How AI Can Help Reduce Medical Malpractice Risk and Improve Clinical Accountability

Medical malpractice remains a critical issue in healthcare. It undermines patient trust, increases litigation costs, and leads to preventable harm. Artificial intelligence (AI) offers new opportunities to detect risks early, support clinical accountability, and build a safer care environment.

Understanding the Scope of Medical Malpractice

Medical errors rank among the leading causes of death in the United States. A study by researchers at Johns Hopkins University estimates that more than 250,000 deaths each year result from medical mistakes,¹ making it the third-leading cause of death² after heart disease and cancer. 

Common causes include:

• Misdiagnosis or delayed diagnosis: 32% of medical malpractice claims³

• Surgical errors: 25% of claims against negligent providers³

• Anesthesia errors: 2.7% of malpractice claims⁴ 

These claims often involve complex clinical decisions, communication breakdowns, or procedural lapses. The root causes usually span across systems, roles, and workflows, not just individual errors.

Beyond statistics, malpractice events carry emotional and operational costs. Patients and families endure trauma and loss, while clinicians experience guilt, stress, or fear. The second victim phenomenon⁵ has drawn increasing attention, as clinicians involved in adverse events report burnout, insomnia, and in some cases, withdrawal from clinical practice. These ripple effects highlight the need for solutions that go beyond incident response and address prevention at a systemic level.

From Legal Risk to Learning Opportunity

Historically, adverse events have triggered defensive posturing, legal reviews, and blame. AI-powered platforms can help to shift that mindset. They provide:

• Post-event clarity through objective data

• Actionable insights to prevent recurrence

• Opportunities for education and process improvements

Hospitals that adopt a learning health system⁶ approach can transform legal risk into organizational learning. This fosters a just culture, where clinicians feel safe discussing mistakes and improving care without fear of punishment.

Several hospitals now integrate AI-based insights into morbidity and mortality (M&M) reviews. One example includes using anonymized video and audio to highlight deviations from protocols during surgery, such as skipped safety checks or lapses in communication. These insights are shared in interdisciplinary team reviews to support systemic improvements, not individual blame.

This shift requires leadership buy in and support. Clinical teams must see that the goal is better care, not disciplinary action. Legal and risk leaders must work in partnership with clinicians and quality teams to align policy, education, and culture. When used effectively, AI can become a bridge between events and action—replacing speculation with facts and blame with improvement.

Why Early Detection Reduces Litigation Risk

Early detection helps prevent harm before it happens. AI systems can detect early warning signs of risk. These may include skipped safety steps, poor communication, or frequent workflow interruptions. Teams can use this information to address problems and improve workflows, ensuring the problems do not persist. Early action helps prevent adverse events and protects patients.

AI also improves documentation. Traditional notes can be incomplete or delayed. Real-time data capture creates an objective record of care. This includes timestamps, communication patterns, and safety checks. It can show that protocols were followed or explain why a decision was made. This makes it easier to resolve cases and avoid drawn-out litigation.

Some malpractice insurers now recognize the role of AI in reducing risk and improving clinical and operational outcomes.⁷ Hospitals that use AI tools may see reduced premiums or better coverage. Some participate in risk-sharing programs that reward safety performance. These changes show a shift in how liability is managed. Organizations that use data to improve care are seen as lower risk.

Early detection builds trust with patients and families. It shows a commitment to transparency and accountability. When people see that hospitals are learning from data and working to improve, they are more likely to feel safe and supported. This reduces the likelihood of legal action after an unexpected outcome.

How the Black Box Platform™ Improves Quality and Safety

The Black Box Platform is an AI-driven, clinical intelligence platform designed to facilitate honest reflection of care delivery and drive systemic improvement in healthcare. The platform is a powerful combination of multi-modal data capture and AI, providing a 360-degree view of the entire clinical experience.

The Black Box Platform de-identifies facial images and voices to provide psychological safety and highlights best practices as well as areas of opportunity. It’s a powerful tool for driving excellence - not assigning blame. Organizations leveraging the platform are able to overcome operational blindness and deliver care at its highest potential. 

Unlike traditional root cause analysis, which relies on memory and subjective interpretation, the Black Box Platform provides real-time, multi-dimensional data. This includes handoff errors, excessive noise levels, checklist compliance, and response times. Organizations use the insights to:

• Improve patient safety

• Increase operational efficiency

• Support clinical education and training

• Accelerate continuous quality improvement

• Streamline clinical research initiatives

• Enhance team dynamics and performance

• Collaborate across institutions

The platform operates with a strong focus on privacy, consent, and data security. Patient identities remain protected, and data access is limited to authorized personnel. This makes it possible to learn from care delivery in a way that is ethical, scalable, and aligned with clinical governance.

Conclusion

AI cannot eliminate all risk from healthcare, but it can reduce avoidable harm and enhance accountability. AI-enabled technologies like the Black Box Platform enable a path forward built on data, insight, and continuous improvement.

Success depends not only on adopting technology but also on building a multidisciplinary governance structure. Hospitals must bring together clinicians, legal teams, and risk managers to support these initiates. These groups shape how data gets used, how feedback is delivered, and how safety becomes part of organizational culture.

Hospitals must ask:

• How will we use this data to learn, not to punish?

• How can we ensure transparency while protecting privacy?

• How can we align AI with ethics, trust, and care quality?

As malpractice concerns persist and patient expectations evolve, hospitals that take a proactive, data-driven approach will be better equipped to protect both patients and clinicians.

Learn more about mitigating legal risks by downloading our fact sheet, “AI in Healthcare: Mitigating Legal and Security Risks”.⁸

Ready to take the next step to improve quality and safety at your organization? Request a personalized demo. 

Recommended Reading 

1. Makary, M., & Daniel, M. (2016). Medical error–the third leading cause of death in the US. BMJ;353:i2139. https://www.bmj.com/content/353/bmj.i2139

2. Bieber, C., & Ramirez, A. (2024). Medical Malpractice Statistics of 2025. Forbes. https://www.forbes.com/advisor/legal/personal-injury/medical-malpractice-statistics/

3. Finnegan, J. (2020, February 25). Surgery is the 2nd most common reason for medical malpractice claims, report says [blog post]. Fierce Healthcare. https://www.fiercehealthcare.com/practices/surgery-second-most-common-reason-for-medical-malpractice-claims-report-says

4. National Practitioner Data Bank (2025). Data Analysis Tool. https://www.npdb.hrsa.gov/analysistool/ 

5. Sachs, C.J., & Wheaton, N. (2023). Second Victim Syndrome. National Library of Medicine. https://www.ncbi.nlm.nih.gov/books/NBK572094/

6. Agency for Healthcare Research and Quality (2019). About Learning Health Systems. https://www.ahrq.gov/learning-health-systems/about.html

7. Surgical Safety Technologies. (2025, January 14). MedPro Group Endorses Surgical Safety Technologies’ OR Black Box® as Ground-breaking Technology to Improve Surgical Quality and Safety [press release]. https://www.surgicalsafety.com/company/news/medpro-group-endorses-surgical-safety-technologies-or-black-box-as-ground-breaking-technology-to-improve-surgical-quality-and-safety

8. Surgical Safety Technologies. (2025). AI in Healthcare: Mitigating Legal and Data Security Risks [fact sheet]. https://www.surgicalsafety.com/resources/ai-in-healthcare-mitigating-legal-and-data-security-risks