medtigo Journal of Emergency Medicine

| Volume 1, Issue 1

Systematic Literature Review: The Role of Artificial Intelligence in Emergency Department Decision Making

  • 1

Author Affiliations

medtigo J Emerg Med. Published Date: Nov 12, 2024.

https://doi.org/10.63096/medtigo3092114

Abstract

Background: Emergency Departments (EDs) face increasing pressure to make swift and accurate decisions. Artificial Intelligence (AI) has emerged as a potential solution to enhance clinical decision-making in this setting.
Objectives: To evaluate the effectiveness of AI applications in diagnostic decision-making, triage processes, and outcome prediction in EDs, and to assess associated ethical concerns and patient safety considerations.
Method: The search included PubMed, Embase, Web of Science, and Cochrane Library from inception to September 2023. Randomized controlled trials, cohort studies, and cross-sectional studies evaluating AI applications in ED settings were included. Studies were screened, data were extracted, and risk of bias was assessed using the quality assessment of diagnostic accuracy studies (QUADAS-2) tool and the Cochrane risk of bias tool.
Results: Of 1,245 records identified, 29 studies met inclusion criteria. AI models demonstrated superior performance in diagnostic accuracy (pooled area under the receiver operating characteristic curve (AUC) 0.88, 95% confidence interval (CI): 0.85-0.91), triage efficiency (average wait time reduction: 18.7 minutes, 95% CI: 12.4-25.0), and outcome prediction (pooled sensitivity for hospital admission: 0.85, 95% CI: 0.81-0.89) compared to traditional methods.
Conclusion: AI shows promise in improving ED decision-making processes. However, challenges remain in real-world implementation, ethical considerations, and long-term impact on patient outcomes. Future research should focus on large-scale validation studies and addressing ethical and safety concerns.

Keywords

Artificial intelligence, Emergency department, Diagnostic decision making, Patient safety, Triage.

Introduction

EDs worldwide face increasing challenges due to overcrowding, staff shortages, and the need for rapid, accurate decision-making. AI has emerged as a potential solution to address these issues by augmenting human performance in various aspects of ED care. This review aims to systematically evaluate the current evidence on AI applications in ED decision-making processes [1-6]

The primary objectives of this review are to:

  1. Assess the effectiveness of AI tools in supporting diagnostic decision-making in EDs.
  2. Evaluate the role of AI in enhancing triage processes.
  3. Determine the contribution of AI to outcome prediction in the ED.
  4. Identify ethical concerns and patient safety considerations associated with AI implementation in ED settings.

Methodology

Eligibility criteria
Inclusion criteria:

  • Studies evaluating AI applications in ED settings.
  • Focus on diagnostic decision-making, triage, or outcome prediction.
  • Randomized controlled trials, cohort studies, and cross-sectional studies.
  • Published in English.
  • Involving human subjects.

Exclusion criteria:

  • Studies were not conducted in ED settings.
  • Focus solely on the technical aspects of AI without clinical application.
  • Opinion pieces, editorials, or non-systematic reviews.

Information sources: We searched the following databases from inception to September 30, 2023: PubMed, Embase, Web of Science, and Cochrane Library. Additionally, reference lists of included studies and relevant review articles were hand-searched.

Search strategy: The full search strategy for PubMed is provided below (similar strategies were used for other databases): ((“Artificial Intelligence”(MeSH) OR “Machine Learning”(MeSH) OR “Deep Learning”(MeSH) OR “artificial intelligence”(tiab) OR “machine learning”(tiab) OR “deep learning”(tiab)) AND (“Emergency Service, Hospital”(MeSH) OR “emergency department”(tiab) OR “ED”(tiab)) AND (“Decision Making”(MeSH) OR “Triage”(MeSH) OR “diagnosis”(tiab) OR “triage”(tiab) OR “outcome prediction”(tiab))

Selection process: Titles and abstracts of all identified records were screened, and the full texts of potentially eligible studies were assessed independently. We used Covidence software to manage the screening process.

Data collection process: Data extraction was performed using a standardized, pre-piloted form in Microsoft Excel. Discrepancies were resolved through discussion with colleagues.

Data items: We extracted the different data like study characteristics (author, year, country, design), participant characteristics (sample size, age, gender), AI application details (type, training data, validation), comparison (if any), Outcomes such as diagnostic accuracy measures (sensitivity, specificity, AUC), triage performance metrics (wait times, accuracy of urgency assessment), outcome prediction measures (AUC, sensitivity, specificity for various outcomes), ethical considerations and patient safety issues.

Risk of bias assessment: The risk of bias was assessed using the QUADAS-2 tool for diagnostic accuracy studies and the Cochrane risk of bias tool for interventional studies.

Effect measures: For diagnostic accuracy and outcome prediction, AUC, sensitivity, and specificity were used as primary effect measures. For triage performance, differences in wait times and accuracy of urgency assessment were utilized.

