Leveraging Machine Learning for Better Prior Authorization Outcomes

The prior authorization process, essential for ensuring that medical treatments are necessary and covered by insurance, has long been a bottleneck in healthcare. It involves obtaining approval from payers before certain services or medications are provided. While its intent is to manage costs and ensure appropriate care, the current manual process often leads to significant delays and financial strain for both patients and healthcare providers – we can do better.

The Challenges of Prior Authorization

The traditional prior authorization process is plagued by inefficiencies and fragmentation. Providers, administrative staff, and payers grapple with a lack of transparency around clinical information. Information ambiguity leaves providers in the dark about what payers deem medically necessary.

The Toll on Providers and Health Systems

Providers and health systems bear the brunt of this burden with repercussions extending beyond mere inconvenience, manifesting in tangible delays in patient care. Shockingly, approximately 90% of providers report care delays on the back of this process, with a notable percentage citing that some delays even lead to unnecessary patient hospitalizations.

The financial toll of prior authorization is staggering. Claim denials and associated administrative costs alone amount to billions of dollars annually, according to analysis and primary customer research. Manual associated processes  further exacerbate the strain, running into hundreds of millions each year, according to senior leaders at hospitals and our discovery interviews,  from already strained healthcare budgets.

The Role of Machine Learning in Prior Authorization

We now have the technology to address these challenges. Leveraging real-time clinical data and machine learning can offer a transformative solution; improving patient outcomes and creating efficiencies for healthcare systems. Here’s what will be improved – 

• Improving Prior Authorization Outcomes: Integrating into the Electronic Health Record (EHR) system enables access to crucial patient data, allowing Machine Learning (ML) methods to extract and interpret relevant clinical indications and documents. This process can identify missing prerequisites and ensures that all necessary documentation is complete and accurate before submission.

• Enhancing Transparency and Reducing Opacity: The fragmentation of information flow across stakeholders—providers, administrative staff, and payers—creates significant opacity in the prior authorization process. This can be  addressed by leveraging technology to offer clinical recommendations based on clinical patient data in the EHR, aligning expectations and reducing miscommunication.

• Predicting Outcomes and Providing Insights: Machine Learning can predict  the likelihood of prior authorization approval based on historical patient data and documentation.  This is critical as it offers insights into potential delays and suggests improvements to the order, enhancing the chances of a quicker authorization turnaround.

• Reducing Care Delays: By automating the retrieval and analysis of clinical documents, software can significantly reduce the time taken to process prior authorizations. This leads to faster patient care, reducing the risk of unnecessary hospitalizations and improving overall health outcomes.

• Minimizing Financial Burden: With a more efficient prior authorization process, healthcare providers can reduce the incidence of claim denials and the associated administrative costs. Ensuring that all necessary documentation is complete at the time of submission increases the likelihood of approval, positively impacting revenue reimbursement for doctors and health systems.

• Empowering Healthcare Stakeholders

The right technology will empower all stakeholders involved in the prior authorization process:

• Providers: By improving the accuracy and completeness of submissions, providers can focus more on patient care rather than administrative tasks.
• Administrative Staff: Equipped with the best tools and information, administrative staff can handle prior authorizations more efficiently, reducing the time and effort required for manual processes.
• Payers: With clear, complete, and accurate documentation, payers can process prior authorizations more quickly, reducing their administrative burden and improving relationships with providers.

Conclusion

The prior authorization process is a critical but often cumbersome aspect of healthcare delivery. By leveraging machine learning and patient clinical data, you can streamline the process and enhance patient care and financial outcomes. Through improved transparency, accurate documentation, and predictive insights, prior authorization can be transformed from a bottleneck to a seamless, efficient component of healthcare administration.

In the ever-evolving landscape of healthcare, leveraging technological advancements like machine learning is essential while continuing to provide transparency into the latest provided standards around medical necessity. At Fig Medical, our commitment is to continuously refine our solutions to meet the needs of providers, health systems, and administrative staff, ultimately contributing to better patient care and a more sustainable healthcare system.