With years of experience in healthcare technology, Carm has been instrumental in developing solutions that allow providers to concentrate on patient care rather than getting bogged down in data. In this episode, Carm discusses Credo's approach to using machine learning models for diagnosis identification, exploring the potential advantages and challenges that come with this technology. Carm's vast knowledge and expertise in the healthcare sector make him an indispensable source of information for those looking to enhance their understanding of health data interoperability.
Taking inspiration from the recent advancements in large language models by companies like OpenAI and Google, Credo began using machine learning to enhance the experience of their risk adjustment coding team. The goal was to create a structured longitudinal record for patients, transforming unstructured data into accurate diagnoses and coding. Battling challenges presented by hallucinations, gaps in knowledge, and ensuring precision, Credo continues to refine its models with human input. Carm Huntress envisions a future where healthcare is much more efficient, and with Credo's relentless pursuit, that future is just around the corner.
In this episode, you will be able to:

Gain insights into Credo's utilization of machine learning models for more accurate diagnosis determination.

Contemplate the potential drawbacks and hazards arising from machine learning incorporation in healthcare.

Examine the progress in healthcare interoperability driven by TEFCA regulatory measures.

Discern the obstacles patients may face when obtaining access to their own medical information.

Comprehend the significance of streamlined healthcare data management and the usefulness of machine learning technologies.

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