Digitizing clinical guideline recommendations involves the identification of Action Type, Action(s), Condition(s), Logic, Recommendation Strength, and Evidence Quality. Further, successfully implementing recommendations requires that they be executable and decidable. We also need to identify its effect on the process of care, its degree of novelty, and finally its measurability. Each of these dimensions is described in detail in the GuideLine Implementability Appraisal instrument developed at the Yale Center for Medical Informatics. The reason they are important is that they answer the questions that implementers ask when implementing a clinical guideline recommendation. Questions such as, “How does this recommendation impact the users?”, “How does it impact my patients?”, and “Can we measure its use?” are all critical to a successful implementation strategy. We are going to create an interface that will allow us to capture this critical metadata and also support it with some of the enhancements we have been building.

PMI Growth

With our preliminary results, we expanded the PMI set to the whole corpus and have some very interesting results. High PMI values indicate more clinically relevant terms and less pedestrian, although clinical in nature, terms. Pairs which include terms such as “should” and “therapy” score lower PMI values, since they occur in locations outside the current pair being considered. Sorting of the table also allows one to see the progression of terms as they become more or less “clinical.”