Improving the quality of hospital services using the QFD approach and integration with kano analysis under budget constraint

Document Type: Research Paper


Industrial Engineering Department, Faculty of Engineering, Semnan University, Semnan, Iran


Background and Objectives: One of the main concerns of hospital managers is their ability in improving their organization's performance. The use of quality management and decision-making techniques facilitates managers to achieve this goal. In this research, the corrective activities to increase the quality of hospital services are determined and selected using an integration of the QFD method with the kano analysis and knapsack problem mathematical model (KPMM). This approach is implemented in a private hospital in Iran.
Methods: First, the customers’ wants are identified. The corrective activities are then identified to meet these want, and the relationship between each corrective activity and each want is determined. Next, the types of the wants are identified based on the kano analysis, and their weights are determined using their degree of importance and type. Then, the final weight of each corrective activities is obtained based on the wants’ weights and the relationship matrix. Finally, KPMM is used to select the optimum list of corrective activities under budget constraint.
Findings: This study identifies 30 customers’ wants among them “Professional and experienced doctors and nurses" and " Healthy and sufficient consumables" obtained the highest weights. Results show that there are 2 “Attractive” customer wants, 15 “One-dimensional” customer wants and 13 “Must-be” customers’ wants. Finally, 30 corrective activities were identified which are placed at house of quality. The corrective activities "Training of physicians and nurses" and "Increasing staff sense of responsibility" obtained the highest weights.
Conclusions: Utilizing the kano analysis for determining the weight of customers’ wants in the house of quality approach causes the organization's strategies taken in to account in prioritizing corrective activities. Moreover, KPMM leads to an optimum selection of corrective activities.


1.             Torkzad, A. and M.A. Beheshtinia Evaluating and prioritizing hospital service quality. International Journal of Health Care Quality Assurance. 2019; 32(2): 332-346.

2.             Hatefi, S.M. and A. Haeri Evaluating hospital performance using an integrated balanced scorecard and fuzzy data envelopment analysis. Journal of Health Management and Informatics. 2019; 6(2): 66–76.

3.             Lupo, T. A fuzzy framework to evaluate service quality in the healthcare industry: An empirical case of public hospital service evaluation in Sicily. Applied Soft Computing. 2016; 40: 468-478.

4.             Askari, R., Z. Hatamizadeh, F. Sepaseh, R. Montazerolfaraj, F. Shamsi, and S. Rafiei Evaluation of Critical Thinking Disposition among College Students: A Study among Healthcare Management Students. International Journal of Hospital Research. 2017; 6(4): 76-90.

5.             Ghatreh Samani, M. and S.-M. Hosseini-Motlagh Evaluation and Selection of Most Preferable Supplementary Blood Centers in The Case of Tehran. International Journal of Hospital Research. 2018; 7(2): 81-101.

6.             Ghannadpour, S.F., A. Rezahoseini, and E. Ahmadi Selection of Sustainable Supplier for Medical Centers with Data Envelopment Analysis (DEA) & Multi-Attributed Utility Theory (MAUT) Approaches. International Journal of Hospital Research. 2018; 7(1): 82-96.

7.             Sedady, F. and M.A. Beheshtinia A novel MCDM model for prioritizing the renewable power plants’ construction. Management of Environmental Quality: An International Journal. 2019; 30(2): 383-399.

8.             Jokar, A. and S.-M. Hosseini-Motlagh Impact of Capacity of Mobile Units on Blood Supply Chain Performance: Results from a Robust Analysis. International Journal of Hospital Research. 2015; 4(3): 101-105.

9.             Hosseini-Motlagh, S.-M., M.R.G. Samani, and S. Cheraghi Robust and stable flexible blood supply chain network design under motivational initiatives. Socio-Economic Planning Sciences. 2019: 100725.

10.          Samani, M.R.G., S.-M. Hosseini-Motlagh, and S.F. Ghannadpour A multilateral perspective towards blood network design in an uncertain environment: Methodology and implementation. Computers & Industrial Engineering. 2019; 130: 450-471.

11.          Materla, T., E.A. Cudney, and D. Hopen Evaluating factors affecting patient satisfaction using the Kano model. International journal of health care quality assurance. 2019; 32(1): 137-151.

12.          Wang, C.-H. and H.-Y. Fong Integrating fuzzy Kano model with importance-performance analysis to identify the key determinants of customer retention for airline services. Journal of Industrial and Production Engineering. 2016; 33(7): 450-458.

13.          Singh, A. and A. Prasher Measuring healthcare service quality from patients’ perspective: using Fuzzy AHP application. Total Quality Management & Business Excellence. 2019; 30(3-4): 284-300.

