Design of a smart medical service quality evaluation system based on a hybrid multi-criteria decision model.

Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE

Original Publication: London : Nature Publishing Group, copyright 2011-

Hospitals*; Humans ; Decision Support Techniques ; Quality of Health Care ; Decision Making ; Models, Theoretical

As medical informatization continues to progress, the sophistication of smart medicine (SM) systems has led to a marked enhancement in the caliber of medical services provided. Nevertheless, the existing body of literature is sparse when it comes to frameworks for evaluating service quality, specifically within the domain of smart medicine (SM). Drawing on a hybrid multi-criteria decision-making model that fuses the best-worst method (BWM) with the VIKOR approach, this research has crafted an innovative framework for assessing the service quality of SM. BWM was used to obtain the weights of all dimensions and indicators under each dimension. Then, the service quality of hospitals H1, H2 and H3 in Xiamen was evaluated using the VIKOR method. The results showed that smart appointments, diagnosis and treatment are three important dimensions to evaluate SM service quality in medical institutions. The stability and robustness of the model were proved through sensitivity analysis. Findings suggest that hospitals can strengthen the construction of their appointment information platforms, the quality management of internal doctors, and the information connection between self-service terminals and information platforms to improve hospital service quality in the construction of smart medicine.
(© 2024. The Author(s).)

