The prior authorization process, integral for confirming the necessity and insurance coverage of medical treatments, has historically been an obstacle in healthcare. In its current state, it has the potential to cause considerable delays and financial burdens for patients and healthcare providers – and the inefficiencies of this process are evident, as approximately 90% of providers report care delays due to this process (AMA), with a significant percentage indicating that some delays even lead to unnecessary patient hospitalizations. Improvements are needed now.
Healthcare providers and systems bear the brunt of these inefficiencies, resulting in more than just inconvenience. These delays significantly impact patient care. Current technology offers solutions to these challenges. Utilizing real-time clinical data and machine learning can transform the process, and in turn, improve patient outcomes and create an overall more effective and efficient healthcare system.
As hospitals and insurers seek competitive advantages in managing the $4 trillion in annual medical expenses in the U.S., (Politico) they are increasingly turning to AI-powered tools. Providers dream of AI that can quickly and effectively code procedures and file claims, while insurers and government agencies look for technology to scrutinize and process these claims efficiently. The push for AI in healthcare is further validated by the success of companies driving these initiatives: Waystar, a payment solutions company, recently achieved nearly a $1 billion IPO, highlighting the significant demand and potential.
It’s clear to see why there’s so much enthusiasm for AI’s potential in healthcare when we consider five key outcomes that provides and patients alike could benefit from:
1. Streamlining Prior Authorization Outcomes: Integrating prior authorization systems into Electronic Health Records (EHR) enables access to essential patient data. Machine Learning (ML) can then extract and interpret relevant clinical information, identifying missing prerequisites and ensuring that all necessary documentation is complete and accurate before submission.
2. Enhancing Transparency and Reducing Fragmentation: The fragmented flow of information among providers, administrative staff, and payers creates significant opacity in the prior authorization process. Leveraging technology can address this by offering clinical recommendations based on patient data in the EHR, aligning expectations and reducing miscommunication.
3. Predicting Outcomes and Providing Insights: Machine Learning can predict the likelihood of prior authorization approval based on historical patient data and documentation. This capability offers critical insights into potential delays and suggests improvements, enhancing the chances of a quicker authorization turnaround.
4. Reducing Care Delays: Automating the retrieval and analysis of clinical documents can significantly reduce the time needed to process prior authorizations. This leads to faster patient care, decreasing the risk of unnecessary hospitalizations and improving overall health outcomes.
5. Minimizing Financial Burden: A more efficient prior authorization process reduces the incidence of claim denials and associated administrative costs. Ensuring that all necessary documentation is complete at submission increases the likelihood of approval, positively impacting revenue reimbursement for healthcare providers.
So especially in the dynamic landscape of healthcare, adopting technological advancements like machine learning is essential. Luckily, there is a growing and disruptive force working at the intersection of healthcare and AI that is committed to uncovering and perfecting solutions to meet the needs of providers, health systems, and administrative staff, ultimately contributing to better patient care and a more sustainable healthcare system. The integration of AI tools not only streamlines billing and claims processing but also helps combat fraud, making the overall healthcare system more efficient and reliable.
About
As a first generation American, Florence was uninsured the first half of her life and uniquely understands the impact of inequitable healthcare access. After a personal experience with negative health impacts due to delays in prior authorization, she realized how administrative processes can significantly impact a patient’s health.
Her lifelong passion for improving the state of healthcare in the United States drove her to roles focused on healthtech investing across roles in the VC and entrepreneurial ecosystem. During her Cornell Tech MBA, she focused on gaining expertise within healthtech through coursework and experiential learning. Fig Medical was spun out of Cornell Tech during her final year and the team has since invested hundreds of hours interviewing and shadowing critical stakeholders as well as developing our software with the goal of improving equitable healthcare access.
