Patient-led, evidence-informed insights on medical AI

Practical takeaways for clinicians, health leaders, and recruiters.

Read latest insights Subscribe for updates

Stay current on AI in patient care

Join clinicians and recruiters receiving practical, evidence-informed updates.

Subscribe now About Dan
Sign in Subscribe
Healthcare AI

My Sentinel Event

My Sentinel Event
Strategies to reduce sentinel events through the use of AI

Leveraging Artificial Intelligence to Proactively Reduce Medical Sentinel Events

Executive Summary

This is personal. I had a life-altering sentinel event that haunts me to this day. The neurologist made a mistake. A big mistake. He overprescribed a medication by nearly 200 times the recommended dosage. I am fortunate to be alive. I spent time in the ICU, eight weeks in the hospital, and I still carry the bruises of that incident. This is partly why I am committed to the medical application of AI. I believe AI can reduce the risk of sentinel events.

The incidence of medical sentinel events represents a critical challenge within healthcare, leading to severe patient harm and significant operational burdens. While these events are often unexpected, they are frequently the result of systemic vulnerabilities rather than isolated errors. A comprehensive analysis indicates that artificial intelligence (AI) offers a powerful new layer of defense to address these systemic issues. This report identifies and details five key applications of AI that can dramatically improve patient safety by shifting the paradigm from reactive to proactive care.

The analysis demonstrates that AI can:

  1. Enhance Diagnostic Precision by augmenting human analysis of medical images and clinical data, thereby reducing diagnostic errors and delays.
  2. Enable Proactive Fall Prevention through predictive models and real-time monitoring systems that identify high-risk patients before an event can occur.
  3. Strengthen Medication Safety Protocols with intelligent systems that detect potential errors, interactions, and contraindications in real time.
  4. Provide Real-Time Clinical Deterioration Predictions by synthesizing complex patient data to identify subtle signs of conditions like sepsis hours before traditional methods.
  5. Reduce Avoidable Hospital Readmissions by accurately predicting patient risk and enabling targeted, post-discharge interventions.

Supported by numerous case studies and quantitative evidence, these AI applications not only promise to improve patient outcomes but also address key challenges such as workforce shortages, cognitive overload, and rising costs. While the path to widespread adoption involves navigating complex ethical and logistical challenges, the evidence overwhelmingly points to a future where a thoughtful human-AI partnership sets a new, safer standard of care.

READ NEXT

Understanding NNT and NNH

Understanding NNT and NNH

Understanding the clinical metric and a transformative mindset: the Number Needed to Treat (NNT) and its…

Dan Noyes · 25 Jun 2025
Understanding ELSA

Understanding ELSA

ELSA is an internal generative AI tool developed by the FDA. Its primary objective is to enhance the efficiency…

Dan Noyes · 21 Jun 2025

The Imperative of Safety: Understanding Sentinel Events

Patient safety is the cornerstone of high-quality healthcare, yet unexpected adverse occurrences, known as sentinel events, continue to pose a significant risk. A sentinel event is defined as an occurrence involving death, serious physical or psychological injury, or the risk thereof, which signals the need for immediate investigation and response.1 It is a critical distinction that these events are not synonymous with medical errors; an error may not result in a sentinel event, and not all sentinel events are caused by an error.1 The emphasis is on the profound and often preventable harm to the patient.

Common types of sentinel events include wrong-site surgery, unintended retention of a foreign object after a procedure, patient suicide, medication errors leading to death or serious injury, and patient falls resulting in permanent harm or death.3 These events are not random accidents; they are often the culmination of multiple failures across a healthcare system. This systemic vulnerability can be understood through the "Swiss cheese model" of patient safety, where each layer of a system's defense—from institutional policy to individual practitioner vigilance—has inherent "holes" or weaknesses.5 A sentinel event occurs when these holes align, allowing a hazard to reach the patient.

The advent of AI introduces a new layer of defense, a new slice of Swiss cheese designed to prevent the holes from aligning. By addressing root causes such as cognitive overload, communication gaps, and information processing limitations, AI can proactively close critical vulnerabilities that contribute to sentinel events.5 For healthcare leaders, the case for AI adoption extends beyond clinical benefits alone. Financial and regulatory drivers, such as the Centers for Medicare and Medicaid Services (CMS) Hospital Readmissions Reduction Program and new quality reporting measures for falls, link patient safety directly to a health system's financial viability.7 For example, a successful AI-powered readmission reduction initiative at Zuckerberg San Francisco General Hospital helped the institution retain $7.2 million in at-risk pay-for-performance funding.9 This creates a powerful business imperative for technology adoption, transforming the conversation from a clinical "nice-to-have" to a strategic "must-have."

