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HERALD (Health Economics using Routine Anonymised Linked Data) / Muhammad J Husain; Sinead Brophy; Steven Macey; Leila M Pinder; Mark D Atkinson; Roxanne Cooksey; Ceri J Phillips; Stefan Siebert

BMC Medical Informatics and Decision Making, Volume: 12, Issue: 1

Swansea University Author: Cooksey, Roxanne

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Abstract

AbstractBackground: Health economic analysis traditionally relies on patient derived questionnaire data, routine datasets and outcome data from experimental randomised control trials or clinical studies. These datasets are generally used as distinct datasets. Herein, we outline the potential of link...

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Published in: BMC Medical Informatics and Decision Making
ISSN: 1472-6947
Published: 2012
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URI: https://cronfa.swan.ac.uk/Record/cronfa12638
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fullrecord <?xml version="1.0"?><rfc1807><datestamp>2017-12-18T10:49:43Z</datestamp><bib-version>v2</bib-version><id>12638</id><entry>2012-09-10</entry><title>HERALD (Health Economics using Routine Anonymised Linked Data)</title><alternativeTitle></alternativeTitle><author>Roxanne Cooksey</author><firstname>Roxanne</firstname><surname>Cooksey</surname><active>true</active><ORCID>0000-0002-6763-9373</ORCID><ethesisStudent>false</ethesisStudent><sid>df63826249b712dcb03cb0161d0f3daf</sid><email>ce21541891fa8ff8bd0c7f2c49bae442</email><emailaddr>x7tAxxTB4pnYxmIX/tdeJwgr5y2nBRz3haj4DmVVDsQ=</emailaddr><date>2012-09-10</date><deptcode>PMSC</deptcode><abstract>AbstractBackground: Health economic analysis traditionally relies on patient derived questionnaire data, routine datasets and outcome data from experimental randomised control trials or clinical studies. These datasets are generally used as distinct datasets. Herein, we outline the potential of linking these datasets to give a unified, joined-up data-resource to be used in 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 data with the routinely collected general practitioner (GP) data, inpatient (IP), outpatient (OP) and Accident and Emergency datasets 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 measures of the costs of chronic conditions for a longer time period, 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 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 &#x2018;drug&#x2019; read codes; and (c) the presence of a GP &#x2018;diagnostic&#x2019; 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.Results are presented under several patient stratification schemes based on disease severity, functions, age, sex and disease symptom onsetConclusion: 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 maximises clinical efficiency.</abstract><type>Journal article</type><journal>BMC Medical Informatics and Decision Making</journal><volume>12</volume><journalNumber>1</journalNumber><paginationStart/><paginationEnd/><publisher></publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>1472-6947</issnElectronic><keywords></keywords><publishedDay>29</publishedDay><publishedMonth>3</publishedMonth><publishedYear>2012</publishedYear><publishedDate>2012-03-29</publishedDate><doi>10.1186/1472-6947-12-24</doi><url></url><notes></notes><college>Swansea University Medical School</college><department>Medicine</department><CollegeCode>CMED</CollegeCode><DepartmentCode>PMSC</DepartmentCode><institution/><researchGroup>None</researchGroup><supervisor/><sponsorsfunders/><grantnumber/><degreelevel/><degreename>None</degreename><lastEdited>2017-12-18T10:49:43Z</lastEdited><Created>2012-09-10T15:04:15Z</Created><path><level id="1">Swansea University Medical School</level><level id="2">Medicine</level></path><authors><author><firstname>Muhammad J</firstname><surname>Husain</surname><orcid/><order>1</order></author><author><firstname>Sinead</firstname><surname>Brophy</surname><orcid/><order>2</order></author><author><firstname>Steven</firstname><surname>Macey</surname><orcid/><order>3</order></author><author><firstname>Leila M</firstname><surname>Pinder</surname><orcid/><order>4</order></author><author><firstname>Mark D</firstname><surname>Atkinson</surname><orcid/><order>5</order></author><author><firstname>Roxanne</firstname><surname>Cooksey</surname><orcid/><order>6</order></author><author><firstname>Ceri J</firstname><surname>Phillips</surname><orcid/><order>7</order></author><author><firstname>Stefan</firstname><surname>Siebert</surname><orcid/><order>8</order></author></authors><documents/></rfc1807>
