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Is data the key to population health success?

In a recent blog, Pavel discussed data’s critical role in negotiating risk-based contracts. Data, in all areas of health care, has become the needle in our compass. But especially with population health. Data analytics can help us understand where we are, how we got there, and how to reach our goals. 

As hospitals and providers are increasingly evaluated on performance and outcomes, access to data is critical. Real world examples of this may come in the form of a Chief Quality Officer needing to know how providers are performing on quality measures required for the organization’s participation in a Medicare Shared Savings Program (MSSP) Accountable Care Organization (ACO), or a Care Manager wanting to track patients’ HbA1c levels before and after enrolling in a diabetes care management program.

In both situations, data is necessary to answer the questions at hand and guide decision making.

Looking beyond the data

Let’s take a closer look at that example involving MSSP ACO quality measure performance. For simplicity’s sake, we’ll focus on one quality measure: Body Mass Index (BMI) Screening and Follow-up.

To meet this measure, providers need to screen patients for BMI and document a follow-up plan when BMI is outside normal parameters. The Chief Quality Officer turns to the analytics folks and asks for a report or dashboard with performance data on this measure.

At first glance, this sounds like a straightforward ask. However, upon reviewing the reports, it’s quickly noticed that there are holes in the data. When looking at how the providers are performing with documenting BMI follow-up plans, the data looks terrible! Surely, analytics made some sort of mistake, right? Wrong. Unfortunately, this is a classic case of “garbage in, garbage out.” 

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In this scenario, the data doesn’t exist because the information is not documented discretely, regardless of whether the providers actually are developing a follow-up plan for their patients. Today, there is no way for analytics to produce accurate performance data from unstructured documentation. But this is poised to change in the future as natural language processing and machine learning matures in healthcare.

Physicians and other clinical users, more so than any other users, are incredible at forging ahead when the EHR system doesn’t meet their documentation needs. They find a way to document what they need to document, no matter what. While I have huge respect and empathy for these users (confession: I used to be one of them!), that approach doesn’t always lend itself to maximizing an EHR system that has incredible power for us to leverage, such as Epic.

Accessing the valuable data we need to guide population health decision making—whether in the C-suite or at the bedside—requires that we have as much of an appreciation for capturing quality data as we do for reporting quality data.

 While there is a smattering of reasons why we might find holes in the data we need, a few common culprits include:

  • Lack of tools. Discrete documentation tools, such as SmartForms, SmartTexts, Documentation Flowsheets, and others are not available
  • Inadequate tools. Tools are available, but are either too labor intensive, poorly integrated with the workflow, or are outdated, and therefore go unused
  • Lack of consistency. Quality tools and thorough training are in place, but nonetheless, clinicians prefer their own workarounds, resulting in lots of documentation variation
  • Lack of training. Tools are available, but clinical users are not aware how to access or use them

Set your organization up for population health success

Population health success hinges on discrete, quality documentation as a cornerstone for producing the valuable clinical analytics we need to for important tasks, such as gauging and improving performance, clinical decision making, or negotiating contracts.

If your organization is starting down the road of harnessing analytics for its population health efforts, or if your organization has been down this path and has concerns about its data quality, now is a great time take a step back and revisit the workflows and documentation tools that are going to give you the data you need. 

This may seem like a daunting, nebulous task, but it is worthwhile, we promise! We’ve walked through this challenge with customers and we’re here to help.

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