Information Discovery on Electronic Health Records Using Authority Flow Techniques
- Equal contributors
1 School of Computing and Information Sciences, Florida International University, Miami, Florida, USA
2 Department of Computer Sciences, University of Wisconsin-Madison, Wisconsin, USA
3 Regenstrief Institute, Inc., Indianapolis, Indiana, USA
4 Indiana University School of Medicine, Indianapolis, Indiana, USA
5 Indiana University Center for Health Services and Outcomes Research, Indianapolis, Indiana, USA
6 VA HSR&D Center of Excellence on Implementing Evidence-Based Practice, Richard L. Roudebush VA Medical Center, Indianapolis, Indiana, USA
BMC Medical Informatics and Decision Making 2010, 10:64 doi:10.1186/1472-6947-10-64Published: 22 October 2010
As the use of electronic health records (EHRs) becomes more widespread, so does the need to search and provide effective information discovery within them. Querying by keyword has emerged as one of the most effective paradigms for searching. Most work in this area is based on traditional Information Retrieval (IR) techniques, where each document is compared individually against the query. We compare the effectiveness of two fundamentally different techniques for keyword search of EHRs.
We built two ranking systems. The traditional BM25 system exploits the EHRs' content without regard to association among entities within. The Clinical ObjectRank (CO) system exploits the entities' associations in EHRs using an authority-flow algorithm to discover the most relevant entities. BM25 and CO were deployed on an EHR dataset of the cardiovascular division of Miami Children's Hospital. Using sequences of keywords as queries, sensitivity and specificity were measured by two physicians for a set of 11 queries related to congenital cardiac disease.
Our pilot evaluation showed that CO outperforms BM25 in terms of sensitivity (65% vs. 38%) by 71% on average, while maintaining the specificity (64% vs. 61%). The evaluation was done by two physicians.
Authority-flow techniques can greatly improve the detection of relevant information in EHRs and hence deserve further study.