If, for example, a provider could request a comprehensive medical history for a patient and receive it in one easy-to-read document — rather than the tens to hundreds of pages of records a patient will accrue throughout their lifetime — they could walk into a clinical encounter with a list of diagnoses and make sure to address each one while talking to the patient.

This “shift-left” on the timeline - starting risk adjustment pre-encounter - adds enormous value for at-risk providers and also improves outcomes for patients, as their caregivers will be armed with a better understanding of their medical histories and current needs.


Can artificial intelligence offer a solution?
With an enormous amount of data available and the need for a system that can process that data in a variety of formats, artificial intelligence and machine learning are uniquely positioned to change the way we compile a patient’s medical history.

Rather than expecting providers to sift through every document from a patient’s lifetime to look for potential diagnoses, these large language models could complement the work of manual risk adjusters, summarizing key information and pulling out diagnostic codes.

There’s also enormous potential for AI to help coders extract value from the unstructured progress or SOAP notes, including diagnoses, prescriptions, and even specific treatment plan details.

Whatever solution health systems choose, it’s important to remember that the clock is ticking. If risk adjusters want to remain compliant and ensure they’re receiving maximum value for each patient, we must find a medical records solution that supports accurate, compliant coding as soon as possible.

About the Author: Carm Huntress is the founder and CEO of Credo, the leader in automated patient medical record retrieval. Credo is working to radically simplify and update the current medical record and retrieval process to support patients receiving better overall care.

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