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Exploring opportunities for AI supported medication error categorization: A brief report in human machine collaboration

Affiliation
National Center for Human Factors in Healthcare ,MedStar Health Research Institute ,Hyattsville ,MD ,United States
Fong, Allan;
Affiliation
National Center for Human Factors in Healthcare ,MedStar Health Research Institute ,Hyattsville ,MD ,United States
Bonk, Christopher;
Affiliation
National Center for Human Factors in Healthcare ,MedStar Health Research Institute ,Hyattsville ,MD ,United States
Vasilchenko, Varvara;
Affiliation
Center for Drug Evaluation and Research, Food and Drug Administration ,Office of Surveillance and Epidemiology ,Silver Spring ,MD ,United States
De, Suranjan;
Affiliation
Center for Drug Evaluation and Research, Food and Drug Administration ,Office of Surveillance and Epidemiology ,Silver Spring ,MD ,United States
Kovich, Douglas;
Affiliation
Center for Drug Evaluation and Research, Food and Drug Administration ,Office of Surveillance and Epidemiology ,Silver Spring ,MD ,United States
Wyeth, Jo

Understanding and mitigating medication errors is critical for ensuring patient safety and improving patient care. Correctly identifying medication errors in the United States Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) reports can be difficult because of the complexities of medication error concepts. We took a user-centered design approach to support the medication error categorization workflow process with artificial intelligence (AI). We developed machine learning models to categorize medication error terms. The average F1-score, precision, recall, and area under the precision recall curve for 18 Medical Dictionary for Regulatory Activities (MedDRA) Lower Level Term (LLT) relating to medication errors were 0.88, 0.92. 0.85, and 0.83 respectively. We developed a framework to help evaluate opportunities for artificial intelligence integration in the medication error categorization workflow. The framework has four attributes: technical deployment, process rigidity, AI assistance, and frequency. We used the framework to compare two AI integration opportunities and concluded that the quality assurance (QA) opportunity to be a more feasible initial option for AI integration. We then extended these insights into the development and user testing of a prototype application. The user testing identified the highlighting and commenting capabilities of the application to be more useful and sliders and similar report suggestions to be less useful. This suggested that different AI interactions with human highlighting should be explored. While the medication error quality assurance prototype application was developed for supporting the review of direct FAERS reports, this approach can be extended to assist in the workflow for all FAERS reports.

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License Holder: Copyright © 2022 Fong, Bonk, Vasilchenko, De, Kovich and Wyeth.

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