One of the most widely heard terms in healthcare circles today is ‘outcomes.’ This is both good and bad. It is certainly positive that outcomes appear to act as a rallying flag for improvement efforts. A potential downside exists, however, depending on how things are done once an outcome is deemed important enough for management to allocate improvement resources to it.
As someone with a lifelong involvement in problem-solving and IT, I see a trend that gives me pause. It concerns users who, overwhelmed by the increasing complexity of their work, employ ever more sophisticated computing tools and technology in haphazard fashion, often lacking the know how to be able to gauge the validity, usefulness, or limits of applicability of a given answer or result. Many are left with one option, to simply believe what the tool tells them and accept it as fact, a reluctance to ask questions coming both from a sheer inability to do it as well as from the sizable capital investment often made.
A pressing need exists to become more evidence-based, and to practice and deliver healthcare accordingly. Given fast-paced technological and regulatory changes, being able to develop a probabilistic outlook as to the range of possible outcomes in decision-making is increasingly important. This material introduces Bayes’ theorem, which is central to reassessing probabilities in light of accumulating evidence.