Falls Prevention within the Australian General Practice Data Model: Methodology, Information Model, and Terminology Issues
- Siaw-Teng Liaw, MBBS, PhD,
- Nabil Sulaiman, MBBS, PhD,
- Christopher Pearce, MBBS, MMed,
- Jane Sims, PhD,
- Keith Hill, PhD,
- Heather Grain, Gr Dip Data Processing,
- Justin Tse, MBBS,
- Choon-Kiat Ng, BEng
- Affiliations of the authors: The University of Melbourne Department of General Practice, Melbourne, Victoria, Australia (S-TL, NS, CP, JS, JT); National Ageing Research Institute, Melbourne, Victoria, Australia (KH); La Trobe University, Melbourne, Victoria, Australia (HG); Swinburne University, Melbourne, Victoria, Australia (C-KN)
- Correspondence and reprints: Professor Siaw-Teng Liaw, MBBS, PhD, Department of General Practice, The University of Melbourne, 200 Berkeley Street, Carlton, VIC 3053, Australia; e-mail: <t.liaw{at}unimelb.edu.au>.
- Received 29 October 2002
- Accepted 15 May 2003
Abstract
The iterative development of the Falls Risk Assessment and Management System (FRAMS) drew upon research evidence and early consumer and clinician input through focus groups, interviews, direct observations, and an online questionnaire. Clinical vignettes were used to validate the clinical model and program logic, input, and output. The information model was developed within the Australian General Practice Data Model (GPDM) framework. The online FRAMS implementation used available Internet (TCP/IP), messaging (HL7, XML), knowledge representation (Arden Syntax), and classification (ICD10-AM, ICPC2) standards. Although it could accommodate most of the falls prevention information elements, the GPDM required extension for prevention and prescribing risk management. Existing classifications could not classify all falls prevention concepts. The lack of explicit rules for terminology and data definitions allowed multiple concept representations across the terminology–architecture interface. Patients were more enthusiastic than clinicians. A usable standards-based online-distributed decision support system for falls prevention can be implemented within the GPDM, but a comprehensive terminology is required. The conceptual interface between terminology and architecture requires standardization, preferably within a reference information model. Developments in electronic decision support must be guided by evidence-based clinical and information models and knowledge ontologies. The safety and quality of knowledge-based decision support systems must be monitored. Further examination of falls and other clinical domains within the GPDM is needed.
Footnotes
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The authors thank the GP Computing Group for funding this project, FRAMS project team and clinicians, and consumers who contributed to the terminology and the clinical and information models.
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↵* Included five general practitioners, a practice manager, a pharmacist, two aged care and rehabilitation specialists, two health promotion specialists, a physiotherapist, two rehabilitation specialists, an integrated care manager, and an information technology specialist.
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↵† Included a consumer advocate and five older patients with a range of chronic diseases such as arthritis and diabetes.
