AI Playbook for Prescription Medication MRO Verification
A safety-sensitive railway employee tested positive for opioids (codeine 620 ng/mL, morphine 8,400 ng/mL) on a random DOT test. The donor claims a legitimate prescription for hydrocodone for post-surgical pain. The MRO has the laboratory results and a prescription copy from the donor. The DOT return-to-duty clock is running.
When to use this playbook
- Use this playbook when the decision looks like the situation above: A safety-sensitive railway employee tested positive for opioids (codeine 620 ng/mL, morphine 8,400 ng/mL) on a random DOT test.
- It is a fit when you have source files in hand and need a structured, reviewable analysis — not a generic chat answer about "Prescription Medication MRO Verification".
- Do not use it as a substitute for licensed, legal, clinical, or authorized official judgment in the domain.
What you'll need
- Laboratory result (codeine and morphine levels and cutoffs)
- Donor's prescription copy for hydrocodone
- Donor interview notes
- DOT FRA return-to-duty requirements
- DOT MRO manual and opioid verification guidance
Attachments: Documents (Documents)
The Prompt
You are a Medical Review Officer verifying a prescription medication explanation for a DOT positive opioid result. I am attaching: Work only from the attached source files. If a conclusion is not supported, say so. Produce: 1. Assess the laboratory result: codeine and morphine are both present — is this consistent with hydrocodone metabolism, or does the pattern suggest a different drug source? 2. Verify the prescription legitimacy: what specific information must the MRO confirm from the prescribing physician, and is a copy alone sufficient? 3. Assess whether the prescribed medication use is consistent with DOT safety-sensitive duty under FRA regulations. 4. Determine the MRO reporting outcome: verified negative, verified positive, or additional information needed. 5. Tell me the exact documentation the MRO file must contain and the timeline for reporting to the employer DER. Call out where independent models are likely to disagree, and list follow-up documents a reviewer should request.
What to expect
- Opioid metabolite pattern analysis
- Prescription verification requirements
- Safety-sensitive duty compatibility assessment
- MRO reporting outcome determination
- File documentation and DER reporting timeline
Review before you act
- Validate this output against source files before relying on it: Assess the laboratory result: codeine and morphine are both present — is this consistent with hydrocodone metabolism, or does the pattern suggest a different drug source?.
- Validate this output against source files before relying on it: Verify the prescription legitimacy: what specific information must the MRO confirm from the prescribing physician, and is a copy alone sufficient?.
- Validate this output against source files before relying on it: Assess whether the prescribed medication use is consistent with DOT safety-sensitive duty under FRA regulations.
- Validate this output against source files before relying on it: Determine the MRO reporting outcome: verified negative, verified positive, or additional information needed.
- Confirm every cited figure, date, counterparty, or requirement against the attached originals — models compress and can drop a qualifier.
- Treat disagreement between models as a review item, especially on classification, materiality, and recommended next action.
- Do not authorize an operational, clinical, legal, credit, or enforcement action solely because the models agree.
Why compare models on this
For Prescription Medication MRO Verification, running the same attachments across independent models is useful because the hard part is classification and completeness, not fluency. The workflow is already designed to surface opioid metabolite pattern analysis; prescription verification requirements; safety-sensitive duty compatibility assessment; mro reporting outcome determination. Those are comparison artifacts — they only exist if more than one model runs. Models disagree on whether an irregularity is fatal to custody, whether a prescription explains a result, and whether observation is authorized. Those splits are MRO work, not auto-verification.
See governed multi-model AI on your own prompt
Compare GPT-5, Claude, and Gemini side by side, with human review and a decision record built in.

