In the first post of this series, I discussed one of the most common reasons that healthcare analytics initiatives fail to deliver on results: being too open-ended. It turns out that without at least a little guidance about how they should act on the information being presented, decision makers often won’t. Next, we explored the opposite end of that spectrum, which is the tendency for data governance programs to strangle results by keeping a too-tight grip on the type of data that makes it into the mix, and how. This week I’m covering the last two missing links between you and the results you’ve been waiting for from your analytics strategy: transparency and visualization.
Build in Windows, Not Walls
Assuming that you were able to successfully tighten your analytics initiative at the top and loosen it at the bottom, that still won’t be enough to unleash the full power of your data unless the people who are using it have complete confidence in its accuracy. Without assurance that they can defend their decisions, executives, managers and others are more likely to revert to old modes of decision-making rather than sticking their neck out on analytics that came from data and algorithms they don’t fully understand.
Gaining their trust requires building transparency into your data sources and processes. You can do this by providing intuitive tools for data stewardship in an organized and integrated fashion, across your organization. I like to offer Wikipedia as a good model here. Consider that for any given entry on the platform, Wikipedia contributors are granted unrestricted permission to develop, edit and polish content. They do so not because they’re paid but because they care—a trait often shared by health care workers. Given a clear path to updating, commenting on and improving critical care-related data, it is highly likely that they will voluntarily comply once they see the connection. This sense of autonomy and ownership, in combination with an equally open approach around access to data source material, serves to cultivate the widespread confidence necessary for data analytics success.
Paint a Picture
The power of good data visualization is a well-documented aspect of any successful analytics strategy. That’s not news. But in healthcare, what is news is how visualizations are already being put to work among leaders in the field. Part of their power lies in their ability to spark action. To see a scatter chart showing an alarming outlier is quite a different experience than the same issue buried in a jumble of numbers on a report—and one that is far likelier to move you to action.
Thankfully, there are a plethora of commercially available visualization tools that can be plugged into existing analytics solutions without much trouble. Not using them? You should be.
There’s More to This Story
Now that I’ve outlined all four steps to better healthcare analytics outcomes, you might assume that this is the end of the road. But if you stop here, you’ll be missing out on key information about a data management model that can go a long way in helping you put these recommendations into action. Check back next week for details or download our latest eBook to get the full scoop right now.