Machine Learning Model of Emergency Department Use for Patients Undergoing Treatment for Head and Neck Cancer Using Comprehensive Multifactor Electronic Health Records

A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy

BACKGROUND AND PURPOSE

Artificial Intelligence (AI) models in radiation therapy are being developed with increasing pace. Despite this, the radiation therapy community has not widely adopted these models in clinical practice. A cohesive guideline on how to develop, report and clinically validate AI algorithms might help bridge this gap.

METHODS AND MATERIALS

A Delphi process with all co-authors was followed to determine which topics should be addressed in this comprehensive guideline. Separate sections of the guideline, including Statements, were written by subgroups of the authors and discussed with the whole group at several meetings. Statements were formulated and scored as highly recommended or recommended.

RESULTS

The following topics were found most relevant: Decision making, image analysis, volume segmentation, treatment planning, patient specific quality assurance of treatment delivery, adaptive treatment, outcome prediction, training, validation and testing of AI model parameters, model availability for others to verify, model quality assurance/updates and upgrades, ethics. Key references were given together with an outlook on current hurdles and possibilities to overcome these. 19 Statements were formulated.

CONCLUSION

A cohesive guideline has been written which addresses main topics regarding AI in radiation therapy. It will help to guide development, as well as transparent and consistent reporting and validation of new AI tools and facilitate adoption.

Order From Chaos: The Benefits of Standardized Nomenclature in Radiation Oncology

Although standardization has been shown to improve patient safety and improve the efficiency of workflows, implementation of standards can take considerable effort and requires the engagement of all clinical stakeholders. Engaging team members includes increasing awareness of the proposed benefit of the standard, a clear implementation plan, monitoring for improvements, and open communication to support successful implementation. The benefits of standardization often focus on large institutions to improve research endeavors, yet all clinics can benefit from standardization to increase quality and implement more efficient or automated workflow. The benefits of nomenclature standardization for all team members and institution sizes, including success stories, are discussed with practical implementation guides to facilitate the adoption of standardized nomenclature in radiation oncology.

Development of a claims-based risk-scoring model to predict emergency department visits in older patients receiving anti-neoplastic therapy

This study developed and validated a risk-scoring model, with a particular emphasis on medication-related factors, to predict emergency department (ED) visits among older Korean adults (aged 65 and older) undergoing anti-neoplastic therapy. Utilizing national claims data, we constructed two cohorts: the development cohort (2016-2018) with 34,642 patients and validation cohort (2019) with 10,902 patients. The model included a comprehensive set of predictors: demographics, cancer type, comorbid conditions, ED visit history, and medication use variables. We employed the least absolute shrinkage and selection operator (LASSO) regression to refine and select the most relevant predictors. Out of 120 predictor variables, 12 were integral to the final model, including seven related to medication use. The model demonstrated acceptable predictive performance in the validation cohort with a C-statistic of 0.76 (95% CI 0.74-0.77), indicating reasonable calibration. This risk-scoring model, after further clinical validation, has the potential to assist healthcare providers in the effective management and care of older patients receiving anti-neoplastic therapy.

Enhancing Triage Efficiency and Accuracy in Emergency Rooms for Patients with Metastatic Prostate Cancer: A Retrospective Analysis of Artificial Intelligence-Assisted Triage Using ChatGPT 4.0

BACKGROUND

Accurate and efficient triage is crucial for prioritizing care and managing resources in emergency rooms. This study investigates the effectiveness of ChatGPT, an advanced artificial intelligence system, in assisting health providers with decision-making for patients presenting with metastatic prostate cancer, focusing on the potential to improve both patient outcomes and resource allocation.

METHODS

Clinical data from patients with metastatic prostate cancer who presented to the emergency room between 1 May 2022 and 30 April 2023 were retrospectively collected. The primary outcome was the sensitivity and specificity of ChatGPT in determining whether a patient required admission or discharge. The secondary outcomes included the agreement between ChatGPT and emergency medicine physicians, the comprehensiveness of diagnoses, the accuracy of treatment plans proposed by both parties, and the length of medical decision making.

RESULTS

Of the 147 patients screened, 56 met the inclusion criteria. ChatGPT had a sensitivity of 95.7% in determining admission and a specificity of 18.2% in discharging patients. In 87.5% of cases, ChatGPT made the same primary diagnoses as physicians, with more accurate terminology use (42.9% vs. 21.4%, p = 0.02) and more comprehensive diagnostic lists (median number of diagnoses: 3 vs. 2, p < 0.001). Emergency Severity Index scores calculated by ChatGPT were not associated with admission (p = 0.12), hospital stay length (p = 0.91) or ICU admission (p = 0.54). Despite shorter mean word count (169 ± 66 vs. 272 ± 105, p < 0.001), ChatGPT was more likely to give additional treatment recommendations than physicians (94.3% vs. 73.5%, p < 0.001).

CONCLUSIONS

Our hypothesis-generating data demonstrated that ChatGPT is associated with a high sensitivity in determining the admission of patients with metastatic prostate cancer in the emergency room. It also provides accurate and comprehensive diagnoses. These findings suggest that ChatGPT has the potential to assist health providers in improving patient triage in emergency settings, and may enhance both efficiency and quality of care provided by the physicians.