Research On Prediction Of Diagnosis And Treatment Demand And Patient Allocation Mechanism In Medical Union

As an important means of my country’s medical and health system reform,the medical association has far-reaching significance for hierarchical diagnosis and treatment and two-way referral,and plays an important role in optimizing the allocation of medical resources.At the same time,the hierarchical collaborative operation of different levels of institutions in the medical association has also produced The new problem of how to allocate patients to appropriate medical resources according to the needs of diagnosis and treatment.Combining the current era background of smart medical care,this research uses new information technology methods such as mathematical modeling and intelligent algorithms to study the diagnosis and treatment demand prediction and patient allocation methods within the medical consortium,aiming to establish a framework for realizing the optimization of graded diagnosis and treatment in the medical consortium.A reliable implementation path.At the same time,discuss and design around how to build the corresponding guarantee system and mechanism to ensure the stable and effective practical application of technical means and achieve the expected application effect and level of the research.This research first summarizes the current status,mode and role of the current medical consortium construction through literature analysis.Analyze the needs of collaborative diagnosis and treatment in the medical union and the current situation of patient allocation,and conduct theoretical discussions on the direction of improvement based on the shortcomings and the development direction of the construction of the medical union,and confirm the research on the needs of collaborative diagnosis and treatment and patient allocation necessity.On this basis,this study first selected the naive Bayes algorithm commonly used in disease prediction in medical research.Taking coronary heart disease as an example,after studying the literature,the risk factors of coronary heart disease were screened and evaluated.Seven variables such as gender and age are selected,and the probability of each variable in the disease state is the conditional probability,and the probability of the disease in the presence of each variable is the posterior probability.After the discretization of the data and the definition of the domain,the construction The corresponding Bayesian prediction model predicts the probability of the patient’s illness and the severity of the illness to determine the classification of different types of medical needs.This model is used for simple preliminary prediction of coronary heart disease incidence.When the prediction probability is greater than 0.9,a definite conclusion is made on the prediction result.Based on the results obtained by the above prediction model,analyze the diagnosis and treatment needs reflected by it.Based on the Markov process,a Markov model of the natural course of coronary heart disease is established,which includes three natural states:high-risk or severe patients,low-risk or mild patients,and death.The state transition probability matrix is reasonably assumed,and the model is analyzed by using Matlab to determine that it is a non-periodic,reducible,and non-ergodic Markov chain.Through the analysis of the state distribution probability table,it is concluded that the Markov chain has a steady-state distribution,and the time when it is close to the steady-state distribution is obtained.Using this model,this study evaluates the effectiveness of the treatment plan based on the utility-cost ratio of the treatment plan,so as to select the optimal treatment plan,and use this as the basis for the allocation of patients within the medical consortium.Then,based on the above quantitative research results,in order to ensure that the technical means established in the research can be effectively used in practical applications,from a management perspective,the construction of corresponding guarantee systems and mechanisms has been carried out from several aspects.The first is to improve the top-level design of the medical consortium’s collaborative medical business,the second is to improve the relevant internal management system of the medical consortium,the third is to build a hardware foundation based on the internal information sharing platform of the medical consortium,and the fourth is to improve the residents’ health data archives.The main data foundation,the fifth is to establish a corresponding incentive and guarantee system.This research is combined with the actual situation of the medical and health industry and has high social application value.Through scientific diagnosis and treatment demand forecasting and patient allocation mechanism,it not only contributes to the efficient use of medical resources,but also improves the quality of patient diagnosis and treatment services,which has important theoretical value and practical significance.The technology and system guarantee mechanism under study is popularized and operable.