The Five Pillars of AI-Powered Patient Safety

The application of AI is a force multiplier, augmenting human expertise across the clinical spectrum. Its strength lies not in replacing clinicians but in freeing them from repetitive tasks and providing insights that far exceed human capacity. The following five applications demonstrate how AI can fundamentally reshape patient safety.

1. Precision Diagnostics through AI-Augmented Analysis

Diagnostic errors and delays are a significant cause of patient harm. Clinicians, particularly in data-heavy fields like radiology and pathology, face immense workloads that can lead to fatigue and missed findings.10 The human brain is limited in its capacity to process the vast amounts of data—from imaging scans to genomic information—that now constitute a patient’s health record.6

AI addresses this by acting as a "second set of eyes," using machine learning and deep learning to analyze medical images with superhuman pattern recognition.10 For instance, AI systems have been trained on massive datasets of medical images to identify subtle abnormalities in mammograms or CT scans that may be difficult for the human eye to detect.10 A study published in

Nature Medicine found that an AI system could detect skin cancer more accurately than dermatologists.6 In fields that rely on skilled techniques and large-scale data processing, such as radiology and pathology, AI has been shown to improve accuracy and reduce diagnostic time by approximately 90% or more.11

Beyond image analysis, AI-powered clinical decision support systems (CDSS) can analyze patient data in real time to suggest diagnostic options, identify rare conditions, and check for drug interactions based on the latest medical evidence.10 Natural language processing (NLP) is also a key component, capable of extracting important symptoms and patterns hidden in unstructured text from clinical notes and discharge summaries that other systems may overlook.10 By automating these repetitive, tiring tasks, AI allows clinicians to focus on the most critical diagnostic challenges and spend more time engaging directly with their patients.5

2. Predictive Analytics for Proactive Fall Prevention

In-hospital patient falls are a prevalent safety concern, causing preventable harm, raising costs, and increasing regulatory risk.8 Traditional fall prevention methods, which often rely on periodic manual risk assessments and pressure-based alarms, can be burdensome for staff and ineffective, sometimes leading to alarm fatigue.8

AI-powered systems are shifting this paradigm by moving from a reactive to a truly proactive model of fall prevention. These systems use predictive analytics to analyze electronic health record (EHR) data for changes in clinical factors that may indicate an increased fall risk, providing a real-time alert to nurses.15 A predictive model implemented at Community Health Network, for example, contributed to a 22% decrease in falls over six months, resulting in an estimated savings of 197,000 dollars.15

Furthermore, AI-enabled camera monitoring systems can detect patient movement and identify the intent to exit a bed or chair before a fall occurs, sending an immediate alert to staff.14 Systems like VSTOne have been shown to reduce patient falls by up to 85%.14 Another solution, OK2StandUP, provides predictive alerts within 3-6 seconds of a patient's intent to sit up, enabling caregivers to intervene before an incident.8 In five clinical evaluations covering over 4,500 monitored hours with 44 high-risk patients, this system recorded zero fall incidents.8 These technologies not only prevent harm but also address a significant cause of staff burden by reducing false alarms and automating the risk assessment process.14

3. Enhancing Medication Safety with Intelligent Systems

Medication errors are a highly frequent type of sentinel event, causing at least 1.5 million preventable adverse events annually in the U.S..5 These errors often occur due to human factors such as fatigue, cognitive overload, and stress from manual data entry and translation between different systems.5

AI provides a robust defense against these vulnerabilities. Algorithms can scan EHRs in real time to identify potential medication interactions, improper dosages, and allergic reactions before a drug is administered.6 This real-time notification can be a critical line of defense. The use of large language models (LLMs) represents a significant advancement in this area. Researchers from Stanford and Amazon developed an AI-based system called MEDIC, a "medication direction copilot," specifically to translate prescription instructions with greater accuracy.5 This system was designed to flag prescriptions that match error-related patterns, thereby reducing near-misses. In testing, the MEDIC system reduced prescription near-misses by about 33%, a notable improvement that helps to close a critical hole in the medication safety process.5 By automating repetitive tasks like transcription, these systems free pharmacists to focus on higher-level work, such as the pharmacokinetics of medication, further augmenting patient safety.5

