Improving care gap closure and efficiency with AI chart abstraction
Business challenge
A large health plan needed a more efficient and precise approach to chart abstraction. Manual processes were placing a strain on staff time, and undetected clinical evidence was causing the plan to underperform on key quality measures.
The plan was interested in technology that could help it close more gaps in care and find evidence to satisfy quality measures.
Smarter AI abstraction with better outcomes
The health plan partnered with Reveleer to implement its natural language processing (NLP) solution for automated chart abstractions.
The NLP technology filters out charts unlikely to contain evidence for care gap closure. It then automatically identifies relevant details within each chart including unstructured medical notes.
The result was better care gap detection as well as improved abstractor efficiency, as abstractors only reviewed charts with potential evidence.
Greater accuracy, fewer errors, and streamlined workflows
Partnering with Reveleer delivered substantial value to the health plan in terms of care gap identification and efficiency. Results included:
Next steps: Unlocking ongoing value with AI-driven Quality Improvement
The health plan successfully improved the accuracy and efficiency of its abstraction efforts, powering better care gap closure across nine critical quality measures. Looking ahead, the plan can leverage Reveleer’s advanced NLP technology to identify additional diagnoses across its member base, increasing the precision of its risk adjustment factor scores and enabling targeted quality improvement efforts on additional measures. As the health plan continues to advance its value-based care objectives, Reveleer’s robust healthcare technology will play a key role in supporting its ongoing success.
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