Administrative workflows have been a vital proving ground, with different types of AI applied to different tasks.
No matter the use cases for AI in healthcare administration, the desired outcomes tend to be the same: increased efficiency and decreased burden on staff.
Robert Potts, senior principal analyst at Gartner, notes there are different flavors of AI organizations can deploy for administrative tasks:
- Generative AI tools and large language models can create billing summaries, prior authorizations and appeals documents; they can also act as ambient scribes during patient visits.
- Natural language processing takes healthcare's stockpiles of unstructured data and makes it computer-readable for business applications.
- Machine learning can analyze data and detect patterns, such as increases in patient no-show rates or main causes of claims denials.
- AI agents can perform tasks based on a loose set of business rules or guidelines.
While healthcare is no stranger to AI, there's still a lot for organizations to consider as AI tools and use cases evolve rapidly.
To determine where to get started, Potts recommends prioritizing value over complexity. A task that's difficult but not done frequently may not justify the cost of automation, for example. Ideal tasks for automation couple high volume with little complexity.
Other important considerations include the following: governance, cost, and risk.
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