Abstract
Providing better hospital service quality is one of the major concerns of healthcare industry in the world. Since health services in Turkey are provided in a very competitive environment, for making a better choice, the services delivered by the public and private hospitals should be evaluated according to the viewpoint of stakeholders in terms of satisfaction. In this study, a model proposal is presented based on the concept of Pythagorean fuzzy analytic hierarchy process and Pythagorean fuzzy technique for order preference by similarity to ideal solution method to provide an accurate decision-making process for evaluating the hospital service quality. We study under fuzzy environment to reduce uncertainty and vagueness, and use linguistic variables parameterized by Pythagorean fuzzy numbers. The proposed approach is separated from others with the integration of the methods in a way providing a systematic fuzzy decision-making process. A case study including 32 service quality criteria and two public and one private hospitals in Turkey assessed by 32 evaluators by medical staff, hospital executives, auxiliaries, and patients is performed to demonstrate the applicability and validity of the proposed approach. On conclusion, integrated model produces reliable and suggestive outcomes better representing the vagueness of decision-making process.
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Afkham L, Abdi F, Komijan A (2012) Evaluation of service quality by using fuzzy MCDM: a case study in Iranian health-care centers. Manag Sci Lett 2(1):291–300
Akdag H, Kalaycı T, Karagöz S, Zülfikar H, Giz D (2014) The evaluation of hospital service quality by fuzzy MCDM. Appl Soft Comput 23:239–248
Aktas A, Cebi S, Temiz I (2015) A new evaluation model for service quality of health care systems based on AHP and information axiom. J Intell Fuzzy Syst 28(3):1009–1021
Ali M, Raza SA (2017) Service quality perception and customer satisfaction in Islamic banks of Pakistan: the modified SERVQUAL model. Total Qual Manag Bus Excell 28(5–6):559–577
Altuntas S, Dereli T, Yilmaz MK (2012) Multi-criteria decision-making methods based weighted SERVQUAL scales to measure perceived service quality in hospitals: a case study from Turkey. Total Qual Manag Bus Excell 23(11–12):1379–1395
Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96
Carpitella S, Certa A, Izquierdo J, La Fata CM (2018) A combined multi-criteria approach to support FMECA analyses: a real-world case. Reliab Eng Syst Saf 169:394–402
Celik E, Gul M, Gumus AT, Guneri AF (2012) A fuzzy TOPSIS approach based on trapezoidal numbers to material selection problem. J Inf Technol Appl Manag 19(3):19–30
Chakravarty A (2011) Evaluation of service quality of hospital outpatient department services. Med J Armed Forces India 67(3):221–224
Chang TH (2014) Fuzzy VIKOR method: a case study of the hospital service evaluation in Taiwan. Inf Sci 271:196–212
Chen C (2000) Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets Syst 114:1–9
Demir P, Gul M, Guneri AF (2018) Evaluating occupational health and safety service quality by SERVQUAL: a field survey study. Total Qual Manag Bus Excell. https://doi.org/10.1080/14783363.2018.1433029
Demirer Ö, Bülbül H (2014) Kamu ve özel hastanelerde hizmet kalitesi, hasta tatmini ve tercihi arasındaki ilişki: Karşılaştırmalı bir analiz. Amme İdaresi Dergisi 47(2):95–119
Gul M (2018) Application of Pythagorean fuzzy AHP and VIKOR methods in occupational health and safety risk assessment: the case of a gun and rifle barrel external surface oxidation and colouring unit. Int J Occup Saf Ergon. https://doi.org/10.1080/10803548.2018.1492251
Gul M, Ak MF (2018) A comparative outline for quantifying risk ratings in occupational health and safety risk assessment. J Clean Prod 196:653–664
Gul M, Guneri AF (2018) Use of FAHP for occupational safety risk assessment: an application in the aluminum extrusion industry. In: Emrouznejad A, Ho W (eds) Fuzzy analytic hierarchy process. Chapman and Hall/CRC, New York, pp 249–271
Gul M, Celik E, Aydin N, Gumus AT, Guneri AF (2016) A state of the art literature review of VIKOR and its fuzzy extensions on applications. Appl Soft Comput 46:60–89