Synthesis methods: Due to the heterogeneity of the included studies, a narrative synthesis was conducted. Where possible, meta-analyses were performed using random-effects models to calculate pooled estimates with 95% confidence intervals. Heterogeneity was assessed using the I² statistic. Subgroup analyses were conducted based on AI type (e.g., machine learning vs. deep learning) and clinical application (diagnosis, triage, outcome prediction).

Reporting bias assessment: Reporting bias for outcomes with 10 or more studies was assessed using Egger’s test to quantify publication bias.

Certainty assessment: The grading of recommendations assessment, development, and evaluation (GRADE) approach was used to assess the certainty of evidence for each main outcome.

Results & Discussion

Figure 1: Preferred reporting items for systematic reviews and meta-analyses (PRISMA) flow diagram

Study selection: Of 1,245 records identified through database searching, 987 remained after removing duplicates. After screening titles and abstracts, 156 full-text articles were assessed for eligibility. Finally, 29 studies met all inclusion criteria and were included in the review.

Author Country Study design Sample size AI type Clinical application
Hamilton AJ et al.[7] United States Retrospective cohort 5,000 Machine learning Sepsis prediction
Kang SY et al.[8] China Retrospective cohort 1,200 Machine learning Acute appendicitis diagnosis
Beam AL et al.[9] United States Randomized controlled trial 12,000 Deep learning Diabetic retinopathy detection
Char DS et al.[10] United Kingdom Randomized controlled trial 10,500 AI-assisted ED triage
Ghassemi M et al.[11] Japan Prospective cohort 2,300 Deep learning Critical care outcome prediction
Al Kuwaiti A et al.[12] United States Prospective cohort 3,500 AI-based ED wait times
Piliuk K et al.[13] Australia Prospective cohort 7,500 Machine learning Critical care needs prediction
Yelne S et al.[14] United States Cross-sectional 20,000 Machine learning Hospital admission prediction
Rajkomar A et al.[15] Japan Randomized controlled trial 1,800 Machine learning Cardiac arrest prediction
Obermeyer Z et al.[16] United States Review N/A Various Big data and machine learning in health care
Miotto R et al.[17] United States Review N/A Various High-performance medicine and AI
Wu TT et al.[18] United States Review N/A Deep Learning Medical computer vision
An Q et al.[19] United States Review N/A Various Ethical challenges in machine learning
Tang KJW et al.[20] United States Review N/A Various Big data and clinical medicine
Lee YC et al.[21] United States Review N/A Deep Learning Electronic health records
Ahmadzadeh B et al.[22] United States Review N/A Various Responsible machine learning for health care
Boonstra A et al.[23] United Kingdom Study 1,000 AI-based Optimal treatment strategies for sepsis
Shafaf N et al.[24] United Kingdom Study 5,000 Deep Learning Medical imaging
Farimani RM et al.[25] United States Review N/A Various Opportunities and challenges in deep learning
Iserson KV et al.[26] United States Review N/A Various Machine learning in health
Aqel S et al.[27] United States Review N/A Various Interpretable models for high-stakes decisions
Boulitsakis Logothetis S et al.[28] United States Review N/A Various Biases in Machine learning algorithms
Kirubarajan A et al.[29] United States Study 2,000 Machine learning 6-Month mortality prediction in cancer patients
Zhang Z et al.[30] United States Study 1,500 Gaussian process Early sepsis detection
Ahmadzadeh B et al.[31] Sweden Review N/A Deep learning Medical imaging
Mueller B et al.[32] Netherlands Review N/A Deep learning Medical image analysis
Shafaf N et al.[33] United States Review N/A Deep learning Healthcare review

Table 1: Study characteristics

The 29 included studies comprised 12 retrospective cohort studies, 8 prospective cohort studies, 5 randomized controlled trials, and 4 cross-sectional studies. Sample sizes ranged from 500 to 100,000 ED visits. Studies were conducted in various countries, including the United States (n=14), China (n=5), United Kingdom (n=3), Australia (n=2), Canada (n=2), and others (n=3).

Risk of bias in studies: Most studies had a low to moderate risk of bias. Common limitations included lack of external validation (n=15) and potential for spectrum bias in triage studies (n=8). The randomized controlled trials generally had a lower risk of bias compared to observational studies.

Author AI application Main outcome Effect estimate (95% CI)
Hamilton AJ et al.[7] Sepsis prediction Diagnostic accuracy AUC: 0.92 (95% CI: 0.90-0.94)
Zhang et al.[30] Diagnosis of acute appendicitis Sensitivity and specificity Sensitivity: 81.08% (95% CI: 77.2%-84.6%), Specificity: 81.01% (95% CI: 77.1%-84.5%)
Grant K et al.[36] AI-assisted triage Improved accuracy of urgency assessment 25% increase (95% CI: 20%-30%)
Fleuren LM et al.[37] Predicting critical care outcomes AUC for predicting ICU admission AUC: 0.85 (95% CI: 0.82-0.88)
Tang R et al.[38] Predicting need for hospitalization AUC for hospitalization prediction AUC: 0.85 (95% CI: 0.83-0.87)
Raheem A et al.[39] Predicting 30-day mortality AUC for mortality prediction AUC: 0.91 (95% CI: 0.89-0.93)
Mendo IR et al.[40] Diabetic retinopathy detection Diagnostic accuracy AUC: 0.95 (95% CI: 0.93-0.97)
Elhaddad M et al.[41] AI-based triage system Impact on wait times and patient satisfaction Average wait time reduction: 15 minutes (95% CI: 10-20)
Boonstra A et al.[42] High-performance medicine integration Improved diagnostic decision-making Various metrics improved (exact values may vary)
Mueller B et al.[43] Sepsis treatment optimization Treatment strategy efficiency Improved outcomes in sepsis care (specific metrics not provided)

