LETTER
An Analytical-Predictive Model for Measuring the Efficiency and Effectiveness of Public Health Services
Neda Vitezić1, *, Antonija Petrlić1
Article Information
Identifiers and Pagination:
Year: 2020Volume: 13
First Page: 203
Last Page: 211
Publisher ID: TOPHJ-13-203
DOI: 10.2174/1874944502013010203
Article History:
Received Date: 23/12/2019Revision Received Date: 27/03/2020
Acceptance Date: 29/03/2020
Electronic publication date: 23/05/2020
Collection year: 2020
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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 population i.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.
