Michael Buck

Senior Vice President of Clinical Informatics

Michael joined Health Catalyst in December 2016. Prior to coming to Catalyst, he worked as the Senior Director of Biomedical Informatics for the Primary Care Information Project in the NYC Department of Health and Mental Hygiene. He is also an Associate Research Scientist in the Department of Biomedical Informatics at Columbia University. His professional interests include healthcare analytics, health information exchange, value-based payment initiatives, public health informatics and clinical decision support. Michael completed his PhD in Biomedical Informatics at the University of Utah, School of Medicine and his postdoctoral fellowship at Columbia University. His work has received several awards including the 2012 1st place Innovator Award featured in Healthcare Informatics magazine, the 2011 HIMSS Public Health Davies Award of Excellence, and the 2011 Best Application Serving the Public, New York City Excellence in Technology Awards Program.

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Five Solutions to Widespread Self-Service Analytics Concerns

As data becomes more widely available, healthcare organizations are turning to self-service analytics to empower team members to make data-informed decisions. Rather than waiting for manual reports from analytics teams, self-service analytics allows individual team members to perform queries, run reports, and dive into the data details on their own. However, this broader distribution of data also presents concerns, such as deciding who will oversee the data, who can access which data, and how to best deliver the data to end users. Leaders can address these concerns and reap the benefits of self-service analytics by focusing on answers to five common hesitations:

1. Invest in advance data platforms to standardize data.
2. Teach analytics best practices and data literacy.
3. Leverage tools to take data beyond historical analytics.
4. Shift the analytics teams’ mindset.
5. Follow data security procedures and policies.

Self-Service Data Tools Unlock Healthcare’s Most Valuable Asset

Data is increasingly critical to the delivery of healthcare. However, due to its complexity and scope, frontline clinicians and other end users can’t always access the data they need when they need it. In addition, expectations for data at the point of care unduly burden data analysts, keeping them from advancing more sophisticated organizational analytics goals.

In response to data productivity and efficiency challenges, self-service data solutions models only the high-value data, versus all available data, giving analysts and nontechnical users immediate and direct access to the data. These reusable models address three key challenges healthcare analytics programs face:

1. Cost—avoid additional expense and labor of producing single-use models.
2. Efficiency—save times associated with routinely producing new models.
3. Maintenance—allow updates across the organization’s models, versus separate updates.

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