Branch F, Santana I, Hegdé J. Diagnostics (Basel). 2022;12:105.
Anchoring bias is relying on initial diagnostic impression despite subsequent information to the contrary. In this study, radiologists were asked to read a mammogram and were told a random number which researchers claimed was the probability the mammogram was positive for breast cancer. Radiologists' estimation of breast cancer reflected the random number they were given prior to viewing the image; however, when they were not given a prior estimation, radiologists were highly accurate in diagnosing breast cancer.
Haimi M, Brammli-Greenberg S, Baron-Epel O, et al. BMC Med Inform Decis Mak. 2020;20.
This retrospective mixed-methods study explored patient safety within a pediatric telemedicine triage service by assessing the appropriateness and reasonableness of the diagnosis reached by the online physician. The researchers analyzed a random sample of telephone consultations and conducted qualitative interviews with physicians to obtain their perspectives about factors impacting their reaching diagnosis and deciding on reasonable and appropriate treatment. Analysis of telephone consultations found high levels of diagnosis appropriateness, decision reasonableness and accuracy. Physician interviews revealed six themes for appropriate diagnosis and decision-making: (1) use of intuition, (2) experience, (3) use of rules of thumb and protocols, (4) making shared decisions with parents, (5) considering non-medical factors, and (6) using additional tools such as video chat or digital photos when necessary.
Perea-Pérez B, Labajo-González E, Acosta-Gío AE, et al. J Patient Saf. 2020;16.
Based on malpractice claims data in Spain, the authors propose eleven recommendations to mitigate preventable adverse events in dentistry. These recommendations include developing a culture of safety, improving the quality of clinical records, safe prescribing practices, using checklists in oral surgical procedures, and having an action plan for life-threatening emergencies in the dental clinic.
Kim H-E, Kim HH, Han B-K, et al. The Lancet Digital Health. 2020.
There is increasing interest in the use of artificial intelligence (AI) to improve breast cancer detection. This study developed and validated an AI algorithm using mammography readings from five institutions in South Korea, the United States, and the United Kingdom. The AI algorithm alone showed better diagnostic performance in breast cancer detection compared to radiologists without AI assistance (area under the curve [AUC] of 0.94 vs. 0.81, p<0.0001) or radiologists with AI assistance (0.88; p<0.0001). AI improved performance of radiologists and was better at detecting mass cancers, distortion, asymmetry, or node-negative cancers compared with radiologist alone.
Zhu L, Reychav I, McHaney R, et al. Int J Risk Saf Med. 2019;30:129-153.
Understanding the contributors to adverse events helps to identify ways to prevent future events. This study used natural language processing (NLP) strategies and social network analysis (SNA) to explore the underlying behaviors contributing to adverse events, and suggested institutional-level approaches to reducing these events.
Liew TM, Lee CS, Shawn KLG, et al. Ann Fam Med. 2019;17:257-266.
Many older patients experience medication-related harm due to inappropriate prescribing. This meta-analysis found that potentially inappropriate medication prescribing in older patients worsened health-related quality of life and increased emergency department visits and hospitalizations. A WebM&M commentary discussed strategies for safer medication management for older patients.
Shimizu T, Nemoto T, Tokuda Y. Int J Med Inform. 2018;109:1-4.
This retrospective study found that clinicians who had access to a commercial clinical decision support tool made fewer diagnostic errors than clinicians who did not have access to the tool. The authors conclude that online clinical decision support from this platform improved diagnosis.
Solanki R, Mondal N, Mahalakshmy T, et al. Arch Dis Child. 2017;102:651-654.
Pediatric patients are at high risk for medication errors. Researchers conducted a cross-sectional study on 166 infants younger than 3 months who were discharged from the hospital. They found a high frequency of medication errors by caregivers. In keeping with prior research, dose administration errors were the most common type of error.
Clinicians may prescribe split pills for many different reasons, including dosing flexibility and patient affordability; however, this practice presents potential hazards. Splitting medications that are formulated to be extended-release or enteric-coated can lead to possibly dangerous changes in the drug's functionality. This study discusses the introduction of a clinical decision support warning that created a "hard stop" for any time an outpatient clinician attempted to prescribe a split pill for these special formulation medications. The study site was an academic medical center in Taiwan that performs more than 2.5 million ambulatory visits per year. The intervention resulted in a sharp decline in inappropriate medication splitting from a rate of approximately 0.61% to below 0.2%, where it has remained for at least 10 consecutive months. The use of a hard stop order can be controversial, as this method has resulted in unintended consequences in the past. A prior AHRQ WebM&M perspective discussed some of the tensions related to implementing medication decision support systems.
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