Research Article
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Prioritizing Digital Health: Key Municipal Services Identified Through Fuzzy Methods

Year 2024, Volume: 4 Issue: 2, 76 - 106, 01.10.2024

Abstract

The integration of digital technologies into healthcare systems within municipalities has elicited a transformative change in the delivery of health services. This paper explores the importance of the digitalization of health services in municipalities and represents relatively selected the most important services by employing fuzzy methods. By examining existing literature and employing a combination of qualitative and quantitative methods, including the Pythagorean Fuzzy CRITIC (PF-CRITIC) and Interval Valued Pythagorean Fuzzy WASPAS (IVPF-WASPAS) methods, this research evaluates the importance of digital transformation of several health services in municipalities. Key findings highlight that mobile health services and medical center services are the two most important municipal health activities regarding digital transformation. Through evidence-based strategies, municipalities can harness the power of digitalization to develop patient-centered, efficient, and responsive healthcare services. Therefore, this study contributes to a more inclusive approach to digitalization in healthcare, aiming to obtain the opinions of individuals who have experience with health activities in municipalities.

References

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  • [2] Schou, J. and Pors, A. S. (2018). Digital by default? a qualitative study of exclusion in digitalised welfare. Social Policy &Amp; Administration, 53(3), 464-477. https://doi.org/10.1111/spol.12470.
  • [3] Lloyd, C. and Payne, J. (2021). Fewer jobs, better jobs? an international comparative study of robots and ‘routine’ work in the public sector. Industrial Relations Journal, 52(2), 109-124. https://doi.org/10.1111/irj.12323.
  • [4] Collington, R. (2021). Disrupting the welfare state? digitalisation and the retrenchment of public sector capacity. New Political Economy, 27(2), 312-328. https://doi.org/10.1080/13563467.2021.1952559.
  • [5] Tiainen, M., Ahonen, O., Hinkkanen, L., Rajalahti, E., & Värri, A. (2021). The definitions of health care and social welfare informatics competencies. Finnish Journal of eHealth and eWelfare, 13(2), 147-159.
  • [6] Baumgartner, M., Sauer, C., Blagec, K., & Dorffner, G. (2022). Digital health understanding and preparedness of medical students: a cross-sectional study. Medical education online, 27(1), 2114851.
  • [7] Gopal, G. V., Suter‐Crazzolara, C., Toldo, L., & Eberhardt, W. (2018). Digital transformation in healthcare – architectures of present and future information technologies. Clinical Chemistry and Laboratory Medicine (CCLM), 57(3), 328-335.
  • [8] Scarano, G. and Colfer, B. (2022). Linking active labour market policies to digitalisation–a review between remote and automated possibilities. International Journal of Sociology and Social Policy, 42(13/14), 98-112.
  • [9] Holm, S. G., Mathisen, T. A., Sæterstrand, T. M., & Brinchmann, B. S. (2017). Allocation of home care services by municipalities in norway: a document analysis. BMC Health Services Research, 17(1). https://doi.org/10.1186/s12913-017-2623-3.
  • [10] Shava, E. and Vyas-Doorgapersad, S. (2022). Fostering digital innovations to accelerate service delivery in south african local government. International Journal of Research in Business and Social Science (2147- 4478), 11(2), 83-91.
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  • [12] Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87-96. https://doi.org/10.1016/S0165-0114(86)80034-3.
  • [13] Wang, J. C., & Chen, T. Y. (2018). Multiple criteria decision analysis using correlation-based precedence indices within Pythagorean fuzzy uncertain environments. International Journal of Computational Intelligence Systems, 11(1), 911-924.
  • [14] Peng, X., & Selvachandran, G. (2019). Pythagorean fuzzy set: state of the art and future directions. Artificial Intelligence Review, 52(3), 1873–1927. https://doi.org/10.1007/s10462-017-9596-9.
  • [15] Zhang, X., & Xu, Z. (2014). Extension of TOPSIS to multiple criteria decision making with Pythagorean fuzzy sets. International journal of intelligent systems, 29(12), 1061-1078.
  • [16] Peng, X., & Yang, Y. (2016). Fundamental properties of interval‐valued Pythagorean fuzzy aggregation operators. International Journal of Intelligent Systems, 31(5), 444-487.
  • [17] Sancar, S. (2022). Pisagor bulanık AHP ve pisagor bulanık WASPAS yöntemleri ile bakım stratejisi seçimi: Gazete matbaası örneği (Master's thesis, İbn Haldun Üniversitesi, Lisansüstü Eğitim Enstitüsü).
  • [18] Peng, X., Zhang, X., & Luo, Z. (2020). Pythagorean fuzzy MCDM method based on CoCoSo and CRITIC with score function for 5G industry evaluation. Artificial Intelligence Review, 53(5), 3813-3847. https://doi.org/10.1007/s10462-019-09780-x.
  • [19] Kabak, M., & Erdebilli, B. (2021). Bulanık Çok Kriterli Karar Verme Yöntemleri - MS Excel ve Software Çözümlü Uygulamalar. Ankara: Nobel Yayın.
  • [20] Turskis, Z., Zavadskas, E. K., Antucheviciene, J., & Kosareva, N. (2015). A hybrid model based on fuzzy AHP and fuzzy WASPAS for construction site selection. International Journal of Computers communications & control, 10(6), 113-128.
  • [21] Ilbahar, E., & Kahraman, C. (2018). Retail store performance measurement using a novel interval-valued Pythagorean fuzzy WASPAS method. Journal of Intelligent & Fuzzy Systems, 35(3), 3835-3846.
  • [22] Görçün, O. F., Senthil, S., & Küçükönder, H. (2021). Evaluation of tanker vehicle selection using a novel hybrid fuzzy MCDM technique. Decision Making: Applications in Management and Engineering, 4(2), 140-162.
  • [23] Pérez-Domínguez, L., Rodríguez-Picón, L. A., Alvarado-Iniesta, A., Luviano Cruz, D., & Xu, Z. (2018). MOORA under Pythagorean fuzzy set for multiple criteria decision making. Complexity, 2018. https://doi.org/10.1155/2018/2602376
  • [24] O'cathain, A., Murphy, E., & Nicholl, J. (2008). The quality of mixed methods studies in health services research. Journal of health services research & policy, 13(2), 92-98.
  • [25] Haktanir, E., & Kahraman, C. (2022). A novel picture fuzzy CRITIC & REGIME methodology: Wearable health technology application. Engineering Applications of Artificial Intelligence, 113, 104942.
  • [26] Wang, Y., Wang, W., Wang, Z., Deveci, M., Roy, S. K., & Kadry, S. (2024). Selection of sustainable food suppliers using the Pythagorean fuzzy CRITIC-MARCOS method. Information Sciences, 664, 120326.
  • [27] Gedikli, T., & Cayir Ervural, B. (2022). Identification of optimum COVID-19 vaccine distribution strategy under integrated Pythagorean fuzzy environment. In Digitizing Production Systems: Selected Papers from ISPR2021, October 07-09, 2021 Online, Turkey (pp. 65-76). Springer International Publishing.
Year 2024, Volume: 4 Issue: 2, 76 - 106, 01.10.2024

