Patients now receive laboratory and imaging results in real time via electronic health record (EHR) portals—often before the ordering physician reviews the findings. This newfound ability to receive and act on clinical information before speaking with a medical professional may carry liability implications.
In addition, many patients use publicly available generative AI (GenAI) tools to interpret their test results and diagnoses. Although GenAI-driven errors don’t create liability for MPL insurers, they can sow misunderstanding and disrupt doctor-patient communications.
Risk management and patient experience professionals need to act quickly. Leading healthcare systems should review their operational procedures, digital platforms, and peer benchmarking to improve patient experience and reduce risk.
Patient Access Has Changed the Communication Timeline
Historically, clinicians typically reviewed sensitive findings before patients saw them. That sequence allowed time for physician interpretation, care planning, and patient outreach.
The 21st Century Cures Act altered the sequence that placed providers in the role of gatekeeper by accelerating patient access to electronic health information and limiting healthcare organizations' ability to delay the release of many test results. As a result, many systems now automatically release results once they are finalized in the EHR.
Early access enables patients to interpret test results outside traditional clinical settings. Many use internet searches, social media communities, or GenAI to translate clinical language into layperson explanations in seconds. For example, a patient who receives results outside business hours can now obtain a GenAI interpretation before the physician even sees them. That interpretation may be incomplete, inaccurate, overly reassuring, or unnecessarily alarming. Good or bad, GenAI will likely shape the patient’s perception before the physician reaches them.
In fact, medical professionals often delay assessment until all results come in, which can take hours or even days. When patient access outpaces expert assessment, communication failures arise and become harder to defend. A delayed callback, an unclear portal response, or an inconsistent documentation trail may seem more consequential once the patient has actively sought clarification.
Plaintiffs and their attorneys may scrutinize whether the organization maintained reasonable systems for:
- Monitoring abnormal findings
- Escalating time-sensitive results
- Responding to patient outreach
- Documenting communication efforts consistently
In other words, the workflow itself may become part of the claim.
Documentation Exposure Will Likely Intensify
A friend’s elderly mother recently had a hospital stay for suspected pneumonia. Her primary care physician carefully documented the patient’s fragile condition and designated her as a fall risk. The care team posted fall risk notices in the patient’s room and acted accordingly.
The patient's oncologist, an attending physician at the same hospital, saw her daily during her stay. On each visit, the oncologist checked “No fall risk,” despite having seen her in a wheelchair for years.
The patient was treated and discharged without a fall. But if she had fallen, would sloppy record-keeping constitute a liability exposure?
“Cut-and-paste” documentation already poses defensibility challenges. AI-assisted documentation raises additional questions about authorship, verification, review, and accuracy when organizations apply inconsistent governance controls. Delayed documentation of outreach attempts weakens defense arguments, even when care is appropriate.
Future claim files may include:
- EHR audit trails
- Portal release timestamps
- Patient-generated messages
- Automated notification logs
Healthcare organizations need to demonstrate reliable operational processes for communication and follow-up.
Adoption of GenAI Outpacing Operational Governance
Patients began using GenAI on a large scale to better understand their medical conditions in late 2022 and early 2023, following ChatGPT's public release. GenAI has since evolved from a basic "symptom checker" into a core part of healthcare navigation.1
The American Medical Association (AMA) reported in March that more than 80% of physicians use GenAI professionally. GenAI was used for disease detection (11.8%), diagnosis (8.7%), and screening processes (7.5%) in radiology (10.6%); also for cardiology (7.5%), gastrointestinal medicine (2.5%), and diabetes care (3.7%), according to JMIR Medical Informatics.
Yet many healthcare organizations are still developing policies governing AI-assisted documentation, patient communication, and portal escalation procedures.
MPL carriers already assess how generative AI may affect standards-of-care arguments and litigation strategies. One emerging concern is that plaintiffs may argue both sides of the issue—criticizing providers for relying on AI and for failing to use available AI tools.
The velocity of data strains governance structures, particularly in radiology, emergency medicine, pathology, and primary care. Here, large volumes of results flow through portal systems with limited physician control over release timing.
Traditional MPL underwriting criteria remain important. However, underwriters should also consider:
- Portal release protocols
- Abnormal-result escalation workflows
- Response-time expectations
- After-hours communication coverage
- Documentation of auditing practices
- Governance surrounding AI-enabled workflows
Exposures like these should be part of standard risk management, as they may influence claim frequency and claim defensibility.
Risk Implications for MPL Insurers
Communication surrounding healthcare delivery is changing faster than many risk models anticipated.
For risk managers and insurers, the opportunity lies in identifying where communication workflows, escalation procedures, and documentation practices no longer align with patient expectations in a real-time information environment.
MPL insurers can partner with clients to support good outcomes and a positive patient experience:
- Monitor communication-driven claim trends
- Benchmark response-time and escalation workflows for abnormal findings
- Assess governance controls surrounding AI-assisted documentation tools
- Incorporate “informed patient” dynamics into underwriting and patient safety models
- Consider advanced digital platforms that connect safety, patient experience, and compliance
Healthcare has adapted to major operational transitions before, including EHR adoption and the expansion of telemedicine. Addressing patient communication represents the next significant shift affecting liability exposure.
Reference
1“Generative AI in consumer health; leveraging large language models for health literacy and clinical safety with a digital health framework,” Frontiers Digital Health, Aug. 25, 2025, https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2025.1616488/full; “GenAI in Healthcare: Transforming Diagnostics and Patient Care,” Feb. 28, 2025, https://centricconsulting.com/blog/genai-in-healthcare-transforming-diagnostics-and-patient-care_ai/; “Generative AI for Health Information: A Guide To Safe Use,” Yale Medicine, Jan. 8, 2024, https://www.yalemedicine.org/news/generative-ai-artificial-intelligence-for-health-info