title: Model-driven approach to data collection and reporting for quality improvement.
abstract: 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.
authors.affiliation: Department of Primary Care and Public Health, Imperial College London, Reynolds Building, St. Dunstan's Road, London W6 8RP, United Kingdom. Electronic address: vasa.curcin@imperial.ac.uk.
authors.forename: Vasa
authors.initials: V
authors.lastname: Curcin
authors.affiliation: NIHR CLAHRC for NWL Unit, Chelsea and Westminster Hospital, 369 Fulham Road, London SW10 9NH, United Kingdom. Electronic address: thomas.woodcock99@imperial.ac.uk.
authors.forename: Thomas
authors.initials: T
authors.lastname: Woodcock
authors.affiliation: NIHR CLAHRC for NWL Unit, Chelsea and Westminster Hospital, 369 Fulham Road, London SW10 9NH, United Kingdom. Electronic address: a.poots@imperial.ac.uk.
authors.forename: Alan J
authors.initials: AJ
authors.lastname: Poots
authors.affiliation: Department of Primary Care and Public Health, Imperial College London, Reynolds Building, St. Dunstan's Road, London W6 8RP, United Kingdom. Electronic address: a.majeed@imperial.ac.uk.
authors.forename: Azeem
authors.initials: A
authors.lastname: Majeed
authors.affiliation: NIHR CLAHRC for NWL Unit, Chelsea and Westminster Hospital, 369 Fulham Road, London SW10 9NH, United Kingdom. Electronic address: d.bell@imperial.ac.uk.
authors.forename: Derek
authors.initials: D
authors.lastname: Bell
mesh_terms: D035843:Biomedical Research / Q000379:methods*; D003625:Data Collection / Q000379:methods*; D006801:Humans; D008131:London; D008490:Medical Informatics / Q000379:methods*; D008962:Models, Theoretical; D029424:Pulmonary Disease, Chronic Obstructive; D058996:Quality Improvement; D012984:Software; D014584:User-Computer Interface
keywords: D2.1 (Software engineering) requirements/specification J.3 (life and medical sciences): Health model-driven architectures; Data collection; Healthcare analytics; Metrics; Performance analytics; Quality improvement