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Associate Professor earns IMIA Best Paper of 2020 recognition

Yang Gong, MD, PhD <br/>Associate Professor
Yang Gong, MD, PhD
Associate Professor

The Editorial Board of the International Medical Informatics Association (IMIA) Yearbook of Medical Informatics 2020 recently recognized a paper co-authored by UTHealth School of Biomedical Informatics (SBMI) Associate Professor Yang Gong, MD, PhD. IMIA named the paper one of the best papers published in 2019 in the Human Factors and Organizational Issues subfield.

Gong’s co-authored paper was one of three papers named “Best Paper” because it was both outstanding and advances the field. Additionally, the paper provides “examples of applying existing frameworks together in novel and highly illuminating ways, showing the value of theory development in human factors.” The winning paper, titled “Understanding health information technology induced medication safety events by two conceptual frameworks,” was co-authored by Gong, SBMI Postdoctoral Fellows Hong Kang, PhD and Ju Wang, PhD, and Visiting Scholar Hongyuan Liang, MD, PhD.

In order to be considered for the recognition, papers must make “original and high impact contributions in the area of human factors and organizational issues in biomedical informatics.” To find applicable papers, IMIA conducted a rigorous extraction process based on queries from Web of Science and PubMed/Medline. The query process resulted in 626 papers that included “interesting and rigorous methods and important studies in human factors that move the field forward, particularly in clinical informatics and emerging technologies such as brain-computer interfaces.” Next, titles and abstracts of the papers were independently screened by two IMIA editors and the list was reduced to 30 papers. Of the 30 papers evaluated, only 15 were selected as finalist papers that were then reviewed by the two editors and by three external reviewers from internationally renowned research teams. In the end, three papers were named the best.

In their research, the team of co-authors identified a lack of studies on medication errors that present systematic views on health IT in the context of medication errors. Their paper aimed to fill that knowledge gap.

“Medication errors can occur at any point within the medication-use system and can be a double-edged sword that results in the introduction of new paths to errors,” says Gong. “An effective way to prevent errors is to learn from the event reports, including unsafe conditions, near misses, and incidents.”

In the paper, the researchers investigated health IT-specific safety reports related to medication errors through two views – a composite classification of contributing factors guided by Sittig and Singh's sociotechnical model (written by SBMI Professor Dean Sittig, PhD and Adjunct Professor Hardeep Singh, MD, MPH) and Coiera's information value chain. Using those two conceptual frameworks provides an opportunity to understand contexts and impacts of health IT-induced medication safety reports from cause to outcome.

Gong stated that “the sociotechnical model focuses on causal factors of event occurrence while the information value chain focuses on the health care process and patient outcomes that could be impacted by errors or unsafe conditions when health IT products are utilized.”

The U.S. Food and Drug Administration (FDA) indicates that it “receives more than 100,000 U.S. reports each year associated with a suspected medication error.” The FDA’s Manufacturer and User Facility Device Experience (MAUDE) database includes a vast list of medical device reports submitted to the FDA by both mandatory reporters like manufacturers, importers, and device user facilities and voluntary reporters such as health care professionals, patients, and consumers.

“The FDA’s MAUDE database is a large, publicly accessible resource that is rich with diverse health IT-related medication error report data,” noted Gong. “We used the database to understand, identify, and prioritize the most significant risks to patient safety.”

Based on the research completed by Gong and his colleagues, they anticipate that the high number of health IT-related medication error reports will only increase, “based upon the observed increment of incoming reports and the pervasive application of health IT,” says Gong.

To learn more about their study, the full paper is available online. You can also find the complete IMIA Yearbook of Medical Informatics for 2020 online.

published on 10/05/2020 at 11:30 a.m.

Chelsea Overstreet

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