Blog | Banking and Financial Services

The Importance of Analytics in Healthcare

Read the importance of analytics in healthcare and how to best leverage data analytics in healthcare's digital era.

MAY 22, 2018

Our last blog post was dedicated to the foundations of healthcare in the digital era. In this post – the second in our five-part series devoted to the future of digital solutions in healthcare, we’ll examine the role that analytics can play in strengthening that digital foundation.

After all, with the capacity to improve patient outcomes, streamline care coordination, and minimize administrative costs, analytics are a key component of healthcare in the digital age. But too often, the promise of analytics is hobbled by ineffective underlying data.

So let’s start this examination of analytics by exploring the best ways to leverage the data that you probably already have.

Getting Started

The power of your analytic output will always be dependent on the strength of the data that serves as its input. And the volume of data that can be mined for analytics has never been greater; everything from membership, pharmacy, and lab data, to data from electronic health records (EHR) is readily available.

But despite the abundance of data, the full benefits of analytics frequently fails to materialize. Much of the information in EHRs, for instance, never enters the analytics lifecycle, but instead remains lost as unstructured data in the “text” and “notes” fields. Alternatively, the capture and cycling of unnecessary data hinders the gathering of actionable insights. In fact, well over 90% of data collected from EHRs is never applied to clinical functions, but is used instead for billing, scheduling, and other applications [source: American Hospital Association].

Clean, effective data management that produces complete and consistent reporting is the key to analytics in the digital age. It is no longer enough to simply digitize health records or other sources of data. Instead, organizations should focus not only on the records themselves, but on the content they contain. Is all relevant data scrubbed and available for use? Is irrelevant data omitted? And is the data that is being used interoperable, so it realizes its greatest potential throughout your organization, touching both front and back office functions?

Until clean data that is also interoperable becomes the norm, the full scope of cost savings and clinical improvements that analytics can provide will not be fully realized.

Gaining Actionable Insights

Once effective data management protocols are in place, it is time to use that data to gain actionable insights. To do that, organizations should emphasize agile analytic processes, design thinking, and rapid prototyping to realize the most quick and effective translation of data into action.

Agile analytics based on short, iterative work cycles, allows for the rapid development of new insights. Design thinking, which identifies the operational barriers facing new insights and rapidly isolates the most elegant solution, increases both the speed and efficiency with which new insights can be made operational. The final step - maximizing the value of actionable insights - is through the application of rapid prototyping.

Rapid prototyping allows the real-world use of insights so that they can be assessed and refined not only in terms of immediate operational efficacy but also in terms of your organizations’ overall clinical and savings goals. A more in-depth exploration of the real-world advantages of agile analytics, design thinking, and rapid prototyping is available here.

But it is important to remember that agile analytics, design thinking, and rapid prototyping are not ends in themselves. After they are used to operationalize initial actionable insights, they can then be harnessed to leverage the value of an increasingly broad range of analytics-based improvements. For instance, predictive and prescriptive analytics can be channeled in support of a variety of clinical decisions, from helping to lower rates of unnecessary readmissions to realizing appropriate levels of post-acute care. Predictive and prescriptive analytics can also be applied to operational areas including billing and patient outreach.

Moving Forward

Today, the power of analytics to offer clinical and operational insights when and where they are needed most is no longer a market advantage: it is a competitive necessity. And tapping the power of analytics can begin with a simple maturity assessment to determine where on the analytics adoption spectrum your organization lies. Because when you know what your organization’s analytic strengths are today, you can make an informed decision about what direction you should take to maximize the value of your analytic position in the days to come.

The reason this matters is because in the digital age, the future of analytics is the future of healthcare. As machine learning and artificial intelligence speed the development and reach of analytics even as they underscore analytics as a necessary tool of market survival, no organization can afford to be left behind.

Want to find out more about how Sutherland can help you harness the power of analytics to transform your operations? Contact us at

Up next in our series? The Direction of Digital Healthcare, Part 3: an investigation of how your organization can benefit from the advantages of design thinking.

Enhance CX. Drive Growth and Reduce Costs.

Sutherland Editorial


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