June 30, 2021 | Net Health

5 min read

Solutions for 3 Critical Data Mistakes in Healthcare Analytics

As healthcare analytics continue shifting from descriptive (the past and the now) in nature to predictive (the future), it’s becoming more and more important that common data collection and analysis challenges are addressed and resolved.

Why? Two reasons. Number one—predictive analytics are poised to drive integral parts of the clinical decision-making process. Things like helping to diagnose patients, suggest treatment regimens, and automate clinical processes are all on the horizon. And number two—predictive analytics is an abstract concept with a lot of moving parts. The better we can dial in the inputs (the data) now, the better and the faster we’ll see meaningful results we can trust.

In light of this, let’s look at three of the most common data mistakes made in healthcare analytics and how to solve them.

1. Not Starting With the Problem First

Often, providers start with the data available and look to see what conclusions can be drawn from there. However, the more effective way of leveraging data is to start with the problem first. 

Begin by looking at what you want to accomplish and then look at what data you need to make that specific process more effective. Sometimes we accept that data is going to solve all problems across the board without truly understanding what the problem or the root of the problem is first.

Start by determining what your team wants to achieve through healthcare analytics. Remember that the even though new technology may be complex, the goals should be the same as they’ve always been.

The Solution – Steps in Order

All of this begins with a clearly defined understanding of what an organization wants out of analytics functionality. By getting every department on the same page, it ensures that the data processes support the end goal. Some examples of goals that are or will soon be possible include:

  • Reduction in costly penalties associated with readmissions and hospital-acquired conditions
  • Reduction in a patient’s length of stay without compromising quality of care and patient satisfaction
  • Improved clinic scheduling, billing, and operations

2. Overloading for the Sake of Overloading

Not everything measurable is important and not everything important is measurable. The first half of this phrase speaks volumes to how collected data should, or more importantly, should not be presented to clinicians and decision-makers. Too often, it’s tempting to fall into the trap of wanting to present every piece of data collected in a beautiful dashboard filled with graphs, charts, tabs, pop-outs, and more.

The problem? Not all of this data is meaningful and you can end up creating confusion and decreasing efficiency and accuracy by adding too much noise to the discussion.

The Solution—The Right KPIs

The solution here is to make sure you’re measuring the right Key Performance Indicators (KPIs). What data points really tell the story of what’s going on? What do your decision-makers need to know to impact the quality and effectiveness of their choices?

And then on that same token, what data points are less impactful or possibly distracting? It’s okay to measure these things, especially if you may need them on rare occasions, but put some thought into what’s immediately in the faces of your people. The better you can streamline the data presentation, the better the results.

The bottom line is it’s important to target objective data instead of subjective data that can lead to misleading or incorrect conclusions. Additionally, look for opportunities to leverage technology to turn what is currently a subjective data point into an objective one. For example, the capability to automatically measure wounds with Tissue Analytics is a great way to remove the subjectivity of a critical metric.

3. Absence of Clearly Defined Definitions

Using data specific to a single department can certainly bring value to the table. However, the real value comes when different departments can quickly and easily share standardized data and outcomes across multiple departments and specialties. Data standardization can be a major problem for an organization, especially when one uses multiple electronic systems with multiple “sources of truth”.

Data points must be intentionally standardized across the entire organization. For example, if specialized providers use one set of terminology in their clinical documentation and non-specialists use another, these documentation fields should be harmonized. If clinicians use different terminology for the same clinical observations, it can create quality reporting problems down the line. And those problems become more critical when you start leveraging predictive analytics to drive clinical and operational decision making.

The Solution – Define, Define, Define

Start by defining your critical data points organization-wide. Note that this goes further than just defining the ‘what’. It also needs to define things like how the data is collected, what the parameters for collection look like, and who is doing the collection and the aggregation.

How Can I Learn More?

If you’re interested in more ways to get your analytics and data plans poised for the future, or you’re interested in learning more about the future of predictive analytics, we have a few resources we’d like to invite you to check out.

For those looking to learn more, start by checking out our recent webinar where our expert panel discussed these topics and more, entitled, The Modern Healthcare CMIO: Best Practices for Implementing Digital Innovations.

And for those looking for some actionable help in getting their organization ready to start leveraging the power of data to drive more effective clinical decision-making, we’d encourage you to reach out and chat with Net Health today.

The Modern Healthcare CMIO

Best Practices for Implementing Digital Innovations

 
 
 
 
 
 
 
 

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