4. Real-Time Prediction of Clinical Deterioration

Early detection is paramount for managing life-threatening conditions like sepsis, but its symptoms—such as fever and confusion—are often non-specific and easily missed.20 An hour-long delay in intervention can be the difference between life and death in severe sepsis cases.21

AI-driven early warning systems address this challenge by continuously tracking and synthesizing a vast amount of patient data, including lab results, vital signs, and clinical notes, to identify subtle patterns that signal early decline.6 A breakthrough system developed at Johns Hopkins University, the "Targeted Real-Time Early Warning System," successfully caught sepsis symptoms an average of nearly six hours earlier than traditional methods.21 The system was utilized by over 4,000 clinicians and successfully reduced patient mortality from sepsis by 20%.21 A key factor in its success was the incorporation of explainable AI (XAI) features, which allowed doctors to see the rationale behind the tool's recommendations, thereby building crucial trust and confidence in the system.21 This type of AI moves beyond simple pattern recognition to provide a meaningful understanding of a patient's condition, enabling clinicians to make more informed decisions.

5. Reducing Avoidable Hospital Readmissions

Unplanned hospital readmissions are a major clinical and financial burden, with approximately 15% of patients being readmitted within 30 days of discharge.7 Traditional predictive models have shown limited success in identifying high-risk patients.7

AI, particularly machine learning (ML) and LLMs, has demonstrated a significant ability to improve readmission predictions by analyzing the complex, non-linear relationships within patient data that traditional methods struggle to capture.7 At NYU Langone Health, a new LLM called NYUTron was designed to read unaltered text directly from EHRs to assess a patient’s health status.22 The system could predict 80% of patients who were readmitted, a 5% improvement over a standard model that required pre-formatted data.22 This ability to learn from the rich, unstructured data in clinical notes and physician dictations is a fundamental shift in predictive capabilities.

A technology-based initiative at Zuckerberg San Francisco General Hospital (ZSFG) combined predictive AI algorithms with EHR-based automation to identify and proactively manage patients with the highest risk of readmission.9 This initiative successfully reduced readmission rates from 27.9% to 23.9%.9 A critical component of this success was the system's ability to incorporate and address social determinants of health (SDOH), which had an outsized effect on readmission rates in their patient population.9 By including SDOH in the model, the system helped eliminate a significant gap in readmission rates between Black/African American patients and the general population, demonstrating AI's potential as a tool for health equity and not just efficiency.9

AI Application

Sentinel Event Reduced

Mechanism

Supporting Evidence & Metrics

Case Study

Precision Diagnostics

Diagnostic Errors, Delays

NLP, Image Analysis, Predictive Analytics, Clinical Decision Support

AI detects skin cancer more accurately than dermatologists 6; reduces diagnostic time by up to 90%.11

IDx-DR, UI Health Care 11

Predictive Fall Prevention

Patient Falls with Injury

Real-time monitoring of patient movement, Predictive EHR Models

22% decrease in falls over six months 15; Up to 85% reduction with camera systems 14; Zero falls recorded in a study with a predictive alert system.8

Community Health Network 15, MGH 16, VSTOne 14, OK2StandUP 8

Medication Safety

Medication Errors

Real-time EHR scanning, LLMs for prescription review

The MEDIC system reduced near-misses by approximately 33%.5

Stanford/Amazon (MEDIC) 5

Clinical Deterioration

Sepsis, Clinical Decline

Continuous patient data synthesis, Early Warning Systems

20% reduction in sepsis mortality 21; Detected sepsis an average of nearly 6 hours earlier than traditional methods.21

Johns Hopkins (Targeted Real-Time Early Warning System) 21

Avoidable Readmissions

Unplanned Readmissions

ML and LLMs for risk stratification

NYUTron predicted 80% of readmitted patients 22; ZSFGH reduced readmission rates from 27.9% to 23.9%.9

NYU Langone (NYUTron) 22, Zuckerberg San Francisco General Hospital 9

Other Related Articles in Healthcare AI

While the promise of AI in patient safety is compelling, its widespread adoption is not without significant hurdles. The challenges are not purely technical but are deeply human and organizational, requiring a thoughtful approach that goes beyond simple technological implementation.

