EPIDEMIOLOGY AND HEALTH DATA INSIGHTS
Review Article
Oct 20, 2026 3 1

Natural Language Processing and Machine Learning Solutions for Patient Wait Time-Related Safety Risks in High-Risk Clinical Care

Oluwatoyin Aanu Ayeni 1,
Daniel Chukwuebuka Unigwe 2,
Baqi Atimah 3,
Bright Kofi Ladzro 4,
Voke Harriet Akpughe 5,
John Alaba Stephen 6 *
1 Department of Social Works, University of Ibadan, Nigeria2 Ulster University Business School, University of Ulster, UK3 Department of Chemical Engineering, Federal University of Technology, Minna, Niger State, Nigeria4 Department of Mathematics and Statistics, American University, Washington DC, USA5 Department of Public Health/Community Medicine, Delta State University Teaching Hospital, Nigeria6 Department of Banking and Finance, University of Ibadan, Nigeria* Corresponding Author
Epidemiology and Health Data Insights, 2(5), 2026, ehdi058, https://doi.org/10.63946/ehdi/19646
Publication date: Oct 20, 2026
Full Text (PDF)

ABSTRACT

Patient wait times remain a major challenge in healthcare systems worldwide, particularly in high-risk clinical care environments such as emergency departments, intensive care units, trauma centres, and acute medical wards. Prolonged delays in treatment can contribute to patient deterioration, medication errors, adverse events, increased complications, and even mortality. Despite ongoing efforts to improve patient flow and operational efficiency, many healthcare organisations continue to struggle with overcrowding, workforce shortages, resource constraints, and growing patient demand. This study adopted a critical literature review approach to examine the applications of NLP and ML in managing patient wait time-related safety risks in high-risk clinical care environments. Relevant studies published between 2014 and 2026 were identified through systematic searches of PubMed, Scopus, Web of Science, IEEE Xplore, ScienceDirect, and Google Scholar. The findings show that NLP and ML have the potential to improve healthcare delivery through risk prediction, adverse event detection, clinical decision support, patient prioritisation, and operational workflow optimisation. The literature consistently demonstrates that AI systems can extract invaluable information from electronic health records, clinical notes, incident reports, and other healthcare data sources to support earlier identification of high-risk patients and more timely interventions. However, challenges such as algorithmic bias, lack of transparency, limited external validation, data quality concerns, ethical issues, and difficulties associated with real-world implementation persist. This review concludes that effective use of AI in high-risk clinical care requires the combination of intelligent technologies, high-quality data, organisational support, ethical governance, and continuous human oversight. Therefore, this review recommends that healthcare organisations should move beyond viewing AI solely as a diagnostic tool and begin adopting it as part of broader patient safety and operational management strategies.

KEYWORDS

NLP ML Patient Safety Risks Patient Wait Times Clinical Delays High-Risk Clinical Care

CITATION (Vancouver)

Ayeni OA, Unigwe DC, Atimah B, Ladzro BK, Akpughe VH, Stephen JA. Natural Language Processing and Machine Learning Solutions for Patient Wait Time-Related Safety Risks in High-Risk Clinical Care. Epidemiology and Health Data Insights. 2026;2(5):ehdi058. https://doi.org/10.63946/ehdi/19646
APA
Ayeni, O. A., Unigwe, D. C., Atimah, B., Ladzro, B. K., Akpughe, V. H., & Stephen, J. A. (2026). Natural Language Processing and Machine Learning Solutions for Patient Wait Time-Related Safety Risks in High-Risk Clinical Care. Epidemiology and Health Data Insights, 2(5), ehdi058. https://doi.org/10.63946/ehdi/19646
Harvard
Ayeni, O. A., Unigwe, D. C., Atimah, B., Ladzro, B. K., Akpughe, V. H., and Stephen, J. A. (2026). Natural Language Processing and Machine Learning Solutions for Patient Wait Time-Related Safety Risks in High-Risk Clinical Care. Epidemiology and Health Data Insights, 2(5), ehdi058. https://doi.org/10.63946/ehdi/19646
AMA
Ayeni OA, Unigwe DC, Atimah B, Ladzro BK, Akpughe VH, Stephen JA. Natural Language Processing and Machine Learning Solutions for Patient Wait Time-Related Safety Risks in High-Risk Clinical Care. Epidemiology and Health Data Insights. 2026;2(5), ehdi058. https://doi.org/10.63946/ehdi/19646
Chicago
Ayeni, Oluwatoyin Aanu, Daniel Chukwuebuka Unigwe, Baqi Atimah, Bright Kofi Ladzro, Voke Harriet Akpughe, and John Alaba Stephen. "Natural Language Processing and Machine Learning Solutions for Patient Wait Time-Related Safety Risks in High-Risk Clinical Care". Epidemiology and Health Data Insights 2026 2 no. 5 (2026): ehdi058. https://doi.org/10.63946/ehdi/19646
MLA
Ayeni, Oluwatoyin Aanu et al. "Natural Language Processing and Machine Learning Solutions for Patient Wait Time-Related Safety Risks in High-Risk Clinical Care". Epidemiology and Health Data Insights, vol. 2, no. 5, 2026, ehdi058. https://doi.org/10.63946/ehdi/19646

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