Aspiration to be a “Learning Health System” (LHS) is a staple of the advanced health systems. It means the health system continuously measures, analyzes and, most importantly, rapidly implements processes that produce demonstrably better patient outcomes, at reduced costs and with fewer risks to patient safety.
While the concept has been around since 2007, recent advances in technology have seen this idea receiving renewed attention (sometimes by the name “Medicine 4.0”). Indeed, digital technology already permeates every aspect of medical care; data is collected everywhere from bedside to back offices, entered manually, collected automatically, and provides a valuable resource for evidence-based improvements.
It therefore might come as a surprise that technology has had little impact on the way hospitals become more efficient, healthcare becomes more effective, and patient harm is prevented. Indeed, the lack of technological impact against all odds, only serves to frustrate stakeholders, advocates, and implementers of Learning Health Systems.
Meanwhile, solutions that are used in place of LHS prove ineffective: root cause analyses lack actionable outcomes; evidence is unavailable and / or out of date; and AI-based predictor discovery fails to translate across sites.
To clarify this seemingly paradoxical state of healthcare informatics, look at the LHS cycle below: Data collection represents only one part of the LHS cycle. Another three critical components still remain: preparing data for analysis, identifying meaningful questions and finally, implementing change. We examine the problems and compare failed and successful solutions.
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