Barriers to Adoption

Many healthcare organizations are hesitant to invest in AI, with some leaders citing a desire to wait for the technology to mature or for well-established reference cases.23 This cautious approach, however, may delay their ability to address existing pressures such as rising costs and workforce shortages. One of the most significant barriers is the need for a robust data infrastructure to support AI models.23 Many legacy systems are not equipped to handle the scale and complexity of data required for effective AI training and deployment.

Furthermore, a substantial challenge lies in the human element. Healthcare professionals often lack the digital literacy and skills needed to fully embrace AI on the front lines.18 A study found that more than one in three healthcare professionals cite education and skills as the biggest barrier to AI adoption, highlighting the need for comprehensive training and a cultural shift.24 This suggests that for AI to be successful, a health system must invest as much in change management and workforce empowerment as it does in the technology itself.

Critical Ethical Concerns

Beyond the logistical barriers, a host of ethical challenges must be addressed for AI to be implemented responsibly in healthcare. A primary concern is data bias. AI systems can inherit and even amplify biases present in their training data, which can lead to unfair or discriminatory outcomes.6 The use of AI to predict readmission risk, for example, could perpetuate existing health disparities if the model is not trained on diverse, representative data and continually monitored for bias.9 The ZSFG case study provides a positive example of how new AI models can be developed to explicitly mitigate bias by incorporating social determinants of health.9

Another key issue is transparency and accountability. Many advanced AI algorithms, particularly deep learning models, are considered "black boxes" because their decision-making process is difficult to interpret or understand.25 This lack of transparency poses a significant challenge for user trust and a lack of clear regulatory framework for accountability when an AI system makes a mistake.5 Healthcare providers must be able to trust the recommendations made by AI systems and understand how they were derived, a point which the Johns Hopkins sepsis system addressed with its explainable AI feature.21

The Human-in-the-Loop Imperative

The research consistently emphasizes that AI is not a replacement for human expertise but a supportive tool. The most effective approach is a collaborative model where technology augments the clinician, a concept often referred to as a "human in the loop".5 As one expert noted, AI can "take out the repetitive, less intellectual tasks so clinicians can focus on caring for the patient, which is presumably more rewarding for them".5 By automating tasks like data processing and analysis, AI frees up mental space for critical decision-making, direct patient interaction, and "higher-level work".5 The true value of AI lies in this partnership, where the strengths of both human and machine are leveraged to create a safer, more efficient, and more equitable healthcare system.

Conclusion

The data confirms that medical sentinel events are not inevitable; they are, in many cases, a symptom of complex, systemic vulnerabilities that can be mitigated with advanced technology. AI serves as a powerful new defense, offering practical and evidence-based solutions to reduce patient harm across five critical areas: diagnostics, fall prevention, medication safety, clinical deterioration, and readmission reduction. Each of these applications represents a move toward a more proactive, predictive, and personalized model of care.

The successful integration of AI, however, is not a simple technological implementation. It requires a strategic and ethical approach that addresses organizational inertia, fosters digital literacy among the workforce, and vigilantly guards against bias and a lack of transparency. The most impactful innovations in patient safety will be the result of a thoughtful collaboration between human intelligence and artificial intelligence. By embracing AI as a partner, a vigilant guardian that augments human capabilities, healthcare leaders can create a safer, more resilient system that not only prevents harm but also enables clinicians to deliver a higher, more compassionate standard of care.

I cringe as I conclude this article because it brings up many bad memories of my sentinel event, but it also provides hope for what could be and the lives that could be spared through the use of technology to reduce potential harm. I'd love to speak with you about your thoughts and insights into this article and the points it raises.

