Journal article
Managing patient satisfaction in a blood-collection room by the probabilistic linguistic gained and lost dominance score method integrated with the best-worst method
Computers & industrial engineering, v 145, 106547
Jul 2020
Abstract
•We construct a criteria system for evaluating the satisfaction degrees of patients.•We extend the best worst method with probabilistic linguistic information.•We propose a method for qualitative multiple criteria decision-making problems.•We measure the satisfaction degrees of patients in a blood collection room.
Evaluating patient satisfaction on medical services in terms of multiple aspects is critical for hospital management. To settle this problem, a criteria system for medical service evaluation is first structured through a survey on experts. The probabilistic linguistic representation model is used to portray the qualitative evaluations that are unquantifiable in nature or unable to be accurately measured. Then, the best-worst method is extended to the probabilistic linguistic context to determine the weights of criteria information. Afterwards, we propose a comprehensive method by combing the probabilistic linguistic best-worst method with the gained and lost dominance score method to measure both the collective and worst performances of alternatives. Finally, we investigate a practical case about managing patient satisfaction in the blood collection room of a Chinese hospital which undergoes a reformation. The results show that the patient satisfaction has greatly improved after the reformation.
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Details
- Title
- Managing patient satisfaction in a blood-collection room by the probabilistic linguistic gained and lost dominance score method integrated with the best-worst method
- Creators
- Yan Ming - Sichuan UniversityLi Luo - Sichuan UniversityXingli Wu - Sichuan UniversityHuchang Liao - Sichuan UniversityBenjamin Lev - Drexel UniversityLi Jiang - Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital
- Publication Details
- Computers & industrial engineering, v 145, 106547
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems)
- Web of Science ID
- WOS:000542180000048
- Scopus ID
- 2-s2.0-85085237372
- Other Identifier
- 991019168822404721
InCites Highlights
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Engineering, Industrial