Abstract
Background:The main function of public health services is to improve people’s health and therefore, efficiency and effectiveness are constantly a subject of various world-wide research works. Today, in the era of digitalization, when numerous data are created and built, it is much easier to develop and implement a measurement system. It is possible to quickly use a wide variety of accurate and reliable data, aiming to create different measures that will help in the assessment and the decision-making process. For a long time, public health services have been facing a problem of finding an appropriate solution for measuring efficiency and effectiveness.Objective:The aim of this research is to find an appropriate analytical-predictive model for measuring efficiency and effectiveness of public health institutes. Public health is oriented to monitoring, analysis, and evaluation of the health of a populationi.e., prevention activities. It is a complex and interdependent process of different realisation of services, programmes, and activities the results of which are sometimes visible only after a long period of time. Therefore, the results of their activities should be evaluated using an appropriate performance measurement system.Methods:The adjusted Balanced Scorecard (BSC) combined with the non-parametric Data Envelopment Analysis (DEA) technique is used to help identify the possibilities for improving the efficiency and effectiveness of public health service activities.Results:The result of this study is the proposed Analytical-Predictive Model (APE) that uses Balanced Scorecard combined with Data Envelopment Analysis to measure relative and technical efficiency as well as long-term effectiveness. The model used DEA as a benchmark for targets set in each perspective within the BSC. Using the BSC model, we selected the goals and common indicators for all DMUs, and using DEA, we identified relative efficiency of the DMUs. Efficient DMUs are considered a benchmark and used as targets for measuring effectiveness.Conclusion:This research has confirmed the appropriateness of the combination of BSC and DEA methods for measuring efficiency and effectiveness of public health institutions. To be able to measure and predict the long-term effectiveness of the activities and programmes, we had to combine the realised outputs and the set outcomes. The implementation of the APE model in the institutes of public health will contribute to the improvement of analysis, forecast, and optimisation of all their activities. The model is applicable to other public health institutions.
Highlights
Public health services are organised differently in the world, but their common task is improving populations’ health.Public health services are usually provided by institutes or agencies that organise professional activities for the improvement and protection of a population’s health as well as reducing mortality, premature deaths, and disabilities
The result of this study is the proposed Analytical-Predictive Model (APE) that uses Balanced Scorecard combined with Data Envelopment Analysis to measure relative and technical efficiency as well as long-term effectiveness
The model used Data Envelopment Analysis (DEA) as a benchmark for targets set in each perspective within the Balanced Scorecard (BSC)
Summary
Public health services are organised differently in the world, but their common task is improving populations’ health.Public health services are usually provided by institutes or agencies that organise professional activities for the improvement and protection of a population’s health as well as reducing mortality, premature deaths, and disabilities. Today’s digital environment and the ‘Big Data Age’ provide new capabilities and organisational, managerial, and strategic benefits [2] that will contribute to a more accurate and comprehensive measurement system These datasets that contain volumes of different unstructured, semi-structured and structured data are, besides other 'Vs', characterised by data from different sources, data accuracy, and reliability (veracity) that examine the cost-benefit of data collecting (value) [3, 4]. Manyika et al [6], point out that properly applied data analytics helps cut costs by approximately $300 million annually in the health care industry Wang et al [2] point out the following potential operational benefits of big data analytics in health care: it improves workflow efficiency, monitors quality, improves costs and outcomes, and reduces the time for extraction of information from research studies on large databases. Public health services have been facing a problem of finding an appropriate solution for measuring efficiency and effectiveness
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