Perfect ratings with negative comments: Learning from contradictory patient survey responses

This research explores why patients give perfect domain scores yet provide negative comments on surveys. In order to explore this phenomenon, vendor-supplied in-patient survey data from eleven different hospitals of a major U.S. health care system were utilized. The dataset included survey scores an...

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Autores principales:	Andrew Gallan, Marina Girju, Roxana Girju
Formato:	article
Lenguaje:	EN
Publicado:	The Beryl Institute 2017
Materias:	patient experience likelihood to recommend patient comments patient surveys text analytics natural language analysis Medicine (General) R5-920 Public aspects of medicine RA1-1270
Acceso en línea:	https://doaj.org/article/b1bd920a047d49fe8de03f07d1692740
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spelling	oai:doaj.org-article:b1bd920a047d49fe8de03f07d16927402021-11-15T04:22:14ZPerfect ratings with negative comments: Learning from contradictory patient survey responses2372-0247https://doaj.org/article/b1bd920a047d49fe8de03f07d16927402017-11-01T00:00:00Zhttps://pxjournal.org/journal/vol4/iss3/6https://doaj.org/toc/2372-0247This research explores why patients give perfect domain scores yet provide negative comments on surveys. In order to explore this phenomenon, vendor-supplied in-patient survey data from eleven different hospitals of a major U.S. health care system were utilized. The dataset included survey scores and comments from 56,900 patients, collected from January 2015 through October 2016. Of the total number of responses, 30,485 (54%) contained at least one comment. For our analysis, we use a two-step approach: a quantitative analysis on the domain scores augmented by a qualitative text analysis of patients’ comments. To focus the research, we start by building a hospital recommendation model using logistic regression that predicts a patient’s likelihood to recommend the hospital; we use this to further evaluate the top four most predictive domains. In these domains (personal issues, nurses, hospital room, and physicians), a significant percentage of patients who rated their experience with a perfect domain score left a comment categorized as not positive, thus giving rise to stark contrasts between survey scores and comments provided by patients. Within each domain, natural language analysis of patient comments shows that, despite providing perfect survey scores, patients have much to say to health care organizations about their experiences in the hospital. A summary of comments also shows that respondents provide negative comments on issues that are outside the survey domains. Results confirm that harvesting and analyzing comments from these patients is important, because much can be learned from their narratives. Implications for health care professionals and organizations are discussed.Andrew GallanMarina GirjuRoxana GirjuThe Beryl Institutearticlepatient experiencelikelihood to recommendpatient commentspatient surveystext analyticsnatural language analysisMedicine (General)R5-920Public aspects of medicineRA1-1270ENPatient Experience Journal (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	patient experience likelihood to recommend patient comments patient surveys text analytics natural language analysis Medicine (General) R5-920 Public aspects of medicine RA1-1270
spellingShingle	patient experience likelihood to recommend patient comments patient surveys text analytics natural language analysis Medicine (General) R5-920 Public aspects of medicine RA1-1270 Andrew Gallan Marina Girju Roxana Girju Perfect ratings with negative comments: Learning from contradictory patient survey responses
description	This research explores why patients give perfect domain scores yet provide negative comments on surveys. In order to explore this phenomenon, vendor-supplied in-patient survey data from eleven different hospitals of a major U.S. health care system were utilized. The dataset included survey scores and comments from 56,900 patients, collected from January 2015 through October 2016. Of the total number of responses, 30,485 (54%) contained at least one comment. For our analysis, we use a two-step approach: a quantitative analysis on the domain scores augmented by a qualitative text analysis of patients’ comments. To focus the research, we start by building a hospital recommendation model using logistic regression that predicts a patient’s likelihood to recommend the hospital; we use this to further evaluate the top four most predictive domains. In these domains (personal issues, nurses, hospital room, and physicians), a significant percentage of patients who rated their experience with a perfect domain score left a comment categorized as not positive, thus giving rise to stark contrasts between survey scores and comments provided by patients. Within each domain, natural language analysis of patient comments shows that, despite providing perfect survey scores, patients have much to say to health care organizations about their experiences in the hospital. A summary of comments also shows that respondents provide negative comments on issues that are outside the survey domains. Results confirm that harvesting and analyzing comments from these patients is important, because much can be learned from their narratives. Implications for health care professionals and organizations are discussed.
format	article
author	Andrew Gallan Marina Girju Roxana Girju
author_facet	Andrew Gallan Marina Girju Roxana Girju
author_sort	Andrew Gallan
title	Perfect ratings with negative comments: Learning from contradictory patient survey responses
title_short	Perfect ratings with negative comments: Learning from contradictory patient survey responses
title_full	Perfect ratings with negative comments: Learning from contradictory patient survey responses
title_fullStr	Perfect ratings with negative comments: Learning from contradictory patient survey responses
title_full_unstemmed	Perfect ratings with negative comments: Learning from contradictory patient survey responses
title_sort	perfect ratings with negative comments: learning from contradictory patient survey responses
publisher	The Beryl Institute
publishDate	2017
url	https://doaj.org/article/b1bd920a047d49fe8de03f07d1692740
work_keys_str_mv	AT andrewgallan perfectratingswithnegativecommentslearningfromcontradictorypatientsurveyresponses AT marinagirju perfectratingswithnegativecommentslearningfromcontradictorypatientsurveyresponses AT roxanagirju perfectratingswithnegativecommentslearningfromcontradictorypatientsurveyresponses
_version_	1718428890113769472

Perfect ratings with negative comments: Learning from contradictory patient survey responses

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