Clinical research staff perceptions on a natural language processing-driven tool for eligibility prescreening: An iterative usability assessment

Betina Idnay; Yilu Fang; Caitlin Dreisbach; Karen Marder; Chunhua Weng; Rebecca Schnall

doi:10.1016/j.ijmedinf.2023.104985

Clinical research staff perceptions on a natural language processing-driven tool for eligibility prescreening: An iterative usability assessment

Int J Med Inform. 2023 Mar:171:104985. doi: 10.1016/j.ijmedinf.2023.104985. Epub 2023 Jan 6.

Authors

Betina Idnay¹, Yilu Fang², Caitlin Dreisbach³, Karen Marder⁴, Chunhua Weng², Rebecca Schnall⁵

Affiliations

¹ Columbia University, School of Nursing, New York, NY, USA; Columbia University, Department of Neurology, New York, NY, USA; Columbia University, Department of Biomedical Informatics, New York, NY, USA. Electronic address: bsi2102@cumc.columbia.edu.
² Columbia University, Department of Biomedical Informatics, New York, NY, USA.
³ Columbia University, Data Science Institute, New York, NY, USA.
⁴ Columbia University, Department of Neurology, New York, NY, USA.
⁵ Columbia University, School of Nursing, New York, NY, USA; Columbia University, Mailman School of Public Health, Department of Population and Family Health, New York, NY, USA.

Abstract

Background: Participant recruitment is a barrier to successful clinical research. One strategy to improve recruitment is to conduct eligibility prescreening, a resource-intensive process where clinical research staff manually reviews electronic health records data to identify potentially eligible patients. Criteria2Query (C2Q) was developed to address this problem by capitalizing on natural language processing to generate queries to identify eligible participants from clinical databases semi-autonomously.

Objective: We examined the clinical research staff's perceived usability of C2Q for clinical research eligibility prescreening.

Methods: Twenty clinical research staff evaluated the usability of C2Q using a cognitive walkthrough with a think-aloud protocol and a Post-Study System Usability Questionnaire. On-screen activity and audio were recorded and transcribed. After every-five evaluators completed an evaluation, usability problems were rated by informatics experts and prioritized for system refinement. There were four iterations of system refinement based on the evaluation feedback. Guided by the Organizational Framework for Intuitive Human-computer Interaction, we performed a directed deductive content analysis of the verbatim transcriptions.

Results: Evaluators aged from 24 to 46 years old (33.8; SD: 7.32) demonstrated high computer literacy (6.36; SD:0.17); female (75 %), White (35 %), and clinical research coordinators (45 %). C2Q demonstrated high usability during the final cycle (2.26 out of 7 [lower scores are better], SD: 0.74). The number of unique usability issues decreased after each refinement. Fourteen subthemes emerged from three themes: seeking user goals, performing well-learned tasks, and determining what to do next.

Conclusions: The cognitive walkthrough with a think-aloud protocol informed iterative system refinement and demonstrated the usability of C2Q by clinical research staff. Key recommendations for system development and implementation include improving system intuitiveness and overall user experience through comprehensive consideration of user needs and requirements for task completion.

Keywords: Clinical research; Cognitive walkthrough; Cohort identification; Eligibility prescreening; Natural language processing; Usability.

Publication types

Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, P.H.S.

MeSH terms

Adult
Computers
Electronic Health Records
Female
Humans
Middle Aged
Natural Language Processing*
Records
User-Computer Interface*
Young Adult

Abstract

Publication types

MeSH terms

Grants and funding