Missed diagnosis of stroke in emergency medicine settings is an important patient safety problem. In this study, researchers interviewed emergency medicine physicians about their perspectives on diagnostic neurology and use of clinical decision support (CDS) tools. Themes emerged related to challenges in diagnosis, neurological complaints, and challenges in diagnostic decision-making emergency medicine, more generally. Participating physicians were enthusiastic about the possibility of involving CDS tools to improve diagnosis for non-specific neurological complaints.
Rodriguez-Ruiz A, Lång K, Gubern-Merida A, et al. J Natl Cancer Inst. 2019;111:916-922.
Artificial intelligence (AI) may have the capacity to improve diagnosis. Researchers found that an AI system was able to detect breast cancer using mammography with accuracy similar to that of the average of the 101 radiologists whose interpretations were included in the study.
Massalha S, Clarkin O, Thornhill R, et al. Can J Cardiol. 2018;34:827-838.
Decision support tools can help reduce diagnostic uncertainty. Discussing how artificial intelligence can be utilized to inform diagnostic decision making and improve the accuracy of cardiac image interpretation, this review suggests that use of such technology can reduce production pressure and cognitive load for imaging physicians.
Meyer AND, Thompson PJ, Khanna A, et al. J Am Med Inform Assoc. 2018;25:841-847.
Clinical decision support is a widely recommended patient safety strategy. This study examined whether a mobile application created by the Centers for Disease Control and Prevention improved clinician decision-making about anticoagulation test ordering for simulated case vignettes. Each participating physician completed a series of vignettes; half used the application and half did not. When using the application, physicians demonstrated greater diagnostic accuracy and confidence, and they needed less time to complete each vignette. The authors suggest that mobile applications may be useful for providing decision support.
Liberman AL, Newman-Toker DE. BMJ Qual Saf. 2018;27:557-566.
Patient safety measurement remains challenging. This article describes a framework to address gaps in measuring diagnostic error. The authors propose utilizing big data to develop diagnostic performance dashboards and benchmarking tools that support proactive learning and improvement strategies.
Bejnordi BE, Veta M, van Diest PJ, et al. JAMA. 2017;318:2199-2210.
Diagnostic error is a growing area of focus within patient safety. Artificial intelligence has the potential to improve the diagnostic process, both in terms of accuracy and efficiency. In this study, investigators compared the use of automated deep learning algorithms for detecting metastatic disease in stained tissue sections of lymph nodes of women with breast cancer to pathologists' diagnoses. The algorithms were developed by researchers as part of a competition and their performance was assessed on a test set of 129 slides, 49 with metastatic disease and 80 without. A panel of 11 pathologists evaluated the same slides with a 2-hour time limit and one pathologist evaluated the slides without any time constraints. The authors conclude that some of the algorithms demonstrated better diagnostic performance than the pathologists did, but they suggest that further testing in a clinical setting is warranted. An accompanying editorial discusses the potential of artificial intelligence in health care.
Brush JE, Brophy JM. JAMA Intern Med. 2017;177:1245-1246.
Improving diagnosis has been recently promoted as a core area in patient safety. This commentary discusses how physicians can utilize cognitive decision making and likelihood ratios to address uncertainty and augment diagnosis. The authors suggest that clinicians learn to assess their diagnostic practices and skills to improve their performance.
Stafos A, Stark S, Barbay K, et al. Am J Nurs. 2017;117:26-31.
This study compared nurses' identification of patients at risk for harm to an electronic predictive model and found that nurses more commonly identified psychological or social risks as relevant to harm. The nurses did not identify some patients whom the predictive model deemed high risk in cases where the risk had been incorporated into the plan of care. The authors suggest that nurse perceptions could inform more accurate predictive models, though neither approach was tested against an actual safety outcome.
Information technology approaches have been advocated as a means of preventing diagnostic error. This study compared the diagnostic accuracy of computerized symptom checkers (software programs that use diagnostic algorithms based on patients' self-reported symptoms to suggest diagnoses) with that of practicing physicians. Physicians consistently arrived at more accurate diagnoses across a variety of simulated cases.
Amland RC, Hahn-Cover KE. Am J Med Qual. 2016;31:103-10.
Sepsis is a clinical condition that can be rapidly fatal, thus prompt recognition and treatment is critical. This multicenter retrospective study describes the performance of a cloud-based computerized decision support system aimed at identifying sepsis in patients before infection was suspected.
Riches N, Panagioti M, Alam R, et al. PLoS One. 2016;11:e0148991.
Despite increasing focus on diagnostic error, it remains a controversial patient safety issue. The Institute of Medicine recently suggested that further research is needed regarding electronic tools to improve diagnosis. Differential diagnosis generators provide a list of possible diagnoses for a problem. The investigators conducted a systematic review and found that differential diagnosis generators have been shown to improve diagnostic accuracy when a clinician has an opportunity to re-review the case using the software in pre-post studies. The degree of improvement varied between studies. The effect on actual clinician behaviors—such as test ordering, clinical outcomes, and cost—is unclear. Clinicians need prospective studies in order to determine whether such tools enhance diagnosis in actual practice. A recent PSNet perspective discussed future research avenues to ensure progress in diagnostic safety.
Jutel A, Lupton D. Diagnosis (Berl). 2015;2:89-96.
This study examined currently available smartphone and software applications (or apps) designed to aid with accurate diagnosis. Although the authors described some of the potential benefits of these apps, they note that their research suggests apps should be used with caution by both clinicians and consumers, due to problems with transparency regarding sources, evidence, and credentials.
Nishikawa RM, Schmidt RA, Linver MN, et al. American Journal of Roentgenology. 2012;198.
A computerized clinical decision support system helped radiologists reduce diagnostic errors in mammogram interpretation. However, radiologists ignored more than two-thirds of the prompts provided by the system.
Etchells E, Adhikari NKJ, Wu R, et al. BMJ Qual Saf. 2011;20:924-30.
In this study, clinicians were notified in real time about critical lab test abnormalities and provided with immediate decision support. However, this intervention did not prevent adverse events attributable to the critical test results, nor did it seem to result in more timely management.
Fitzgerald M, Cameron P, Mackenzie C, et al. Arch Surg. 2011;146:218-25.
Accurate initial assessment and resuscitation of trauma patients is critical to ensuring correct treatment and survival, and although standardized algorithms have been developed for initial trauma evaluation, errors are not uncommon. This innovative randomized controlled trial implemented a computerized clinician decision support system (CDSS) to ensure adherence to standardized protocols for trauma resuscitation, and used video capture of trauma resuscitations to assess the effects of the CDSS on patient outcomes. Use of the CDSS resulted in significantly reduced errors, and also reduced morbidity compared to standard treatment. This study demonstrates the utility of a CDSS in a fast-paced, high-acuity environment.
Ramnarayan P, Cronje N, Brown R, et al. Emerg Med J. 2007;24:619-24.
Diagnostic errors are common and often related to cognitive processes, with many retrospectively discovered through review of closed malpractice claims or at time of autopsy. This study used a web-based clinical decision support system called Isabel to determine its ability to accurately diagnose acute medical problems compared with final discharge diagnoses and a panel of experts. Building on a past study, investigators discovered that the system displayed the final discharge diagnosis in 95% of inpatients. The authors highlight the potential benefits of integrating such a system into daily practice and call for further study on whether it reduces diagnostic error. An AHRQ WebM&M conversation with Dr. Britto, the co-founder of Isabel Healthcare Inc., discusses eradicating diagnostic errors through such decision support systems.
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