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A Protocol for a Mixed-Methods Process Evaluation of a Local Population Health Management System to Reduce Inequities in COVID-19 Vaccination Uptake
Population health management is an emerging technique to link and analyse patient data across several organisations in order to identify population needs and plan care. It is increasingly used in England and has become more important as health policy has sought to drive greater integration across he...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9029748/ https://www.ncbi.nlm.nih.gov/pubmed/35457461 http://dx.doi.org/10.3390/ijerph19084588 |
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author | Watson, Georgia Moore, Cassie Aspinal, Fiona Boa, Claudette Edeki, Vusi Hutchings, Andrew Raine, Rosalind Sheringham, Jessica |
author_facet | Watson, Georgia Moore, Cassie Aspinal, Fiona Boa, Claudette Edeki, Vusi Hutchings, Andrew Raine, Rosalind Sheringham, Jessica |
author_sort | Watson, Georgia |
collection | PubMed |
description | Population health management is an emerging technique to link and analyse patient data across several organisations in order to identify population needs and plan care. It is increasingly used in England and has become more important as health policy has sought to drive greater integration across health and care organisations. This protocol describes a mixed-methods process evaluation of an innovative population health management system in North Central London, England, serving a population of 1.5 million. It focuses on how staff have used a specific tool within North Central London’s population health management system designed to reduce inequities in COVID-19 vaccination. The COVID-19 vaccination Dashboard was first deployed from December 2020 and enables staff in North London to view variations in the uptake of COVID-19 vaccinations by population characteristics in near real-time. The evaluation will combine interviews with clinical and non-clinical staff with staff usage analytics, including the volume and frequency of staff Dashboard views, to describe the tool’s reach and identify possible mechanisms of impact. While seeking to provide timely insights to optimise the design of population health management tools in North Central London, it also seeks to provide longer term transferable learning on methods to evaluate population health management systems. |
format | Online Article Text |
id | pubmed-9029748 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90297482022-04-23 A Protocol for a Mixed-Methods Process Evaluation of a Local Population Health Management System to Reduce Inequities in COVID-19 Vaccination Uptake Watson, Georgia Moore, Cassie Aspinal, Fiona Boa, Claudette Edeki, Vusi Hutchings, Andrew Raine, Rosalind Sheringham, Jessica Int J Environ Res Public Health Protocol Population health management is an emerging technique to link and analyse patient data across several organisations in order to identify population needs and plan care. It is increasingly used in England and has become more important as health policy has sought to drive greater integration across health and care organisations. This protocol describes a mixed-methods process evaluation of an innovative population health management system in North Central London, England, serving a population of 1.5 million. It focuses on how staff have used a specific tool within North Central London’s population health management system designed to reduce inequities in COVID-19 vaccination. The COVID-19 vaccination Dashboard was first deployed from December 2020 and enables staff in North London to view variations in the uptake of COVID-19 vaccinations by population characteristics in near real-time. The evaluation will combine interviews with clinical and non-clinical staff with staff usage analytics, including the volume and frequency of staff Dashboard views, to describe the tool’s reach and identify possible mechanisms of impact. While seeking to provide timely insights to optimise the design of population health management tools in North Central London, it also seeks to provide longer term transferable learning on methods to evaluate population health management systems. MDPI 2022-04-11 /pmc/articles/PMC9029748/ /pubmed/35457461 http://dx.doi.org/10.3390/ijerph19084588 Text en © 2022 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 | Protocol Watson, Georgia Moore, Cassie Aspinal, Fiona Boa, Claudette Edeki, Vusi Hutchings, Andrew Raine, Rosalind Sheringham, Jessica A Protocol for a Mixed-Methods Process Evaluation of a Local Population Health Management System to Reduce Inequities in COVID-19 Vaccination Uptake |
title | A Protocol for a Mixed-Methods Process Evaluation of a Local Population Health Management System to Reduce Inequities in COVID-19 Vaccination Uptake |
title_full | A Protocol for a Mixed-Methods Process Evaluation of a Local Population Health Management System to Reduce Inequities in COVID-19 Vaccination Uptake |
title_fullStr | A Protocol for a Mixed-Methods Process Evaluation of a Local Population Health Management System to Reduce Inequities in COVID-19 Vaccination Uptake |
title_full_unstemmed | A Protocol for a Mixed-Methods Process Evaluation of a Local Population Health Management System to Reduce Inequities in COVID-19 Vaccination Uptake |
title_short | A Protocol for a Mixed-Methods Process Evaluation of a Local Population Health Management System to Reduce Inequities in COVID-19 Vaccination Uptake |
title_sort | protocol for a mixed-methods process evaluation of a local population health management system to reduce inequities in covid-19 vaccination uptake |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9029748/ https://www.ncbi.nlm.nih.gov/pubmed/35457461 http://dx.doi.org/10.3390/ijerph19084588 |
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