How Do You Fix an Error Your Doctor Made… With an AI?
Here’s the New AI Skill That Finally Lets You Fix It.
By Paul J. Swider · May 2026
I have spent years building mission-critical health applications and AI.
But last week, something hit me harder than any technical breakthrough.
A patient walked into a specialist appointment. The AI wrote the clinical note. The doctor signed it. And nine factual errors survived review. Wrong medications, omitted history that was literally in the referral letter, and even the wrong number of pregnancies. The doctor had the correct information in front of them and still missed it.
That story is not from a dystopian future. It is from Dr. Linda McIver’s recent post, “How many errors are too many?” And it is happening right now, every single day, in clinics across the country.
We have entered an era in which AI is drafting your medical record. And the system we use to correct those records is still stuck in the microcassette-tape days.
That changes today.
The Problem No One Wants to Talk About
Under HIPAA, you have the legal right to request an amendment to your protected health information. Providers must respond within 60 days. If they refuse, you can file a Statement of Disagreement that is supposed to travel with your record forever.
In theory, that is patient protection.
In practice, it is a paper exercise that gets buried in a “Patient Correspondence” tab no clinician ever opens.
Now layer on AI-generated notes that hallucinate details, omit critical history, and sound so professional that even doctors skim and approve them. The error rate is not theoretical anymore. It is documented, widespread, and growing.
As Josh Mandel, MD (Chief Architect for Health at Microsoft Research) pointed out in the LinkedIn thread that sparked this: denying a correction actually creates more procedural work for the provider than simply accepting it. Yet the defaults in most EHRs still make corrections invisible.
This is not sustainable.
The Tool I Built to Fix It
Last week I added a new skill to Tula, my open-source personal health AI agent that runs on OpenClaw.
The skill is called request-amendment.
It does three things that did not exist before:
It finds the errors for you. Point Tula at a new clinical note (FHIR Bundle or PDF from your portal). It cross-references every detail against your full longitudinal record, labs, prior notes, wearables, even your own memory-diff entries. It flags hallucinations and omissions with evidence attached.
It drafts the correction like a pro. In one conversation it generates a HIPAA-compliant amendment request that specifies the exact wrong text, the correct text, the supporting evidence, and the legal citation. If the provider refuses, it instantly drafts the Statement of Disagreement. Both versions explicitly ask for the correction to be prominently appended to the original AI-generated note so future clinicians actually see it.
It tracks the whole process and never forgets. It stores everything locally on your VM (nothing leaves your machine). It calculates real calendar deadlines (60 days, plus the one allowed 30-day extension), sends you reminders through your daily pulse, and even prepares escalation language for the privacy officer or HHS complaint if needed.
And because Tula already does multi-hospital SMART on FHIR pulls, the skill can optionally generate a proper FHIR Task + Communication payload using the HL7 Patient Request for Corrections Implementation Guide, the standard the Patient Empowerment Work Group built exactly for this moment.
Everything runs on a 30-dollar-per-month Linux VM you control. No vendor sees your PHI. No black-box SaaS. Just you and your agent fixing your own record.
Why This Matters More Than It First Appears
This is not just a convenience feature. It is built using Microsoft Frontier AI patterns. This is an enterprise capable skill with agent evaluations included. All open-sourced.
It is the first practical bridge between two realities that have been on a collision course:
AI is going to generate more and more of our clinical documentation.
Patients are going to see those notes in their portals faster than ever.
If we do not give patients an easy, enforceable, visible way to correct the inevitable errors, we will erode trust in the entire system.
The beautiful part? The law is already on our side. HIPAA has always allowed this. The HL7 IG already exists. All we had to do was build the patient-side tool that makes exercising those rights frictionless.
Tula’s request-amendment skill does exactly that.
What You Can Do Right Now
If you want this capability for yourself:
The skill is fully open-source under Apache 2.0. The SKILL.md, Waza eval spec, and fixture examples are already public.
I will be publishing the complete build instructions in the repo this week. If you want early access or help deploying it, reply to this post or DM me. I am happy to walk the first wave of users through it.
If you are a clinician reading this: thank you for the work you do. This tool is not anti-doctor. It is pro-truth. Accurate records help you deliver better care.
If you are a patient who has already spotted an error in an AI-generated note: you are not alone, and you are no longer powerless.
We just gave you the pen.
What error have you already found in your own records?
Drop it in the comments. The more we talk about this, the faster the system has to adapt.
And if this post resonates, share it. The patients who need this tool the most are often the ones who do not yet know it exists.
Paul J. Swider Mission-critical healthcare solutions. Microsoft Strategic Partner. AI pioneer. pswider@realactivity.com 617-817-7720
Sources & Further Reading
McIver, Dr. Linda. “How many errors is too many?” Australian Data Science Education Institute, March 2026.
Mandel, Joshua C., MD. Chief Architect for Health, Microsoft Research.
HIPAA Privacy Rule. 45 CFR § 164.526 — Amendment of protected health information. Electronic Code of Federal Regulations.
HL7 International. Patient Request for Corrections Implementation Guide, STU1. Patient Empowerment Work Group, 2025.
OpenClaw. Open-source autonomous AI agent runtime by Peter Steinberger.