spelling 2017-12-18T10:49:43Z v2 12638 2012-09-10 HERALD (Health Economics using Routine Anonymised Linked Data) Roxanne Cooksey Roxanne Cooksey true 0000-0002-6763-9373 false df63826249b712dcb03cb0161d0f3daf ce21541891fa8ff8bd0c7f2c49bae442 x7tAxxTB4pnYxmIX/tdeJwgr5y2nBRz3haj4DmVVDsQ= 2012-09-10 PMSC AbstractBackground: Health economic analysis traditionally relies on patient derived questionnaire data, routine datasets and outcome data from experimental randomised control trials or clinical studies. These datasets are generally used as distinct datasets. Herein, we outline the potential of linking these datasets to give a unified, joined-up data-resource to be used in 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 data with the routinely collected general practitioner (GP) data, inpatient (IP), outpatient (OP) and Accident and Emergency datasets 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 measures of the costs of chronic conditions for a longer time period, 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 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.Results are presented under several patient stratification schemes based on disease severity, functions, age, sex and disease symptom onsetConclusion: 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 maximises clinical efficiency. Journal article BMC Medical Informatics and Decision Making 12 1 1472-6947 29 3 2012 2012-03-29 10.1186/1472-6947-12-24 Swansea University Medical School Medicine CMED PMSC None None 2017-12-18T10:49:43Z 2012-09-10T15:04:15Z Swansea University Medical School Medicine Muhammad J Husain 1 Sinead Brophy 2 Steven Macey 3 Leila M Pinder 4 Mark D Atkinson 5 Roxanne Cooksey 6 Ceri J Phillips 7 Stefan Siebert 8
title HERALD (Health Economics using Routine Anonymised Linked Data)
spellingShingle HERALD (Health Economics using Routine Anonymised Linked Data)
Cooksey, Roxanne
title_short 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_sort HERALD (Health Economics using Routine Anonymised Linked Data)
author_id_str_mv df63826249b712dcb03cb0161d0f3daf
author_id_fullname_str_mv df63826249b712dcb03cb0161d0f3daf_***_Cooksey, Roxanne
author Cooksey, Roxanne
author2 Muhammad J Husain
Sinead Brophy
Steven Macey
Leila M Pinder
Mark D Atkinson
Roxanne Cooksey
Ceri J Phillips
Stefan Siebert
format Journal article
container_title BMC Medical Informatics and Decision Making
container_volume 12
container_issue 1
publishDate 2012
institution Swansea University
issn 1472-6947
doi_str_mv 10.1186/1472-6947-12-24
college_str Swansea University Medical School
hierarchytype
hierarchy_top_id swanseauniversitymedicalschool
hierarchy_top_title Swansea University Medical School
hierarchy_parent_id swanseauniversitymedicalschool
hierarchy_parent_title Swansea University Medical School
department_str Medicine{{{_:::_}}}Swansea University Medical School{{{_:::_}}}Medicine
document_store_str 0
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description AbstractBackground: Health economic analysis traditionally relies on patient derived questionnaire data, routine datasets and outcome data from experimental randomised control trials or clinical studies. These datasets are generally used as distinct datasets. Herein, we outline the potential of linking these datasets to give a unified, joined-up data-resource to be used in 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 data with the routinely collected general practitioner (GP) data, inpatient (IP), outpatient (OP) and Accident and Emergency datasets 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 measures of the costs of chronic conditions for a longer time period, 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 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.Results are presented under several patient stratification schemes based on disease severity, functions, age, sex and disease symptom onsetConclusion: 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 maximises clinical efficiency.
published_date 2012-03-29T04:11:14Z
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