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Stan Pestotnik, MS, RPh

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.

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Stan Pestotnik, MS, RPh

Improving Patient Safety: Machine Learning Targets an Urgent Concern

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:

Identifies risk: provides concurrent daily surveillance for all-cause harm events in a health system population.
Stratifies patients at risk: places at-risk patients into risk categories (e.g., high, medium, and low risk).
Shows modifiable risk factors: by understanding patient risk factors that can be modified, clinicians know where to intervene to prevent harm.
Shows impactability: helps clinicians identify high-risk patients and prioritize treatment by patients who are most likely to benefit from preventive care.
Makes risk prediction accessible: integrates risk prediction into workflow tools for immediate access.

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Stan Pestotnik, MS, RPh
Valere Lemon, MBA, RN

How to Use Data to Improve Patient Safety

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.

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