The Great Resignation and what it means for healthcare data analytics

Photo: Matt Grahl

Hospitals and health systems have gotten better at using analytics to fine-tune their clinical, financial and operational improvement strategies. But it’s time to revisit and prioritize data that gives the most bang for the buck, said Matt Grahl, a senior advisor at Impact Advisors, a healthcare consulting firm.

The Great Resignation has highlighted the need to strategize and prioritize to more fully leverage a leaner staff, encouraging a hyper-focus on key analytical data that could net the most positive impact instead of being bogged down by focusing on other data that, while it could prove useful, should be deprioritized and placed on the backburner temporarily to make the most out of a smaller staff, said Grahl, who has 25 years of analytics, operations and risk management experience.

Healthcare IT News sat down with Grahl to discuss his views on the role of healthcare data analytics today and get some lessons on how to approach data analysis.

Q. Why do you say it is time to revisit and prioritize data that gives the most bang for the buck?

A. While the creation of and access to data is rapidly increasing, the internal capacity to turn that data into decision-making tools – analytic products – remains constrained. A good analogy here is a factory that suddenly sees a large amount of excess and varied raw materials dumped on its receiving dock.

There’s certainly potential there, but all that has truly happened is that work in progress just went up and nothing more is coming out of the factory in terms of finished products – more than likely less is being produced due to the excess work in progress. More raw materials injected into a production process do not equate to increased production where other parts of the system are constrained.

In terms of data, operational leaders, with the help of their supporting analytic teams, need to step back and clarify what decisions need to be enabled through their analytic products. It’s ok to not have that answer cross-walked to every chunk of data generated.

Right now, we need to be sure we are enabling decision making tightly coupled to the enterprise strategy and near-term initiatives. Think about what decisions need to be made that fully support the advancement of the enterprise in alignment with the overall strategy.

With those decisions in mind, now prioritize which data to leverage. With this exercise complete, we now have a high-level analytic production plan with a good understanding of the raw material (data) required.

Now, what about the rest of it? As operational leaders consume analytic products in alignment with the overarching business strategy, new questions will be formulated and there’s a great chance data that has been set aside will come into play to help answer those questions.

The difference in this approach is that you have addressed the most important pieces first to the best of your knowledge and are now iterating in an orderly fashion. The order is derived from your strategy and tightly aligned execution of initiatives supporting that strategy.

Q. In your opinion, the so-called Great Resignation has highlighted the need to strategize and prioritize to leverage a leaner staff more fully, encouraging a hyper-focus on key analytical data. Please elaborate.

A. Everything we just discussed relates to this statement, but there is some more nuance to this and a huge opportunity for employee engagement that can translate into positive team retention. The engagement of analytic teams can potentially be damaged when their hard work and expertise goes unrealized with an analytic product that just isn’t used to any significant degree.

The tight coupling of their work with operational stakeholders aligned to the enterprise strategy ensures analytic products will be leveraged once complete and the realization of those efforts fuels an already aligned analytics team and truly connects them to the overall organization.

The potential for a true win-win scenario can emerge. The organization has prioritized what is critically important and at the end of development, the analytic team understands the connection between their work and the wider enterprise while also seeing the fruit of their labor being used to enable decision making in the march down the organization’s strategic path.

The other subtle win here is that the analytic team has potentially become even more engaged in their work and the organization as a whole.

The Great Resignation has taught some tough lessons around the competitive environment of analytic talent acquisition. You can go a long way in retaining and securing valuable analytic team members simply by aligning their efforts in the laser-focused execution of your desired strategy and the initiatives supporting that strategy.

Q. You caution healthcare leaders to avoid “Shiny Toy Syndrome” of new analytics technology that could distract from the most impactful technologies. Please give an example of this.

A. I love shiny new toys as much as anybody, but proceed with caution here. An example of this is when health systems want to focus on artificial intelligence/machine learning solutions without first having a foundation in place and a clear operational use case.

I have seen again and again that analytic issues within organizations are rarely a technology gap at the root cause. Lack of a strategy or a strategy that is not well understood is usually problem No. 1. Next up is lack of meaningful data governance. A fully functioning data governance construct is a foundational element, and the lack of that element will defeat any technology over time.

Here’s what I would recommend regarding shiny new toys: focus on what decisions will be enabled and then first look at your legacy toolbox and see if those tools are being leveraged to the maximum extent possible. Let the enablement of key decisions lead you to the right technology and not the other way around. The desire to acquire new technology is also a great opportunity to review your analytic foundations – strategy and data governance – before adding another floor to the house.

It is critical to avoid “build it and they will come” as an analytics strategy. If the slickest new analytic platform is implemented without consideration to your analytics program foundation and that implementation isn’t coupled to the overall enterprise strategy, you are going to have your new toy lying dormant while incurring the requisite maintenance overhead.

If you have gone through all the steps in the right order and decide that the shiny new toy is the tool you need, be sure to also go ahead and plan for the sunset of those legacy platforms with duplicative functionality.

Q. With regard to analytics, why should CIOs and other health IT leaders bring operational leaders into the fold?

A. Simply put, operational leaders are the customer and should know what are the most important decisions that need to be enabled. Chances are analytic development requests need to be prioritized due to constrained resources, and operational leaders need to be at the table during that prioritization.

I would contend they need to be the decision makers regarding what needs to be built next. To inform that decision, analytic leaders need to be transparent and as granular as possible on the capacity side of the discussion.

A natural offshoot to these conversations is possible gaps in capability. Those gaps could be skill gaps or true gaps in supporting technology. When these gaps are highlighted through the lens of true prioritized demand, an emergent gap closure effort becomes clear and easily justified.

When it comes to analytic demand management, I have seen a few processes that work well. One was informal and just involved a discussion between the senior analytic leader and the respective VP who owned a particular analytic request immediately after the request was submitted. The discussion was around validating the request, current capacity, and getting approval to go forward or not from that VP.

This worked great for a health system on the smaller end of the scale. On the larger end of the scale, I have seen operational leaders meet with analytics leadership on a three-week cycle in a more formal setting and accomplish the same task.

In both cases, operational leaders made the decision on what was most important and approved for development. It is important to note that for either of these methods to work, there absolutely needs to be a defined and disciplined request intake process.

Twitter: @SiwickiHealthIT
Email the writer: [email protected]
Healthcare IT News is a HIMSS Media publication.

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