Finding best evidence for evidence-based best practice recommendations in health care: the initial decision support system design

• Published in: Knowledge and information systems 2011-10, Vol.29 (1), p.159-201
• Main Authors: Cercone, Nick, An, Xiangdong, Li, Jiye, Gu, Zhenmei, An, Aijun
• Format: Article
• Language: English
• Subjects: Applied sciences; Bayesian analysis; Best practice; Computer Science; Computer science; control theory; systems; Data mining; Data Mining and Knowledge Discovery; Data processing. List processing. Character string processing; Database Management; Decision support systems; Drugs; Exact sciences and technology; Health; Health care; Information Storage and Retrieval; Information Systems and Communication Service; Information Systems Applications (incl.Internet); Information systems. Data bases; IT in Business; Management; Markov analysis; Memory organisation. Data processing; Patients; Prescription drugs; Quality control; Quality of life; Regular Paper; Software; Studies; Systems design
• ISSN: 0219-1377; 0219-3116
• DOI: 10.1007/s10115-011-0439-8

A major problem for Canadian health organizations is finding best evidence for evidence-based best practice recommendations. Medications are not always effectively used and misuse may harm patients. Drugs are the fastest-growing element of Canadian health care spending, second only to hospital spending. Three hundred million prescriptions are filled annually. Prescription drugs accounted for 5.8% of total health care spending in 1980 and close to 18% today. A primary long-term goal of this research is to develop a decision support system for evidence-based management, quality control and best practice recommendations for medical prescriptions. Our results will improve accessibility and management of information by: (1) building an prototype for adaptive information extraction, text and data mining from (online) documents to find evidence on which to base best practices; and (2) employing multiply sectioned Bayesian networks (MSBNs) to infer a probabilistic interpretation to validate evidence for recommendations; MSBNs provide this structure. Best practices to improve drug-related health outcomes; patients’ quality of life; and cost-effective use of medications by changing knowledge and behavior. This research will support next generation eHealth decision support systems, which routinely find and verify evidence from multiple sources, leading to cost-effective use of drugs, improve patients’ quality of life and optimize drug-related health outcomes.