AI Agents for Healthcare
FISTA Solutions builds healthcare AI agents that remove administrative load: drafting clinical documentation, assembling prior-authorization packets, supporting coding, triaging patient messages, and working denials. Agents run under HIPAA controls with clinician review on anything clinical, and are evaluated before they touch live work.
- 150+
- projects delivered
- 50+
- companies served
- 99.9%
- verified uptime
- 47%
- efficiency gains
- 12+
- countries reached
What we build
What can AI agents do in healthcare?
Healthcare agents draft encounter notes and summaries, assemble and submit prior-authorization packets, suggest codes with chart evidence, triage inbound patient messages, work denials and appeals, and coordinate referrals — each returning evidence a clinician or specialist reviews before anything is final.
- 01
Clinical documentation agent
Drafts notes and discharge summaries from the encounter record for clinician review, measured on editing effort and time saved.
Documentation - 02
Prior authorization agent
Assembles the clinical packet against payer rules, submits, tracks status, and escalates anything ambiguous to a coordinator.
Access - 03
Coding and CDI agent
Suggests codes with the supporting chart text quoted, so certified coders validate evidence instead of searching for it.
Revenue - 04
Patient message triage agent
Classifies portal messages by urgency and topic, drafts routine replies, and routes clinical questions to the right queue.
Access - 05
Denials and appeals agent
Reads the denial, retrieves supporting documentation, and drafts the appeal for a revenue-cycle specialist to send.
Revenue - 06
Referral coordination agent
Collects required records, checks network and authorization requirements, and prepares the referral for staff confirmation.
Coordination
Requirements
What guardrails do healthcare agents need?
Healthcare agents touch protected health information and sit next to clinical decisions, so the guardrails are not optional: access is scoped and logged, clinical output is reviewed by a licensed human, uncertainty is expressed rather than hidden, and every run leaves a trace.
| Guardrail | Why it matters here | How FISTA implements it |
|---|---|---|
| PHI protection | Agents read charts, messages, and payer data covered by HIPAA. | Business associate agreement, encrypted transport and storage, minimum-necessary scopes per tool, and audit logging on every retrieval. |
| Clinician accountability | Clinical content must be reviewed and signed by a licensed human. | Review gates before anything enters the record, reviewer identity captured, and no autonomous clinical decisions in any workflow. |
| Uncertainty handling | A confident wrong answer in a chart is a safety issue. | Explicit abstention behavior, confidence signaling, and escalation paths when retrieval or evidence is insufficient. |
| Evidence and citation | Suggestions must be checkable against the record. | Every clinical assertion quotes the source chart text with a link back to the document and encounter. |
| Evaluation before use | Behavior must be known before live exposure. | Golden test set from your own cases, shadow runs on real inputs, and eval gates on every prompt, model, or tool change. |
Where AI fits
Which healthcare workflow should you automate first?
Start where volume is high, the rules are written down, and a human already reviews the output: prior authorization and documentation usually qualify. Both have measurable baselines, existing review steps, and clear escalation paths, which makes the first agent provable rather than merely impressive.
- 01
1. Pick a workflow with a queue
Prior auth, denials, and message triage have visible backlogs and measurable cycle times, so improvement is not a matter of opinion.
- 02
2. Baseline it honestly
Measure current handling time, error rate, and escalation frequency before the agent exists, or savings later will be contested.
- 03
3. Shadow on real inputs
Run the agent alongside staff on live cases without acting, and compare its output to what the team actually did.
- 04
4. Graduate with approval gates
Let the agent act on the safest subset first, with human approval on everything else, widening scope as evidence accumulates.
- 05
5. Instrument continuously
Track quality, escalation, and cost per case in production, with alerts when behavior drifts from the evaluated baseline.
Cost and timeline
How much does an AI agent for healthcare cost, and how long does it take?
Cost is driven by EHR integration depth, the number of payer or system interfaces, and evaluation rigor; timeline by interface access and clinical reviewer availability. FISTA does not quote blind: the scoping call returns an agent design, a guardrail plan, and a phased estimate.
Integration, not the model, dominates the budget. Reading a chart through a sanctioned FHIR interface is one cost; writing back into the record with organizational policy honored and audited is another. FISTA prices read-only and write-capable phases separately so you can prove value before taking on the harder path.
Evaluation is the second real line item and the one that makes the agent trustworthy. Building a golden set from your own cases, running shadow mode, and maintaining evals as models change is ongoing work. It is also the difference between an agent you can defend and a demo you quietly retire.
