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Prior to Health Catalyst Stan has held several executive, clinical and research roles. Most recently he was the Chief Strategy Officer for Pascal Metrics a federally-certified Patient Safety Organization. Prior to that Stan was the founding CEO of TheraDoc, which he led for 10+years until its acquisition. For 2+ decades Stan was a clinician, researcher and educator at the University of Utah School of Medicine, College of Pharmacy and at IHC-LDS Hospital. Stan is clinically trained as a pharmacist specializing in infectious diseases as well and has an advanced degree in medical informatics specializing in clinical surveillance and expert system decision support technologies.
Even though medication-associated errors affect over 7 million patients and cost more than $40 billion each year, healthcare often falls short when it comes to prioritizing patient safety. For example, in October 2021, a draft of the Department of Health and Human Services Strategic Plan FY 2022–2026 didn’t include reducing preventable harm as part of its mission to improve the quality of care. Meanwhile, other complex and adaptive industries, such as aviation and nuclear, give top precedence to safety oversight and compliance. To catch up to other sectors and actively pursue patient safety improvement, healthcare needs a straightforward framework for integrating patient safety across the continuum of care—an approach involving culture, clinical analytics, and frontline adoption of best practices.
The operational military concept known as “left of bang” endorses continuous situational awareness to avoid harm proactively—before it occurs. Healthcare, however, operates reactively in response to patient harm, often intervening once a patient safety event has occurred, versus using practices and tools to recognize and respond to threats.
Applied to patient safety, a left-of-bang approach teaches frontline clinicians to increase sensitivity to and stay in constant vigilance for threats before they happen, moving down a scale of situational awareness levels:
1. White: Tending to the tasks at hand but largely unprepared for disruption and unaware of the conditions around them.
2. Yellow: Constantly understanding the safety vulnerabilities of day-to-day healthcare.
3. Orange: Ready to use the needed skills and tools to react to an event.
4. Red: Taking action and laser focused on the issue at hand.
Healthcare organizations have worked hard to improve patient safety over the past several decades, however harm is still occurring at an unacceptable rate. Though the healthcare industry has made efforts (largely regulatory) to reduce patient harm, these measures are often not integrated with health system quality improvement efforts and may not result in fewer adverse events. This is largely because they fail to integrate regulatory data with improvement initiatives and, thus, to turn patient harm information into actionable insight.
Fully integrated clinical, cost, and operational data coupled with predictive analytics and machine learning are crucial to patient safety improvement. Tools that leverage this methodology will identify risk and suggest interventions across the continuum of care.
Preventable patient harm costs healthcare billions annually, making strategies to improve patient safety an imperative for health systems. To improve patient safety, organizations must establish a safety culture that prioritizes safety throughout the system, supports blame-free reporting of safety events, and ensures that healthcare IT solutions functions and accessibility align with safety goals.
A sociotechnical framework gives health systems a seven-part roadmap to improving patient safety culture:
1. Leverages qualitative and quantitative data.
2. Doesn’t rely on HIMSS stage levels to tell the complete safety picture.
3. Gives frontline clinicians a voice in decision making.
4. Makes IT solutions accessible to non-technical users.
5. Encourages frontline clinicians to report safety and quality issues.
6. Treats a safety issue in one area as a potential systemwide risk.
7. Performs thorough due diligence before taking safety IT solutions live.
With a potential industry-wide savings of almost $21 billion and an impact on more than seven million patient lives, preventing harmful medication error is a significant improvement opportunity for health systems. Also known as adverse drugs events (ADEs), harmful medication errors comprise about 37 percent of all medical harm. Approximately 50 percent of ADEs are preventable, making their reduction a highly impactable area of patient safety.
Current data and analytics workflow tools are making ADE surveillance, monitoring, and prevention increasingly more effective with four key capabilities:
1. Perspective surveillance for ADEs and identification of previously undescribed ADEs.
2. Identification of the root cause of many ADEs by drug class.
3. Prescription at appropriate doses for patients with compromised kidney or liver functions.
4. Identification of different types of harm to find causes.
Unlike the standard post-event reporting process, the Patient Safety Monitor Suite: Surveillance Module is a trigger-based surveillance system, enabled by the unique industry-first technological capabilities of the Health Catalyst Data Operating System platform, including predictive analytic models and AI.
The Health Catalyst PSO creates a secure and safe environment where clients can collect and analyze patient safety events to learn and improve, free from fear of litigation. Coupled with patient safety services, an organization’s active all-cause harm patient safety system is fully enabled to deliver measurable and meaningful improvements.
With over 400,000 patient-harm related deaths annually and costs of more the $1 billion, health systems urgently need ways to improve patient safety. One promising safety solution is patient harm risk assessment tools that leverage machine learning.
An effective patient safety surveillance tool has five core capabilities:
1. Identifies risk: provides concurrent daily surveillance for all-cause harm events in a health system population.
2. Stratifies patients at risk: places at-risk patients into risk categories (e.g., high, medium, and low risk).
3. Shows modifiable risk factors: by understanding patient risk factors that can be modified, clinicians know where to intervene to prevent harm.
4. Shows impactability: helps clinicians identify high-risk patients and prioritize treatment by patients who are most likely to benefit from preventive care.
5. Makes risk prediction accessible: integrates risk prediction into workflow tools for immediate access.