14.          Rezvani, M., M. Beheshtinia, and M. Forozeshfard A New Fuzzy AHP- Fuzzy VIKOR Approach in Control and Management of The Angiography Procedure to Prevent Disruptions: A Case Study. International Journal of Hospital Research. 2018; 7(1): 97-108.

15.          Beheshtinia, M. and V. Nemati-Abozar A Novel Hybrid Fuzzy Multi-Criteria Decision-Making Model for Supplier Selection Problem (A Case Study in Advertising industry). Journal of Industrial and Systems Engineering. 2017; 9(4): 65-79.

16.          Gul, M., E. Celik, A.T. Gumus, and A.F. Guneri Emergency department performance evaluation by an integrated simulation and interval type-2 fuzzy MCDM-based scenario analysis. European Journal of Industrial Engineering. 2016; 10(2): 196 - 223.

17.          Keshavarz Ghorabaee, M., M. Amiri, E.K. Zavadskas, and J. Antucheviciene Supplier evaluation and selection in fuzzy environments: a review of MADM approaches. Economic Research-Ekonomska Istraživanja. 2017; 30(1): 1073-1118.

18.          Kano, N. Attractive quality and must-be quality. Hinshitsu (Quality, The Journal of Japanese Society for Quality Control). 1984; 14: 39-48.

19.          Chen, L.-H. and Y.-F. Kuo Understanding e-learning service quality of a commercial bank by using Kano's model. Total Quality Management. 2011; 22(1): 99-116.

20.          Behdioğlu, S., E. Acar, and H.A. Burhan Evaluating service quality by fuzzy SERVQUAL: a case study in a physiotherapy and rehabilitation hospital. Total Quality Management & Business Excellence. 2017: 1-19.

21.          Zobnina, M. and A. Rozhkov Listening to the voice of the customer in the hospitality industry: Kano model application. Worldwide Hospitality and Tourism Themes. 2018(just-accepted): 00-00.

22.          Ali, S.S., A. Basu, and N. Ware Quality measurement of Indian commercial hospitals–using a SERVQUAL framework. Benchmarking: An International Journal. 2018; 25(3): 815-837.

23.          Farokhnia, M. and M.A. Beheshtinia A three-dimensional house: extending quality function deployment in two organizations. Management Decision. 2019; 57(7): 1589-1608.

24.          Cetinkaya, C., O.N. Kenger, Z.D. Kenger, and E. Ozceylan, Quality Function Deployment Implementation on Educational Curriculum of Industrial Engineering in University of Gaziantep, in Industrial Engineering in the Big Data Era. 2019, Springer. p. 67-78.

25.          Wood, L.C., C. Wang, H. Abdul-Rahman, and N.S.J. Abdul-Nasir Green hospital design: integrating quality function deployment and end-user demands. Journal of Cleaner Production. 2016; 112: 903-913.

26.          Akao, Y., Quality function deployment: integrating customer requirements into product design. 1990: Productivity press.

27.          Fauziah, F., E. Surachman, and A. Muhtadi Integration of service quality and quality function deployment as an effort of pharmaceutical service improvement on outpatient in a referral Hospital, Karawang, Indonesia. Journal of Advanced Pharmacy Education & Research| Apr-Jun. 2019; 9(2).

28.          Raziei, Z., S.A. Torabi, S. Tabrizian, and B. Zahiri A Hybrid GDM-SERVQUAL-QFD Approach for Service Quality Assessment in Hospitals. Engineering Management Journal. 2018; 30(3): 179-190.

29.          Wibawa, J., H.A. Widjaja, and A.N. Hidayanto. Integrating IS success model, SERVQUAL and Kano model into QFD to improve hospital information system quality. in 2016 International Conference on Information Management and Technology (ICIMTech). 2016. IEEE.

30.          Gao, N.-n. and Y. Zhang. Healthcare service hidden quality cost estimation based the SERVQUAL and QFD method. in Proceedings of the 22nd International Conference on Industrial Engineering and Engineering Management 2015. 2016. Springer.

31.          Camgöz-Akdağ, H., M. Tarım, S. Lonial, and A. Yatkın QFD application using SERVQUAL for private hospitals: a case study. Leadership in Health Services. 2013; 26(3): 175-183.

32.          Osorio-Gómez, J.C. and D.F. Manotas-Duque, Fuzzy QFD and TOPSIS for Dispatching Prioritization in Maritime Transportation Considering Operational Risk, in Best Practices in Manufacturing Processes. 2019, Springer. p. 97-116.

33.          Garibay, C., H. Gutiérrez, and A. Figueroa Evaluation of a digital library by means of quality function deployment (QFD) and the Kano model. The Journal of Academic Librarianship. 2010; 36(2): 125-132.

34.          Kuo, C.-M., H.-T. Chen, and E. Boger Implementing city hotel service quality enhancements: integration of Kano and QFD analytical models. Journal of Hospitality Marketing & Management. 2016; 25(6): 748-770.

