From Notification to Prevention: How AI Is Transforming Patient Safety
Summary:
Patient safety is entering a new stage of evolution. Over the past decades, hospitals and healthcare institutions worldwide have strengthened their reporting, investigation and learning processes based on adverse events. Artificial intelligence is now expanding that capability by enabling increasingly preventive approaches.
In the seventh article of the series “AI in Healthcare: Credibility, Safety, and Impact in Clinical Practice,” we discuss how AI applied to healthcare is transforming patient safety management by identifying risk patterns, supporting incident analysis and anticipating adverse events before harm occurs. The article explores how predictive models, AI agents, and structured clinical data can strengthen decision-making, expand the capabilities of patient safety teams and institutional committees, and generate meaningful improvements in quality of care and risk prevention.
Key Topics Covered:
- The evolution of patient safety: from reaction to prevention
- Artificial intelligence applied to healthcare risk management
- Limitations of traditional reporting and investigation models
- Early identification and prevention of adverse events
- Practical applications of AI in patient safety
- Governance, structured data and human oversight
- AI agents and the future of patient safety
Content:
Patient safety has come a long way since the publication of To Err Is Human by the U.S. Institute of Medicine in 2000.
In response to the findings of this and subsequent studies, healthcare systems around the world established national patient safety programs, implementing requirements for incident reporting, investigation and management. The World Health Organization (WHO) recognized patient safety as a global priority, and accreditation initiatives began incorporating increasingly rigorous standards for healthcare risk management.
Over the last few decades, patient safety has evolved significantly. National programs, specific regulations and structured investigation frameworks have enabled hospitals and other healthcare organizations to develop more robust processes for identifying, analyzing, and reducing healthcare-related risks.
These advances have been essential, but an important limitation remains: most current systems still learn primarily from events that have already occurred.
When an incident occurs, it is recorded, investigated and used as a learning opportunity to prevent recurrence. While this approach has generated significant improvements in quality of care, it remains fundamentally reactive.
This is precisely where artificial intelligence begins to transform patient safety management.
The Challenge of Reactive Models
The regulatory frameworks and quality systems that support patient safety have matured significantly over the past decade. The WHO Global Patient Safety Action Plan (2021–2030), Joint Commission International (JCI) standards, and national regulations across Western countries have established requirements that have strengthened patient safety committees in healthcare organizations of all sizes and contexts.
Nevertheless, important gaps remain. Studies published in the New England Journal of Medicine indicate that adverse events occur in approximately one in four hospitalizations in high-income countries, and that nearly 23% of these events are preventable. The U.S. Office of Inspector General found that 43% of adverse events among Medicare beneficiaries were preventable. Evidence from different healthcare systems suggests that this pattern is consistent regardless of the level of development or regulatory structure.
It is estimated that only 3% to 5% of adverse events identified through inpatient medical record reviews are actually reported by healthcare professionals. Current safety approaches rely heavily on voluntary reporting systems, which capture fewer than 10% of adverse events overall. These events are often subjected to extensive root cause analyses long after they occur, producing conclusions that may be superficial or inaccurate, and recommendations that are not always implemented.
The result is a paradox: highly qualified professionals spend much of their energy trying to understand the past when they could be devoting more time to preventing future risks.
A Paradigm Shift: From Recording Events to Anticipating Risks
Underreporting remains one of the most critical gaps in patient safety management. Cultural barriers, operational overload, and limitations in reporting processes mean that many incidents are never documented.
The primary contribution of artificial intelligence to patient safety lies in its ability to identify risk patterns before harm occurs. By analyzing large volumes of clinical, care-related, and operational data, AI systems can detect signals that are often overlooked in human analyses or manual processes.
This capability allows organizations to move beyond responding solely after incidents occur and begin acting proactively. In practical terms, AI applied to patient safety and incident management operates across three dimensions that traditional models do not address in an integrated manner:
- Aggregating and analyzing large volumes of incident reports, care episodes, and investigation records to identify patterns that human analysts could not detect manually.
- Identifying contributing factors over time, not only within isolated events but throughout the chain of conditions leading up to them.
- Continuously monitoring the effectiveness of corrective actions, closing the improvement cycle through evidence rather than perception alone.
To address these gaps, technology must evolve beyond simple documentation and toward active surveillance based on pattern recognition.
