Buyer's Guide
How to Choose a Healthcare AI Implementation Partner: RFP & Due Diligence Guide
What to put in the RFP, what to ask in due diligence, and how to tell an implementation partner from an advisory deck.
The right healthcare AI implementation partner is the one that can show a working audit trail and CHAI- or ONC-aligned governance controls already built into a live system, not just a framework slide deck. Put RFP requirements around model documentation, fairness testing, and clinician oversight in place before you compare price or timeline.
criteria
What to Put in the RFP
Model documentation and an inspectable audit trail
A serious implementation partner should be able to produce, on request, the kind of documentation ONC's HTI-1 rule now requires of certified health IT with predictive decision support features: training data characteristics, intended use, validation approach, and performance across patient subgroups. Ask for a sample, not a description of one.
Clinician oversight built into the workflow
AI that changes a clinical or operational decision needs a defined human-in-the-loop point, not a disclaimer buried in a settings page. The RFP should require the vendor to name where clinician review sits in the workflow and what happens when a clinician overrides the model.
Fairness testing across patient subgroups
A model that performs well in aggregate can still underperform for specific patient populations. Require evidence of subgroup performance testing as part of the deliverable, not a one-time claim made during the sales process.
Implementation, not just advisory
Framework design is the easier half of the engagement. The harder half is building the monitoring, documentation, and review gates into a running system. Ask whether the team doing the work includes engineers who build, not only consultants who advise.
regulatory context
The Rules Health Systems Now Have to Plan Around
ONC's HTI-1 final rule requires certified health IT developers to support source attributes covering training data, intended use, validation methods, and fairness processes for predictive decision support interventions, with developers attesting annually that this documentation has been reviewed and updated. An implementation partner building or integrating AI into certified health IT should already be building to this bar, not treating it as a future compliance project.
Separately, the Coalition for Health AI (CHAI) released governance playbooks on May 27, 2026 covering eight domains, including AI policy, risk and impact assessment, and third-party vendor management, developed with more than 150 health AI leaders across 100-plus healthcare organizations. CHAI and the Joint Commission had already published joint guidance on responsible AI adoption in September 2025, aimed at the Joint Commission's more than 22,000 accredited healthcare organizations, with a voluntary AI certification program planned to follow. A partner that can map its own delivery process to these playbooks, rather than treating them as background reading, is easier to hold accountable during and after implementation.
kriv fit
Where Kriv AI Fits
Kriv AI is a boutique, implementation-focused firm, not a Big 4-style advisory practice. Our healthcare work is built around the documentation and oversight expectations above from the start, including a healthcare AI governance accelerator built on Azure and a clinical copilot accelerator with human-in-the-loop review on every AI-generated output. Both ship as named, fixed-scope engagements with a defined deliverable, not open-ended advisory retainers.
Kriv's healthcare and life-sciences engagements start at a $200/hr floor or an $8,000 fixed-scope minimum, reflecting the documentation and audit-trail work that regulated healthcare AI actually requires.
evaluation questions
Questions to Ask Before You Sign
1. Can you show a sample of the model documentation from a live engagement?
A partner that has actually built ONC- or CHAI-aligned documentation can show you what it looks like, not just describe it.
2. Where does clinician review sit in the workflow, and what happens on override?
A named review point and override path is a concrete answer. A general statement about 'human oversight' is not.
3. Who runs the fairness testing, and across which patient subgroups?
Ask for the specific subgroups tested and how the results were documented, not just a claim that testing happened.
4. Is the engagement fixed-scope or open-ended advisory?
A fixed-scope engagement with a named deliverable is easier to evaluate and budget against than an ongoing advisory retainer with no defined end state.
5. Who builds the monitoring and documentation, an engineer or a policy writer?
Governance documents alone do not catch a model drifting in production. Ask who implements the actual monitoring.
get a quote
How to Get a Real Quote
Engagement cost depends on how many models are in scope, how much existing documentation already exists, and whether the work is a one-time implementation or an ongoing governance retainer. See our AI governance consulting cost breakdown for Kriv AI's published rate floors, or book a discovery call to scope your specific situation.
Straight answers
Frequently asked questions about How to Choose a Healthcare AI Implementation Partner: RFP & Due Diligence Guide
What should a healthcare AI implementation partner RFP include?
Require model documentation covering training data and validation, a named clinician oversight point in the workflow, evidence of fairness testing across patient subgroups, and a fixed-scope deliverable rather than open-ended advisory language.
What does ONC's HTI-1 rule require for predictive AI in certified health IT?
It requires developers of certified health IT to support documentation covering training data, intended use, validation, and fairness processes for predictive decision support interventions, and to attest annually that the documentation has been reviewed.
What are CHAI's health system AI governance playbooks?
The Coalition for Health AI released governance playbooks on May 27, 2026 across eight domains, including AI policy, risk assessment, and third-party vendor management, developed with more than 150 health AI leaders.
How much does a healthcare AI implementation partner cost?
Kriv AI's healthcare engagements start at a $200/hr floor or an $8,000 fixed-scope minimum, scoped to how many models and how much existing documentation are in play. See our AI governance consulting cost breakdown for the full rate structure.
Does Kriv AI only advise, or does it implement the governance system?
Kriv AI builds the monitoring, documentation, and review workflow directly as part of the engagement, rather than only delivering a governance framework document.
Talk to the team that would do the work
Bring your requirements to a working session with the person who'll actually deliver.
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