Analytics are alive and kicking: Getting every healthcare user to leverage data analytics

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David Hall-Tipping, Solutions Manager, Logi Analytics

Healthcare organizations are under increasing pressure to streamline operations and run more efficiently, with the ultimate goal of improving patient care while reducing costs. One way to do this is to ensure every worker has easy access to data analytics from health records systems and information portals – and that they actually use that information to make decisions. However, the latter part has proven to be trickier than expected for many organizations.

The analytics adoption problem is something that affects organizations across all industries, and it is only getting compounded as organizations require their employees to be more data-driven. In Logi’s 2015 State of Self-Service Analytics Report we found the analytics adoption rate of business users was 22 percent, even though users in most organizations have plenty of analytics tools on hand.

After taking a step back and looking at the issue, many organizations have quickly realized that the reason many analytics tools result in poor user adoption is the over-generalization of those tools.

Analytics is not one-size-fits-all: In any healthcare setting, different users will need to utilize data in vastly different ways. At a hospital, nurses on the floor need to quickly access high-level, comprehensive patient data on the go. On the other hand, hospital administrators may prefer to dive deep into detailed efficiencies dashboards on their desktops.

If healthcare organizations want to ensure user adoption of analytics tools, it’s essential that the capabilities be tailored to individual users’ roles and skills.

Understanding user personas

Deciding who should be consuming and acting on information is just as important as determining the relevant KPIs for your organization. By taking a step back and understanding the individualized needs and levels of expertise, it will be significantly easier to provide all users with the ability to understand data and derive insights to make their own conclusions and decisions, no matter their skill levels.

The first step to tailoring analytics to your users is breaking down the various user types. In any healthcare setting, nearly every analytics user will fall into one of three categories.

  • Consumers are users who prefer a defined experience. They have a standard set of metrics or KPIs to track in order to do their job effectively, and those metrics don’t change very often. Within a hospital, this may be the on-call nurse who is responsible for monitoring the intensive care unit. He or she would refer to a preformatted dashboard or report on an iPad to keep track of patient data, such as how many people were readmitted to the floor or the number of occupied beds at a given time.
  • Creators are users who value a managed experience, but want to supplement standard metrics with new dashboards and reports that they can create themselves. Within a hospital, this could be the head of the clinical department who wants to study and link the most common diagnoses among readmitted patients, so that he or she can share that information with other teams and management executives on a weekly basis. 
  • Analysts are users who prefer a completely self-guided experience. These users measure data daily, and often don’t have any particular direction or specific questions they need answered. They are open and want the data to speak to them. Within the hospital, this might be the finance analyst who frequently performs cost studies and allocations based on settlement claims in order to provide future recommendations for improvement.

Once you’ve identified the end users in your organization, ask yourself whether your current analytics solution meets everyone’s needs – or just the needs of a select group. The number one barrier to adoption is complexity of use. Many analytics solutions are too complicated for anyone but power users or data analysts.

But when your users are given access to the right data in the right way, it allows everyone to make real-time data more visual and actionable. The result of this is overall performance improvements, and the ability to reallocate resources to use for cure, rather than care.

 

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