engleski

An analytical-predictive model for measuring the efficiency and effectiveness of public health services

The task of public health services is to improve population's health and therefore efficiency and effectiveness is constantly a subject of various researches over the world. Today, in the era of digitalization when numerous of data has been created and build, measurement system could be developed and implemented much easier. It is possible to use a wide variety of accurate and reliable data very fast with aim to create different measures which will help in assessment and decision making process. For a long time public health services are faced with a problem of appropriate solution for efficiency and effectiveness measurement. Public health is oriented to prevention activities i.e. monitoring, analysis, and evaluation of the health of the population. It is the complex and interdependent process of different services realization, programs and activities whose results are sometimes visible after a long period of time. Therefore the results of their activity and effects in the long run should be measured. This research, which is carried out in an institute of public health in Croatia, has attempt to find an appropriate analytical- predictive model for measuring the efficiency and effectiveness in the overall system of public health in Croatia. Adjusted balanced- score card (BSC) combining with non-parametric technique Data Envelopment Analysis (DEA) helps to identify the possibilities for improving the efficiency and effectiveness of public health services activities. Using BSC model we will select the goals and common indicators of all DMUs within the Institute, and using DEA identify relative efficiency between them. Efficient DMUs will be considered as a benchmark and used as targets for measuring efficiency. Combining the realized outputs with the set outcomes, we will make a conceptual model for measuring and predicting the effectiveness of the programs and activities in the long run.

public health services, efficiency, effectiveness, DEABSC, predictive model

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