Healthcare Case Studies
Case Study:
Improving Healthcare Efficiencies and Cost of Care with AI
Challenge:
Clients for a healthcare insurance company demanded reductions in their insurance costs without compromising care quality and outcomes for covered employees.
Traditional Approach:
Healthcare insurers typically use more pre-authorizations to limit unnecessary medical use and manage costs, but this increases demand for clinicians and in turn raises costs. The direct correlation between the use of pre-authorization and costs was a real problem for this health insurers as noted in the chart.
Solution:
A proof of concept was conducted to develop a custom AI solution that applies natural language processing (NLP) and machine learning to improve medical records reviews and pre-authorization recommendations. The approach utilized a “human-in-the-loop” process, with licensed clinicians validating responses, and making all care decisions. The solution included explainability features to clarify how and why recommendations were generated and enabling clinicians to provide feedback for model improvement. Additionally, checks and balances were integrated into the workflow review process to maintain compliance with industry managed care guidelines, regulations, and standards.
Results - "Bent the Curve"
The POC “bent the curve”, changing the relationship between auth volume and staffing requirements, enabling a new capability to "do more with existing staff”. Additionally benefits realized:
- Increased clinical staff efficiency by 27%, reduced administrative costs, and decreased inconsistencies and errors in medical reviews
- Helped clinicians to effectively manage caseloads, reduce administrative burden, & maintain comply care guidelines
- Approval of a multi-million, multi-year technology investment with an estimated ROI of 46% once scaled