Diving into Data Context
For the last several weeks, I’ve been discussing the most common reasons that healthcare analytics initiatives fall short through a series of posts that also offer recommendations about how to beat the odds. My thoughts essentially boil down to four critical steps:
- Tighten at the Top
Offer specific guidance to decision makers about how to act on insight
- Loosen at the Bottom
Design a data governance framework that allows for a wider range of data types and sources
- Build in Windows, not Walls
Build transparency into your data sources and processes
- Paint a Picture
Make good use of powerful data visualization tools
For a deeper dive into each of these points, I would encourage you to download the full (free!) eBook now!
Today though, I’m going to focus on a specific data management model that can go a long way in helping to implement these recommendations in your organization. I do have a specific tool in mind, but at the end of the day, what we’re really talking about here is a fairly basic concept that serves as a common thread through all of the recommendations I’ve made thus far: data context.
Data Context is the Foundation of Action
In healthcare, analytics insights shouldn’t be a report decision-makers receive once a day or even once an hour. These insights should be the air they breathe, providing a steady call to action just as quickly as data becomes available.
In this environment, data context is nearly as important as the data itself. Which report or data source did these numbers come from? Who uses those reports, who enters the data, how and when did they do it? What real-world actions does this data represent? How does this impact our strategic objectives? Smart decision-makers in healthcare never take such information at face value, because they know the consequences of making the wrong decision based on a faulty understanding of the data. The faster they can understand its provenance and full context, the better prepared they’ll be to make timely decision on it.
The Origin of Origen
This is where Amitech’s Origen Platform comes in. The Origen model delivers an open, collaborative, Wikipedia-like experience for everyone that touches data at every level across the organization. As data collection processes are widened to accept more types of information from more sources, Origen gives users an intuitive, easy-to-use tool for understanding its full context.
Make it Happen
The level of transparency and openness introduced by the Origen platform is transforming the world of healthcare analytics—and it could transform your organization as well, without disrupting any progress you’ve made so far.
If you’re ready to turn your data into action, contact us today.