Statistical modelling of general practice medicine for computer assisted data entry in electronic medical record systems.
Electronic medical record (EMR) systems have much potential, however, there are still a number of issues that need to be resolved before EMRs are widely accepted. One of these issues is the data input task, a potentially serious practical barrier to on-line medical computer usage. This paper reports the empirical modelling of data input requirements for physicians who use a problem-orientated medical record system. Three statistical models (Bayesian conditional probability, multiple linear regression and discriminant analysis) to predict drug treatment given problem diagnoses are derived from EMRs of 2500 general Practice encounters. Two metrics are used to measure the predictive power of the models considering both the number of drugs correctly predicted and the strength with which the models predict them. The models are tested on 500 unseen records from the same patient-physician population and the data used to build the models. The Bayesian model produces the best predictions on unseen data and is also the easiest model to compute. A prototype interface that enables new patient cases to be entered is constructed to demonstrate how the predictive power of the model can translate into benefits in the data entry task.
George
S E
SE
The School of Computer and Information Science, University of South Australia, Mawson Lakes, Australia. s.george@unisa.edu.au
Warren
J R
JR
D001499:Bayes Theorem; D016002:Discriminant Analysis; D011307:Drug Prescriptions; D005194:Family Practice; D006801:Humans; D016014:Linear Models; D016347:Medical Records Systems, Computerized; D015233:Models, Statistical; D014584:User-Computer Interface