Authors :
Saifuddin Shaik Mohammed
Volume/Issue :
Volume 10 - 2025, Issue 6 - June
Google Scholar :
https://tinyurl.com/3czmfu3a
DOI :
https://doi.org/10.38124/ijisrt/25jun1818
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Note : Google Scholar may take 30 to 40 days to display the article.
Abstract :
Before treating a patient, Prior Authorization requires healthcare providers to contact payers to obtain approval
for a specific service or medication. Although it was meant to limit wasteful healthcare costs, PA has become a significant
barrier, slowing patient care, adding costs, and making physicians' jobs more stressful. This study examines the growing
use of machine learning (ML) as an effective solution to current challenges. ML boosts the PA process by automatically
collecting data, providing estimates for approvals, and simplifying the entire process. The work examines the current state
of PA, explores various applications of ML in this field, shares real-life examples, highlights the clear benefits, considers the
broader implications of ML, and discusses the ethical issues and challenges that arise from using ML for PA. This research
suggests that when ML is used in prior authorization, it is a breakthrough that can improve both the care provided and the
sustainability of healthcare.
Keywords :
Prior Authorization, Machine Learning, Artificial Intelligence, Healthcare Administration, Revenue Cycle Management, Natural Language Processing, Predictive Analytics, Healthcare Costs, Automation.
References :
- CDW. "Revolutionizing Prior Authorizations with AI." CDW, 28 Mar. 2025
- Lubell, J. "How AI is leading to more prior authorization denials." American Medical Association, 10 Mar. 2025
- IRJMETS. (2025). The Impact of AI and Automation on Prior Authorization in Healthcare. International Research Journal of Modernization in Engineering Technology and Science, vol. 7, no. 2, 2025: pp. 123-131.
- Experian. "Use automated prior authorizations to expedite patient care." Experian, 29 Nov. 2023
- American Medical Association. "Don't fall for these myths on prior authorization." American Medical Association, 17 Apr. 2025.
- Tenasol. "Prior Authorization NLP and Tenasol." Tenasol, 25 Jan. 2025
- Waystar. "RCM resource roundup: Activating AI for prior authorization + denial prevention." Waystar, 19 Feb. 2025
- Experian. "5 benefits of automating prior authorizations." Experian, 24 Jun. 2024, https://www.experian.com/blogs/healthcare/5-benefits-of-automating-prior-authorizations/.
- Pickern, J. S. Prior authorizations and the adverse impact on continuity of care. The American Journal of Managed Care, vol. 31, no. 4, 2025: pp. 163-165.
- Kumar, P.D. et al. (2025) Ai in health care: The black box of prior authorization, KevinMD.com.
- Putty, C. "How To Solve The Most Common Prior Authorization Challenges with AI." Thoughtful.ai, 12 Mar. 2025
- TRIARQ Health. "Prior Authorization Statistics: The Impact of Prior Authorizations." TRIARQ Health, 25 Jul. 2024
- Clearlink Partners. "How AI is Transforming Prior Authorization: Real-World Examples & Future Implications." Clearlink Partners, 5 Jun. 2025
- Staffingly, Inc. "How Data Analytics Transforms Prior Authorization?." Staffingly, Inc., n.d.
- eClaimStatus. "Prior Authorization Automation Services: Save Time and Reduce Errors." eClaimStatus, n.d.
Before treating a patient, Prior Authorization requires healthcare providers to contact payers to obtain approval
for a specific service or medication. Although it was meant to limit wasteful healthcare costs, PA has become a significant
barrier, slowing patient care, adding costs, and making physicians' jobs more stressful. This study examines the growing
use of machine learning (ML) as an effective solution to current challenges. ML boosts the PA process by automatically
collecting data, providing estimates for approvals, and simplifying the entire process. The work examines the current state
of PA, explores various applications of ML in this field, shares real-life examples, highlights the clear benefits, considers the
broader implications of ML, and discusses the ethical issues and challenges that arise from using ML for PA. This research
suggests that when ML is used in prior authorization, it is a breakthrough that can improve both the care provided and the
sustainability of healthcare.
Keywords :
Prior Authorization, Machine Learning, Artificial Intelligence, Healthcare Administration, Revenue Cycle Management, Natural Language Processing, Predictive Analytics, Healthcare Costs, Automation.
