Hospitals and healthcare systems have improved in using analytics to refine their clinical, financial and operational improvement strategies. But it’s time to review and prioritize the data that pays off the most, said Matt Grahl, senior advisor at Impact Advisors, a healthcare consulting firm.

The Great Resignation highlighted the need to strategize and prioritize to take full advantage of a leaner workforce, encouraging a hyper-focus on key analytics that could have the most positive impact , instead of getting bogged down by focusing on other data that might prove useful. but should be deprioritized and mothballed to get the most out of a reduced staff, said Grahl, who has 25 years of experience in analytics, operations and risk management.

Health Informatics News sat down with Grahl to discuss his perspective on the role of healthcare data analytics today and get lessons on how to approach data analytics.

Q. Why do you say it’s time to review and prioritize the most profitable data?

A. While the creation of and access to data is growing rapidly, the internal capacity to turn that data into decision support tools – analytical products – remains limited. A good analogy here is a factory that suddenly sees a large amount of various surplus raw materials dumped on its receiving dock.

There’s definitely potential there, but all that’s really happened is that the work in progress has just increased, and nothing more is coming out of the factory in terms of finished goods – it’s more than likely production is less due to excessive work going on. More raw materials injected into a production process does not equal increased production where other parts of the system are limited.

In terms of data, business leaders, with the help of their supporting analytics teams, need to step back and clarify the decisions that need to be enabled through their analytics products. It is normal not to have this cross-response for each data block generated.

Right now, we need to make sure that we enable decision-making that is closely tied to business strategy and near-term initiatives. Consider the decisions that need to be made to fully support the business moving forward in line with the overall strategy.

With these decisions in mind, now prioritize which data to work with. This exercise completed, we now have a high-level analytical production plan with a good understanding of the raw material needed.

Now what about the rest? As line managers consume analytical products in line with overall business strategy, new questions will be formulated, and chances are, data that has been pushed aside will come into play to help answer these questions. these questions.

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

Q. In your opinion, the so-called big resignation has highlighted the need to strategize and prioritize to take full advantage of a lean workforce, encouraging a hyper-focus on key analytics. Please elaborate.

A. Everything we just talked about relates to this statement, but there are more nuances to this and a huge opportunity for employee engagement that can translate into positive team retention. The commitment of analytics teams can potentially be compromised when their hard work and expertise is not realized with an analytics product that is simply not being used in any meaningful way.

The close coupling of their work with operational stakeholders aligned with the company’s strategy ensures that the analytical products will be leveraged upon completion and carrying out these efforts feeds into an already aligned analytics team and truly connects them to the business. whole organization.

The potential for a true win-win scenario can emerge. The organization has prioritized what is critically important, and by the end of development, the analytics team understands the connection between their work and the wider business, while seeing the fruit of its work be used to enable decision-making in the downward march of the organization. strategic path.

The other subtle win here is that the analytics team has potentially become even more committed to their work and to the organization as a whole.

The Great Resignation taught tough lessons about the competitive environment of acquiring analytical talent. You can do a lot to retain and secure valuable analytics team members simply by aligning their efforts with the targeted execution of the desired strategy and the initiatives supporting that strategy.

Q. You are warning health officials to avoid the “shiny toy syndrome” of new scanning technology that could distract from more impactful technologies. Please give an example.

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

I’ve seen time and time again that analytics problems within organizations are rarely a technology gap at the root. The lack of a strategy or a strategy that is not well understood is usually the #1 problem. Next is the lack of meaningful data governance. A fully functional data governance construct is a fundamental element, and the absence of this element defeats any technology over time.

Here’s what I’d recommend for shiny new toys: focus on what decisions will be activated, then look at your legacy toolbox first and see if those tools are being stretched to the max. Let key decision-making guide you to the right technology, not the other way around. The desire to acquire new technologies is also a great opportunity to review its analytical foundations – strategy and data governance – before adding a floor to the house.

It is essential to avoid “build it and they will come” as a strategy for analysis. If the most sophisticated new analytics platform is implemented without considering the foundation of your analytics program and that implementation is not coupled with the overall business strategy, you will leave your new toy idle while incurring the required maintenance costs.

If you’ve done all the steps in the correct order and decide that the shiny new toy is the tool you need, be sure to also go ahead and plan the sunset of those platforms legacy with duplication functionality.

Q. When it comes to analytics, why should CIOs and other healthcare IT managers integrate with operations managers?

A. Simply put, operational leaders are the customer and need to know which are the most important decisions that need to be activated. It is likely that analytical development requests need to be prioritized due to limited resources, and business leaders need to be at the table during this prioritization.

I would say they need to be the decision makers on what to build next. To inform this decision, analytical leaders need to be transparent and as specific as possible on the capability side of the discussion.

A natural consequence of these conversations is potential gaps in capabilities. These gaps could be skills gaps or genuine gaps in supporting technology. When these gaps are highlighted through the prism of genuine priority demand, an emerging gap reduction effort becomes clear and easily justified.

When it comes to analytical demand management, I’ve seen a few processes that work well. One was informal and simply involved a discussion between the senior analytics manager and the respective VP who owned a particular analytics request immediately after the request was submitted. The discussion was about validation of the request, current capacity and getting approval to go ahead or not from this VP.

This worked very well for a small-scale health system. At the larger end of the scale, I’ve seen operational leaders meet with analytics managers on a three-week cycle in a more formal setting and accomplish the same task.

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

Twitter: @SiwickiHealthIT
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