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Application of Rough Set Theory to Improve Outpatient Medical Service Quality in Public Hospitals Based on the Patient Perspective.
Du, Man-Li; Tung, Tao-Hsin; Tao, Ping; Chien, Ching-Wen; Chuang, Yen-Ching.
  • Du ML; Huadu District of Guangzhou Maternal and Child Health Hospital (Huzhong Hospital), Guangzhou, China.
  • Tung TH; Enze Medical Research Center, Affiliated Taizhou Hospital of Wenzhou Medical College, Taizhou, China.
  • Tao P; Department of Medical Affairs and Planning, Section of Medical Fees Kaohsiung Veterans General Hospital, Kaohsiung City, Taiwan.
  • Chien CW; Institute for Hospital Management, Tsing Hua University, Shenzhen, China.
  • Chuang YC; Institute of Public Health & Emergency Management, Taizhou University, Taizhou, China.
Front Public Health ; 9: 739119, 2021.
Article in English | MEDLINE | ID: covidwho-1775890
ABSTRACT

Purpose:

To analyze the key factors and decision-making behaviors affecting overall satisfaction based on perceptual data of outpatients.

Methods:

The official satisfaction questionnaire developed by the National Health Commission of the People's Republic of China was used. Rough set theory was used to identify the perception patterns between condition attributes (i.e., service factors) and a decision attribute (i.e., overall service level) and to express them in rule form (i.e., if-then).

Results:

The four minimal-coverage rules, with strength exceeding 10% in the good class, and six crucial condition attributes were obtained "Ease of registration (C1)," "Respected by registered staff (C2)," "Registered staff's listening (C3)," "Respected by doctor (C9)," "Signpost (C12)," and "Privacy (C16)." In addition, the average hit rate for 5-fold cross-validation was 90.86%.

Conclusions:

A series of decision rules could help decision-makers easily understand outpatients' situations and propose more suitable programs for improving hospital service quality because these decision rules are based on actual outpatient experiences.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Ambulatory Care / Hospitals, Public Type of study: Observational study / Prognostic study / Qualitative research / Randomized controlled trials Limits: Humans Language: English Journal: Front Public Health Year: 2021 Document Type: Article Affiliation country: Fpubh.2021.739119

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Ambulatory Care / Hospitals, Public Type of study: Observational study / Prognostic study / Qualitative research / Randomized controlled trials Limits: Humans Language: English Journal: Front Public Health Year: 2021 Document Type: Article Affiliation country: Fpubh.2021.739119