Harmful diagnostic errors may occur for as many as one in every 14 hospital patients receiving medical care, a new study based on a single medical center in the U.S. has found. As many as 85 percent of these errors may be preventable, highlighting the need for improved surveillance in hospital settings.
Previously published reports in the U.S. have suggested that existing surveillance tools underestimate the prevalence of diagnostic errors in healthcare settings. To explore these findings, a team of researchers led by Brigham and Women's Hospital in Boston randomly selected records from 675 patients admitted to hospital between July 2019 and September 2021.
"In the majority of cases, the diagnostic process works well, leading to a timely and accurate diagnosis," Anuj Dalal, an associate professor at Harvard Medical School and lead author on the study, told Newsweek. "But sometimes things do break down. Interwoven systems, complex processes, and human factors can contribute to a missed diagnostic opportunity."
In their study, published in the journal BMJ Quality and Safety, Dalal and colleagues concluded that, based on this sample from a single medical center, harmful diagnostic errors occurred in 7 percent of patients, or one in 14, receiving general medical care. They added that the majority of these errors were preventable.
"In our study, the key process breakdowns identified to include breakdowns in initial assessments and diagnostic testing," Dalal said. "It is paramount to note that it is not one individual or process that is at fault."
These findings add to previous work by Dalal and colleagues exploring existing electronic health record systems and their ability to monitor diagnostic errors in medical settings.
"We suspect a mix of underlying issues are driving the problems with test choice and clinical assessments we saw," Andrew Auerbach, a professor of medicine in residence at the University of California San Francisco and co-author on this previous research, told Newsweek.
"These in turn probably fall into system gaps like problems with how handoffs happen or how the electronic health record displays data, as well as cognitive and workload problems such as just being very busy with many tasks simultaneously."
But how applicable are these new findings to hospitals nationwide?
"As a single center study at one tertiary academic medical center, one must be cautious in extrapolating these estimates to other hospitals nationwide," Dalal said. "There is likely variability at different hospitals for a variety of reasons. It is difficult to say without rigorously conducted multicenter research studies."
He continued: "As part of our research efforts, we are conducting studies to measure diagnostic error rates using a similar process at other hospitals."
Once more nationally representative data is gathered, what can we do to minimize these diagnostic errors in hospital settings?
"Measuring error rates routinely as part of hospital quality and safety programs is a first step to understanding the burden of the problem at any one institution," Dalal said.
"Addressing the problem will require multidisciplinary efforts to monitor and address the complex systems and human factors that contribute to these errors; creating a culture of diagnostic safety among clinicians; learning from cases in which a timely and accurate diagnosis was achieved."
These solutions might also be improved with the help of AI. "Artificial intelligence approaches will certainly have a role in improving how we detect cases and trigger interventions," Dalal said.
"I would note that testing out novel AI interventions should be done in context of safely and rigorously conducted research studies that considers potential risks introduced by AI before it can be deployed safely in healthcare settings."
Better diagnostic care helps both doctors and patients, and research in this field is critical for public health. "Focusing on diagnosis and diagnostic thinking is a critical part of not only improving patient care but also improving physician performance and well-being," Dalal said.
"We are optimistic that projects such as our own will help move the field in both directions at once."
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References
Dalal, A. K., Plombon, S., Konieczny, K., Motta-Calderon, D., Malik, M., Garber, A., Lam, A., Piniella, N., Leeson, M., Garabedian, P., Goyal, A., Roulier, S., Yoon, C., Fiskio, J. M., Schnock, K. O., Rozenblum, R., Griffin, J., Schnipper, J. L., Lipsitz, S., & Bates, D. W. (2024). Adverse diagnostic events in hospitalised patients: A single-centre, retrospective cohort study. BMJ Quality & Safety. https://doi.org/10.1136/bmjqs-2024-017183
Dalal, A. K., Schnipper, J. L., Raffel, K., Ranji, S., Lee, T., & Auerbach, A. (2023). Identifying and classifying diagnostic errors in acute care across hospitals: Early lessons from the Utility of Predictive Systems in Diagnostic Errors (UPSIDE) study. Journal of Hospital Medicine, 19(2), 140–145. https://doi.org/10.1002/jhm.13136
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