Works cited

  1. Sentinel Events - New York State Office of Mental Health, accessed August 19, 2025, https://omh.ny.gov/omhweb/dqm/bqi/sentinel_events.html
  2. Sentinel Events | Joint Commission, accessed August 19, 2025, https://www.jointcommission.org/en-us/knowledge-library/sentinel-events
  3. Never Events | PSNet - AHRQ Patient Safety Network, accessed August 19, 2025, https://psnet.ahrq.gov/primer/never-events
  4. Sentinel Events-Patient Safety Events - Maryland Department of Health, accessed August 19, 2025, https://health.maryland.gov/springgrove/Documents/Sentinel%20Events-Patient%20Safety%20Events.pdf
  5. An AI “Copilot” Can Reduce Prescription Errors That Put Patients at ..., accessed August 19, 2025, https://www.gsb.stanford.edu/insights/ai-copilot-can-reduce-prescription-errors-put-patients-risk
  6. How Does Ai Reduce Human Error In Healthcare - Ambula EMR system, accessed August 19, 2025, https://www.ambula.io/how-does-ai-reduce-human-error-in-healthcare/
  7. The Role of Machine Learning in Predicting Hospital Readmissions Among General Internal Medicine Patients: A Systematic Review - PubMed Central, accessed August 19, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC12187041/
  8. Evidence-Based Fall Prevention with real-time AI Insights - OK2StandUP, accessed August 19, 2025, https://www.ok2standup.com/blog/augmenting-evidence-based-fall-prevention-with-ai-technology-a-clinical-imperative
  9. OTC Case Studies: Winter Colds - Pharmacy Times, accessed August 19, 2025, https://www.ajmc.com/view/reducing-readmissions-in-the-safety-net-through-ai-and-automation
  10. How AI Medical Diagnosis Is Reducing Diagnostic Errors and ..., accessed August 19, 2025, https://www.estenda.com/blog/how-ai-medical-diagnosis-is-reducing-diagnostic-errors-and-delays-in-healthcare
  11. Reducing the workload of medical diagnosis through artificial intelligence: A narrative review - PMC, accessed August 19, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC11813001/
  12. Using AI to Interpret Lab Results | Fullscript, accessed August 19, 2025, https://fullscript.com/blog/ai-driven-lab-result-interpretation
  13. Clinical NLP | State-of-the-art Natural Language Processing to extract Clinical Data - John Snow Labs, accessed August 19, 2025, https://www.johnsnowlabs.com/clinical-nlp/
  14. Fall Prevention | Hospital | Remote Patient Monitoring, accessed August 19, 2025, https://www.virtusense.ai/solutions/hospitals
  15. Working Smarter: Using Analytics to Reduce Falls, Improve Patient Outcomes, and Save Time for Nurses - EpicShare, accessed August 19, 2025, https://www.epicshare.org/share-and-learn/community-health-network-quality-dashboard
  16. Study Details | Predictive Analytics and Computer Visualization Enhances Patient Safety to Prevent Falls | ClinicalTrials.gov, accessed August 19, 2025, https://clinicaltrials.gov/study/NCT06339125?cond=falls&aggFilters=status:not%20rec&rank=6
  17. AI Resources - ECRI, accessed August 19, 2025, https://home.ecri.org/pages/ai-resources
  18. AI-Powered Transformation of Healthcare: Enhancing Patient Safety Through AI Interventions with the Mediating Role of Operational Efficiency and Moderating Role of Digital Competence—Insights from the Gulf Cooperation Council Region - MDPI, accessed August 19, 2025, https://www.mdpi.com/2227-9032/13/6/614
  19. www.advisory.com, accessed August 19, 2025, https://www.advisory.com/daily-briefing/2023/03/15/medical-errors#:~:text=To%20prevent%20medication%20errors%2C%20hospitals,could%20be%20out%20of%20place.
  20. Artificial Intelligence in Sepsis Management: An Overview for Clinicians - PMC, accessed August 19, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC11722371/
  21. AI to detect sepsis | Hub, accessed August 19, 2025, https://hub.jhu.edu/magazine/2022/winter/ai-technology-to-detect-sepsis/
  22. New 'AI Doctor' Predicts Hospital Readmission & Other Health ..., accessed August 19, 2025, https://nyulangone.org/news/new-ai-doctor-predicts-hospital-readmission-other-health-outcomes
  23. Why Hospitals Who Wait to Adopt AI May Never Catch-up | EY - Ireland, accessed August 19, 2025, https://www.ey.com/en_ie/insights/health/why-hospitals-who-wait-to-adopt-ai-may-never-catch-up
  24. Editorial – Barriers to adopting AI in everyday healthcare - EITH Think Tank, accessed August 19, 2025, https://thinktank.eithealth.eu/reception/editorial-barriers-to-adopting-ai-in-everyday-healthcare/
  25. The ethical dilemmas of AI | USC Annenberg School for Communication and Journalism, accessed August 19, 2025, https://annenberg.usc.edu/research/center-public-relations/usc-annenberg-relevance-report/ethical-dilemmas-ai

Read next