Developing automated methods for identifying and reducing inappropriate medical care
Inappropriate medical care is an urgent health care challenge, with enormous impact on costs and patient safety. Institute of Medicine estimates that in 2010 unnecessary care cost US approximately $750 billion, or 30% of the total health care expenditure. Numerous articles have reported on the pervasiveness of inappropriate care and on noncompliance concerning established protocols for procedures. Therefore, automated methods are needed that are aimed at identifying and reducing inappropriate medical care.
There is a strong body of research on automated enforcement of evidence-based clinical guidelines and protocols to promote delivery of better care, but the research on using this approach to reduce unnecessary care is scarce. Furthermore, methods used to mitigate unnecessary care are rarely generalizable. We hypothesize that these models can be aligned and a generic model can be developed for automated identification and reduction of unnecessary care.
In this presentation, I will provide a synthesis of current research and discuss the existing gaps in our knowledge. I will further explain the methods that can be used to overcome these gaps, and the challenges involved.