Using AI to Reduce Medical Errors

Medical errors are one of the most pressing challenges in modern healthcare. According to a landmark Johns Hopkins study, preventable errors are responsible for an estimated 250,000 deaths annually,¹ making them the third leading cause of death in the United States. Beyond the tragic human cost, errors erode patient trust, fuel clinician burnout, and drive institutional expenses into the billions. 

Artificial intelligence (AI) is emerging as a transformative solution. By detecting risks earlier, supporting safer workflows, and fostering a culture of accountability, AI offers healthcare institutions a path toward measurable improvements in safety and quality.

The Scope of Medical Errors in Modern Healthcare

The scale of preventable harm is staggering:

• Medical errors rank as the 3rd leading cause of death in U.S.¹

• Misdiagnosis accounts for nearly one-third of malpractice claims²

• Surgical errors contribute to about one-quarter of malpractice claims²

The causes of these errors are systemic: Communication breakdowns, workflow inefficiencies, cognitive overload, and environmental pressures during high-stakes moments in care delivery.

The impact extends beyond statistics. Patients and families endure lasting trauma, while clinicians experience the “second victim” phenomenon—profound emotional distress following adverse outcomes that can lead to burnout, anxiety, and even career changes.

Traditional quality improvement efforts often rely on incident reports, retrospective chart reviews, and subjective recall—methods that are inherently limited by memory bias, incomplete documentation, and underreporting. Healthcare professionals rarely convey near-misses or minor deviations from protocol, and even when incidents are documented, the details can be unclear or contradictory.

Preventing these errors requires more than vigilance; It requires systemic insight into how care is delivered. That’s where AI steps in.

How Does AI Detect Hidden Risks to Reduce Medical Errors?

AI-driven clinical intelligence solutions, such as the Black Box Platform™, are designed to give hospitals an unfiltered view of how care is delivered, while protecting privacy and fostering learning. They continuously capture what happens in clinical environments—without depending on someone to recognize, remember, and report an issue—and provide objective documentation that reveals the complete picture of care delivery. This multi-modal data supports honest reflection and drives systemic improvement across areas including:

• Process variations that occur in real-time but may not be documented in medical records

• Communication breakdowns between team members that contribute to delays or errors

• Workflow inefficiencies that create workarounds or increase cognitive load

• Near-miss events that could have resulted in harm but didn't—yet offer critical learning opportunities

• Environmental factors and system-level issues that impact team performance

De-identified audiovisual information, combined with EHR integration, allows AI to uncover patterns that contribute to adverse events. This data-driven insight makes it possible to identify risks that might otherwise go unnoticed and to address them proactively before harm occurs. Rather than reacting to sentinel events after patients are harmed, healthcare organizations can detect early warning signs and intervene at the system level—transforming patient safety from reactive to predictive. 

AI analysis has revealed risks that routinely go unnoticed in clinical practice, such as:

• Missed steps in the Surgical Safety Checklist:³ In a study of 3,879 elective surgeries, the OR Black Box® revealed that critical checklist items were frequently skipped or poorly executed. For example, fewer than half of surgical teams confirmed patient identity aloud, and overall engagement in the checklist varied significantly.

• Missed surgical time-outs:⁴ A multi-institutional study of 7,243 procedures showed that surgical time-outs were often performed inconsistently. Many lacked essential safety elements such as reviewing imaging or discussing anesthesia risks.

• Breakdowns in team communication:⁵ Data has consistently demonstrated that overlapping conversations, unclear role assignments, and interruptions during high-stress moments contributed to adverse events. These challenges often appeared when cognitive load was already at its peak.

• Excessive workflow interruptions:⁶ Frequent and unnecessary disruptions were shown to compromise team concentration, increasing the likelihood of error. AI-enabled reviews allowed hospitals to systematically categorize and address these interruptions.

• Workflow barriers:⁷ Delays in care caused by systemic and technological workspace conditions that disrupt the timely, accurate, and coordinated delivery of patient care, leading to an increased risk of medical errors, poorer outcomes, and clinician burnout. Research shows that factors such as communication failures, inefficient health information technology (HIT), staff shortages, and heavy documentation burdens are major contributors to these risks.

Together, these insights give hospitals a clear, data-driven understanding of where patient safety is most at risk, and the practical tools to strengthen workflows, improve outcomes, and foster a culture of safety.

