Implementing healthcare service quality enhancement using a cloud-support QFD model integrated with TODIM method and linguistic distribution assessments

Quality function deployment (QFD) is an effective customer-oriented technique for enhancing healthcare service quality. Implementing QFD, patient requirements (PRs) should be firstly identified and prioritized to satisfy patients’ expectation. PRs expressed by human language should be interpreted and converted into technical requirements (TRs) under uncertain situations. The prioritization of TRs should consider patients’ bounded rationality ensuring the quality enhancement fit their psychological behaviors. This paper proposes a cloud-support QFD model to improve above phases. First, a best-worst method based PRs prioritizing phase is built to generate reliable and consistent weights of PRs. Then, a translating phase is constructed based on linguistic distribution assessments and asymmetric normal clouds to interpret patients’ voice and model the correlations. The phase of prioritizing TRs is proposed based on TODIM to better reflect patients’ psychological behaviors. Finally, a case study, sensitive and comparative analyses are provided to show the reliability of the proposed model.