State-of-the-art clinical decision-support systems can sharpen a veteran physician’s reasoning skills, but they're far from perfect.
Among the countless medical conferences I've attended over the years, one presentation stands out. Called "Stump the Professors," it featured a panel of top medical school professors who were challenged to figure out the diagnosis in several tough clinical cases.
One by one, residents would file up to the podium and describe their case, outlining signs and symptoms, lab results, and pathology findings. One thing that stands out in my mind is the lighthearted banter and laughter among the participants as professors struggled to figure out some of these esoteric disorders. The residents relished the fact that they really were stumping the professors.
Among the expert panelists was one professor who remained relatively quiet until the end of each case, only occasionally asking questions about some lab report or symptom. But when all the facts were in, he would nail virtually every diagnosis. What an amazing gift.
Fast forwarding to today and the age of clinical decision support software (CDSS) and IBM's Watson computer, the question that comes to mind is: Can health IT duplicate this kind of diagnostic reasoning, or at least facilitate the process?
Computer-generated diagnostic tools, which are usually part of a larger CDSS, come in a few basic flavors. Many consist of three main components: a knowledge base, an inference engine, and a communication mechanism.
The knowledge base contains reasoning guidelines, including "if/then" rules. For instance, one rule may state: If drug X is taken with drug Y, alert the physician for a possible harmful interaction. The inference engine then combines the rules with each patient's data--in this example, it would be the list of medications he's taking. The communication mechanism provides a way to give the results of the data analysis to the clinician, who then decides what action to take.
Other diagnostic programs rely not on a knowledge base, but on some form of artificial intelligence--neural networks, for instance--to find patterns in the patient's data and associate them with a list of diagnoses.
Among the players in the field of diagnostic software are Simulconsult and Dxplain. Typically, these programs use probabilistic algorithms and pattern-matching to create a list of diagnoses ranked from most likely to least likely disorders. The software generates the list of diseases by looking at each sign, symptom, and lab finding to determine how strongly each of the physical findings is associated with the disease under consideration. For example, how probable is it that a patient with carrot-orange skin is suffering from carotene toxicity.
Many of these programs also incorporate a huge database that condenses clinical research from major clinical journals and clinical practice guidelines to ensure that the final results are best practice.
Taking A Closer Look
The software developed by Simulconsult, which incorporates statistics-enhanced pattern matching, offers a good example of the impressive reasoning skills of these diagnostic tools.
The example provided by Simulconsult starts with a patient presenting with kyphosis, an abnormal curvature of the spine. The initial information screen asks for the patient's age (8 months), gender, and family history. Then it asks the physician to input the most unusual or prominent physical finding.
Once kyphosis is entered in the search engine, the system wants more details: Is the kyphosis accompanied by scoliosis? The clinician observes that there is no scoliosis, enters that fact, and the program then asks: When did the kyphosis begin? A drop-down menu lets him choose its onset.
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