Clinical, information and business process modeling to promote development of safe and flexible software.
Using a factorial vignette survey and modeling methodology, we developed clinical and information models - incorporating evidence base, key concepts, relevant terms, decision-making and workflow needed to practice safely and effectively - to guide the development of an integrated rule-based knowledge module to support prescribing decisions in asthma. We identified workflows, decision-making factors, factor use, and clinician information requirements. The Unified Modeling Language (UML) and public domain software and knowledge engineering tools (e.g. Protégé) were used, with the Australian GP Data Model as the starting point for expressing information needs. A Web Services service-oriented architecture approach was adopted within which to express functional needs, and clinical processes and workflows were expressed in the Business Process Execution Language (BPEL). This formal analysis and modeling methodology to define and capture the process and logic of prescribing best practice in a reference implementation is fundamental to tackling deficiencies in prescribing decision support software.
Liaw
Siaw-Teng
ST
School of Rural Health, The University of Melbourne, Graham Street, Shepparton, Victoria 3630, Australia. t.liaw@unimelb.edu.au
Deveny
Elizabeth
E
Morrison
Iain
I
Lewis
Bryn
B
D001249:Asthma / Q000628:therapy; D001315:Australia; D003625:Data Collection; D020000:Decision Support Systems, Clinical; D019317:Evidence-Based Medicine; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D008490:Medical Informatics / Q000458:organization & administration*; D017146:Models, Organizational; D012985:Software Design; D017432:Unified Medical Language System