Using Data Mining to Predict Errors in Chronic Disease Care

Development of data mining technologies to predict treatment errors in populations of patients represents a major advance in patient safety research. In the work presented here we (1) create a simulation test environment using characteristic models of physician decision strategies and simulated populations of type 2 diabetic patients, (2) employ a specific data mining technology that predicts encounter-specific errors of omission in representative databases of simulated physician-patient encounters, and (3) test the predictive technology in an administrative database of real physician-patient encounter data. Two dominant decision making strategies with different rates of treatment errors are identified: feedback strategies that use the results of past actions to guide treatment decision, and feedforward strategies that make treatment decisions based on anticipated future patient states. Evaluation of data mining results shows that the predictive tools developed from simulated treatment data can predict errors of omission in clinical patient data. The methods developed in this work can have the potential for wide use in identifying decision strategies that lead to encounter-specific treatment errors in chronic disease care.