Content: Clinical Decision Support
Dr Greenes introduced us into the realm of using computers for aiding diagnosis and management of the patient. The first lecture introduced decision trees and modeling in clinical decision support with the secon lecture expanding on these models and methods of analysis.
The earliest of these systems included MYCIN for determining the best antibiotic therapy, HELP used for developing rules to issue medical alerts for inpatients in a hospital.
These systems use Bayesian method of analysis. Statistics is broadly divided into 2 categories based on the way a problem is approached and analyzed. Bayesian system uses new knowledge and research data to make inferences on the problem. The second approach is the frequentist approach (which I still have to understand well) which uses a well defined set of experiments to determine the probabilites and does not allow for new information to change these probabilities.
CDS system uses the Bayesian methods for obvious reasons, as the the entire set of probabilities for all possibilities cannot be pre determined.
What is found most challenging to comprehend in this system was the assignment of these probabilities. Some of these could be determined by previous research and literature search. However, some were based purely on "expert opinion" and these can be subjective.
I definitely think that CDS systems can be useful for development of guidelines, but I doubt if this can ever replace the clinical acumen required in bedside medicine which comes through experience with simple algorithims for diagnosis and even management. Then maybe its too early to say that .

Posted by
Sheetal Shetty