HERALD (Health Economics using Routine Anonymised Linked Data)

BMC Medical Informatics and Decision Making

Research Article

Ceri J Phillips¹  Stefan Siebert²  Leila M Pinder²  Steven Macey²  Sinead Brophy²  Roxanne Cooksey²  Mark D Atkinson²  Muhammad J Husain³ 

[1] College of Human and Health Sciences, Swansea University, SA2 8PP, Wales, UK;College of Medicine, Swansea University, SA2 8PP, Wales, UK;Keele Management School, Keele University, ST5 5BG, Staffordshire, UK;

关键词: General Practice;    Ankylose Spondylitis;    Routine Data;    Read Code;    General Practice Visit;   

DOI  :  10.1186/1472-6947-12-24

 received in 2011-04-21, accepted in 2012-03-29,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundHealth 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.MethodThe 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.ResultsUsing 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.ConclusionThe 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.

【 授权许可】

CC BY   
© Husain et al; licensee BioMed Central Ltd. 2012