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Population segmentation based on healthcare needs: a systematic review
BACKGROUND: Healthcare needs-based population segmentation is a promising approach for enabling the development and evaluation of integrated healthcare service models that meet healthcare needs. However, healthcare policymakers interested in understanding adult population healthcare needs may not be...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6693177/ https://www.ncbi.nlm.nih.gov/pubmed/31409423 http://dx.doi.org/10.1186/s13643-019-1105-6 |
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author | Chong, Jia Loon Lim, Ka Keat Matchar, David Bruce |
author_facet | Chong, Jia Loon Lim, Ka Keat Matchar, David Bruce |
author_sort | Chong, Jia Loon |
collection | PubMed |
description | BACKGROUND: Healthcare needs-based population segmentation is a promising approach for enabling the development and evaluation of integrated healthcare service models that meet healthcare needs. However, healthcare policymakers interested in understanding adult population healthcare needs may not be aware of suitable population segmentation tools available for use in the literature and barring better-known alternatives, may reinvent the wheel by creating and validating their own tools rather than adapting available tools in the literature. Therefore, we undertook a systematic review to identify all available tools which operationalize healthcare need-based population segmentation, to help inform policymakers developing population-level health service programmes. METHODS: Using search terms reflecting concepts of population, healthcare need and segmentation, we systematically reviewed and included articles containing healthcare need-based adult population segmentation tools in PubMed, CINAHL and Web of Science databases. We included tools comprising mutually exclusive segments with prognostic value for clinically relevant outcomes. An updated secondary search on the PubMed database was also conducted as the last search was conducted 2 years ago. All identified tools were characterized in terms of segment formulation, segmentation base, whether they received peer-reviewed validation, requirement for comprehensive electronic medical records, proprietary status and number of segments. RESULTS: A total of 16 unique tools were identified from systematically reviewing 9970 articles. Peer-reviewed validation studies were found for 9 of these tools. DISCUSSION AND CONCLUSIONS: The underlying segmentation basis of most identified tools was found to be conceptually comparable to each other which suggests a broad recognition of archetypical patient overall healthcare need profiles. While many tools operate based on administrative record data, it is noted that healthcare systems without comprehensive electronic medical records would benefit from tools which segment populations through primary data collection. Future work could therefore include development and validation of such primary data collection-based tools. While this study is limited by exclusion of non-English literature, the identified and characterized tools will nonetheless facilitate efforts by policymakers to improve patient-centred care through development and evaluation of services tailored for specific populations segmented by these tools. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13643-019-1105-6) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6693177 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-66931772019-08-16 Population segmentation based on healthcare needs: a systematic review Chong, Jia Loon Lim, Ka Keat Matchar, David Bruce Syst Rev Research BACKGROUND: Healthcare needs-based population segmentation is a promising approach for enabling the development and evaluation of integrated healthcare service models that meet healthcare needs. However, healthcare policymakers interested in understanding adult population healthcare needs may not be aware of suitable population segmentation tools available for use in the literature and barring better-known alternatives, may reinvent the wheel by creating and validating their own tools rather than adapting available tools in the literature. Therefore, we undertook a systematic review to identify all available tools which operationalize healthcare need-based population segmentation, to help inform policymakers developing population-level health service programmes. METHODS: Using search terms reflecting concepts of population, healthcare need and segmentation, we systematically reviewed and included articles containing healthcare need-based adult population segmentation tools in PubMed, CINAHL and Web of Science databases. We included tools comprising mutually exclusive segments with prognostic value for clinically relevant outcomes. An updated secondary search on the PubMed database was also conducted as the last search was conducted 2 years ago. All identified tools were characterized in terms of segment formulation, segmentation base, whether they received peer-reviewed validation, requirement for comprehensive electronic medical records, proprietary status and number of segments. RESULTS: A total of 16 unique tools were identified from systematically reviewing 9970 articles. Peer-reviewed validation studies were found for 9 of these tools. DISCUSSION AND CONCLUSIONS: The underlying segmentation basis of most identified tools was found to be conceptually comparable to each other which suggests a broad recognition of archetypical patient overall healthcare need profiles. While many tools operate based on administrative record data, it is noted that healthcare systems without comprehensive electronic medical records would benefit from tools which segment populations through primary data collection. Future work could therefore include development and validation of such primary data collection-based tools. While this study is limited by exclusion of non-English literature, the identified and characterized tools will nonetheless facilitate efforts by policymakers to improve patient-centred care through development and evaluation of services tailored for specific populations segmented by these tools. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13643-019-1105-6) contains supplementary material, which is available to authorized users. BioMed Central 2019-08-13 /pmc/articles/PMC6693177/ /pubmed/31409423 http://dx.doi.org/10.1186/s13643-019-1105-6 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Chong, Jia Loon Lim, Ka Keat Matchar, David Bruce Population segmentation based on healthcare needs: a systematic review |
title | Population segmentation based on healthcare needs: a systematic review |
title_full | Population segmentation based on healthcare needs: a systematic review |
title_fullStr | Population segmentation based on healthcare needs: a systematic review |
title_full_unstemmed | Population segmentation based on healthcare needs: a systematic review |
title_short | Population segmentation based on healthcare needs: a systematic review |
title_sort | population segmentation based on healthcare needs: a systematic review |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6693177/ https://www.ncbi.nlm.nih.gov/pubmed/31409423 http://dx.doi.org/10.1186/s13643-019-1105-6 |
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