Bakker, K. & Ritts, M. Smart Earth: A meta-review and implications for environmental governance. Glob. Environ. Change 52, 201–211 (2018). (PMID: 10.1016/j.gloenvcha.2018.07.011)
Tian, S. et al. Smart healthcare: Making medical care more intelligent. Glob. Health J. 3(3), 62–65 (2019). (PMID: 10.1016/j.glohj.2019.07.001)
Kumar, A., Dhanagopal, R., Albreem, M. A. & Le, D. N. A comprehensive study on the role of advanced technologies in 5G based smart hospital. Alex. Eng. J. 60(6), 5527–5536 (2021). (PMID: 10.1016/j.aej.2021.04.016)
Ryu, B., Kim, S. & Lee, K. H. Inpatient satisfaction and usage patterns of personalized smart bedside station system for patient-centered service at a tertiary university hospital. Int. J. Med. Inform. 95, 35–42 (2016). (PMID: 2769723010.1016/j.ijmedinf.2016.09.003)
Zhang, M., Dai, D. Y. & Hou, S. L. Thinking on the informatization development of China’s healthcare system in the post-COVID-19 era. Intell. Med. 1(1), 24–28 (2021). (PMID: 34777904857148410.1016/j.imed.2021.03.004)
Liang, J., Zheng, X. & Chen, Z. Y. The experience and challenges of healthcare-reform-driven medical consortia and Regional Health Information Technologies in China: A longitudinal study. Int. J. Med. Inform. 131, 103954 (2019). (PMID: 3151394310.1016/j.ijmedinf.2019.103954)
Chen, J., Zhou, M. Y. & Liu, Q. Y. High time for ease the doctor–patient relationship in China. J. Forensic Leg. Med. 72, 101961 (2020). (PMID: 32452451715137710.1016/j.jflm.2020.101961)
Zhou, P. L. & Rosenbergc, M. W. “Old friend and powerful cadre”: Doctor–patient relationships and multi-dimensional therapeutic landscapes in China’s primary hospitals. Health Place 72, 102708 (2021). (PMID: 3474212010.1016/j.healthplace.2021.102708)
Zhou, Z. L., Zhao, Y. X. & Shen, C. Evaluating the effect of hierarchical medical system on health seeking behavior: A difference-in-differences analysis in China. Soc. Sci. Med. 268, 113372 (2021). (PMID: 3297977610.1016/j.socscimed.2020.113372)
Choi, H., Yi, J. & Cho, S. H. Multifunctional micro/nanomotors as an emerging platform for smart healthcare applications. Biomaterials 279, 121201 (2021). (PMID: 3471563810.1016/j.biomaterials.2021.121201)
Lee, J. S., Chew, C. J. & Liu, J. Y. Medical blockchain: Data sharing and privacy preserving of EHR based on smart contract. J. Inf. Secur. Appl. 65, 103117 (2022).
Krishankumar, R. et al. An integrated decision-making COPRAS approach to probabilistic hesitant fuzzy set information. Complex Intell. Syst. 7, 2281–2298 (2021). (PMID: 10.1007/s40747-021-00387-w)
Ulutaş, A. et al. Development of a novel integrated CCSD-ITARA-MARCOS decision-making approach for stackers selection in a logistics system. Mathematics 8(10), 1672 (2020). (PMID: 10.3390/math8101672)
Nguyen, P. T., Nguyen, P. V. & Nguyen, Q. T. T. H. L. Application of fuzzy analytic network process and TOPSIS method for material supplier selection. In International Conference on Engineering Innovation, vol. 728, 411–415 (2017).
Huynh, V. D. B., Nguyen, Q. L. H. T. T. & Nguyen, P. T. Modeling the quality of urban life using grey analytical hierarchy process. TEM J. 12(1), 53–58 (2023).
Nguyen, P. T., Vu, N. B., Nguyen, L. V., Le, L. P. & Vo, K. D. The application of fuzzy analytic hierarchy process (F-AHP) in engineering project management. In 2018, ICETAS, Bangkok, Thailand.
Rani, P., Mishra, A. R., Krishankumar, R., Ravichandran, K. S. & Kar, S. Multi-criteria food waste treatment method selection using single-valued neutrosophic-CRITIC-MULTIMOORA framework. Appl. Soft Comput. 111, 107657 (2021). (PMID: 10.1016/j.asoc.2021.107657)
Chatterjee, K. & Kar, S. Unified granular-number-based AHP-VIKOR multi-criteria decision framework. Granul. Comput. 2, 199–221 (2017). (PMID: 10.1007/s41066-017-0039-4)
Chatterjee, K. & Kar, S. Supplier selection in telecom supply chain management: A fuzzy-Rasch based COPRAS-G method. Technol. Econ. Dev. Econ. 24(2), 765–791 (2018). (PMID: 10.3846/20294913.2017.1295289)
