Texas SB 1188 (EHR and AI, Health & Safety Code ch. 183)

Binding law — in force

Texas practitioners using diagnostic AI must review every record the AI creates (Texas SB 1188)

Tex. Health & Safety Code 183.005(a) · official text · In force: applies since 1 Sep 2025 · Texas (US-TX)

Texas Health and Safety Code 183.005(a) lets a practitioner use AI for diagnostic purposes, including AI suggestions about a diagnosis or treatment course based on the patient's record, only while acting within the scope of their license, where the use is not otherwise barred by state or federal law, and if the practitioner reviews every record created with AI in line with Texas Medical Board medical-records standards. The code-visible piece is the review step: an AI-drafted note, summary, or diagnostic entry should not become part of the chart as a final record until a practitioner has reviewed it. Detect AI-generated clinical documentation or diagnostic output written to the EHR as final or signed without a recorded practitioner review.

Who it applies to

• Duty falls on: individual professional
• Sectors: healthcare
• Texas health care practitioners (anyone licensed, certified, or otherwise authorized to provide health care in Texas) who use AI for diagnostic purposes, including AI recommendations on a diagnosis or course of treatment drawn from a patient's medical record; it reaches the EHR and clinical-AI software they use. Applies to EHRs prepared on or after 2025-09-01.
• Whether it applies depends on facts outside the code; a person has to decide.

note = client.chat.completions.create(model=MODEL, messages=msgs).choices[0].message.content
requests.post(f'{FHIR_BASE}/DocumentReference', json=doc_ref(patient_id, note, doc_status='final'))

note = client.chat.completions.create(model=MODEL, messages=msgs).choices[0].message.content
requests.post(f'{FHIR_BASE}/DocumentReference',
              json=doc_ref(patient_id, note, doc_status='preliminary'))   # AI draft

def practitioner_review_and_sign(doc_id, practitioner):   # only path to 'final'
    doc = requests.get(f'{FHIR_BASE}/DocumentReference/{doc_id}').json()
    doc['docStatus'] = 'final'
    doc['authenticator'] = {'reference': f'Practitioner/{practitioner.id}'}
    requests.put(f'{FHIR_BASE}/DocumentReference/{doc_id}', json=doc)
    audit.record(doc_id, reviewed_by=practitioner.id, reviewed_at=utcnow())

Standards that recommend the same control

• Health AI/LMMs should keep a clinician accountable with oversight and redress (WHO) (WHO AI-for-Health (LMM) Guidance · WHO LMM Guidance (2024) — Human oversight & accountability recommendation)

Related incidents

No guardrail sits on this exact control; these incidents are cited by guardrails on related controls.

• UnitedHealth nH Predict claim-denial litigation (2023-11; alleged (not proven)). A class action filed in November 2023 alleges that UnitedHealth's nH Predict model had a 90% error rate, measured by denials reversed on appeal, while only about 0.2% of members appealed. UnitedHealth disputes the allegations; the litigation is ongoing. Source: STAT News · evidence grade: primary · cited by Monitor how often adverse AI decisions are reversed, and suspend models that are usually wrong
• Cigna PXDX batch claim denials (reported) (2022; alleged (not proven)). ProPublica, citing internal Cigna records, reported that Cigna's PXDX system was used to reject more than 300,000 claims over two months in 2022, with physicians spending an average of 1.2 seconds on each. Cigna disputes the reporting; related lawsuits are ongoing. Source: ProPublica / The Capitol Forum · evidence grade: press of record · cited by Make human review of adverse AI decisions substantive, not nominal

Rule id tx-sb1188.ai-created-records-practitioner-review · review status: primary source derived

Binding law — in force

Texas practitioners must tell patients when they use AI for diagnostic purposes (Texas SB 1188)

Tex. Health & Safety Code 183.005(b) · official text · In force: applies since 1 Sep 2025 · Texas (US-TX)

Texas Health and Safety Code 183.005(b) requires a health care practitioner who relies on AI for diagnostic purposes, including AI recommendations on a diagnosis or treatment course drawn from a patient's record, to tell their patients that they use the technology. The statute does not prescribe the notice's form or timing, so a patient-facing statement in intake, consent, portal, or visit-summary materials is the practical evidence. Detect a diagnostic-AI path (clinical decision support, a diagnostic model, or an LLM diagnosis prompt) with no patient-facing AI-use disclosure anywhere in the product.

