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HERALD (Health Economics using Routine Anonymised Linked Data)

BACKGROUND: Health economic analysis traditionally relies on patient derived questionnaire data, routine datasets, and outcomes data from experimental randomised control trials and other clinical studies, which are generally used as stand-alone datasets. Herein, we outline the potential implications...

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Autores principales: Husain, Muhammad J, Brophy, Sinead, Macey, Steven, Pinder, Leila M, Atkinson, Mark D, Cooksey, Roxanne, Phillips, Ceri J, Siebert, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3342092/
https://www.ncbi.nlm.nih.gov/pubmed/22458665
http://dx.doi.org/10.1186/1472-6947-12-24
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author Husain, Muhammad J
Brophy, Sinead
Macey, Steven
Pinder, Leila M
Atkinson, Mark D
Cooksey, Roxanne
Phillips, Ceri J
Siebert, Stefan
author_facet Husain, Muhammad J
Brophy, Sinead
Macey, Steven
Pinder, Leila M
Atkinson, Mark D
Cooksey, Roxanne
Phillips, Ceri J
Siebert, Stefan
author_sort Husain, Muhammad J
collection PubMed
description BACKGROUND: Health economic analysis traditionally relies on patient derived questionnaire data, routine datasets, and outcomes data from experimental randomised control trials and other clinical studies, which are generally used as stand-alone datasets. Herein, we outline the potential implications of linking these datasets to give one single joined up data-resource for health economic analysis. METHOD: The linkage of individual level data from questionnaires with routinely-captured health care data allows the entire patient journey to be mapped both retrospectively and prospectively. We illustrate this with examples from an Ankylosing Spondylitis (AS) cohort by linking patient reported study dataset with the routinely collected general practitioner (GP) data, inpatient (IP) and outpatient (OP) datasets, and Accident and Emergency department data in Wales. The linked data system allows: (1) retrospective and prospective tracking of patient pathways through multiple healthcare facilities; (2) validation and clarification of patient-reported recall data, complementing the questionnaire/routine data information; (3) obtaining objective measure of the costs of chronic conditions for a longer time horizon, and during the pre-diagnosis period; (4) assessment of health service usage, referral histories, prescribed drugs and co-morbidities; and (5) profiling and stratification of patients relating to disease manifestation, lifestyles, co-morbidities, and associated costs. RESULTS: Using the GP data system we tracked about 183 AS patients retrospectively and prospectively from the date of questionnaire completion to gather the following information: (a) number of GP events; (b) presence of a GP 'drug' read codes; and (c) the presence of a GP 'diagnostic' read codes. We tracked 236 and 296 AS patients through the OP and IP data systems respectively to count the number of OP visits; and IP admissions and duration. The results are presented under several patient stratification schemes based on disease severity, functions, age, sex, and the onset of disease symptoms. CONCLUSION: The linked data system offers unique opportunities for enhanced longitudinal health economic analysis not possible through the use of traditional isolated datasets. Additionally, this data linkage provides important information to improve diagnostic and referral pathways, and thus helps maximise clinical efficiency and efficiency in the use of resources.
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spelling pubmed-33420922012-05-03 HERALD (Health Economics using Routine Anonymised Linked Data) Husain, Muhammad J Brophy, Sinead Macey, Steven Pinder, Leila M Atkinson, Mark D Cooksey, Roxanne Phillips, Ceri J Siebert, Stefan BMC Med Inform Decis Mak Research Article BACKGROUND: Health economic analysis traditionally relies on patient derived questionnaire data, routine datasets, and outcomes data from experimental randomised control trials and other clinical studies, which are generally used as stand-alone datasets. Herein, we outline the potential implications of linking these datasets to give one single joined up data-resource for health economic analysis. METHOD: The linkage of individual level data from questionnaires with routinely-captured health care data allows the entire patient journey to be mapped both retrospectively and prospectively. We illustrate this with examples from an Ankylosing Spondylitis (AS) cohort by linking patient reported study dataset with the routinely collected general practitioner (GP) data, inpatient (IP) and outpatient (OP) datasets, and Accident and Emergency department data in Wales. The linked data system allows: (1) retrospective and prospective tracking of patient pathways through multiple healthcare facilities; (2) validation and clarification of patient-reported recall data, complementing the questionnaire/routine data information; (3) obtaining objective measure of the costs of chronic conditions for a longer time horizon, and during the pre-diagnosis period; (4) assessment of health service usage, referral histories, prescribed drugs and co-morbidities; and (5) profiling and stratification of patients relating to disease manifestation, lifestyles, co-morbidities, and associated costs. RESULTS: Using the GP data system we tracked about 183 AS patients retrospectively and prospectively from the date of questionnaire completion to gather the following information: (a) number of GP events; (b) presence of a GP 'drug' read codes; and (c) the presence of a GP 'diagnostic' read codes. We tracked 236 and 296 AS patients through the OP and IP data systems respectively to count the number of OP visits; and IP admissions and duration. The results are presented under several patient stratification schemes based on disease severity, functions, age, sex, and the onset of disease symptoms. CONCLUSION: The linked data system offers unique opportunities for enhanced longitudinal health economic analysis not possible through the use of traditional isolated datasets. Additionally, this data linkage provides important information to improve diagnostic and referral pathways, and thus helps maximise clinical efficiency and efficiency in the use of resources. BioMed Central 2012-03-29 /pmc/articles/PMC3342092/ /pubmed/22458665 http://dx.doi.org/10.1186/1472-6947-12-24 Text en Copyright ©2012 Husain et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Husain, Muhammad J
Brophy, Sinead
Macey, Steven
Pinder, Leila M
Atkinson, Mark D
Cooksey, Roxanne
Phillips, Ceri J
Siebert, Stefan
HERALD (Health Economics using Routine Anonymised Linked Data)
title HERALD (Health Economics using Routine Anonymised Linked Data)
title_full HERALD (Health Economics using Routine Anonymised Linked Data)
title_fullStr HERALD (Health Economics using Routine Anonymised Linked Data)
title_full_unstemmed HERALD (Health Economics using Routine Anonymised Linked Data)
title_short HERALD (Health Economics using Routine Anonymised Linked Data)
title_sort herald (health economics using routine anonymised linked data)
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3342092/
https://www.ncbi.nlm.nih.gov/pubmed/22458665
http://dx.doi.org/10.1186/1472-6947-12-24
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