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Author Bio

Taylor Larsen

Taylor Larsen joined Health Catalyst in December 2014 as a Data Architect. Prior to coming to Health Catalyst, he worked for the Colorado Department of Health Care Policy and Financing as a Budget and Data Analyst. Taylor has a Master’s degree in Economics from the University of Colorado

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Taylor Larsen
Michael Barton
Jennifer Van Pelt

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:

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

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Taylor Larsen

Why Health Systems Must Use Data Science to Improve Outcomes

In today’s improvement-driven healthcare environment, organizations must ensure that improvement measures help them reach desired outcomes and focus on the opportunities with optimal ROI. With data science-based analysis, health systems leverage machine learning to determine if improvement measures align with specific outcomes and avoid the risk and cost of carrying out interventions that are unlikely to support their goals.
There are four essential reasons that insights from data science help health systems implement and sustain improvement:

Measures aligned with desired outcomes drive improvement.
Improvement teams focus on processes they can impact.
Outcome-specific interventions might impact other outcomes.
Identifies opportunities with optimal ROI.

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Katherine Foag
Taylor Larsen

Top Five Elements of an Effective Readmission Risk Score

Under value-based healthcare and the 2012 Hospital Readmission Reduction Program, healthcare organizations are more motivated than ever to reduce their incidence of preventable readmissions.
Health systems can reduce risk of hospital readmissions by developing readmission risk scores tailored specifically to their populations. A risk model that meets the following five requirements will have significant predictive value and is most likely to achieve systemwide adoption:

Identifies at-risk patients early.
Separates patients relevant to the disease-specific identification method and intervention strategy from all other in-hospital patients.
Uses organization-specific data to train a disease-specific model.
Exceeds performance of existing models.
Is developed in collaboration with domain experts.

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Taylor Larsen

An Inside Look at Building Machine Learning for Healthcare

Machine learning is a term that crops up often in healthcare lately, but it’s important to understand what really constitutes learning in this context. What some call machine learning is actually unintentional programming, but true learning is derived during the process of building a predictive model. This article delves into the nuts and bolts of a healthcare machine learning model and describes the training process a model undergoes to impact outcomes for patients.
The key ingredient is data and the key deliverable is to complete the feedback loop so those responsible for managing care have actionable information at their disposal.
Machine learning is beyond conceptual; it’s incorporated into a growing list of predictive models for various disease classifications.

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