The most useful first conversation about healthcare AI may not mention a model at all. It may start with a coordinator explaining how a document reaches the right team, or a staff member describing where an everyday question gets stuck.
Look at the handoffs
A workflow is more than a list of tasks. It includes the information people need, the systems they move between, the exceptions they handle, and the responsibility they carry. Mapping those handoffs often reveals friction that a generic chatbot would miss.
For example, a document workflow might need an assistant to organize incoming material and flag an incomplete item. The useful output is a review-ready queue, not an autonomous decision. That distinction changes the data, interface, and evaluation requirements.
Choose a bounded first use case
A good starting point has a clear owner, an understandable input, and an output that a qualified person can review. It should also have a practical fallback when the system is uncertain or unavailable.
- Describe the task in one plain-language sentence.
- Identify what the system must never decide on its own.
- Agree which information may be used and who may access it.
- Define how a reviewer checks the result and corrects a mistake.
Measure usefulness in the workflow
A polished demonstration does not tell you whether a tool makes work easier. Evaluate with representative, appropriately authorized material. Look at the effort required to review outputs, the errors that matter, and the way exceptions are handled. A faster draft that takes longer to verify may not improve the process.
Keep the clinical boundary clear
Administrative assistance and clinical decision support should not be treated as interchangeable. If a use case can influence care, involve the organization’s qualified clinical, privacy, security, and regulatory stakeholders before deployment. Keep human responsibility explicit in the design.
The first question is not “Where can we add AI?” It is “Which piece of work could become clearer, safer to review, or less repetitive?”
At Samach, this is the starting point for a healthcare AI discovery conversation: understand the workflow, define its boundaries, and choose a first implementation that can be meaningfully evaluated.