Michael Barton

Patient Safety Operations, SVP

Michael Barton joined Health Catalyst as an Engagement Executive Vice President in January 2013. He completed his training at the University of Utah Health Sciences Center. Upon graduation in 1994, he was employed with the Pharmacoepidemiology Team, a multidisciplinary team of epidemiologists, infection control practitioners, quality control specialists, pharmacists, and healthcare IT specialists at the University of Utah. After four years, Michael moved his clinical practice to the Shock-Trauma ICU at LDS Hospital. Here, he had the opportunity to apply his infectious disease and critical care knowledge. After eight years of clinical practice in conjunction with five years of IT industry consulting experience, Michael joined HIT startup TheraDoc, Inc. as a consultant in 2000 and full-time in 2001. Michael spent 12 years with TheraDoc, where he served in various roles. The last 5 years Michael served on the senior leadership team as SVP, Knowledge and Product Development where Michael oversaw the Knowledge Management, Product Management, Engineering, and Quality teams. For Michael, joining Health Catalyst means continuing to pursue his passion of improving the quality and safety of patient care through applied healthcare IT solutions.

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Understanding Population Health Management: A Diabetes Example

Diabetes is one of several chronic health conditions at the root of U.S. healthcare challenges. To improve the quality of care and costs associated with diabetes, health systems, clinicians, and patients can benefit from taking a data-centric approach to diabetes management and leveraging population health tools.

Managing individual cases of diabetes require actively involving patients in their care plan, enabling each patient to monitor and understand key data, such as A1c readings, and adjust lifestyle or other factors affecting overall health. Managing diabetes across larger populations, however, is best done through the use of a data and analytics platform that can aggregate data from multiple sources and provide actionable insights. Specifically, a data platform can identify patients who aren’t up to date on tests and those at high risk for other complications, uncover variations in diabetes care across an organization, and more.

Three Ways Evidence-Based Medicine Improves Machine Learning

As health systems continue to adopt machine learning to impact significant outcomes (e.g., reducing readmissions, preventing hospital-acquired infections, and reducing length of stay), they must also leverage evidence-based medicine. Evidence adds critical insight to machine learning models, ensuring that models incorporate all necessary variables in their risk prediction, and builds credibility among clinicians.

Evidence-based medicine brings three essential elements to healthcare machine learning:

1. Boosts machine learning model credibility.
2. Engages data experts around healthcare projects.
3. Saves time and money and increases ROI.

Introducing the Health Catalyst Monitor™ Patient Safety Suite: Surveillance Module

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.

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