<abstract xmlns="http://www.w3.org/1999/xhtml"><p>It is well-known that health-care systems all over the world try to improve efficiency and effectiveness due to high pressure to pursue rational performance. Many of the health-care reforms in different countries are conducted with the objectives to rationalise costs, to make health services more available, and to increase, or at least not to reduce, the quality of health services. Adequate efficient metrics used in assessing health-care services are critical for the quality of decision-making. The subject of this research is efficiency measurement in the institutes of public health in Croatia oriented to preventive activities, i.e. monitoring, analysis, and evaluation of the health of the population. Diversity of the programmes and temporal variability are the characteristics of public health services, and it is therefore harder to measure the effects in the long run. This research aims to analyse and propose a new approach to the measurement system using two methods: the Data Envelopment Analysis (DEA) and the Balanced Score Card (BSC), combined into the DEABSC integrative model, which can be an efficient method for the decision-making process. The starting point of this research is the analysis of the measurement system in public-health services in Croatia. The results confirm a weak and inadequate efficiency measurement system and very low effectiveness measurement. To enhance the efficiency measurement, we have developed the conceptual integrative DEABSC model which has been tested at the Teaching Institute of Public Health – the Department of Health Ecology. Using DEA for 2017, we have identified relatively efficient units that helped establish future goals set in the BSC strategic map. The combination of DEA and BSC allows identifying options for improving the effectiveness of public-health service programmes. The study confirms that it is possible to determine the relative efficiency of different DMUs within the Department of Health Ecology and use best practice for each perspective in the BSC Cause &amp; Effect conceptual model. This paper also provides recommendations for an easier establishment of performance measurement in order to achieve the mission of public health institutions, but also indicates some limitations in the application of the methods.</p></abstract>

It is well-known that health-care systems all over the world try to improve efficiency and effectiveness due to high pressure to pursue rational performance. Many of the health-care reforms in different countries are conducted with the objectives to rationalise costs, to make health services more available, and to increase, or at least not to reduce, the quality of health services. Adequate efficient metrics used in assessing health-care services are critical for the quality of decision-making. The subject of this research is efficiency measurement in the institutes of public health in Croatia oriented to preventive activities, i.e. monitoring, analysis, and evaluation of the health of the population. Diversity of the programmes and temporal variability are the characteristics of public health services, and it is therefore harder to measure the effects in the long run. This research aims to analyse and propose a new approach to the measurement system using two methods: the Data Envelopment Analysis (DEA) and the Balanced Score Card (BSC), combined into the DEABSC integrative model, which can be an efficient method for the decision-making process. The starting point of this research is the analysis of the measurement system in public-health services in Croatia. The results confirm a weak and inadequate efficiency measurement system and very low effectiveness measurement. To enhance the efficiency measurement, we have developed the conceptual integrative DEABSC model which has been tested at the Teaching Institute of Public Health – the Department of Health Ecology. Using DEA for 2017, we have identified relatively efficient units that helped establish future goals set in the BSC strategic map. The combination of DEA and BSC allows identifying options for improving the effectiveness of public-health service programmes. The study confirms that it is possible to determine the relative efficiency of different DMUs within the Department of Health Ecology and use best practice for each perspective in the BSC Cause & Effect conceptual model. This paper also provides recommendations for an easier establishment of performance measurement in order to achieve the mission of public health institutions, but also indicates some limitations in the application of the methods.

Effectiveness Measurement Using DEA &amp; BSC Methods in Public Health Services

NISPAcee Journal of Public Administration and Policy

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Effectiveness Measurement Using DEA & BSC Methods in Public Health Services

{"article-title":"Effectiveness Measurement Using DEA & BSC Methods in Public Health Services"}

It is well-known that health-care systems all over the world try to improve efficiency and effectiveness due to high pressure to pursue rational performance....

Effectiveness Measurement Using DEA & BSC Methods in Public Health Services

Published Online: Jun 18, 2019

Page range: 199 - 216

DOI: https://doi.org/10.2478/nispa-2019-0009

Keywords
efficiency measurement, DEA, BSC, public health services, health ecology

© 2019 Neda Vitezić et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Effectiveness Measurement Using DEA & BSC Methods in Public Health Services

Published Online: Jun 18, 2019

Page range: 199 - 216

DOI: https://doi.org/10.2478/nispa-2019-0009

Keywordsefficiency measurement, DEA, BSC, public health services, health ecology

© 2019 Neda Vitezić et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
efficiency measurement, DEA, BSC, public health services, health ecology