Toward safer health care: a review strategy of FDA medical device adverse event database to identify and categorize health information technology related events

Author:

Kang Hong1,Wang Ju1,Yao Bin1,Zhou Sicheng1,Gong Yang1

Affiliation:

1. School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA

Abstract

Abstract Introduction Health information technology (HIT) is intended to provide safer and better care to patients. However, poorly designed or implemented HIT poses a key risk to patient safety. It is essential for healthcare providers and researchers to investigate the HIT-related events. Unfortunately, the lack of HIT-related event databases in the community hinders the analysis and management of HIT-related events. Objectives Develop a standardized process for identifying HIT-related events from a Federal Drug Administration (FDA) database in order to create an HIT exclusive database for analysis and learning. Methods The FDA Manufacturer and User Facility Device Experience (MAUDE) database, containing over 7-million reports about medical device malfunctions and problems leading to serious injury or death, was considered as a potential resource to identify HIT-related events. We developed a strategy of identifying and categorizing HIT-related events from the FDA reports through the application of a keyword filter and standardized expert review. Ten percent identified reports were reviewed to measure the consistency among experts and to initialize a database for HIT-related events. Results With the proposed strategy, we initialized an HIT-related event database with over 3500 reports, and updated the estimation of the HIT-related event proportion in the FDA MAUDE database to 0.46∼0.69%, up to 50,000 HIT-related events. Conclusion The proposed strategy for HIT-related event identification holds promise in aiding the understanding, characterization, discovery, and reporting of HIT-related events toward improved patient safety. The analysis of contributing factors under the 8-dimensional sociotechnical model shows that hardware and software, clinical content, and human–computer interface were identified more frequently than the other dimensions.

Funder

UTHealth Innovation for Cancer Prevention Research Training Program Post-Doctoral Fellowship

Cancer Prevention and Research Institute of Texas

The University of Texas System Grants Program

The Agency for Healthcare Research and Quality

Agency for Healthcare Research and Quality

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

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