Send the scope you have, even if it is a paragraph. You get a written brief, an architecture sketch, and a phased estimate before any commitment.
Get a scoped quoteDelivery
How does FISTA deliver an AI agent into production?
FISTA delivers agents in four gated phases: a discovery sprint that picks the workflow and writes the agent specification, a design that names tools, permissions, and approval points, a build with an evaluation harness and shadow runs on real work, and a production release with traces, dashboards, and rollback.
- 1
Select and specify
Choose the workflow with a measurable outcome, map its systems and edge cases, and write the agent spec with success metrics.
OutputAgent specification, golden test set
- 2
Design the guardrails
Tool inventory with least-privilege scopes, approval gates, escalation paths, data handling, and the evaluation plan.
OutputTool and permission matrix
- 3
Build and shadow-run
Implement tools as MCP servers or connectors, iterate against the evaluation harness, and run in shadow mode on live inputs.
OutputShadow-mode results, eval scores
- 4
Release and observe
Graduated rollout, full traces, cost and quality dashboards, on-call runbook, and a change process that re-runs the evals.
OutputProduction agent with SLOs
Why FISTA
Why choose FISTA Solutions to build your healthcare agents?
FISTA builds clinical-adjacent agents with HIPAA controls, evidence citation, and clinician sign-off as structural requirements, and is an official Anthropic partner with production experience across model providers. Nothing goes live without shadow-mode evidence.
Healthcare specifics
- PHI handled under a BAA, with de-identified or synthetic data in every non-production environment.
- Every clinical suggestion quotes chart evidence; clinicians review and sign before anything is final.
- Shadow mode on live cases before any agent acts, with the comparison against staff decisions reported to you.
- Evaluation harness re-run on every model, prompt, or tool change, so a provider update cannot silently degrade behavior.
How FISTA engineers
- Spec-Driven Development: every deliverable starts as a written specification with acceptance criteria, so scope is testable before it is built.
- AI-native delivery: engineers direct coding agents under review gates and evaluation harnesses, compressing build time without loosening verification.
- Official Anthropic partner, with production experience across Claude, OpenAI, Google, and open-weight models, chosen per workload rather than by default.
- One accountable delivery lead, weekly demos on your environment, and code in your repositories from week one.
What you get as a client
- 150+ projects delivered for 50+ companies across 12+ countries since 2017, with 99.9% verified uptime on systems we operate.
- A US entity (FISTA Solutions Inc., Wilmington, Delaware) for contracting, invoicing, and IP assignment, with an engineering center in Faisalabad, Pakistan for cost-efficient senior capacity.
- US business-hours overlap for standups and reviews; written decision logs so nothing depends on a meeting you missed.
- Flexible engagement: fixed-scope build, embedded forward deployed engineers, or a dedicated team that you can scale month to month.
Clear answers
What teams ask before deploying agents.
Straightforward guidance for evaluating scope, fit, and the next step.
01Are AI agents HIPAA compliant?
They can be, and FISTA builds them that way: BAA in place, encryption, minimum-necessary tool scopes, audit logging, and no PHI in training. Compliance depends on the deployment and your policies together, so the controls and data flows are documented per workflow.
02Can an agent write directly into our EHR?
Only where your EHR, contract, and governance allow it, and normally as a draft requiring clinician sign-off. Most engagements start read-only with human-mediated entry, then add write capability once shadow-mode evidence supports it.
03How do you stop an agent from hallucinating clinical facts?
Grounding in retrieved chart content, mandatory citation of source text, structured output validation, explicit abstention when evidence is insufficient, and an evaluation harness with a golden set that blocks releases when quality regresses.
04How long before a healthcare agent is in production?
Typically a few weeks to shadow mode and within a quarter to supervised production, depending on interface access and reviewer availability. The discovery sprint identifies those dependencies and dates them before you commit.
05What measurable outcome should we expect?
That depends on the workflow, and FISTA sets the target with you against a measured baseline — for example handling time per prior authorization or clinician minutes per note. No generic percentage is promised, because unmeasured claims are how AI projects lose credibility.
06Which models do you use for healthcare agents?
The model that meets the accuracy, latency, and data-handling requirements of the workflow — Claude models (FISTA is an official Anthropic partner), other commercial models, or self-hosted open-weight models where data residency demands it. The choice is recorded with its rationale.
Continue exploring
Related capabilities
Scoped in writing before you commit
Give the administrative load to an agent, not to another hire.
Bring the queue that never clears. The scoping call returns an agent design, guardrails, an evaluation plan, and a phased estimate.