Linking Data to Healthcare Training and to Measuring Its Effectiveness: Article Excerpt
June 21, 2018
For the past several years, HealthStream and Juice Analytics have worked together to make HealthStream customers’ data more accessible. They use the data that HealthStream collects in partnership with their clients and build outcome applications and analytical tools that then allow clients to make well-informed decisions. HealthStream recently interviewed CEO and founder Zach Gemignani to discuss how Juice Analytics has helped HealthStream customers better understand their data, compare their results against national benchmarks, and make better decisions. You can listen to the full Second Opinions podcast at www.healthstream.com/second-opinions-podcast.
How Juice Analytics Makes Data Useful
There is no shortage of data in the healthcare market, but there is a shortage of data being used to its full potential. Gemignani points out, “The best data in the world is useless if the everyday decision-maker can’t understand and interact with it.” At Juice Analytics, the team of visualization experts changes the way organizations see, discuss, and use data. Gemignani details four ways they make data more useful—resolving the “last mile problem,” using data stories, making training more efficient, and being data-driven.
Last Mile Problem
In studying industries that collect data, the team at Juice Analytics found a lot of companies stuck in what they call the “last mile problem,” where they don’t know how to transform the data they have already gathered into something of value. Gemignani explains, “If the data doesn’t get into the hands of the people who are on the frontlines of your organization in a way they can understand it, then all of that gathered data is essentially wasted.” Juice Analytics helps organizations make that leap by taking their collected data, packaging it up into an understandable form, and delivering it to the decision-makers.
Gemignani emphasizes that the people typically receiving the data are not usually data analysts, and that they need to be able to step back from the data with a clear understanding of what action they should take based on what they’re seeing. “We try to lead the non-analytical person to make the connection between what insights or analysis they’re seeing in the data to what action they should take to achieve a better outcome for their organization,” Gemignani says.
There are a lot of ways to visualize data by using dashboards or analytical tools, but Gemignani explains that the key to making reporting systems useful is through the use of what Juice Analytics calls “data stories.” Juice Analytics applications use data stories to walk people through data like they would walk through a narrative flow, step-by-step on a journey as they explore the data to understand what they should do about it. Gemignani says that this differentiates their technology from other tools. He adds, “First we think about the end users—their jobs, how busy they are, and how comfortable they are with data. If you start from your audience and you immediately think about how you can help those people succeed in their jobs, you’re going to build a better way of exploring, of giving people data, and of giving them access to it in a way that is intuitive and digestible.”
Creating Efficient Training
Juice Analytics’ smarter use of data helps organizations provide more efficient training, which is a major concern in healthcare. Gemignani explains that a key starting point is to find the gaps in knowledge, where students or staff are struggling, and help remediate those situations by recommending the right kind of training that will be helpful to those certain sets of people.
Juice Analytics also provides an analytical tool that helps administrators have clear visibility into which students have and have not completed their work and looks for the most efficient way an organization can deliver training. Gemignani says, “There are always opportunities to think about which people are assigned to which courses and what time and frequency is necessary to achieve proficiency in the workforce. But training is expensive, requires time, and takes people off the job. Our analytical applications help balance those two sides and deliver really efficient training across an organization.”
To Gemignani, hearing that an organization wants to be data-driven is only a starting point. He reminds us that actions speak louder than words, and that a lot of organizations struggle to begin the journey to being data-driven. Gemignani highlights three factors that indicate whether or not an organization is data-driven:
- Does leadership demonstrate data-driven decision-making?
- Have they chosen a narrow set of metrics that are the key performance measures of the organization and communicated that throughout the organization?
- Are they building a workforce that has the skills and capabilities to be data-driven themselves?
Read the full article here.