35.          Vaziri, J. and M.A. Beheshtinia A holistic fuzzy approach to create competitive advantage via quality management in services industry (case study: life-insurance services). Management Decision. 2016; 54(8): 2035-2062.

36.          Baki, B., C. Sahin Basfirinci, I. Murat AR, and Z. Cilingir An application of integrating SERVQUAL and Kano's model into QFD for logistics services: A case study from Turkey. Asia Pacific Journal of Marketing and Logistics. 2009; 21(1): 106-126.

37.          Beheshtinia, M.A. and M. Farzaneh Azad A fuzzy QFD approach using SERVQUAL and Kano models under budget constraint for hotel services. Total Quality Management & Business Excellence. 2019; 30(7-8): 808-830.

38.          Yeh, T.-M. Determining medical service improvement priority by integrating the refined Kano model, Quality function deployment and Fuzzy integrals. African Journal of Business Management. 2010; 4: 2534-2545.

39.          Gupta, P. and R. Srivastava Customer Satisfaction for Designing Attractive qualities of Healthcare Service in India Using Kano Model and Quality Function Deployment. MIT Intl Jl of Mech. Engg. 2011; 1: 101-107.

40.          Chiou, C. and Y. Cheng. An integrated method of Kano model and QFD for designing impressive qualities of healthcare service. in 2008 IEEE International Conference on Industrial Engineering and Engineering Management. 2008.

41.          Materla, T., E.A. Cudney, and J. Antony The application of Kano model in the healthcare industry: a systematic literature review. Total Quality Management & Business Excellence. 2019; 30(5-6): 660-681.

42.          Mustafa, R., ICQI'2002 Building Customer Satisfaction using Kano Model and QFD – A Pakistani Hospital Case Study BUILDING CUSTOMER SATISFACTION USING KANO MODEL AND QFD – A PAKISTANI HOSPITAL CASE STUDY. 2002.

43.          Yeh, T.M. and S.-H. Chen Integrating Refined Kano Model, Quality Function Deployment, and Grey Relational Analysis to Improve Service Quality of Nursing Homes. Human Factors and Ergonomics in Manufacturing & Service Industries. 2014; 24.

44.          Nordin, N. and R. Che Razak, A Conceptual Kano and Quality Function Deployment (QFD) Framework for Healthcare Service. 2010.

45.          Beheshtinia, M.A. and S. Omidi A hybrid MCDM approach for performance evaluation in the banking industry. Kybernetes. 2017; 46(8): 1386-1407.

46.          Nikfarjam, H., M. Rostamy-Malkhalifeh, and A. Noura A New Robust Dynamic Data Envelopment Analysis Approach for Sustainable Supplier Evaluation. Advances in Operations Research. 2018; 2018: 20.

47.          Peykani, P., E. Mohammadi, M.S. Pishvaee, M. Rostamy-Malkhalifeh, and A. Jabbarzadeh A novel fuzzy data envelopment analysis based on robust possibilistic programming: possibility, necessity and credibility-based approaches. RAIRO-Oper. Res. 2018; 52(4-5): 1445-1463.

48.          Peykani, P., E. Mohammadi, A. Jabbarzadeh, and A. Jandaghian Utilizing Robust Data Envelopment Analysis Model for Measuring Efficiency of Stock, A case study: Tehran Stock Exchange. Journal of New Researches in Mathematics. 2016; 1(4): 15-24.

49.          Peykani, P. and E. Mohammadi Interval network data envelopment analysis model for classification of investment companies in the presence of uncertain data. Journal of Industrial and Systems Engineering. 2018; 11(Special issue: 14th International Industrial Engineering Conference): 63-72.

50.          Ghannadpour, S.F. and A. Zarrabi Multi-objective heterogeneous vehicle routing and scheduling problem with energy minimizing. Swarm and Evolutionary Computation. 2019; 44: 728-747.

51.          Ghannadpour, S.F. Evolutionary Approach for Energy Minimizing Vehicle Routing Problem with Time Windows and Customers’ Priority. International Journal of Transportation Engineering. 2019; 6(3): 237-264.

52.          Beheshtinia, M.A., A. Ghasemi, and M. Farokhnia Supply chain scheduling and routing in multi-site manufacturing system (case study: a drug manufacturing company). Journal of Modelling in Management. 2018; 13(1): 27-49.

53.          Borumand, A. and M.A. Beheshtinia A developed genetic algorithm for solving the multi-objective supply chain scheduling problem. Kybernetes. 2018; 47(7): 1401-1419.

54.          Beheshtinia, M.A. and A. Ghasemi A multi-objective and integrated model for supply chain scheduling optimization in a multi-site manufacturing system. Engineering Optimization. 2018; 50(9): 1415-1433.