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Model-driven approach to data collection and reporting for quality improvement

Continuous data collection and analysis have been shown essential to achieving improvement in healthcare. However, the data required for local improvement initiatives are often not readily available from hospital Electronic Health Record (EHR) systems or not routinely collected. Furthermore, improve...

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Detalles Bibliográficos
Autores principales: Curcin, Vasa, Woodcock, Thomas, Poots, Alan J., Majeed, Azeem, Bell, Derek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4266541/
https://www.ncbi.nlm.nih.gov/pubmed/24874182
http://dx.doi.org/10.1016/j.jbi.2014.04.014
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author Curcin, Vasa
Woodcock, Thomas
Poots, Alan J.
Majeed, Azeem
Bell, Derek
author_facet Curcin, Vasa
Woodcock, Thomas
Poots, Alan J.
Majeed, Azeem
Bell, Derek
author_sort Curcin, Vasa
collection PubMed
description Continuous data collection and analysis have been shown essential to achieving improvement in healthcare. However, the data required for local improvement initiatives are often not readily available from hospital Electronic Health Record (EHR) systems or not routinely collected. Furthermore, improvement teams are often restricted in time and funding thus requiring inexpensive and rapid tools to support their work. Hence, the informatics challenge in healthcare local improvement initiatives consists of providing a mechanism for rapid modelling of the local domain by non-informatics experts, including performance metric definitions, and grounded in established improvement techniques. We investigate the feasibility of a model-driven software approach to address this challenge, whereby an improvement model designed by a team is used to automatically generate required electronic data collection instruments and reporting tools. To that goal, we have designed a generic Improvement Data Model (IDM) to capture the data items and quality measures relevant to the project, and constructed Web Improvement Support in Healthcare (WISH), a prototype tool that takes user-generated IDM models and creates a data schema, data collection web interfaces, and a set of live reports, based on Statistical Process Control (SPC) for use by improvement teams. The software has been successfully used in over 50 improvement projects, with more than 700 users. We present in detail the experiences of one of those initiatives, Chronic Obstructive Pulmonary Disease project in Northwest London hospitals. The specific challenges of improvement in healthcare are analysed and the benefits and limitations of the approach are discussed.
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spelling pubmed-42665412014-12-16 Model-driven approach to data collection and reporting for quality improvement Curcin, Vasa Woodcock, Thomas Poots, Alan J. Majeed, Azeem Bell, Derek J Biomed Inform Article Continuous data collection and analysis have been shown essential to achieving improvement in healthcare. However, the data required for local improvement initiatives are often not readily available from hospital Electronic Health Record (EHR) systems or not routinely collected. Furthermore, improvement teams are often restricted in time and funding thus requiring inexpensive and rapid tools to support their work. Hence, the informatics challenge in healthcare local improvement initiatives consists of providing a mechanism for rapid modelling of the local domain by non-informatics experts, including performance metric definitions, and grounded in established improvement techniques. We investigate the feasibility of a model-driven software approach to address this challenge, whereby an improvement model designed by a team is used to automatically generate required electronic data collection instruments and reporting tools. To that goal, we have designed a generic Improvement Data Model (IDM) to capture the data items and quality measures relevant to the project, and constructed Web Improvement Support in Healthcare (WISH), a prototype tool that takes user-generated IDM models and creates a data schema, data collection web interfaces, and a set of live reports, based on Statistical Process Control (SPC) for use by improvement teams. The software has been successfully used in over 50 improvement projects, with more than 700 users. We present in detail the experiences of one of those initiatives, Chronic Obstructive Pulmonary Disease project in Northwest London hospitals. The specific challenges of improvement in healthcare are analysed and the benefits and limitations of the approach are discussed. Elsevier 2014-12 /pmc/articles/PMC4266541/ /pubmed/24874182 http://dx.doi.org/10.1016/j.jbi.2014.04.014 Text en © 2014 The Authors https://creativecommons.org/licenses/by-nc-nd/3.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).
spellingShingle Article
Curcin, Vasa
Woodcock, Thomas
Poots, Alan J.
Majeed, Azeem
Bell, Derek
Model-driven approach to data collection and reporting for quality improvement
title Model-driven approach to data collection and reporting for quality improvement
title_full Model-driven approach to data collection and reporting for quality improvement
title_fullStr Model-driven approach to data collection and reporting for quality improvement
title_full_unstemmed Model-driven approach to data collection and reporting for quality improvement
title_short Model-driven approach to data collection and reporting for quality improvement
title_sort model-driven approach to data collection and reporting for quality improvement
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4266541/
https://www.ncbi.nlm.nih.gov/pubmed/24874182
http://dx.doi.org/10.1016/j.jbi.2014.04.014
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