Appl Clin Inform 2017; 08(01): 35-46
DOI: 10.4338/ACI-2016-09-CR-0148
Case Report
Schattauer GmbH

Using Active Learning to Identify Health Information Technology Related Patient Safety Events

Allan Fong
1  MedStar Institute for Innovation –National Center for Human Factors in Healthcare, 3007 Tilden St. NW, Suite 7M, Washington, D.C. 20008, USA
,
Jessica L. Howe
1  MedStar Institute for Innovation –National Center for Human Factors in Healthcare, 3007 Tilden St. NW, Suite 7M, Washington, D.C. 20008, USA
,
Katharine T. Adams
1  MedStar Institute for Innovation –National Center for Human Factors in Healthcare, 3007 Tilden St. NW, Suite 7M, Washington, D.C. 20008, USA
,
Raj M. Ratwani
1  MedStar Institute for Innovation –National Center for Human Factors in Healthcare, 3007 Tilden St. NW, Suite 7M, Washington, D.C. 20008, USA
2  Georgetown University Medical Center, 3800 Reservoir Rd NW, Washington, DC 20007
› Author Affiliations
FundingThis project was funded under contract/grant number Grant R01 HS023701–02 from the Agency for Health-care Research and Quality (AHRQ), U.S. Department of Health and Human Services. The opinions expressed in this document are those of the authors and do not reflect the official position of AHRQ or the U.S. Department of Health and Human Services.
Further Information

Correspondence to:

Allan Fong
National Center for Human Factors in Healthcare
3007 Tilden St. NW, Suite 7M
Washington, D.C. 20008, USA
202–244–9807

Publication History

Received: 01 September 2016

Accepted: 09 January 2016

Publication Date:
20 December 2017 (online)

 

Summary

The widespread adoption of health information technology (HIT) has led to new patient safety hazards that are often difficult to identify. Patient safety event reports, which are self-reported descriptions of safety hazards, provide one view of potential HIT-related safety events. However, identifying HIT-related reports can be challenging as they are often categorized under other more predominate clinical categories. This challenge of identifying HIT-related reports is exacerbated by the increasing number and complexity of reports which pose challenges to human annotators that must manually review reports. In this paper, we apply active learning techniques to support classification of patient safety event reports as HIT-related. We evaluated different strategies and demonstrated a 30% increase in average precision of a confirmatory sampling strategy over a baseline no active learning approach after 10 learning iterations.


#

 


#

Conflict of interest

The authors have no conflict of interest.


Correspondence to:

Allan Fong
National Center for Human Factors in Healthcare
3007 Tilden St. NW, Suite 7M
Washington, D.C. 20008, USA
202–244–9807