Influence of artificial intelligence on the work design of emergency department clinicians a systematic literature review

Boonstra, Albert; Laven, Mente

doi:10.1186/s12913-022-08070-7

Cited by 38 publications

(23 citation statements)

References 47 publications

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“…For example, preliminary consultations using GPT-4 can reduce physician workload and improve patient experience. The use of AI in emergency rooms has already been shown to improve clinical decision-making and reduce physician workload [22]. As predicted by Topol [23], AI technology is expected to be widely adopted by health care professionals across multiple specialties.…”

Section: Principal Results and Comparison With Prior Workmentioning

confidence: 99%

The Accuracy and Potential Racial and Ethnic Biases of GPT-4 in the Diagnosis and Triage of Health Conditions: Evaluation Study

Ito,

Kadomatsu,

Fujisawa

et al. 2023

JMIR Med Educ

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Background Whether GPT-4, the conversational artificial intelligence, can accurately diagnose and triage health conditions and whether it presents racial and ethnic biases in its decisions remain unclear. Objective We aim to assess the accuracy of GPT-4 in the diagnosis and triage of health conditions and whether its performance varies by patient race and ethnicity. Methods We compared the performance of GPT-4 and physicians, using 45 typical clinical vignettes, each with a correct diagnosis and triage level, in February and March 2023. For each of the 45 clinical vignettes, GPT-4 and 3 board-certified physicians provided the most likely primary diagnosis and triage level (emergency, nonemergency, or self-care). Independent reviewers evaluated the diagnoses as “correct” or “incorrect.” Physician diagnosis was defined as the consensus of the 3 physicians. We evaluated whether the performance of GPT-4 varies by patient race and ethnicity, by adding the information on patient race and ethnicity to the clinical vignettes. Results The accuracy of diagnosis was comparable between GPT-4 and physicians (the percentage of correct diagnosis was 97.8% (44/45; 95% CI 88.2%-99.9%) for GPT-4 and 91.1% (41/45; 95% CI 78.8%-97.5%) for physicians; P=.38). GPT-4 provided appropriate reasoning for 97.8% (44/45) of the vignettes. The appropriateness of triage was comparable between GPT-4 and physicians (GPT-4: 30/45, 66.7%; 95% CI 51.0%-80.0%; physicians: 30/45, 66.7%; 95% CI 51.0%-80.0%; P=.99). The performance of GPT-4 in diagnosing health conditions did not vary among different races and ethnicities (Black, White, Asian, and Hispanic), with an accuracy of 100% (95% CI 78.2%-100%). P values, compared to the GPT-4 output without incorporating race and ethnicity information, were all .99. The accuracy of triage was not significantly different even if patients’ race and ethnicity information was added. The accuracy of triage was 62.2% (95% CI 46.5%-76.2%; P=.50) for Black patients; 66.7% (95% CI 51.0%-80.0%; P=.99) for White patients; 66.7% (95% CI 51.0%-80.0%; P=.99) for Asian patients, and 62.2% (95% CI 46.5%-76.2%; P=.69) for Hispanic patients. P values were calculated by comparing the outputs with and without conditioning on race and ethnicity. Conclusions GPT-4’s ability to diagnose and triage typical clinical vignettes was comparable to that of board-certified physicians. The performance of GPT-4 did not vary by patient race and ethnicity. These findings should be informative for health systems looking to introduce conversational artificial intelligence to improve the efficiency of patient diagnosis and triage.

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Section: Principal Results and Comparison With Prior Workmentioning

confidence: 99%

The Accuracy and Potential Racial and Ethnic Biases of GPT-4 in the Diagnosis and Triage of Health Conditions: Evaluation Study

Ito,

Kadomatsu,

Fujisawa

et al. 2023

JMIR Med Educ

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“…The benefits of digitisation of ED services lie in not only the delivery of its services, but also the opportunity to mine the captured data from the patients and the electronic records to support the application of artificial intelligence (AI) to improve ED operations, patient care, and population and public health. [ 34 ] Examples include decision support of clinicians in the risk stratification of patients with diseases such as syncope,[ 35 ] prediction of frequent ED use,[ 36 ] prediction models of severity of patient illnesses in ED for their timely triage and management,[ 37 38 ] and even risk prediction of illnesses and management for better public health and prevention of emergency visits before they happen. [ 39 ] These emerging AI models are in their infancy as emergency medicine is just starting its digitisation journey, and the number and variety of AI applications are expected to grow rapidly in the near future.…”

Section: Artificial Intelligencementioning

confidence: 99%

Digitisation of emergency medicine: opportunities, examples and issues for consideration

2024

Singapore Medical Journal

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“…Artificial intelligence may even match patients to triage levels even more accurately than emergency specialists themselves [21]. Further possibilities for implementation of artificial intelligence in the field of emergency medicine was systematically reviewed by Boonstra and Laven [22].…”

Section: How To Optimize Workflow In Paediatric Emergency Departments?mentioning

confidence: 99%

Modern Paediatric Emergency Department: Potential Improvements in Light of New Evidence

Kula¹,

Popela²,

Klučka³

et al. 2023

Preprint

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The increasing attendance of paediatric emergency departments becomes a serious health issue. To reduce an elevated burden of medical errors, inevitably caused by high level of stress exerted on emergency physicians, we propose potential areas for improvement in regular paediatric emergency departments. In effort to guarantee demanded quality of care to all incoming patients, the workflow in paediatric emergency departments should be sufficiently optimized. The key component remains implementing one of the validated paediatric triage systems upon patient's arrival at emergency department and fast-tracking patients with low level of risk according to the triage system. To ensure the patient's safety, emergency physicians should follow issued guidelines. Cognitive aids, such as well-designed checklists, posters or flow charts, generally improve physicians' adherence to guidelines and should be therefore available in every paediatric emergency department. To sharpen diagnostic accuracy, the use of ultrasound in paediatric emergency department, according to ultrasound protocols, should be targeted to answer specific clinical questions. Combining all mentioned improvements might reduce number of errors linked with overcrowding. The review serves not only as a blueprint for modernizing paediatric emergency departments, but also as a bin of useful literature which can be suitable in the paediatric emergency field.

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Influence of artificial intelligence on the work design of emergency department clinicians a systematic literature review

Cited by 38 publications

References 47 publications

The Accuracy and Potential Racial and Ethnic Biases of GPT-4 in the Diagnosis and Triage of Health Conditions: Evaluation Study

The Accuracy and Potential Racial and Ethnic Biases of GPT-4 in the Diagnosis and Triage of Health Conditions: Evaluation Study

Digitisation of emergency medicine: opportunities, examples and issues for consideration

Modern Paediatric Emergency Department: Potential Improvements in Light of New Evidence

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