Diagnosis-intervention packet-based Pareto chart of the proportion of high-cost cases and the analysis of the structure of hospitalization expenses.

BACKGROUND: In order to prevent the occurrence of high-cost cases, it is necessary to know the proportion of high-rate cases and the hospitalization cost of patients. OBJECTIVE: Through the analysis of the high-rate cases of various specialties in a first-class hospital in a province, the profit and loss situation of medical institutions under the diagnosis-intervention packets (DIP) payment reform was discussed to seek a more effective way of medical insurance payment reform. METHODS: Data of 1955 inpatients who participated in DIP settlement in January 2022 were retrospectively selected. The Pareto chart was used to analyze the distribution trend of high-cost cases and the composition of hospitalization expenses in each specialty. RESULTS: The high-cost cases are the main reason for the loss of medical institutions at the time of DIP settlement. Neurology, respiratory medicine and other specialties are the focus of high-cost cases. CONCLUSION: The cost composition of inpatients with high-cost cases is in urgent need of optimization and adjustment. DIP payment method can control the use of medical insurance funds more effectively, which is the guarantee for the refined management of medical institutions.

Potential savings in the treatment pathway of liver transplantation: an inter-sectorial analysis of cost-rising factors.

INTRODUCTION: Identification of cost-driving factors in patients undergoing liver transplantation is essential to target reallocation of resources and potential savings. AIM: The aim of this study is to identify main cost-driving factors in liver transplantation from the perspective of the Statutory Health Insurance. METHODS: Variables were analyzed with multivariable logistic regression to determine their influence on high cost cases (fourth quartile) in the outpatient, inpatient and rehabilitative healthcare sectors as well as for medications. RESULTS: Significant cost-driving factors for the inpatient sector of care were a high labMELD-score (OR 1.042), subsequent re-transplantations (OR 7.159) and patient mortality (OR 3.555). Expenditures for rehabilitative care were significantly higher in patients with a lower adjusted Charlson comorbidity index (OR 0.601). The indication of viral cirrhosis and hepatocellular carcinoma resulted in significantly higher costs for medications (OR 21.618 and 7.429). For all sectors of care and medications each waiting day had a significant impact on high treatment costs (OR 1.001). Overall, cost-driving factors resulted in higher median treatment costs of 211,435 €. CONCLUSIONS: Treatment costs in liver transplantation were significantly influenced by identified factors. Long pre-transplant waiting times that increase overall treatment costs need to be alleviated by a substantial increase in donor organs to enable transplantation with lower labMELD-scores. Disease management programs, the implementation of a case management for vulnerable patients, medication plans and patient tracking in a transplant registry may enable cost savings, e.g., by the avoidance of otherwise necessary re-transplants or incorrect medication.

High cost pool or high cost groups-How to handle high(est) cost cases in a risk adjustment mechanism?

Competitive social health insurance systems (at least) in Western Europe have implemented systems of morbidity based risk adjustment to set a level playing field for insurers. However, many high cost insured still are heavily underfunded despite risk adjustment, leaving incentives for risk selection. In most of these health care systems, there is an ongoing debate about how to deal with such underpaid high cost cases. This study develops four distinct concepts by adding variables to risk adjustment or by setting up a high cost pool for underpaid insured besides the risk adjustment system. Their features, incentives and distributional effects are discussed. With a data set of 6 million insured, performance is demonstrated for Germany. All models achieve a substantial improvement in model fit, measured in terms of R(2) as well as CPM. As the results of the various models are different in different dimensions, the trade-offs that have to be dealt with and should be addressed, when implementing a model to reduce underfunding of high cost cases.