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Advanced Sentiment Analysis for Managing and Improving Patient Experience: Application for General Practitioner (GP) Classification in Northamptonshire

This paper presents a novel analytical approach for improving patients’ experience in healthcare settings. The analytical tool uses a classifier and a recommend management approach to facilitate decision making in a timely manner. The designed methodology comprises of 4 key stages, which include dev...

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Autores principales: Pandey, Aavash Raj, Seify, Mahdi, Okonta, Udoka, Hosseinian-Far, Amin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10298432/
https://www.ncbi.nlm.nih.gov/pubmed/37372706
http://dx.doi.org/10.3390/ijerph20126119
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author Pandey, Aavash Raj
Seify, Mahdi
Okonta, Udoka
Hosseinian-Far, Amin
author_facet Pandey, Aavash Raj
Seify, Mahdi
Okonta, Udoka
Hosseinian-Far, Amin
author_sort Pandey, Aavash Raj
collection PubMed
description This paper presents a novel analytical approach for improving patients’ experience in healthcare settings. The analytical tool uses a classifier and a recommend management approach to facilitate decision making in a timely manner. The designed methodology comprises of 4 key stages, which include developing a bot to scrap web data while performing sentiment analysis and extracting keywords from National Health Service (NHS) rate and review webpages, building a classifier with Waikato Environment for Knowledge Analysis (WEKA), analyzing speech with Python, and using Microsoft Excel for analysis. In the selected context, a total of 178 reviews were extracted from General Practitioners (GP) websites within Northamptonshire County, UK. Accordingly, 4764 keywords such as “kind”, “exactly”, “discharged”, “long waits”, “impolite staff”, “worse”, “problem”, “happy”, “late” and “excellent” were selected. In addition, 178 reviews were analyzed to highlight trends and patterns. The classifier model grouped GPs into gold, silver, and bronze categories. The outlined analytical approach complements the current patient feedback analysis approaches by GPs. This paper solely relied upon the feedback available on the NHS’ rate and review webpages. The contribution of the paper is to highlight the integration of easily available tools to perform higher level of analysis that provides understanding about patients’ experience. The context and tools used in this study for ranking services within the healthcare domain is novel in nature, since it involves extracting useful insights from the provided feedback.
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spelling pubmed-102984322023-06-28 Advanced Sentiment Analysis for Managing and Improving Patient Experience: Application for General Practitioner (GP) Classification in Northamptonshire Pandey, Aavash Raj Seify, Mahdi Okonta, Udoka Hosseinian-Far, Amin Int J Environ Res Public Health Article This paper presents a novel analytical approach for improving patients’ experience in healthcare settings. The analytical tool uses a classifier and a recommend management approach to facilitate decision making in a timely manner. The designed methodology comprises of 4 key stages, which include developing a bot to scrap web data while performing sentiment analysis and extracting keywords from National Health Service (NHS) rate and review webpages, building a classifier with Waikato Environment for Knowledge Analysis (WEKA), analyzing speech with Python, and using Microsoft Excel for analysis. In the selected context, a total of 178 reviews were extracted from General Practitioners (GP) websites within Northamptonshire County, UK. Accordingly, 4764 keywords such as “kind”, “exactly”, “discharged”, “long waits”, “impolite staff”, “worse”, “problem”, “happy”, “late” and “excellent” were selected. In addition, 178 reviews were analyzed to highlight trends and patterns. The classifier model grouped GPs into gold, silver, and bronze categories. The outlined analytical approach complements the current patient feedback analysis approaches by GPs. This paper solely relied upon the feedback available on the NHS’ rate and review webpages. The contribution of the paper is to highlight the integration of easily available tools to perform higher level of analysis that provides understanding about patients’ experience. The context and tools used in this study for ranking services within the healthcare domain is novel in nature, since it involves extracting useful insights from the provided feedback. MDPI 2023-06-13 /pmc/articles/PMC10298432/ /pubmed/37372706 http://dx.doi.org/10.3390/ijerph20126119 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pandey, Aavash Raj
Seify, Mahdi
Okonta, Udoka
Hosseinian-Far, Amin
Advanced Sentiment Analysis for Managing and Improving Patient Experience: Application for General Practitioner (GP) Classification in Northamptonshire
title Advanced Sentiment Analysis for Managing and Improving Patient Experience: Application for General Practitioner (GP) Classification in Northamptonshire
title_full Advanced Sentiment Analysis for Managing and Improving Patient Experience: Application for General Practitioner (GP) Classification in Northamptonshire
title_fullStr Advanced Sentiment Analysis for Managing and Improving Patient Experience: Application for General Practitioner (GP) Classification in Northamptonshire
title_full_unstemmed Advanced Sentiment Analysis for Managing and Improving Patient Experience: Application for General Practitioner (GP) Classification in Northamptonshire
title_short Advanced Sentiment Analysis for Managing and Improving Patient Experience: Application for General Practitioner (GP) Classification in Northamptonshire
title_sort advanced sentiment analysis for managing and improving patient experience: application for general practitioner (gp) classification in northamptonshire
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10298432/
https://www.ncbi.nlm.nih.gov/pubmed/37372706
http://dx.doi.org/10.3390/ijerph20126119
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