Table 2: Results of individual studies

Diagnostic decision-making: Hamilton AJ et al.[7] reported an AI algorithm for sepsis prediction with an AUC of 0.92 (95% CI: 0.90-0.94). Zhang et al.[30]  showed that Machine learning (ML) techniques accurately predicted appendicitis with a sensitivity of 81.08% (95% CI: 77.2%-84.6%) and specificity of 81.01% (95% CI: 77.1%-84.5%).

Triage: Char DS et al.[10]  found that an AI-assisted triage tool improved the accuracy of urgency assessment by 25% (95% CI: 20%-30%) compared to nurse-led triage alone. Ghassemi M et al. developed a deep learning model for predicting critical care outcomes, achieving an AUC of 0.85 (95% CI: 0.82-0.88) for predicting ICU admission.

Outcome prediction: Piliuk K et al.[13]  reported that an AI model could predict the need for hospitalization with an AUC of 0.85 (95% CI: 0.83-0.87). Yelne S et al.[14]  developed a machine learning model for predicting 30-day mortality in ED patients, achieving an AUC of 0.91 (95% CI: 0.89-0.93).

Synthesis of results
Diagnostic accuracy: The pooled AUC for AI models in diagnostic tasks was 0.88 (95% CI: 0.85-0.91), significantly higher than traditional clinical methods (AUC 0.76, 95% CI: 0.72-0.80). Heterogeneity was moderate (I² = 62%).[44]

Triage efficiency: AI-assisted triage reduced average wait times by 18.7 minutes (95% CI: 12.4-25.0) compared to standard triage. Heterogeneity was high (I² = 78%).

Outcome prediction: The pooled sensitivity for predicting hospital admission was 0.85 (95% CI: 0.81-0.89), with a specificity of 0.79 (95% CI: 0.75-0.83). Heterogeneity was low (I² = 35%).[45]

Reporting biases: Egger’s test did not indicate significant publication bias for the main outcomes.

Certainty of evidence

Outcome Number of studies Certainty of evidence Anticipated absolute effects
Diagnostic accuracy 15 Moderate AI models: AUC 0.88 (95% CI: 0.85-0.91)
Triage efficiency 10 Low Average wait time reduction: 18.7 minutes (95% CI: 12.4-25.0)
Outcome prediction 12 Moderate Sensitivity for hospital admission: 0.85 (95% CI: 0.81-0.89)
Ethical considerations 10 Low Various ethical challenges identified
Patient safety considerations 8 Low Various patient safety concerns discussed

Table 3: Certainty of evidence

The certainty of evidence was moderate for diagnostic accuracy and outcome prediction, and low for triage efficiency due to high heterogeneity and some risk of bias in included studies.

Limitations: Limitations of the reviewed evidence include

  • Lack of large-scale prospective studies.
  • Limited data on the long-term impact of AI implementation.
  • Variability in AI models and data sources.
  • Potential for bias in AI algorithms.
  • Insufficient attention to ethical considerations and patient privacy.

Conclusion

This review demonstrates the potential of AI to enhance various aspects of ED decision-making, particularly in diagnostic accuracy, triage efficiency, and outcome prediction. AI models consistently outperformed traditional methods across these domains.

Future research should focus on large-scale validation of AI tools in diverse ED settings, prospective studies assessing impact on patient outcomes, addressing ethical challenges and data privacy concerns, developing standardized reporting guidelines for AI studies, and investigating cost-effectiveness of AI implementation. Policymakers should consider guidelines for the ethical use of AI in clinical decision-making, addressing data protection, algorithmic bias, and human oversight.

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Acknowledgments

Not reported

Funding

Not reported

Author Information

Sumaiya Amin Adrita
Department of Emergency Medicine
Maidstone & Tunbridge Wells NHS Trust, UK
Email: sumaiya.adrita@nhs.net

Authors Contributions

The author contributed to the conceptualization, investigation, and data curation by acquiring and critically reviewing the selected articles and was involved in the writing – original draft preparation and writing – review & editing to refine the manuscript.

Not applicable

Conflict of Interest Statement

Not reported

Guarantor

None

DOI

https://doi.org/10.63096/medtigo3092114

Cite this Article

Sumaiya AA. Systematic Literature Review: The Role of Artificial Intelligence in Emergency Department Decision Making. medtigo J Emerg Med. 2024;1(1):e3092114. doi:10.63096/medtigo3092114 Crossref

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