Jennifer Van Pelt

Writer, Sr

Jennifer joined Health Catalyst in June 2015 as a Knowledge Developer/Medical Writer. She has 25 years of experience providing research, writing, and consulting services to hospitals and health systems. Prior to coming to Health Catalyst, she worked for Hayes, Inc., as a senior research analyst and manager of healthcare provider research services, providing custom evidence-based research reports on medical technologies and clinical issues for hospitals and health systems. Jennifer also worked for ECRI, first as a hospital consultant, then as a senior clinical writer and product manager for their Health Technology Forecast, an online database of emerging healthcare technologies expected to impact hospital costs, clinical outcomes, and operations. She has also authored articles on health technology assessment and evidence analysis inHealthcare Purchasing News, OR Manager, andAmerica’s Health Insurance Plans Coverage, and presented a national hospital supply chain association meetings.

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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.

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