on the dataset of UA patients collected from a tertiary hospital in Tianjin, China, to screen out the features that have an important impact on the model performance; next, the CatBoost model was used to train and test the dataset; finally the experimental results show that the CatBoost model exhibits superior performance compared with five mainstream machine learning; meanwhile, by interpreting the features extracted from the CatBoost model, it is found that adding TCM indicators in the risk prediction model has some practical value.

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.