Medical AI needs more than implementation. It needs governance.

Flagship Offer

Medical AI Governance Review

A focused, clinician-led assessment for organizations using AI in clinical documentation, risk adjustment, coding, decision support, utilization workflows, or other healthcare operations.

01 / INVENTORY

Know what is in use

Document the model, intended use, users, affected population, data inputs, vendor dependencies, decision authority, and downstream consequences.

02 / VALIDATE

Test real performance

Compare AI output with independent clinical review. Measure false positives, false negatives, agreement, documentation sufficiency, subgroup variation, and failure patterns.

03 / CONTROL

Build defensible oversight

Define human review, escalation, monitoring thresholds, incident response, change control, documentation, accountability, and corrective-action processes.

STARTER ENGAGEMENT

90-Day Governance & Validation Sprint

Governance inventory, risk-tiering, representative chart sample, clinician benchmark, failure-mode analysis, executive findings, governance policies, monitoring scorecard, and prioritized remediation roadmap.

ONGOING ENGAGEMENT

Clinical AI Monitoring Retainer

Recurring sample review, KPI tracking, drift surveillance, model or workflow change assessment, incident review, governance committee reporting, and independent clinical challenge.

Governance Framework

From purchase decision to post-deployment monitoring.

Governance is not a policy binder. It is the operating system that keeps intended use, real-world performance, human judgment, and organizational accountability aligned.

Govern

Ownership, policies, risk appetite, role clarity, documentation, vendor accountability, and governance committee structure.

Map

Context of use, users, patient or member populations, data flows, decisions influenced, harms, benefits, dependencies, and affected stakeholders.

Measure

Clinical validity, reliability, false positives, false negatives, subgroup performance, workflow effects, human factors, and evidence quality.

Manage

Thresholds, monitoring, escalation, mitigation, change control, incident response, suspension criteria, and retirement.

Clinical safety and validity

Does the output make sense in the full record, not merely in a single note, code, medication, or data field?

Compliance and defensibility

Can the organization explain why the system was used, how it was tested, who reviewed it, and what happened when it failed?

Equity and population fit

Does performance vary by facility, provider, condition, demographic group, acuity, documentation style, or data completeness?

Change and drift

Are model updates, configuration changes, new data sources, or altered workflows changing performance over time?

Why Independent Validation Matters

A model can look productive and still fail in clinically important ways.

Vendor dashboards show what a system produces. Governance asks whether those outputs are correct, complete, appropriate for the intended use, and safely integrated into human decision-making.

Longitudinal synthesis failures

Important evidence may be distributed across imaging, labs, wound care, treatment history, and multiple encounters. A single-note workflow can miss the clinical conclusion.

Medication-inferred false positives

A medication can suggest many possible conditions. It does not independently establish a diagnosis or its current status.

Hidden false negatives

Organizations can count accepted AI findings while never measuring clinically supportable findings the system failed to surface.

Model and workflow drift

Performance can shift after updates, configuration changes, population changes, data-quality changes, or new user behavior.

Prior validation finding: clinician review surfaced the majority of confirmed misses

In a de-identified Medicare Advantage validation engagement, approximately three-quarters of confirmed missed opportunities were surfaced through independent clinician review and approximately one-quarter through the AI-assisted workflow. The result does not establish a universal model detection rate. It demonstrates the governance problem: without an independent benchmark, an organization may not know the size or clinical character of what its AI misses.

What You Receive

Evidence your leadership team can use.

AI system inventory

Intended use, owners, users, data, dependencies, decisions, vendor information, risk tier, and regulatory relevance.

Validation report

Sampling methodology, clinical benchmark, performance measures, failure modes, limitations, and high-risk examples.

Governance controls

Human oversight, escalation, monitoring thresholds, incident response, change control, and documentation standards.

Executive scorecard

Concise view of performance, risk, unresolved issues, corrective actions, owners, and due dates.

Remediation roadmap

Prioritized actions across configuration, workflow, education, vendor management, data quality, and further testing.

Monitoring plan

Sampling cadence, metrics, subgroup review, drift triggers, committee reporting, and revalidation requirements.

Why IMHIRS

Governance led by someone who can challenge the clinical output.

Medical AI governance requires more than policy language. It requires the ability to read the chart, understand the workflow, interrogate the data, recognize coding and documentation risk, and explain the result to clinical, compliance, technical, and executive stakeholders.

IMHIRS is led by Dawn Krysa, a Physician Associate with 14 years of clinical practice, graduate training in health informatics and information management, healthcare analytics experience, and direct experience validating AI-assisted CDI and risk-adjustment output.

[PA-C]14 years of clinical practice across primary care, internal medicine, telehealth, and complex longitudinal care.

[MSHIIM]Master of Science in Health Informatics & Information Management.

[CHDA]Certified Health Data Analyst, AHIMA.

[CRC]Certified Risk Adjustment Coder, AAPC.

[AIGP]Artificial Intelligence Governance Professional candidate; examination scheduled October 19, 2026.

Contact

Find out what your medical AI is not telling you.

IMHIRS works with health plans, Medicare Advantage and I-SNP organizations, risk-bearing provider groups, healthcare AI vendors, and clinical leaders who need independent evidence, not another vendor performance claim.

Book a discovery call

Email
dawn@imhirs.com

Phone
(406) 239-1990

LinkedIn
Dawn Krysa

Location
Darby, Montana · Available nationwide