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Application of Rough Set Theory to Improve Outpatient Medical Service Quality in Public Hospitals Based on the Patient Perspective.

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 (C ₁)," "Respected by registered staff (C ₂)," "Registered staff's listening (C ₃)," "Respected by doctor (C ₉)," "Signpost (C ₁₂)," and "Privacy (C ₁₆)." 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.

SUBMITTER: Du ML

PROVIDER: S-EPMC8654147 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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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 ML Tung Tao-Hsin TH Tao Ping P Chien Ching-Wen CW Chuang Yen-Ching YC

Frontiers in public health 20211124

<b>Purpose:</b> To analyze the key factors and decision-making behaviors affecting overall satisfaction based on perceptual data of outpatients. <b>Methods:</b> 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., i ...[more]

PMID: 34900895

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Project description:In China, most ambulatory care is provided by public hospitals. However, many public hospitals' outpatient services fall short of meeting patients' demands. This study aimed to assess the quality gap of outpatient service in public hospitals by using an indicator system developed based on the SERVQUAL model. This cross-sectional study was conducted from June to July 2019 at 13 public hospitals in Shenzhen. In total, 1876 outpatients were included in the study, and participants were asked to fill out the modified SERVQUAL questionnaire. The scale consists of 23 items scattered in 6 dimensions, including Safety, Reliability, Responsiveness, Tangibles, Assurance, and Empathy. Descriptive analysis, t-test or F-test, and optimal scale analysis were performed. The patients' expectation of outpatient services exceeded the hospitals' services they perceived and resulted in negative gaps between the mean expectation and perception scores for each indicator. The gaps were as follows: Reliability = Empathy > Responsiveness > Safety > Assurance > Tangibles. Quality gaps differed significantly between age, education, and hospital type. The overall impression mean scores are 7.45 ± 1.18 (out of 9 points) and 7.27 ± 1.23 for general and specialized hospitals. And the willingness to recommend the hospital to others mean scores are 4.06 ± 0.62 (out of 5 points) and 3.92 ± 0.65 for general and specialized hospitals. Patients' impression of the hospital and willingness to recommend the hospital were significantly correlated with age, citizenship, health insurance, referral type, frequency of visits, Safety, Tangibles, Reliability, and Assurance. The outpatient service provided by Shenzhen's public hospitals did not meet patients' needs. Hospital administrators should evaluate the quality gap of outpatient services to identify the flaws within the delivery of ambulatory care and make improvements according to the findings to drive patient-centered care.

Project description:BackgroundAntimicrobial peptides attract considerable interest as novel agents to combat infections. Their long-time potency across bacteria, viruses and fungi as part of diverse innate immune systems offers a solution to overcome the rising concerns from antibiotic resistance. With the rapid increase of antimicrobial peptides reported in the databases, peptide selection becomes a challenge. We propose similarity analyses to describe key properties that distinguish between active and non-active peptide sequences building upon the physicochemical properties of antimicrobial peptides. We used an iterative supervised machine learning approach to classify active peptides from inactive peptides with low false discovery rates in a relatively short computational search time.ResultsBy generating explicit boundaries, our method defines new categories of active and inactive peptides based on their physicochemical properties. Consequently, it describes physicochemical characteristics of similarity among active peptides and the physicochemical boundaries between active and inactive peptides in a single process. To build the similarity boundaries, we used the rough set theory approach; to our knowledge, this is the first time that this approach has been used to classify peptides. The modified rough set theory method limits the number of values describing a boundary to a user-defined limit. Our method is optimized for specificity over selectivity. Noting that false positives increase activity assays while false negatives only increase computational search time, our method provided a low false discovery rate. Published datasets were used to compare our rough set theory method to other published classification methods and based on this comparison, we achieved high selectivity and comparable sensitivity to currently available methods.ConclusionsWe developed rule sets that define physicochemical boundaries which allow us to directly classify the active sequences from inactive peptides. Existing classification methods are either sequence-order insensitive or length-dependent, whereas our method generates the rule sets that combine order-sensitive descriptors with length-independent descriptors. The method provides comparable or improved performance to currently available methods. Discovering the boundaries of physicochemical properties may lead to a new understanding of peptide similarity.

Dataset Information

Application of Rough Set Theory to Improve Outpatient Medical Service Quality in Public Hospitals Based on the Patient Perspective.

Publications

Application of Rough Set Theory to Improve Outpatient Medical Service Quality in Public Hospitals Based on the Patient Perspective.

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