Abstract

References

  • [1] Buchert, U., Kemppainen, L., Olakivi, A., Wrede, S., & Kouvonen, A. (2022). Is digitalisation of public health and social welfare services reinforcing social exclusion? the case of russian-speaking older migrants in finland. Critical Social Policy, 43(3).
  • [2] Schou, J. and Pors, A. S. (2018). Digital by default? a qualitative study of exclusion in digitalised welfare. Social Policy &Amp; Administration, 53(3), 464-477. https://doi.org/10.1111/spol.12470.
  • [3] Lloyd, C. and Payne, J. (2021). Fewer jobs, better jobs? an international comparative study of robots and ‘routine’ work in the public sector. Industrial Relations Journal, 52(2), 109-124. https://doi.org/10.1111/irj.12323.
  • [4] Collington, R. (2021). Disrupting the welfare state? digitalisation and the retrenchment of public sector capacity. New Political Economy, 27(2), 312-328. https://doi.org/10.1080/13563467.2021.1952559.
  • [5] Tiainen, M., Ahonen, O., Hinkkanen, L., Rajalahti, E., & Värri, A. (2021). The definitions of health care and social welfare informatics competencies. Finnish Journal of eHealth and eWelfare, 13(2), 147-159.
  • [6] Baumgartner, M., Sauer, C., Blagec, K., & Dorffner, G. (2022). Digital health understanding and preparedness of medical students: a cross-sectional study. Medical education online, 27(1), 2114851.
  • [7] Gopal, G. V., Suter‐Crazzolara, C., Toldo, L., & Eberhardt, W. (2018). Digital transformation in healthcare – architectures of present and future information technologies. Clinical Chemistry and Laboratory Medicine (CCLM), 57(3), 328-335.
  • [8] Scarano, G. and Colfer, B. (2022). Linking active labour market policies to digitalisation–a review between remote and automated possibilities. International Journal of Sociology and Social Policy, 42(13/14), 98-112.
  • [9] Holm, S. G., Mathisen, T. A., Sæterstrand, T. M., & Brinchmann, B. S. (2017). Allocation of home care services by municipalities in norway: a document analysis. BMC Health Services Research, 17(1). https://doi.org/10.1186/s12913-017-2623-3.
  • [10] Shava, E. and Vyas-Doorgapersad, S. (2022). Fostering digital innovations to accelerate service delivery in south african local government. International Journal of Research in Business and Social Science (2147- 4478), 11(2), 83-91.
  • [11] Yager, R. R. (2013, June). Pythagorean fuzzy subsets. In 2013 joint IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS) (pp. 57-61). IEEE.".
  • [12] Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87-96. https://doi.org/10.1016/S0165-0114(86)80034-3.
  • [13] Wang, J. C., & Chen, T. Y. (2018). Multiple criteria decision analysis using correlation-based precedence indices within Pythagorean fuzzy uncertain environments. International Journal of Computational Intelligence Systems, 11(1), 911-924.
  • [14] Peng, X., & Selvachandran, G. (2019). Pythagorean fuzzy set: state of the art and future directions. Artificial Intelligence Review, 52(3), 1873–1927. https://doi.org/10.1007/s10462-017-9596-9.
  • [15] Zhang, X., & Xu, Z. (2014). Extension of TOPSIS to multiple criteria decision making with Pythagorean fuzzy sets. International journal of intelligent systems, 29(12), 1061-1078.