The difference is not merely one of speed or scale. AI changes the central question of patient safety management from “What happened?” to “What is about to happen, and what can we do about it now?”
Where AI Is Already Making an Impact
Although often viewed as a future development, several AI applications are already delivering measurable results in clinical practice.
1. Fall Risk
Falls remain among the most common adverse events in healthcare organizations. AI-based predictive models can simultaneously analyze factors such as clinical history, medication use, mobility, neurological status and vital signs to estimate an individual patient’s fall risk in real time.
This enables preventive interventions before an event occurs, making risk management more precise and dynamic.
2. Healthcare-Associated Infections (HAIs)
Healthcare-associated infections remain one of the leading causes of preventable harm. Algorithms trained on large datasets can identify clinical patterns associated with infection development before symptoms become evident, increasing opportunities for early intervention.
3. Medication-Related Adverse Events
Medication administration involves a high volume of clinical decisions and multiple risk factors. AI can support this process by analyzing prescriptions, medical history, renal function, drug interactions, and prior records, flagging situations that require attention before medication is administered.
AI in Healthcare Requires More Than Technology
The potential of artificial intelligence depends on more than algorithm quality. In healthcare, value creation is directly linked to data quality, clinical validation, transparency and appropriate human oversight.
For this reason, leading organizations treat AI as a decision-support tool rather than a replacement for clinical judgment. Trust in technology is built when professionals understand how it works, what data it uses and what evidence supports its recommendations. In short, trustworthy AI requires trustworthy governance.
Recent advances in artificial intelligence extend beyond data analysis. AI agents represent a new generation of systems capable of performing specific tasks autonomously within predefined and supervised parameters.
In patient safety, these agents can continuously monitor available information, identify risk patterns, support incident classification, organize investigation data and direct teams’ attention toward priority situations.
In practice, they function as an additional layer of operational intelligence, expanding the capabilities of Patient Safety Centers and safety teams without replacing human evaluation. The objective is not to automate clinical decisions but to ensure that professionals have faster access to relevant information and can act more strategically.
The Foundation for This Transformation Already Exists
If artificial intelligence depends on structured data, the question becomes operational rather than purely technological. This is where years of accumulated experience become highly relevant.
Epimed has built its patient safety journey on internationally recognized methodologies, standardized taxonomies and structured investigation and monitoring processes.
Over the years, thousands of incident reports, investigations, and action plans have been recorded in a structured and traceable manner. This foundation represents a critical element for developing clinically relevant AI applications: consistent, comparable data generated within real-world healthcare environments.
Patient safety has evolved from reaction to systematic risk management. The technology required to support this transformation already exists, and so does the scientific knowledge.
The differentiating factor will increasingly be the ability of organizations to structure their data, strengthen governance and create the conditions necessary for artificial intelligence to generate real value for professionals and patients.
Organizations already operating structured safety management systems are in a significantly stronger position to adopt AI and achieve meaningful clinical outcomes. Those that have not yet established this foundation are effectively delaying access to capabilities that are already available.
Because in patient safety, the most important piece of data is not always the one that has already been recorded. It may be precisely the one that still allows the next incident to be prevented.
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References:
- World Health Organization (WHO). Global Patient Safety Action Plan 2021–2030. Geneva: WHO; 2021.
- Bates DW, Levine D, Syrowatka A, et al. The potential of artificial intelligence to improve patient safety: a scoping review. NPJ Digit Med. 2021;4(1):54. doi:10.1038/s41746-021-00423-6
- Kohn LT, Corrigan JM, Donaldson MS (Eds.). To Err Is Human: Building a Safer Health System. Washington, DC: National Academies Press; 2000.
- Lucian Leape Institute. Patient Safety and Artificial Intelligence: Opportunities and Challenges for Care Delivery. Boston: Institute for Healthcare Improvement; 2024.
- Ratwani RM, Bates DW, Classen DC. Patient safety and artificial intelligence in clinical care. JAMA Health Forum. 2024;5(2):e235514.
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This is the seventh article in the editorial series “AI in Healthcare: Credibility, Safety, and Impact on Clinical Practice,” produced by Epimed Solutions.
Author: Laiane Silva, Registered Nurse, MBA in Quality Management (UFF-RJ) and Project Management (USP). Product Manager at Epimed Solutions, responsible for the company’s patient safety solutions. She has extensive experience in quality management, healthcare accreditation, and the development of digital products and AI applications.
