白金玉
Abstract：In recent years， the problem of population health in China has been increasingly serious. The aggravation of the aging population， the deterioration of the environment， the change of the living habits in modern society， and the shortage of medical resources all have added pressure to the population health management system in China. In this paper， the studies of reliability theory relating to health management are reviewed.
Keywords：Population health management ，reliability ，life prediction
Introduction
With the development of modern technology in recent years， the emergency of the big data， artificial intelligence， block chain and cloud storage have provided new approaches and technologies for health management. Although the health management and its system has been relatively mature after years of development， the application of reliability theory in this field is still in its early stage.In terms of population health management， reliability theories such as control chart theory， delay time theory and random filter theory can also be used to analyze health management problems. The following three subsections introduced three most common reliability theories that may be used in health management.
Ⅰ.Health risk assessment and control based on control chart theory
Health risk （HR） factors are also known as health hazards or health risk factors， while population health risk assessment and control is to evaluate and control the risks faced by the health of the whole population.Qin Liping and Ma Jiaqi （2008） analyzed the advantages and disadvantages of using control chart theory in early warning of infectious diseases.They believe that there are many methods for early warning， but most rely on relatively complex statistical models. The control chart method can effectively buffer the impact of health risk factors on the health of the population.In addition， for daily behavior health management can also promote the overall health of the population.
Ⅱ. Design and optimization of disease screening strategy based on delay time theory
Delay time theory was first proposed to simulate problems in industrial engineering （Christer， 1999; Wang， 2007 b， 2008; Liu et al.， 2015）. In this theory， the degradation process of a piece of production equipment can be viewed as a two-stage process： the first stage of normal operation and the second stage of potential failur.In order to maximize the cost-effectiveness of the government' s screening strategy， the optimization objective is to extend the cost of each year. However， there are some limitations. Studies can consider using more complex formulas to simulate more realistic situations to reduce errors.
Ⅲ. Prediction of population residual life based on random filtering theory
At present， stochastic filtering theory is widely used in industry. Many scholars have done a lot of research on the remaining life prediction method of gear box based on data drive.Si et al. summarized residual life estimation methods on data-driven aspects， and introduced prediction models based on direct state information and indirect state information in detail.The random filtering theory is rarely used in the field of population residual life prediction. It was first used to predict the mortality model. Status data is also not updated for prediction. The later Gompertz model and its improved version were predicted using current state information without considering the impact of past health conditions on future survival probabilities. .
Ⅳ.Summery
In general， especially for medical career， is urgently needing to cover more areas， other domain knowledge， highlight integration of management and technology， paying attention to the crowd health management， quantitative management control chart and the application of the delay time is mature， and based on the theory of stochastic filtering population life prediction remains to be further explored， subject to draw lessons from the advanced management concept and combined with our country people health management current situation of the development of further improving people health management.
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