Learn more about Valere Lemon, MBA, RN

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Valere Lemon, MBA, RN

Valere joined Health Catalyst in March of 2017 as Senior Subject Matter Expert. Prior to joining HC, Valere worked at Pascal Metrics as a Client Services Manager. She has worked as a Pediatric Hematology/Oncology nurse at 2 children’s hospitals, Children’s National Medical Center and Riley Children’s Hospital. Valere holds a BSN from Purdue University and a MBA from University of Maryland University College

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

How to Use Data to Improve Quality and 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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Valere Lemon, MBA, RN
Alejo Jumat

Meaningful Machine Learning Visualizations for Clinical Users: A Framework

Health systems can leverage the predictive potential of machine learning to improve outcomes, lower costs, and save lives. Machine learning, however, doesn’t inherently produce insights that are actionable in the clinical setting, and frontline clinicians need information that’s accessible and meaningful at the point of care. Thoughtfully designed visualizations of machine learning insights are a powerful way to give clinical users the information they need, when and how they need it, to support informed decision making.
A design framework for machine learning visualizations addresses three key questions about who will use the decision-support insights and how:

People: who are the targeted users?
Context: in what context or environment do they work?
Activities: what activities do they perform?

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