| The fundamental idea here is that doctors find it difficult to ensure that their recommendations are actually up-to-date with the latest clinical research. Further, that by virtue of being at the centre of action in research, doctors in prestige medical centres have an advantage that could be available to all doctors. It's a pretty important point, sometimes referred to as the dissemination of knowledge problem. Currently, this is best approached by publishing systematic reviews according to the Cochrane Criteria [0]. Such reviews are quite labour-intensive and done all too rarely, but are very valuable when done. One aspect of such reviews, when done, is how often they discard published studies for reasons such as bias, incomplete datasets, and so forth. The approach described by Geiger in the link is commendable for its intentions but the outcome will be faced with the same problem that manual systematic reviews face. I wonder if the author considered included rules-based approaches (e.g. Cochrane guidelines) in addition to machine learning approaches? [0] https://training.cochrane.org/handbook |
NCCN guidelines and Cochrane Reviews serve complementary roles in medicine - NCCN provides practical, frequently updated cancer treatment algorithms based on both research and expert consensus, while Cochrane Reviews offer rigorous systematic analyses of research evidence across all medical fields with a stronger focus on randomized controlled trials. The NCCN guidelines tend to be more immediately applicable in clinical practice, while Cochrane Reviews provide a deeper analysis of the underlying evidence quality.
My main goal here was to show what you could do with any set of medical guidelines that was properly structured. You can choose any criteria you want.