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F Marasigan

Publications and source records attributed to F Marasigan.

2 recordsLinked to original sources

Scoring performance on computer-based patient simulations: beyond value of information.

As computer based clinical case simulations become increasingly popular for training and evaluating clinicians, approaches are needed to evaluate a trainee's or examinee's solution of the simulated cases. In 1997 we developed a decision analytic approach to scoring performance on computerized patient case simulations, using expected value of information (VOI) to generate a score each time the user requested clinical information from the simulation. Although this measure has many desirable characteristics, we found that the VOI was zero for the majority of information requests. We enhanced our original algorithm to measure potential decrements in expected utility that could result from using results of information requests that have zero VOI. Like the original algorithm, the new approach uses decision models, represented as influence diagrams, to represent the diagnostic problem. The process of solving computer based patient simulations involves repeated cycles of requesting and receiving these data from the simulations. Each time the user requests clinical data from the simulation, the influence diagram is evaluated to determine the expected VOI of the requested clinical datum. The VOI is non-zero only it the requested datum has the potential to change the leading diagnosis. The VOI is zero when the data item requested does not map to any node in the influence diagram or when the item maps to a node but does not change the leading diagnosis regardless of it's value. Our new algorithm generates a score for each of these situations by modeling what would happen to the expected utility of the model if the user changes the leading diagnosis based on the results. The resulting algorithm produces a non-zero score for all information requests. The score is the VOI when the VOI is non-zero It is a negative number when the VOI is zero.

Algorithms↗

A decision analytic method for scoring performance on computer-based patient simulations.

As computer based clinical case simulations become increasingly popular for training and evaluating clinicians, approaches are needed to evaluate a trainee's or examinee's solution of the simulated cases. We developed a decision analytic approach to scoring performance on computerized patient case simulations. We developed decision models for computerized patient case simulations in four specific domains in the field of infectious disease. The decision models were represented as influence diagrams. A single decision node represents the possible diagnoses the user may make. One chance node represents a probability distribution over the set of competing diagnoses in the simulations. The value node contains the utilities associated with all possible combinations of diagnosis and disease. All relevant data that the user may request from the simulation are represented as chance nodes with arcs to or from the diagnosis node and/or each other. Probabilities in the decision model were derived from the literature, where available, or expert opinion. Utilities were assessed by standard gamble from clinical experts. The process of solving computer based patient simulations involves repeated cycles of requesting data (history, physical examination or laboratory) and receiving these data from the simulations. Each time the user requests clinical data from the simulation, the influence diagram is evaluated with and without an arc from the corresponding chance node to the decision node. The difference in expected utility between the two solutions of the influence diagram represents the expected value of information (VOI) from the requested clinical datum. The ratio of the expected VOI from the data requested and the expected value of perfect information about the diagnosis is a normative measure of the quality of each of the user's data requests. This approach provides a continuous measure of the quality of the user's data requests in a way that is sensitive to the previous data collected. The score distinguishes serious from minor misdiagnoses. And the same influence diagram can be used to evaluate performance on multiple simulations in the same clinical domain.

Computer Simulation↗