Who it applies to

• Duty falls on: individual professional
• Sectors: healthcare
• Texas health care practitioners (anyone licensed, certified, or otherwise authorized to provide health care in Texas) who use AI for diagnostic purposes, including AI recommendations on a diagnosis or course of treatment drawn from a patient's medical record; it reaches the EHR and clinical-AI software they use. Applies to EHRs prepared on or after 2025-09-01.
• Whether it applies depends on facts outside the code; a person has to decide.

@app.post('/applications')
def apply(app_in: Application):
    resp = client.chat.completions.create(model=MODEL, messages=underwriting_prompt(app_in))
    return {'decision': underwrite(resp.choices[0].message.content)}

@app.post('/applications')
def apply(app_in: Application):
    notice = send_admt_notice(app_in.applicant_id, template='ai_decision_notice_v2')
    resp = client.chat.completions.create(model=MODEL, messages=underwriting_prompt(app_in))
    decision = underwrite(resp.choices[0].message.content)
    db.decisions.insert(app_in.id, decision, notice_id=notice.id)
    return {'decision': decision, 'ai_notice': notice.text}

Standards that recommend the same control

• Financial AI decisions should offer enquiry, appeal, and review channels to affected people (MAS FEAT) (MAS FEAT Principles · MAS FEAT Principles — Accountability (External), Principles 10-11)

Rule id tx-sb1188.ai-diagnostic-use-patient-disclosure · review status: primary source derived

Binding law — in force

Treatment-decision algorithms in Texas EHRs must use the patient's recorded biological sex (Texas SB 1188)

Tex. Health & Safety Code 183.007 · official text · In force: applies since 1 Sep 2025 · Texas (US-TX)

Texas Health and Safety Code 183.007, added by SB 1188, tells the Health and Human Services Commission, the Texas Medical Board and the Texas Department of Insurance to jointly make sure of two things for electronic health records that covered entities in Texas prepare or maintain. First, the record has its own field for the patient's biological sex, entered as male or female from the sex a practitioner observed and recorded at birth, and a field for any sexual development disorder. Second, any algorithm or decision assistance tool built into the record to help a practitioner make treatment decisions takes the biological sex from that dedicated field as an input. Additional sex or gender identity fields remain allowed (183.007(b)). The statute states the duty as one the agencies must ensure; the chapter's investigation, licensing-discipline and Attorney General penalty provisions reach covered entities that violate the chapter, and how they apply here is flagged for legal review. Detect a treatment-decision tool, including an AI or LLM recommendation path, that runs on EHR patient context without the recorded biological sex or substitutes an administrative-gender or gender-identity value for it, and an EHR data model with no dedicated biological-sex field.

Who it applies to

• Duty falls on: deployer, individual professional
• Sectors: healthcare
• Covered entities in Texas (including health care practitioners) whose EHRs contain an algorithm or decision assistance tool that assists treatment decisions; in practice it reaches the EHR and clinical decision-support software they use. The statute frames the requirement as something HHSC, the Texas Medical Board and TDI must jointly ensure, not as a direct command to the covered entity or its vendor. Applies to EHRs prepared on or after 2025-09-01.
• Not covered:
  • EHRs of entities outside 'covered entity' (183.001(2)(A)-(G)): licensed home and community support services agencies, nursing facilities, continuing care facilities, assisted living facilities, intermediate care facilities, day activity and health services facilities, and TxHmL or HCS waiver providers
  • EHRs prepared before 2025-09-01 (SB 1188 Section 2(a))
  • Algorithms or tools in the EHR that do not assist a practitioner in making medical treatment decisions (183.007(a)(2) reaches only those)
• Whether it applies depends on facts outside the code; a person has to decide.

@app.post('/cds-services/dose-check')
def dose_check():
    patient = request.json['prefetch']['patient']
    sex = patient.get('gender')            # administrative gender
    return {'cards': dose_cards(sex, request.json['context'])}

BIRTHSEX_URL = 'http://hl7.org/fhir/us/core/StructureDefinition/us-core-birthsex'

@app.post('/cds-services/dose-check')
def dose_check():
    patient = request.json['prefetch']['patient']
    ext = next((e for e in patient.get('extension', []) if e['url'] == BIRTHSEX_URL), None)
    if ext is None or ext.get('valueCode') not in ('M', 'F'):
        return {'cards': [missing_recorded_sex_card()]}   # no recommendation without it
    return {'cards': dose_cards(ext['valueCode'], request.json['context'])}

Rule id tx-sb1188.ehr-treatment-tool-biological-sex-input · review status: primary source derived