April 8, 2020

Building a Healthcare Data Strategy With Big Rocks

Shawn Sutherland

Topic:   Industry Focus

Last updated: July 29th, 2020

The long-awaited 21st Century Cures Act became a Final Rule just before the COVID-19 pandemic escalated in the U.S. Primarily addressing information interoperability, the rule was expected to be announced at the HIMSS conference in early March, where more than 45,000 attendees from around the world – including me! We were looking forward to gathering and discussing the power of life-changing innovation in healthcare – including the impact of the passage of this very rule.

This is a topic that is near and dear to our hearts. My colleague Bill Moroz (@BizIntelligent) is working feverishly with our partners at @Ready_Computing on an HL7 task force developing new interoperability standards to better manage healthcare resources in the midst of the coronavirus. Their efforts remind me of a favorite quote from the ancient Roman philosopher Seneca: “Luck is what happens when preparation meets opportunity.” If the Office of the National Coordinator for Health Information Technology (ONC) had not clearly signaled its emphasis on interoperability with the Proposed Rules released during HIMSS19, healthcare in the U.S. would have been caught completely flat-footed by this pandemic.

The same holds true for becoming data-driven as an organization. Without diligent prioritization and preparedness guided by an enterprise strategy, healthcare organizations risk falling short in both ordinary and extraordinary times.

Data Strategy Priorities: Starting With the Big Rocks

You may be familiar with the analogy demonstrated by a professor who filled a jar with big rocks and then asked their students if the jar was full. Perhaps predictably, the class replied, “Yes.”

Rocks Jar

Adding smaller and smaller rocks followed finally by sand, the professor posed the same question after each new addition with the class now responding that the jar was not full. Reflecting on the point of the exercise, the students concluded “you can always fit more.” I’m sure that’s the way most of us in healthcare feel these days, as we are asked to try and fit in more and more.

However the professor explained that the actual point of the exercise is this: if you don’t put the big rocks in first, you will never get them in at all.

Rocks Jar 2

I love this analogy because it fits perfectly with what has happened with healthcare data strategy during the past decade, and offers a valuable lesson as healthcare embarks on a new decade under these unprecedented circumstances, which are certain to reshape policy and systems as we know them.

There continue to be many “little rocks” that received a lot of attention and hype, including big data, predictive and prescriptive analytics, artificial intelligence (AI), machine learning, natural language processing (NLP), blockchain, IoT and yes, interoperability. I’ve witnessed many departmental purchases of these technologies because of local budget availability. Some of these have created pockets of success, but nothing that fully transforms organizations into data- and insight-driven entities. Each of these smaller rocks, when incorrectly prioritized, inevitably crowd out and delay the foundational big rocks of an enterprise data strategy.

The Healthcare Data Value Chain

So what are the “big rocks” of enterprise-level healthcare data strategy? They are what we call the “Healthcare Data Value Chain”:

Healthcare Value Chain

Healthcare data is uniquely complex compared to any other industry. Think about the tens of thousands of data types needed in healthcare and the 12M+ codes required to classify them including acute and ambulatory observations, pharmaceuticals, orders (including lab, radiology, patient care, meds, nutritional, PT, OT, etc.), problems, diagnoses, procedures, vitals, and many more.

Then layer in social determinants of health and other public data sources that help with population health management – particularly relevant right now for mitigating risk with COVID-19 patients. An organizational data strategy must harmonize these complexities into a single version of organizational truth and create a scalable and sustainable data strategy that will optimize the enterprise and keep it prepared for the future.

Reflecting on my decades of work in data management and analytics for world-renowned and innovative health care systems, I saw first-hand how despite significant investment in both master data management (MDM) and business intelligence (BI) technology and resources, these organizations experienced the law of diminishing returns when trying to do things manually or with a “Frankenstack” of point solution technologies. Inevitably, these organizations lost their appetites for growing FTE count needed as more small rocks were added. This experience was validated through my national work with the Healthcare Data Analytics Association and as part of the National Quality Forum (NQF) Task Force, where I saw dozens of well-known healthcare organizations struggling with the same issues.

To summarize, start with your big rocks: the healthcare data value chain and a unified data and analytics platform to manage it from end to end. By focusing here first, you will be well positioned to achieve significant improvements in clinical outcomes, reduced cost, population health, business development, and patient satisfaction.

A solid and integrated data foundation will enable you to capitalize on myriad opportunities presented in value-based care, shared risk contracts, population health, AI/ML, and the patient experience. You will also be ready for new regulatory and payment models, mergers and acquisitions, and the next opportunity – or crisis – that comes your way.

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