Moving to an Online Framework for Knowledge-Driven Healthcare
Bruce Shadbolt (Canberra Clinical School, Australia), Rui Wang (National Health Sciences Centre, Australia) and Paul S. Craft (Canberra Hospital, Australia)
Copyright: © 2005
The acquisition of knowledge in healthcare is mostly piecemeal and irregular. Consequently, we believe that the integration of science and patient care into a seamless framework is the key to establishing widespread knowledge-based healthcare organizations. Over the last five years, we have developed a dynamic methodology that completes the full information cycle using a generic online framework that merges science with clinical practice over the continuum of care. Called Protocol Hypothesis Testing (PHT), the framework is an extremely flexible web-enabled system that provides authors (expert groups) with the ability to instantly modify the structure of the system to meet the changing needs of clinical practice and incremental knowledge generation. The fully relational, centralised approach caters to the diversity of local needs whilst providing a global focus. The PHT System: • helps drive collaboration between clinicians, researchers, patients, and healthcare organizations to continually improve and use the latest and best evidence; • interfaces between clinical practice and bio-technology research;conducts randomised clinical trial research; centrally runs local clinical investigations and health service research;provides clinicians and patients with user-generated, decision-support algorithms and evidence-based summaries that are applicable to specific patients and their treatment choices; manages individual patient’s information, automatically distributing information to where it is needed, and providing patients with probable paths their treatment may follow; and provides a process to explore improvements in cost-effectiveness.In sum, the PHT system creates a centralised, seamless framework between research and clinical practice that is responsive to instant change based on hypothesis testing (science), data mining (exploration & thresholds) and expert opinion (authors) — all in the context of the needs of different diseases, clinical specialties and healthcare organisations.