Classification models for the prediction of clinicians' information needs.
OBJECTIVE
Clinicians face numerous information needs during patient care activities and most of these needs are not met. Infobuttons are information retrieval tools that help clinicians to fulfill their information needs by providing links to on-line health information resources from within an electronic medical record (EMR) system. The aim of this study was to produce classification models based on medication infobutton usage data to predict the medication-related content topics (e.g., dose, adverse effects, drug interactions, patient education) that a clinician is most likely to choose while entering medication orders in a particular clinical context.


METHODS
We prepared a dataset with 3078 infobutton sessions and 26 attributes describing characteristics of the user, the medication, and the patient. In these sessions, users selected one out of eight content topics. Automatic attribute selection methods were then applied to the dataset to eliminate redundant and useless attributes. The reduced dataset was used to produce nine classification models from a set of state-of-the-art machine learning algorithms. Finally, the performance of the models was measured and compared.


METHODS
Area under the ROC curve (AUC) and agreement (kappa) between the content topics predicted by the models and those chosen by clinicians in each infobutton session.


RESULTS
The performance of the models ranged from 0.49 to 0.56 (kappa). The AUC of the best model ranged from 0.73 to 0.99. The best performance was achieved when predicting choice of the adult dose, pediatric dose, patient education, and pregnancy category content topics.


CONCLUSIONS
The results suggest that classification models based on infobutton usage data are a promising method for the prediction of content topics that a clinician would choose to answer patient care questions while using an EMR system.
Del Fiol
Guilherme
G
Biomedical Informatics Department, University of Utah, 4646 Lake Park Boulevard, Salt Lake City, UT 84120, USA. guilherme.delfiol@utah.edu
Haug
Peter J
PJ
D000465:Algorithms; D019540:Area Under Curve; D001185:Artificial Intelligence; D001499:Bayes Theorem; D016208:Databases, Factual; D020000:Decision Support Systems, Clinical; D006282:Health Personnel; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D016347:Medical Records Systems, Computerized; D015233:Models, Statistical; D009862:Online Systems; D005791:Patient Care / Q000379:methods*; D004364:Pharmaceutical Preparations; D055656:Prescriptions; D012372:ROC Curve; D014584:User-Computer Interface
D004364:Pharmaceutical Preparations