Improving Team Performance and Clinical Culture

Traditional root cause analyses often focused on identifying individual error, asking "who made the mistake?", rather than "what system failures allowed this to happen?". AI changes the narrative. By providing objective, de-identified insights, AI enables hospitals to shift from blame to learning.

The unfiltered view provided by AI-driven clinical intelligence reveals that most errors stem from systemic issues rather than individual failures. Communication breakdowns, workflow inefficiencies, and environmental factors are rarely the fault of a single person. They ate often the result of system-level vulnerabilities that affect entire teams. This reframing is critical to building a culture of safety. Practical applications include:

• Educational value: Hospitals use de-identified AI findings in morbidity and mortality conferences, grand rounds, and simulation training, creating opportunities for structured reflection and team learning without fear of punitive action.

• Training opportunities: Recurring patterns, such as inconsistent checklist execution or communication gaps during high-stress moments help reveal where additional support, coaching, or education is needed. Rather than broad, generic training, teams receive interventions tailored to their specific performance gaps.

• Objective performance feedback: Clinicians can review their own de-identified cases to understand how workflow pressures, interruptions, or communication patterns affected their performance, fostering self-awareness and continuous improvement.

• Cultural impact: Clinicians feel safer engaging in transparent conversations about improvement when data replaces finger-pointing. This reinforces a “just culture” that emphasizes systemic accountability over punishment and encourages reporting of near-misses and safety concerns.

This cultural shift not only reduces burnout but also strengthens overall team performance.

Reducing Risk Beyond the OR

Medical malpractice⁸ claims are often tied to preventable breakdowns—misdiagnoses, surgical mistakes, or communication failures. Studies show that misdiagnosis alone accounts for nearly one-third of malpractice claims, while surgical errors contribute to about one-quarter.⁹

AI-powered platforms provide objective, de-identified documentation of how care was delivered, capturing the process variations, communication breakdowns, and system-level issues that contribute to adverse events. By helping hospitals proactively address these underlying vulnerabilities before they result in patient harm, AI platforms reduce both clinical risk and institutional litigation exposure. 

Importantly, AI-driven documentation is not about defensibility after an event occurs. It’s about creating a transparent, accountable environment where continuous learning prevents harm from happening in the first place.

The healthcare industry is beginning to take notice. In 2025, MedPro Group, the nation’s longest-standing healthcare liability insurer, endorsed the OR Black Box as a groundbreaking technology to improve surgical quality and safety. This endorsement underscores the proven impact of AI in advancing safer care (see the full announcement here).¹⁰

To learn more about how AI improves safety while addressing compliance and privacy concerns, download our fact sheet: AI in Healthcare: Mitigating Legal and Data Security Risks.¹¹

The Path Forward for Safer Care

AI is not a cure-all. It cannot eliminate every risk, but it can significantly reduce blind spots that place patients and clinicians in jeopardy—transforming patient safety from reactive to predictive.

Successful implementation requires:

• Strong leadership buy-in to champion cultural change and sustain investment in quality improvement.

• Multidisciplinary governance that brings together legal, clinical, risk management, and IT stakeholders.

• A commitment to continuous quality improvement that prioritizes learning over blame and system-level solutions over individual accountability.

The future of patient safety will be defined by AI-driven insights that guide proactive interventions, elevate standards of care, and create safer, more trusted healthcare systems for patients and clinicians alike.

Bottom line: AI protects patients, supports clinicians, and strengthens hospitals. With evidence-based platforms and endorsements from leaders like MedPro Group, healthcare institutions now have both the proof and the industry backing to embrace AI solutions that meaningfully reduce preventable harm.

Ready to Learn More?

AI is helping hospitals around the world reduce errors, improve outcomes, and protect patient trust. Discover how AI can measurably reduce risk, protect patients, and lower costs—request a personalized ROI consultation.

Frequently Asked Questions

Why are medical errors considered such a critical issue in healthcare?

Medical errors are the third leading cause of death in the United States, responsible for an estimated 250,000 deaths each year. They not only cause tragic patient harm but also erode trust, increase clinician burnout, and drive billions in institutional costs. 

 What are the most common causes of medical errors?

Errors often result from systemic challenges such as communication breakdowns, workflow inefficiencies, cognitive overload, and environmental pressures in high-stakes care settings. These factors impact entire teams, not just individuals.

Why are traditional methods of tracking errors—like incident reports—insufficient?

