From Analysis to Action: Not All Healthcare Analytics are Created Equal

The demand for analytics within the healthcare continuum continues to increase (projected to reach nearly $15 billion in 2022) as leaders realize significant returns from the effective application of analytics within their organizations. Sensing the potential, those who have not yet adopted programs of their own are now exploring their options.

Yet deciding how best to implement such solutions can prove challenging; healthcare executives face a bewildering glossary of terms from a multitude of vendors that tout solutions with far-ranging claims, many of which overpromise and underdeliver.  Although analytics touted by numerous vendors make similar promises, comparing solutions through the combined lens of context, clarity, and connection helps distinguish analytics as a product (retrospective interpretation by others) from analytics as a service (real-time, action-oriented end-user enablement).

Context: Incorporate the right data

Within healthcare, analytics further a straightforward goal: increasing the efficacy of patient care by making data actionable. Yet that premise can itself be hard to define (what data? which action?), making it difficult to set realistic expectations for both usage and outcomes. Healthcare is full of structured (e.g., lab results or vitals) and unstructured (e.g., histories or physician notes) data. From clinical information and demographics to billing and claims, the inputs run wide – therefore, leaders should have a firm understanding of what their ultimate objective is, ranging from status identification to condition selection, which will inform their data intake needs.

For instance, one of Xsolis’ core predictive analytics models helps case managers determine appropriate level-of-care. This level-of-care model incorporates documented aspects of a patient encounter including unstructured text (like progress notes or patient history), vitals, labs, orders, and medications. This model produces results that are not only informative, but actionable: helping a case manager correctly determine status, in real time.

Clarity: Produce the right results

The goal of analytics within the healthcare setting should be end-user enablement. Timing considerations, required input, and expected output are major determining factors in the usability of certain models or predictive analytics to help staff perform their respective roles. The Xsolis level-of-care model helps predict case outcomes while cases are underway by combining the above information to produce an accurate, interpretable prediction, which augments a case manager’s ability to review patients and intervene when necessary.

To accomplish this, the Xsolis analytics platform must interface with multiple systems and bridge the silos between disparate data in real time—a process that requires an upfront investment in time and energy, yet produces results far outweighing the cost: Xsolis clients report an average eight-fold return on their investment. Leveraging this real-time predictive power means that case managers are more efficient, patients receive the correct level-of-care, and hospitals are properly reimbursed for their services— all of which result in a stronger framework for healthcare delivery.

Connection: Focus on timely action

Understanding not only what can be gleaned from data but when it can prove useful is crucial to leveraging analytics to their full potential. In ever-changing, dynamic situations like patient care, what’s happening now matters. Retrospective analytics, while valuable in other settings, are unable to prevent, while real-time, concurrent analytics lay a framework for immediate action. 

However, whether working with solutions that document relevant information at patient presentation, or with clinical scoring algorithms which ensure cases with the greatest review need are identified early, real-time predictions accomplish little without a concurrent process change and adoption within the organization. An analytics solution must provide users with clear prompts towards action, help them understand what to do with the analysis provided, and encourage educational opportunities when the correct action is not taken. This helps to foster trust in the process and greater engagement with the technology.

With the context, clarity and connection guiding the decision-making process, healthcare leaders are better equipped to improve their organizational profitability with an immediately actionable analytics solution.