Let healthcare teams use AI without exposing PHI.
Clinicians, care managers, and back-office staff already use AI tools like ChatGPT and Claude to be more efficient. LogosGuard swaps patient names, MRNs, dates of birth, and other PHI for placeholders before the prompt is submitted. The AI still summarizes the chart note, drafts the reply, or interprets the claim. The real patient identifiers stay inside the company.
Healthcare AI usage is already happening. The compliance question is no longer 'should we allow it?', it is 'how do we make sure PHI does not ride along with it?'
Healthcare AI usage is already happening.
Clinicians use AI to summarize chart notes and draft patient-friendly explanations. Care managers use it to compose tactful messages and to triage long inboxes. Billing teams use it to interpret denial codes and write appeal language. Back-office staff use it to summarize referral letters and patient correspondence. Every one of those workflows is reasonable. The compliance gap is that PHI ends up in the prompt without anyone deciding it should.
Common PHI leakage scenarios.
- A clinician pastes a chart note into ChatGPT for a one-paragraph summary.
- A care manager pastes a patient message into Claude to draft a tactful reply.
- A coding analyst pastes claim text into Gemini to interpret denial reasons.
- A trainee tests AI-generated documentation against a real encounter for comparison.
- A medical assistant uploads a referral document to an AI summarizer.
How LogosGuard detects and redacts PHI.
LogosGuard treats PHI as a high-severity data class. It detects structured identifiers (MRNs, claim and visit IDs, dates of birth, addresses tied to records) using format-aware patterns. It detects contextual identifiers (named patients combined with clinical content) using context-aware models.
On detection, the real identifiers are swapped for placeholders before the prompt is submitted. A pasted chart note like `Patient Jordan Rivera, DOB 03/14/1993, MRN EMP-40972, presents with...` becomes `Patient [PATIENT], DOB [DOB], MRN [MRN], presents with...` for the AI. The clinical content is preserved. The AI summarizes, drafts, or interprets as if it had the full note. The real identity never leaves your LogosGuard environment. For higher-severity categories (research data, behavioral health), policies hard-block instead of redact. See Prevent PHI in ChatGPT for details.
User review before submission.
LogosGuard does not silently rewrite a clinician's prompt. It shows what was detected and gives the user a one-click option to redact and continue, edit further, or cancel. The user is in the loop, and the prompt that ultimately reaches the AI tool is one the clinician approved.
Admin policy controls.
Compliance and security teams configure which PHI categories are hard-blocked, which are auto-redacted, and which trigger a warning. Different teams can have different rules. Research teams operating on de-identified data may have lighter controls than clinical teams handling full PHI. Settings are auditable and changes are logged.
Audit logs.
Audit logs record event metadata: which user, which tool, which policy fired, which categories of PHI were detected, and what action was taken. The underlying PHI is not retained in the log. This means compliance reviewers can see what happened without the audit trail itself becoming a second exposure surface.
Deployment options for healthcare.
The browser extension is the fastest path to coverage and works for the majority of clinical workflows that happen in the browser. The desktop app extends the same policy to native AI clients used outside the browser. For organizations that want to remove external AI submission entirely from specific workflows, LogosGuard supports private LLM deployment so prompts and outputs stay within the company environment.