Incident reports and retrospective chart reviews are limited by underreporting, memory bias, and incomplete documentation. Near-misses and small deviations from protocol are rarely captured, which means many systemic risks remain invisible.

How can AI help reduce medical errors?

AI platforms like the Black Box Platform™ provide continuous, objective, and de-identified documentation of care delivery. They capture communication patterns, workflow inefficiencies, and near-misses—offering insights into risks that are often missed by traditional reporting.

What kinds of risks has AI uncovered in hospitals?

Studies using AI have revealed:

• Missed steps in surgical safety checklists

• Inconsistent surgical time-outs

• Breakdowns in team communication during high-stress moments

• Excessive workflow interruptions that increase cognitive load Workflow barriers caused by staffing, IT inefficiencies, or documentation burdens

How does AI improve team performance and clinical culture? 

AI shifts the focus from blaming individuals to addressing systemic vulnerabilities. Hospitals use AI insights for education, tailored training, and objective performance feedback. This fosters a “just culture,” encouraging transparency and continuous improvement while reducing burnout.

Does AI reduce malpractice risks?

By identifying and addressing systemic issues before they cause harm, AI reduces the likelihood of adverse events and malpractice claims. For instance, surgical errors—a leading source of claims—can be mitigated with AI-enabled insights.

How does AI balance safety improvement with privacy concerns?

AI platforms rely on de-identified audiovisual data and secure EHR integration. This ensures that valuable insights can be drawn without compromising patient or clinician privacy.

Has AI in healthcare gained industry recognition?

Yes. In 2025, MedPro Group, the nation’s longest-standing healthcare liability insurer, endorsed the OR Black Box® as a groundbreaking tool for improving surgical safety and reducing risk.

Can AI completely eliminate medical errors?

No. AI is not a cure-all, but it significantly reduces blind spots and allows hospitals to shift from reactive to predictive safety practices. The goal is systemic improvement, not perfection.

What’s required for successful AI adoption in hospitals?

Key factors include:

• Strong leadership commitment

• Multidisciplinary governance (clinical, risk, IT, and legal stakeholders)

• A culture that prioritizes learning over blame

• Ongoing investment in quality improvement initiatives 

 What is the bottom-line benefit of AI in patient safety?

AI protects patients, supports clinicians, and strengthens hospitals by reducing preventable harm, lowering institutional risk, and improving trust.

Recommended Resources

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. 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

3. Al Abbas, A.I., Sankaranarayanan, G., Polanco, P.M., et., al. (2022). The Operating Room Black Box: Understanding Adherence to Surgical Checklists. Annals of Surgery;276(6), 995-1001. https://pubmed.ncbi.nlm.nih.gov/36120866/

4. Riley, M.S., Etheridge, J., Palter, V., et., al. (2024). Remote Assessment of Real-World Surgical Safety Checklist Performance Using the OR Black Box: A Multi-Institutional Evaluation. Journal of the American College of Surgeons;238(2), 206-215. https://journals.lww.com/journalacs/abstract/2024/02000/remote_assessment_of_real_world_surgical_safety.7.aspx

5. Greenberg, C.C., Regenbogen, S.E., & Studdert, D.N. (2007). The Patterns of Communication Breakdowns Resulting in Injury to Surgical Patients. Journal of Vascular Surgery;46(2), 395. https://www.jvascsurg.org/article/S0741-5214(07)01006-3/fulltext

6. Møller, K.E., Sørensen, J.L., Topperzer, M.K., et., al. (2022). Implementation of an Innovative Technology Called the OR Black Box: A Feasibility Study. Surgical Innovation;30(1), 64-72. https://pmc.ncbi.nlm.nih.gov/articles/PMC9925891/

7. Cain, C., & Haque, S. (2008). Organizational Workflow and Its Impact on Work Quality. In Hughes, R.G. (Ed), Patient Safety and Quality: An Evidence-Based Handbook for Nurses. Agency for Healthcare Research and Quality. Surgical Safety Technologies. (2025, September 15). How AI Can Help Reduce Medical Malpractice Risk and Improve Clinical Accountability [blog post]. https://www.surgicalsafety.com/blog/ai-reduces-medical-malpractice-risk

8. Surgical Safety Technologies. (2025, September 15). How AI Can Help Reduce Medical Malpractice Risk and Improve Clinical Accountability [blog post]. https://www.surgicalsafety.com/blog/ai-reduces-medical-malpractice-risk

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

10. 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

11. 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