Behdioğlu, S., Acar, E. & Burhan, H. A. Evaluating service quality by fuzzy SERVQUAL: A case study in a physiotherapy and rehabilitation hospital. Total Qual. Manag. Bus. 30(3), 301–319 (2017).
Lupo, T. A fuzzy framework to evaluate service quality in the healthcare industry: An empirical case of public hospital service evaluation in Sicily. App. Soft Comput. 40, 468–478 (2016). (PMID: 10.1016/j.asoc.2015.12.010)
Akdag, H., Kalayci, T. & Karagöz, S. The evaluation of hospital service quality by fuzzy MCDM. Appl. Soft Comput. 23, 239–248 (2014). (PMID: 10.1016/j.asoc.2014.06.033)
Rezaei, J. Best–worst multi-criteria decision-making method. Omega 53, 49–57 (2015). (PMID: 10.1016/j.omega.2014.11.009)
Hezer, S., Gelmez, E. & Özceylan, E. Comparative analysis of TOPSIS, VIKOR and COPRAS methods for the COVID-19 regional safety assessment. J. Infect. Public Health 14, 775–786 (2021). (PMID: 34022737798907410.1016/j.jiph.2021.03.003)
Li, Z., Xing, Y. & Dong, P. A novel q-rung orthopair fuzzy best–worst method, Shannon entropy and MARCOS method for mobile medical app service quality evaluation. Appl. Soft comput. 155, 11147 (2024). (PMID: 10.1016/j.asoc.2024.111417)
Tushar, S. R., Moktadir, M. A., Kusi-Sarpong, S. & Ren, J. Driving sustainable healthcare service management in the hospital sector. J. Clean. Prod. 420, 138210 (2023). (PMID: 10.1016/j.jclepro.2023.138310)
Tuzkaya, G., Sennaroglu, B., Kalender, Z. T. & Mutlu, M. Hospital service quality evaluation with IVIF-PROMETHEE and a case study. Socio Econ. Plan. Sci. 68, 100705 (2019). (PMID: 10.1016/j.seps.2019.04.002)
Handayani, P. W., Hidayanto, A. N., Sandhyaduhita, P. I., Kasiyah, & Ayuningtyas, D. Strategic hospital services quality analysis in Indonesia. Expert Syst. Appl. 42, 3067–3078 (2015). (PMID: 10.1016/j.eswa.2014.11.065)
Shafii, M., Rafiei, S. & Abooee, F. Assessment of service quality in teaching hospitals of Yazd University of Medical Sciences: Using multi-criteria decision making techniques. Osong Public Health Res. Perspect. 7, 239–247 (2016). (PMID: 27635374501475810.1016/j.phrp.2016.05.001)
Wu, C. C., Ho, C. C. & Yang, K. C. Selecting indicators of acupuncture service quality using analytic hierarchy process. Eur. J. Integr. Med. 66, 102324 (2024). (PMID: 10.1016/j.eujim.2023.102324)
Akdag, H., Kalaycı, T. & Karagöz, S. The evaluation of hospital service quality by fuzzy MCDM. Appl. Soft Comput. 23, 239–248 (2014). (PMID: 10.1016/j.asoc.2014.06.033)
Gregório, B. C., Pereira, M. A. & Costa, A. S. Multi-criteria decision-aiding for public hospitals: The role of interactions among pairs of access and quality criteria. Omega 126, 103046 (2024). (PMID: 10.1016/j.omega.2024.103046)
Mete, S., Yucesan, M., Gul, M. & Ozceylan, E. An integrated hybrid MCDM approach to evaluate countries’ COVID-19 risks. Socio-Econ. Plan. Sci. 90, 101744 (2023). (PMID: 10.1016/j.seps.2023.101744)
Krishankumar, R., Dhruva, S., Ravichandran, K. S. & Kar, S. Selection of a viable blockchain service provider for data management within the internet of medical things: An MCDM approach to Indian healthcare. Inf. Sci. 657, 119890 (2024). (PMID: 10.1016/j.ins.2023.119890)
Chowdhury, N. K., Kabir, M. A., Rahman, M. M. & Islam, S. M. S. Machine learning for detecting COVID-19 from cough sounds: An ensemble-based MCDM method. Comput. Biol. Med. 145, 105405 (2022). (PMID: 35318171892694510.1016/j.compbiomed.2022.105405)
Liang, Y. X. & Zhao, L. D. Intelligent hospital appointment system based on health data bank. Procedia Comput. Sci. 159, 1880–1889 (2019). (PMID: 10.1016/j.procs.2019.09.360)
Küçük, A., Demirci, M., Kerman, G. & Özsoy, V. S. Evaluating of hospital appointment systems in Turkey: Challenges and opportunities. Health Policy Technol. 10(1), 69–74 (2021). (PMID: 10.1016/j.hlpt.2020.11.008)
