환자경험관리를 위한 고객의 VOC 텍스트 데이터 분석
Analysis of Voice of Customer for Patient Experience Management
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This study aims to analyze the VOC (Voice of the Customer) data of hospital patients in order to induce customers’ personal experience and evaluation and then provide patient-centered healthcare services. The VOC data were collected over 10 years (2007~2016) from one university hospital in Seoul, Korea. The total 20,649 VOCs were classified into four VOC types (compliments, complaints, suggestions, and inquiries) and analyzed by explanatory variables including year, season, service type, reporting channels, and reporter type. The Chi-Square tests were conducted for testing independence between explanatory variables and VOC types, using the statistical software SAS. The analyses of word frequency and word clouds were also derived using the R program. The VOC analyses resulted in statistically significant findings that distributions of VOC text types are different by year, season, service type, reporting channels, and reporter type. In addition, the word frequency and subsequent word clouds showed different personal concerns between VOC types and also between service types, respectively. The VOC analyses are expected to enable hospital managers to identity patient experiences and strategically apply them in practice. This will ultimately lead to the future of hospitals with improved patient satisfaction and higher service quality.
Abstract
This study aims to analyze the VOC (Voice of the Customer) data of hospital patients in order to induce customers’ personal experience and evaluation and then provide patient-centered healthcare services. The VOC data were collected over 10 years (2007~2016) from one university hospital in Seoul, Korea. The total 20,649 VOCs were classified into four VOC types (compliments, complaints, suggestions, and inquiries) and analyzed by explanatory variables including year, season, service type, reporting channels, and reporter type. The Chi-Square tests were conducted for testing independence between explanatory variables and VOC types, using the statistical software SAS. The analyses of word frequency and word clouds were also derived using the R program. The VOC analyses resulted in statistically significant findings that distributions of VOC text types are different by year, season, service type, reporting channels, and reporter type. In addition, the word frequency and subsequent word clouds showed different personal concerns between VOC types and also between service types, respectively. The VOC analyses are expected to enable hospital managers to identity patient experiences and strategically apply them in practice. This will ultimately lead to the future of hospitals with improved patient satisfaction and higher service quality.
참고문헌 (31)
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