Demand analysis of outpatient whole process service management based on KANO model

Objective To demonstrate the feasibility of introducing KANO model into outpatient whole-process service management, so as to provide decision-making basis for hospitals to accurately improve patient satisfaction under resource limitation. Methods A self-designed questionnaire based on KANO model was used to conduct a cross-sectional survey. The survey covered the whole process of outpatient before, during and after treatment. A total of 392 outpatients in a hospital in Shenzhen were investigated from August 1 to September 30, 2025. Through two rounds of Delphi expert consultation by 8 experts, 83 service indicators of 5 dimensions were formed. The KANO attribute assessment scale was used to classify the needs of patients, which was further refined by Better-Worse coefficient analysis, four-quadrant matrix, and sensitivity ranking to prioritize improvement strategies. Results According to the KANO model, there were 18 must-be needs, 15 expected needs, 25 attractive needs and 19 indifferent needs in the content of comprehensive service process. Item 35 “Patient explanation by doctors regarding etiology, prevention, and treatment pros and cons,” item 44 “Provision of detailed medication guidance sheets,” item 53 “Provision of personalized disease and health guidance,” item 47 “Medical staff maintaining eye contact and focus during communication,” Item 9 “Centralized layout of examination and laboratory departments” are the top 5 requirements with the highest sensitivity. These highly sensitive items were mainly classified as expected needs, highlighting their critical role in improving patient satisfaction. Conclusion The KANO model has applicability in the whole process management of outpatient service, effectively distinguishing the multi-level structure of patients’ needs. Hospitals can adopt a hierarchical improvement strategy: ensuring must-be needs to maintain baseline satisfaction, prioritizing high-sensitivity expected needs to directly improve patient experience, and selectively piloting attractive needs to generate surprise value. This approach enables targeted and efficient allocation of limited resources to the service elements with the greatest impact.