  • [16] Peng, X., & Yang, Y. (2016). Fundamental properties of interval‐valued Pythagorean fuzzy aggregation operators. International Journal of Intelligent Systems, 31(5), 444-487.
  • [17] Sancar, S. (2022). Pisagor bulanık AHP ve pisagor bulanık WASPAS yöntemleri ile bakım stratejisi seçimi: Gazete matbaası örneği (Master's thesis, İbn Haldun Üniversitesi, Lisansüstü Eğitim Enstitüsü).
  • [18] Peng, X., Zhang, X., & Luo, Z. (2020). Pythagorean fuzzy MCDM method based on CoCoSo and CRITIC with score function for 5G industry evaluation. Artificial Intelligence Review, 53(5), 3813-3847. https://doi.org/10.1007/s10462-019-09780-x.
  • [19] Kabak, M., & Erdebilli, B. (2021). Bulanık Çok Kriterli Karar Verme Yöntemleri - MS Excel ve Software Çözümlü Uygulamalar. Ankara: Nobel Yayın.
  • [20] Turskis, Z., Zavadskas, E. K., Antucheviciene, J., & Kosareva, N. (2015). A hybrid model based on fuzzy AHP and fuzzy WASPAS for construction site selection. International Journal of Computers communications & control, 10(6), 113-128.
  • [21] Ilbahar, E., & Kahraman, C. (2018). Retail store performance measurement using a novel interval-valued Pythagorean fuzzy WASPAS method. Journal of Intelligent & Fuzzy Systems, 35(3), 3835-3846.
  • [22] Görçün, O. F., Senthil, S., & Küçükönder, H. (2021). Evaluation of tanker vehicle selection using a novel hybrid fuzzy MCDM technique. Decision Making: Applications in Management and Engineering, 4(2), 140-162.
  • [23] Pérez-Domínguez, L., Rodríguez-Picón, L. A., Alvarado-Iniesta, A., Luviano Cruz, D., & Xu, Z. (2018). MOORA under Pythagorean fuzzy set for multiple criteria decision making. Complexity, 2018. https://doi.org/10.1155/2018/2602376
  • [24] O'cathain, A., Murphy, E., & Nicholl, J. (2008). The quality of mixed methods studies in health services research. Journal of health services research & policy, 13(2), 92-98.
  • [25] Haktanir, E., & Kahraman, C. (2022). A novel picture fuzzy CRITIC & REGIME methodology: Wearable health technology application. Engineering Applications of Artificial Intelligence, 113, 104942.
  • [26] Wang, Y., Wang, W., Wang, Z., Deveci, M., Roy, S. K., & Kadry, S. (2024). Selection of sustainable food suppliers using the Pythagorean fuzzy CRITIC-MARCOS method. Information Sciences, 664, 120326.
  • [27] Gedikli, T., & Cayir Ervural, B. (2022). Identification of optimum COVID-19 vaccine distribution strategy under integrated Pythagorean fuzzy environment. In Digitizing Production Systems: Selected Papers from ISPR2021, October 07-09, 2021 Online, Turkey (pp. 65-76). Springer International Publishing.
There are 27 citations in total.

Details

Primary Language English
Subjects Health Informatics and Information Systems
Journal Section Research Articles
Authors

Aleyna Erdoğan 0009-0000-2661-6485

Gizem Turcan 0000-0002-5530-7777

Onur Doğan 0000-0003-3543-4012

Erman Coşkun 0000-0001-8712-3246

Publication Date October 1, 2024
Submission Date April 24, 2024
Acceptance Date July 16, 2024
Published in Issue Year 2024 Volume: 4 Issue: 2

Cite

APA Erdoğan, A., Turcan, G., Doğan, O., Coşkun, E. (2024). Prioritizing Digital Health: Key Municipal Services Identified Through Fuzzy Methods. Artificial Intelligence Theory and Applications, 4(2), 76-106.