Gul M, Guneri AF, Nasirli SM (2018) A fuzzy-based model for risk assessment of routes in oil transportation. Int J Environ Sci Technol. https://doi.org/10.1007/s13762-018-2078-z
Handayani PW, Hidayanto AN, Sandhyaduhita PI, Ayuningtyas D (2015) Strategic hospital services quality analysis in Indonesia. Expert Syst Appl 42(6):3067–3078
Hwang CL, Yoon K (1981) Multiple attribute decision making: methods and applications, a state of the art survey. Springer, New York
Ilbahar E, Karaşan A, Cebi S, Kahraman C (2018) A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system. Saf Sci 103:124–136
Kayapınar S, Erginel N (2017) Designing the airport service with fuzzy QFD based on SERVQUAL integrated with a fuzzy multi-objective decision model. Total Qual Manag Bus Excell. https://doi.org/10.1080/14783363.2017.1371586
Kubler S, Robert J, Derigent W, Voisin A, Le Traon Y (2016) A state-of the-art survey & testbed of fuzzy AHP (FAHP) applications. Expert Syst Appl 65:398–422
Kutlu AC, Ekmekçioğlu M (2012) Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP. Expert Syst Appl 39(1):61–67
Lee H, Delene LM, Bunda MA, Kim C (2000) Methods of measuring health-care service quality. J Bus Res 48(3):233–246
Lin QL, Liu L, Liu HC, Wang DJ (2013) Integrating hierarchical balanced scorecard with fuzzy linguistic for evaluating operating room performance in hospitals. Expert Syst Appl 40(6):1917–1924
Lupo T (2013) A fuzzy ServQual based method for reliable measurements of education quality in Italian higher education area. Expert Syst Appl 40(17):7096–7110
Lupo T (2016) A fuzzy framework to evaluate service quality in the healthcare industry: an empirical case of public hospital service evaluation in Sicily. Appl Soft Comput 40:468–478
Mete S (2018) Assessing occupational risks in pipeline construction using FMEA based AHP-MOORA integrated approach under Pythagorean fuzzy environment. Hum Ecol Risk Assess Int J. https://doi.org/10.1080/10807039.2018.1546115
Min H, Mitra A, Oswald S (1997) Competitive benchmarking of health care quality using the analytic hierarchy process: an example from Korean cancer clinics. Socio-econ Plan Sci 31(2):147–159
Ministry of Health in Republic of Turkey (2015) Health statistics yearbook 2014. Republic of Turkey Ministry of Health General Directorate of Health Research, Ankara
Ministry of Health in Republic of Turkey (2016) Health statistics yearbook 2015. Republic of Turkey Ministry of Health General Directorate of Health Research, Ankara
Ministry of Health in Republic of Turkey (2017) Health statistics yearbook 2016. Republic of Turkey Ministry of Health General Directorate of Health Research, Ankara
Mohd WRW, Abdullah L (2017) Pythagorean fuzzy analytic hierarchy process to multi-criteria decision making. In: AIP conference proceedings, vol 1905, no 1, p. 040020. AIP Publishing
Oz NE, Mete S, Serin F, Gul M (2018) Risk assessment for clearing and grading process of a natural gas pipeline project: an extended TOPSIS model with Pythagorean fuzzy sets for prioritizing hazards. Hum Ecol Risk Assess Int J. https://doi.org/10.1080/10807039.2018.1495057
Parasuraman A, Zeithaml VA, Berry LL (1988) Servqual: a multiple-item scale for measuring consumer perc. J Retail 64(1):12
Pérez-Domínguez L, Rodríguez-Picón LA, Alvarado-Iniesta A, Luviano Cruz D, Xu Z (2018) MOORA under Pythagorean fuzzy set for multiple criteria decision making. Complexity 2018:1–10
Saaty TL (1990) How to make a decision: the analytic hierarchy process. Eur J Oper Res 48(1):9–26
Samantra C, Datta S, Mahapatra SS (2017) Fuzzy based risk assessment module for metropolitan construction project: an empirical study. Eng Appl Artif Intell 65:449–464
Shieh JI, Wu HH, Huang KK (2010) A DEMATEL method in identifying key success factors of hospital service quality. Knowl Based Syst 23(3):277–282
Taşkin H, Kahraman ÜA, Kubat C (2015) Evaluation of the hospital service in Turkey using fuzzy decision-making approach. J Intell Manuf. https://doi.org/10.1007/s10845-015-1157-y
Teng CI, Ing CK, Chang HY, Chung KP (2007) Development of service quality scale for surgical hospitalization. J Formos Med Assoc 106(6):475–484
