This is an imaginary story of a Health Care CEO
When folks discuss generative AI in healthcare, they're referring to clinical use cases: diagnosis, image interpretation, symptom triage. My experiment, however, wasn't focused on patient-facing technology. It was focused on something more infrastructure-oriented — the back-office day-to-day activity that gets a healthcare organization running.
Imagine if a large language model — appropriately contained, safely trained, and exhaustively tested- could operate the core processes of a healthcare company?
That's the question I attempted to get to the bottom of. So, for 30 days, I granted a private, personalized LLM access to our most important operations. And then, I waited.
Why We Did It
Healthcare administration is drowning in complexity. Credentialing, claims processing, compliance, policy interpretation — it's a tangle of local regulations, payer contracts, evolving regulations, and documentation requirements.
No off-the-shelf AI solution was going to understand our workflows. Public cloud LLMs were out due to privacy and data sensitivity. So we partnered with a vendor to implement a Private Tailored LLM (PT-SLM), entirely within our infrastructure. No internet calls. No third-party data sharing. The model was trained exclusively on our internal documentation: policy guides, reimbursement manuals, licensing rules, SOPs, and structured ops data.
It was also augmented with Retrieval-Augmented Generation (RAG) for supporting exact referencing, and overlaid with Chain-of-Thought reasoning (CoT) and ReAct (Reasoning + Action) capabilities to solve procedural questions with reason and traceability.
We didn't merely provide it with text. We provided context.
What The Model Could Do?
At the conclusion of week one, the PT-SLM was producing useful answers to frequent queries from operations and billing staff: "Can we schedule this provider in State X?", "What forms do we need for payer Y?", "Can we bill for Z under this code?
The model didn't merely respond — it quoted the precise paragraph in a credentialing guidebook, or equaled historic procedure from analogous circumstances. Employees weren't obligated to speculate if it was correct; they could observe how it arrived at its responses.
In week two, we opened up access. It began processing requests from our compliance department, assisting with document audits, identifying out-of-date forms, and reviewing reimbursement procedures against changing payer guidelines.
By week three, we'd incorporated it into our internal ticketing system. The model ran through operational tickets, recommended solutions, and even generated boilerplate responses for human approval. In some instances, it carried out complete approval workflows on its own.
Results
We tracked everything. Compared to the last month:
- Credentialing turnaround time decreased by 55%.
- The new employee policy lookup time was cut by more than 80%.
- Manual verification of reimbursement tickets decreased from 5 per case to less than 1.
- Internal audit compliance accuracy rose by 30%.
- Daily users of the LLM reported 2–3x productivity gain on routine tasks.
Most importantly, perhaps, error rates didn't increase. In fact, they decreased, especially in repetitive and rules-based tasks.
Why It Worked?
Several principles made this a success:
- It was private. All of the processing remained in our secure environment.
- It was tailored. The model didn’t guess — it knew our documents, rules, and edge cases.
- It was explainable. With CoT and ReAct, the model showed its work.
- It was non-invasive. The LLM wasn’t bolted onto the front line; it was integrated gently into existing tools.
- It was respectful of governance. Every request underwent a validation and sanitization layer.
AI did not replace jobs; it augmented sound judgment, reduced decision cycles, and eliminated excess cognitive load from already-burdened teams.
Lessons Learned
There were boundaries. The model still required human beings for uncertainty, conflict resolution, and exception handling. It was not perfect, and it was not innovative. It did not need to be. The actual value was not in artificial intelligence. It was in applied operational intelligence — reliably, predictably, and safely. By day 30, we knew: no longer was it an experiment. It was infrastructure. We didn't construct a chatbot. We put a thinking layer on our company. And we're not looking back.