The Operational AI Evaluation Kit is a vendor-neutral, 21-point scorecard for healthcare operations teams. It turns seven high-stakes decision areas into 28 evidence checks, a consistent score, clear gaps, and an exportable review.
Score the evidence you can inspect, not the confidence of the presentation. Adapt the rubric to your risk, procurement, legal, privacy, security, and clinical governance requirements.
No clear answer or usable evidence was provided.
The answer is stated, but the evidence cannot yet be checked.
The capability works in a controlled scenario or pilot.
The evidence is observable in production and can be independently checked.
Use one representative case with normal complexity, missing data, and at least one exception. A scripted happy path is not enough.
Move from claimed to demonstrated or verified only when the evidence is visible, attributable, and relevant to your environment.
Record unresolved evidence, assign owners, and treat privacy, security, safety, and authority issues as gates that a high total cannot offset.
Check the evidence you received, score each dimension from 0 to 3, and record the gap. Export a CSV for the decision record or print the completed worksheet.
Optional. This tool does not save a decision record. Do not enter PHI or confidential information.
Does the system complete the operational job, or only create another task?
A polished interaction is not the same as a completed workflow. Evaluate the full path from intake to a confirmed operational outcome.
Can the workflow act safely inside the systems your team already uses?
Integration quality determines whether automation removes work or shifts it into reconciliation, duplicate entry, and exception cleanup.
Can people see, pause, correct, and escalate the system at the right moments?
Operational AI should make ownership clearer. Review who approves sensitive actions, who receives exceptions, and how corrections are recorded.
Are data responsibilities, safeguards, access, and retention explicit before sensitive data is used?
Healthcare buyers need a documented data flow and accountable controls. Treat this dimension as a review gate, not a point total to average away.
Is there a credible path from signed agreement to a stable operating workflow?
Implementation risk often sits outside the interface. Surface dependencies, owners, acceptance criteria, training, and support before selection.
Can you tell whether the workflow improved, for whom, and at what cost?
A percentage without a baseline, denominator, timeframe, and scope is not decision-grade evidence. Agree on measurement before the intervention starts.
Are the operating commitments and the path out as clear as the path in?
Commercial fit includes usage growth, service obligations, data portability, transition support, and what happens when the relationship ends.
This is a Linear Health evaluation framework, not a government scorecard or an endorsement by the organizations below. The source set grounds the questions in established risk-management and healthcare data-responsibility guidance.
Risk ownership, context mapping, measurement, and ongoing management.
U.S. Department of Health and Human ServicesHIPAA and Cloud Computing GuidanceData responsibility, business associate agreements, risk analysis, availability, and data return or destruction.
U.S. Department of Health and Human ServicesSummary of the HIPAA Security RuleAdministrative, physical, and technical safeguard review context.
This tool supports operational vendor evaluation. It is not legal, regulatory, privacy, security, clinical, or procurement advice. It does not certify compliance, product quality, or fitness for a specific use. Your accountable reviewers must verify evidence and make the final decision.
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