Tzeng GH, Huang JJ (2011) Multiple attribute decision making: methods and applications. CRC Press, Boca Raton
Wu CR, Chang CW, Lin HL (2008) A fuzzy ANP-based approach to evaluate medical organizational performance. Inf Manag Sci 19(1):53–74
Yager RR (2014) Pythagorean membership grades in multicriteria decision making. IEEE Trans Fuzzy Syst 22(4):958–965
Zeng S, Chen J, Li X (2016) A hybrid method for pythagorean fuzzy multiple-criteria decision making. Int J Inf Technol Decis Mak 15(02):403–422
Zhang X, Xu Z (2014) Extension of TOPSIS to multiple criteria decision making with Pythagorean fuzzy sets. Int J Intell Syst 29(12):1061–1078
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Appendix: Hospital service quality evaluation questionnaire
Appendix: Hospital service quality evaluation questionnaire
This questionnaire is purely related to an academic research entitled “Hospital service quality evaluation: an integrated model based on Pythagorean fuzzy AHP and fuzzy TOPSIS”, aiming at measuring the healthcare service quality level of hospitals. This survey is divided into two sections to explain how we acquire the weights of criteria and sub-criteria and ratings (performances) of hospitals. The first questionnaire is designed for evaluating the relative importance of hospital service quality criteria. The second questionnaire is for evaluating the ranking order of hospitals with respect to each criterion.
The pairwise comparisons in the evaluation forms of first questionnaire will take a considerable time of you. However, findings of this study will contribute to present a guide for the way of highlighting hospital service quality.
Thank you for your attention. Regards.
1.1 General questions
Hospital name:
Working department:
Expertise area (medical/administrative/auxiliary):
Total time of experience (year):
1.2 First questionnaire
Main criteria evaluation
Pairwise comparison of a criterion versus another one | CLI | VLI | LI | BAI | AI | AAI | HI | VHI | CHI |
---|---|---|---|---|---|---|---|---|---|
Certainly low importance | Very low importance | Low importance | Below average importance | Average importance | Above average importance | High importance | Very high importance | Certainly high importance | |
C1 versus C2 | |||||||||
C1 versus C3 | |||||||||
C1 versus C4 | |||||||||
C1 versus C5 | |||||||||
C1 versus C6 | |||||||||
C2 versus C3 | |||||||||
C2 versus C4 | |||||||||
C2 versus C5 | |||||||||
C2 versus C6 | |||||||||
C3 versus C4 | |||||||||
C3 versus C5 | |||||||||
C3 versus C6 | |||||||||
C4 versus C5 | |||||||||
C4 versus C6 | |||||||||
C5 versus C6 |
Sub-criteria evaluation of C1
Pairwise comparison of a criterion versus another one | CLI | VLI | LI | BAI | AI | AAI | HI | VHI | CHI |
---|---|---|---|---|---|---|---|---|---|
Certainly low importance | Very low importance | Low importance | Below average importance | Average importance | Above average importance | High importance | Very high importance | Certainly high importance | |
C11 versus C12 | |||||||||
C11 versus C13 | |||||||||
C11 versus C14 | |||||||||
C12 versus C13 | |||||||||
C12 versus C14 | |||||||||
C13 versus C14 |
1.3 Second questionnaire
Linguistic terms: Extremely low (EL), Very little (VL), Little (L), Middle little (ML)
Middle (M), Middle high (MH), Big (B), Very tall (VT), Tremendously high (TH)
Hospital service quality criteria | Hospitals | ||
---|---|---|---|
Hospital 1 | Hospital 2 | Hospital 3 | |
C11 | |||
C12 | |||
C13 | |||
C14 | |||
C21 | |||
C22 | |||
C23 | |||
C24 | |||
C25 | |||
C26 | |||
C27 | |||
C28 | |||
C31 | |||
C32 | |||
C33 | |||
C34 | |||
C35 | |||
C41 | |||
C42 | |||
C43 | |||
C51 | |||
C52 | |||
C53 | |||
C54 | |||
C55 | |||
C56 | |||
C61 | |||
C62 | |||
C63 | |||
C64 | |||
C65 | |||
C66 |
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Yucesan, M., Gul, M. Hospital service quality evaluation: an integrated model based on Pythagorean fuzzy AHP and fuzzy TOPSIS. Soft Comput 24, 3237–3255 (2020). https://doi.org/10.1007/s00500-019-04084-2
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DOI: https://doi.org/10.1007/s00500-019-04084-2