Based on knowledge capital value for disease cost accounting of diagnosis related groups.

Background: The National Health Commission and the other relevant departments in China have initiated testing of the Diagnosis Related Groups (DRGs) system in 30 pilot locations since 2019. In the process of DRG payment reform, accounting for the costs of diseases has become a highly challenging issue. The traditional method of disease accounting method overlooks the compensation for the knowledge capital value of medical personnel. Objective: The primary objective of this study is to analyze the cost accounting scheme of China's Diagnosis Related Groups (C-DRG), focusing on the value of knowledge capital. Methods: The study initially proposes a measurement index system for the value of knowledge-based capital, including the difficulty of disease treatment, labor intensity of disease treatment, risk of disease treatment, and operation/treatment time for diseases. The Analytic Hierarchy Process (AHP) is then utilized to weigh the features of medical workers' knowledge capital value. First, pairwise comparisons are conducted in this stage to develop a two-pair judgment matrix of the primary indicators. Second, the eigenvectors corresponding to the maximum eigenvalues of the matrix are calculated to generate the weight coefficient of each feature. The consistency test is carried out after this stage. An empirical analysis is conducted by collecting data, including the full costs of treating three types of diseases-hip replacement, acute simple appendicitis, and heart bypass surgery-from one public medical institution. Results: The empirical analysis examines whether this DRG costing accounting can address the issue of neglecting the value of medical workers' knowledge capital. The methods reconfigure the positive incentive mechanism, stimulate the endogenous motivation of the medical service system, foster independent changes in medical behavior, and achieve the goals of reasonable cost control. Conclusion: In the cost accounting system of C-DRG, the value of medical workers' knowledge capital is acknowledged. This acknowledgment not only boosts the enthusiasm and creativity of medical workers in optimizing and standardizing the diagnosis and treatment process but also improves the transparency and authenticity of DRG pricing. This is particularly evident in the optimization and standardization of the diagnosis and treatment processes within medical institutions and in monitoring inadequate medical practices within these institutions.

A Game Model for Medical Service Pricing Based on the Diagnosis Related Groups.

Background: Currently there are various issues that exist in the medical institutions in China as a result of the price-setting in DRGs, which include the fact that medical institutions tend to choose patients and that the payment standard for complex cases cannot reasonably compensate the cost. Objective: The main objective is to prevent adverse selection problems in the operations of a diagnosis-related groups (DRGs) system with the game pricing model for scientific and reasonable pricing. Methods: The study proposes an improved bargaining game model over three stages, with the government and patients forming an alliance. The first stage assumes the alliance is the price maker in the Stackelberg game to maximize social welfare. Medical institutions are a price taker and decide the level of quality of medical service to maximize their revenue. A Stackelberg equilibrium solution is obtained. The second stage assumes medical institutions dominate the Stackelberg game and set an optimal service quality for maximizing their revenues. The alliance as the price taker decides the price to maximize the social welfare. Another Stackelberg equilibrium solution is achieved. The final stage establishes a Rubinstein bargaining game model to combine the Stackelberg equilibrium solutions in the first and second stage. A new equilibrium between the alliance and medical institutions is established. Results: The results show that if the price elasticity of demand increases, the ratio of cost compensation on medical institutions will increase, and the equilibrium price will increase. The equilibrium price is associated with the coefficient of patients' quality preference. The absolute risk aversion coefficient of patients affects government compensation and total social welfare. Conclusion: In a DRGs system, considering the demand elasticity and the quality preference of patients, medical service pricing can prevent an adverse selection problem. In the future, we plan to generalize these models to DRGs pricing systems with the effects of competition of medical institutions. In addition, we suggest considering the differential compensation for general hospitals and community hospitals in a DRGs system, in order to promote the goal of hierarchical diagnosis and treatment.

Novel Case-Based Reasoning System for Public Health Emergencies.

PURPOSE: Several threatening infectious diseases, including influenza, Ebola, SARS, and COVID-19, have affected human society over the past decades. These disease outbreaks naturally inspire a demand for sustained and advanced safety and suppression measures. To protect public health and safety, further research developments on emergency analysis methods and approaches for effective emergency treatment generation are urgently needed to mitigate the severity of the pandemic and save lives. METHODS: To address these issues, a novel case-based reasoning (CBR) system is proposed using three phases. In the first phase, the similarity between the current case and the historical cases is calculated under a variety of heterogeneous information. In the second phase, a filter approach based on grey clustering analysis is created to retrieve relevant cases. In the third phase, the cases retrieved are taken as initial host nests in a cuckoo search (CS) algorithm, and our system searches an optimal solution through iteration of this algorithm. RESULTS: The proposed model is compared with a CBR method improved by particle swarm optimization (PSO) and a CBR method improved by a differential evolution algorithm (DE), to confirm the efficiency of our CS algorithm in adapting solutions for public health emergencies. The results show that the proposed model is better than the existing algorithms. CONCLUSION: The proposed model improves the speed of case retrieval using grey clustering and increases solution accuracy with CS algorithms. The present research can contribute to government, CDC, and infectious disease emergency management fields with regard to the implementation of fast and accurate public biohazard prevention and control measures based on a variety of heterogeneous information.

Predicting Urban Medical Services Demand in China: An Improved Grey Markov Chain Model by Taylor Approximation.

The sharp increase of the aging population has raised the pressure on the current limited medical resources in China. To better allocate resources, a more accurate prediction on medical service demand is very urgently needed. This study aims to improve the prediction on medical services demand in China. To achieve this aim, the study combines Taylor Approximation into the Grey Markov Chain model, and develops a new model named Taylor-Markov Chain GM (1,1) (T-MCGM (1,1)). The new model has been tested by adopting the historical data, which includes the medical service on treatment of diabetes, heart disease, and cerebrovascular disease from 1997 to 2015 in China. The model provides a predication on medical service demand of these three types of disease up to 2022. The results reveal an enormous growth of urban medical service demand in the future. The findings provide practical implications for the Health Administrative Department to allocate medical resources, and help hospitals to manage investments on medical facilities.