6 Essential Data Analyst Skills for Your Healthcare Organization

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.

What Healthcare Analysts Can Learn About Data Analytics From the World of Surfing

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:

Understand the Changing Environment.
Know When to Say No, So You Can Say Yes to the Right Opportunity.
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.

The Healthcare Analytics Ecosystem: A Must-Have in Today’s Transformation

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:

Must-have tools.
People and their skills.
Reactive, descriptive, and prescriptive analytics.
Matching technical skills to analytics work streams.
Interoperability.

When Healthcare Data Analysts Fulfill the Data Detective Role

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:

They are inquisitive and relentless with their questions.
They let the data inform.
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.

Analytics in Community Hospitals: Embracing Data to Thrive in the New Era of Value-Based Care

Value-based care has remade the healthcare landscape for small hospitals. Many are struggling to compete with the larger, better-funded medical centers in the communities they serve. Embracing data and analytics is no longer a luxury for these organizations if they are to succeed and remain competitive. Data analysis can assist senior leaders in identifying opportunities for improvement while balancing long-term goals with short-term pressures. Incorporating data in to the culture and making it a part of everyday decision making will enable smaller hospitals to not only survive, but thrive in the new era of value-based care.

What is the ROI of investing in a healthcare data analyst? A real example of saving millions

Making the most of a healthcare data analyst’s knowledge is a key component to getting the best ROI from a hospital improvement project. But all too often, analysts serve merely as data validators — they justify the data that leadership wants validated. Because analysts aren’t decision makers, they don’t have the authority to ask the questions that can save a health system millions. Empowering analysts, however, enables them to ask the right questions — and find the right answers — that will lead to significant savings.

The Changing Role of Healthcare Data Analysts—How Our Most Successful Clients Are Embracing Healthcare Transformation (Executive Report)

The healthcare industry is undergoing a sea change, and healthcare data analysts will play a central role in this transformation. This report explores how the evolution to value-based care is changing the role of healthcare data analysts, how data analysts’ skills can best be applied to achieve value-based objectives and, finally, how Health Catalyst’s most successful health system clients are making this cultural transformation happen in the real world.

Health Data Stewardship and Its Importance in Healthcare Analytics

Health data stewards are keepers of tribal knowledge, and they’re invaluable when a health system launches or expands a healthcare data analytics initiative. Their intimate and expansive knowledge of how data is collected to represent workflow across different systems can save days’ worth of time (and cost) in the development process while improving the accuracy of the analytics output. But getting anything more than a few spare moments of their time can be difficult because health data stewardship isn’t part of their job description. While it may seem difficult to justify at first, organizations need to formalize the role of the health data steward. The investment will ultimately return many times its value as the organization realizes the advantage of the analytics.

3 Frequent Mistakes in Healthcare Data Analytics

Healthcare organizations are recognizing the value of healthcare analytics, especially in their Big Data, population health management, or accountable care initiatives. This is good because without analytics it is difficult to impossible to run these programs successfully. That said, analytics are not the magic bullet and proper process must be in place. The three most common mistakes health systems makes with their healthcare analytics are: 1. Analytics Whiplash- when the analytics goes from one project to another without being able to fully understand the data and what it’s saying. 2. Coloring the Truth- When analysts don’t feel like they can be completely forthcoming with information and only give leadership the news they want to hear. 3. Deceitful Visualizations- Manipulating charts, graphs, and the like to reflect what the analyst or leadership wants the data to say, rather than what it actually says.

The Best Organizational Structure for Healthcare Analytics

After working with many healthcare organizations to help them implement the appropriate EDW for their needs, we’ve learned how important it is to create cross-functional teams from across the organization. Why? These cross-functional teams will simultaneously improve clinical and financial outcomes and demonstrate ROI. By following this approach, you’ll experience the following advantages:

Removal of organizational barriers between team members
Prioritization of BI and analytic efforts according to institutional readiness and need
Engagement and prioritization from appropriate leadership
Buy in from each level of the organization to improvement goals

Finding $5.7 Million with a Healthcare Data Warehouse

In April 2013, Health Catalyst partnered with North Memorial to develop the Health Catalyst Professional Billing Advanced Application built on our Late-binding™ Data Warehouse platform. The application automates data capture previously done manually by the coding staff. The development process was completed in six weeks. The solution is a perfect marriage of technology supporting workflow processes to become a natural part of billing. The results to date include: 1) 6% increase in notes that had sufficient clinical data for billing, 2) 25% improvement in professional coder efficiency, and 3) a potential to reclaim more than $5.7 million over the next three years. Read our success story to find more details.