Boone, C. E., Celhay, P. & Gertler, P. How scheduling systems with automated appointment reminders improve health clinic efficiency. J. Health Econ. 82, 102598 (2022). (PMID: 3517224210.1016/j.jhealeco.2022.102598)
Schwebal, F. J. & Larimer, M. E. Using text message reminders in health care services: A narrative literature review. Int. Interv. 13, 82–104 (2018).
Nah, J. E. & Kim, S. Workforce planning and deployment for a hospital reservation call center with abandonment cost and multiple tasks. Comput. Ind. Eng. 65(2), 297–309 (2013). (PMID: 10.1016/j.cie.2012.12.024)
Hoti, K., Chivers, P. T. & Hughes, J. D. Assessing procedural pain in infants: A feasibility study evaluating a point-of-care mobile solution based on automated facial analysis. Lancet Digit. Health 3(10), e623–e634 (2021). (PMID: 3448176910.1016/S2589-7500(21)00129-1)
Macano, C. A. W., Kirby, G. C. & Lake, B. Surgical assessment clinic - One stop emergency out-patient clinic for rapid assessment, reduced admissions and improved acute surgical service: A quality improvement study. Ann. Med. Surg. 23, 28–31 (2017). (PMID: 10.1016/j.amsu.2017.09.016)
Su, Y. C. et al. Design and implementation of patient bedside self-service platform. Chin. Med. Equip. 34(1), 81–83 (2019).
Gunasekeran, D. V., Tham, Y. C. & Ting, D. S. W. Digital health during COVID-19: Lessons from operationalizing new models of care in ophthalmology. Lancet Digit. Health 3(2), 124–134 (2021). (PMID: 10.1016/S2589-7500(20)30287-9)
Zhou, M., Li, J. & Wang, Y. Nursing safety management of inpatient identification based on PDA. Contin. Med. Edch. 27, 250–252 (2015).
Keane, C., Regan, M. & Walsh, B. Failure to take-up public healthcare entitlements: Evidence from the medical card system in Ireland. Soc. Sci. Med. 281, 114069 (2021). (PMID: 3412008410.1016/j.socscimed.2021.114069)
Zhou, M. Y. & Zhou, C. R. Discussion on “internet +” hospital medical settlement mode. Mod. Hosp. Manag. 14(4), 72–74 (2016).
Bamgboye, E. A. & Jarallah, J. S. Long-waiting outpatients: Target audience for health education. Patient Educ. Couns. 23(1), 49–54 (1994). (PMID: 797154010.1016/S0738-3991(05)80021-7)
Nourian, M., Askari, G. & Golshiri, P. Effect of lifestyle modification education based on health belief model in overweight/obese patients with non-alcoholic fatty liver disease: A parallel randomized controlled clinical trial. Clin. Nutr. Espen 38, 236–241 (2020). (PMID: 3269016410.1016/j.clnesp.2020.04.004)
Sunet, J. M., Lu, Q. F. & Xu, J. J. Effectiveness research and demand analysis of “health education mobile classroom” outpatient service in Children’s specialized hospital. Chin. J. Pract. 34(28), 2222–2226 (2018).
Grosshandler, J. A., Tulbert, B. & Kaufmann, M. D. The electronic medical record in dermatology. Arch. Dermatol. 146(9), 1031–1036 (2010). (PMID: 2085570710.1001/archdermatol.2010.229)
Opricovic, S. Multicriteria optimization of civil engineering systems. Fac. Civ. Eng. 2, 5–21 (1998).
Wang, T., Chen, D. N. & Fan, G. R. Analysis of outpatient satisfaction and its influencing factors in public medical institutions in Shanghai. Chin. Health Resour. 20(6), 460–480 (2017).
Chen, D. N., Fan, G. R. & Luo, H. W. Analysis of outpatient appointments in public medical institutions and their correlation with medical service satisfaction. Chin. Hosp. Manag. 38(7), 14–16 (2018).
Tang, K., Ye, Q. H. & Chen, Z. B. Status quo of doctor–patient communication quality in general hospitals. Hainan Med. 29(24), 3541–3544 (2018).
Han, W. P., Wang, Y. P. & Liao, X. M. A comparative analysis of the demand degree survey of patients’ self-service registration and window registration. Armed. Police Med. 27(8), 850–852 (2016).

FJKX-2023XKB014 The Science and Technology Innovation Think Tank of Fujian Association of Science and Technology

Keywords: BWM; Service quality; Smart medicine; VIKOR

Date Created: 20241103 Date Completed: 20241103 Latest Revision: 20241105

20241106

PMC11531580

10.1038/s41598-024-71224-6

39488546