State-of-the-art clinical decision-support systems can sharpen a veteran physician’s reasoning skills, but they're far from perfect.
Once that initial data is entered, the program offers several possible differential diagnoses, listed in a chart that shows their relative probability in an easy-to-read graph. At the end of this first run in this example, achondroplasia--a common form of dwarfism--tops the list.
Now the software gets really sophisticated. The physician has to add more physical findings to narrow the possible diagnoses. This is the juncture at which the program will outthink many clinicians who can't bring to mind the long list of possible findings that might accompany kyphosis in an 8-month-old baby who is not suffering from scoliosis.
The program itself provides a list of possible physical signs to look for. In this case, it includes macrocephaly--an enlarged head--which the baby does have. Once the doctor clicks on enlarged head and chooses onset at about 6 months of age, the graph listing all the differential diagnoses changes dramatically, with mucopolysaccharidosis Type I, an inherited metabolic disorder, jumping to the top of the graph.
The program takes the clinician through a series of other physical findings and eventually suggests a blood test to confirm the diagnosis. Quite impressive.
How Well Accepted?
So why aren't diagnostic aids like Simulconsult more widely used? For one thing, this particular program confines itself to genetic, neurological, and metabolic diseases, which narrows down considerably the millions of combinations of physical findings and diseases any one patient may suffer from.
But the obstacles go beyond that one. Many diagnosis-support systems are standalone applications that don't work well with electronic health record systems. Busy clinicians often object to the extra work required to locate patient data and input it into a separate program.
Assuming that these obstacles could be overcome, how effective are diagnostic programs overall? In a review published in 2005 in the Journal of the American Medical Association that included 10 trials of such applications, four of the studies found the programs made a difference because they helped diagnose cardiac ischemia in the emergency room; reduced unnecessary hospital or coronary care admissions; improved detection of mood disorders in a posttraumatic stress clinic; or sped up the diagnosis of bowel obstruction.
So evidently these programs work--at least some of the time. There are two reasons for their selective success rate, according to the JAMA report. When clinicians are automatically prompted to use the software through the workflow process, the programs produce better results. And in general CDSS programs that are home grown--developed by hospitals and other care providers--seem to be more successful, evidently because there are physician champions in-house to encourage others to get on board.
Thinking back to that Stump The Professors presentation, it's hard to imagine artificial intelligence experts developing a diagnostic support program that could duplicate the supercomputer inside that one medical professor's head.
But at the same time, it's hard to imagine a physician who would not benefit from having a user-friendly database of relevant diseases, linked to all the millions of combinations of signs, symptoms, and lab findings. Once clinicians venture past the diagnosis of common, easily recognizable disorders, it seems almost impossible to do all the mathematical calculations needed to find that proverbial needle in a haystack.
Paul Cerrato is editor of InformationWeek Healthcare.
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