John joined Health Catalyst in September 2011 as a senior data architect. Prior to Health Catalyst, he worked for Intermountain Healthcare and for ARUP Laboratories as a data architect. John has a Master of Science degree in biomedical informatics from the University of Utah, School of Medicine.
In today’s high-pressured world of healthcare, health systems don’t need report writers. They need highly valuable healthcare data analysts. A top healthcare data analyst becomes a partner for clinical and operational improvement by using a five-step method for solving complex problems.
This article walks through this step-by-step approach and demonstrates its application using the real-world example of building a diabetes registry. In addition to this specialized approach to solving problems, the article discusses the five essential skills for data analysts needed in the diabetes registry example:
• Data query
• Data movement
• Data modeling
• Data analysis
• Data visualization
Healthcare organizations are turning to the enterprise data warehouse (EDW) as the foundation of their analytics strategy. But simply implementing an EDW doesn’t guarantee an organization’s success.
One obstacle organizations come up against is that their analytics team members don’t have the right skills to maximize the effectiveness of the EDW.
The following six skills are essential for analytics team members: structured query language (SQL); the ability to perform export, transform, and load (ETL) processes; data modeling; data analysis; business intelligence (BI) reporting; and the ability to tell a story with data.
It might sound surprising, but the world of surfing just might hold key observations about the world of healthcare analytics. After watching the Pipeline Masters in Oahu, John Wadsworth, Technical Operations VP at Health Catalyst, took away three key principles from the world of surfing that are important for healthcare analysts:
1. Understand the Changing Environment.
2. Know When to Say No, So You Can Say Yes to the Right Opportunity.
3. Get Good at Positioning.
This article also offers insights on moving from reactive to prescriptive analytics, the top five technical skills data analysts need, and a four step model for problem solving.
Healthcare organizations seeking to achieve the Quadruple Aim (enhancing patient experience, improving population health, reducing costs, and reducing clinician and staff burnout), will reach their goals by building a rich analytics ecosystem. This environment promotes synergy between technology and highly skilled analysts and relies on full interoperability, allowing people to derive the right knowledge to transform healthcare.
Five important parts make up the healthcare analytics ecosystem:
1. Must-have tools.
2. People and their skills.
3. Reactive, descriptive, and prescriptive analytics.
4. Matching technical skills to analytics work streams.
There’s a new way to think about healthcare data analysts. Give them the responsibilities of a data detective. If ever there were a Sherlock Holmes of healthcare analytics, it’s the analyst who thinks like a detective. Part scientist, part bloodhound, part magician, the healthcare data detective thrives on discovery, extracting pearls of insight where others have previously returned emptyhanded. This valuable role comprises critical thinkers, story engineers, and sleuths who look at healthcare data in a different way. Three attributes define the data detective:
1. They are inquisitive and relentless with their questions.
2. They let the data inform.
3. They drive to the heart of what matters.
Innovative analytics leaders understand the importance of supporting the data analyst through the data detective career track, and the need to start developing this role right away in the pursuit of outcomes improvement in all healthcare domains.