GenAI Applications

This page is an index of evolving notes on what healthcare-specific use cases exist for Generative AI. At times I try to connect them with the companies I see out there, with the full realization that I'm only interpreting what these companies claim to be doing based on their online footprint.

Provider Org Operations

There are a lot of workflows in a healthcare organization that depend on manual back and forth of papers, faxes, phone calls. Multi-modal agents focused on logistical use cases can definitely reduce the workforce needed and even make overall efficiency gains (since they can navigate IVR, understand bad responses, escalate if need be, etc). Yes, they need to be integrated with the incumbent EHRs in large organizations. But for smaller, independent deployments I can see these offerings can actually be a wedge that ends up creating a master system that EHRs are subservient to.

1. Scheduling
   1. Patient reminders
2. Billing
   1. Coding: Abstracting clinical documentation and mapping it to medical coding systems (like ICD, CPT, HCPCS etc) so the most viable claims can be submitted for payment. Learning the shorthands, variations of this crosswalk is a big issue.
   2. Payer Compliance: Payers have a lot of policies, regulations and they change frequently. Not adhering to them in can cause payment denials and delayed/lost revenue. Evaluating the adherence and consistently navigating all this bureaucratic maze is a big problem.
3. Banal communications: Multi-modal agents can start dealing with phone calls or fax/email conversations around frequently occurring topics like:
   1. Collecting insurance information from patients
   2. Managing requests for letters of medical necessity
   3. Verifying benefits with payers: Getting some basic eligibility information is possible through EDI (270/71) transactions but high-stakes treatments (like with $$$ drugs, infusions) require making phone calls, exchanging documents to figure out details (like what is the cost share, will a prio-auth be required, etc.). Without those confirmations, treatment can't be started.
4. Prior Authorizations: The friction of PAs costs the average physician practice nearly two business days a week. Figuring out if PA is needed, requirements to submit a PA, status/updates on a submitted one, etc. is all very burdensome. This back and forth is a poster child for LLM-based automation. Which is why multiple startups are in it already.

Care Delivery Support

Gen AI agents have a never-before fluent ability to have conversations and that can be use to support care delivery (for now, mostly in non-clinical contexts). No company is claiming anything that can be construed to replace a clinician, of course. But these use cases need awareness of care pathway - and this context makes them different from the operationally-focused use cases.

1. Clinician's Scribe
2. Medical Record Summarization
3. Referrals
4. Patient Communication
   1. Conversational guidance: A co-pilot for having more successful phone calls with patients. Understanding their personal health contexts and coaching the care navigator/specialists real time. Note LagunaHealth.
   2. Prescription follow-up, renewals
   3. Post-op checkins
   4. Referral follow-up