Why you need to build an analytics culture in the revenue cycle

Data analytics is being embraced across numerous industries as organizations strive to increase revenue, cut costs, and streamline processes. Many healthcare providers are turning to analytics to improve patient outcomes while navigating dramatic changes in the healthcare revenue model. Despite the growing adoption of analytical tools, many are not succeeding with their technology investment.
For healthcare providers, the revenue cycle can strongly benefit from data analytics. But nearly two-thirds of providers (66.3 percent) report doing little to “use available data to make improvements,” according to a 2014 HIStalk poll.1
The failure of most healthcare providers to leverage data can ironically be traced in large part to a culture that is acutely focused on operations success. The incentive to execute day-to- day activities often trumps investing time and resources on the “possibilities” of analysis. An MIT/IBM survey shows the top three impediments for healthcare providers to a strong analytics program are the ability to get data (37 percent), a culture that doesn’t encourage sharing information (35 percent), and no understanding of how to use analytics to improve the business (34 percent).2
This widespread lack of an analytics culture is a breeding ground for missed opportunities in the dynamic healthcare revenue cycle, which is under pressure to make a successful transition from a fee-for-service payment model to a value-based model.
The traditional revenue cycle features a series of linear steps, beginning with processing of patient medical and insurance information, health information management during the process of care, and ending with billing and collecting. 
A data-driven revenue cycle more fully recognizes interdependencies between multiple areas within the organization. Data spanning the revenue cycle can provide insights across the disparate departments to proactively help each other learn and improve. For example, data analysis can help identify opportunities for improvement upstream of the billing office, such as uncovering a recurring clinician coding error leading to rejected claims; evaluating the impact of registration eligibility checks on denials; or determining inaccurate cost estimates that may diminish cash collections.
Harnessing data in the revenue cycle undoubtedly requires the proper tools and some data analytics skills. However, it’s also imperative that healthcare providers foster an analytics culture within the revenue cycle.
That may be accomplished through the following.
  1. Encourage employees to periodically step back from their busy day-to-day activities to reassess how to be more efficient and effective.
  2. Commit firmly to decision-making based on facts.
  3. Invest in skills and training in addition to technology.
While those steps might seem simple, No. 2 particularly is difficult because people (even healthcare workers) sometimes prefer habit and perceptions to facts. In an analytics culture, facts must drive decisions. And fact-based, data-driven decisions can improve the healthcare revenue cycle.
There are numerous examples:
A 600+ bed hospital system in the Southeast used data to determine it had a significant amount of denials related to untimely filing of claims. By analyzing the data, the system identified and fixed a flaw in how claims were being submitted by its HIS. The value of the provider’s future write-off savings has been estimated at approximately   $2 million.
A small hospital in the West was experiencing costly delays in claims. Data revealed problems with physician documentation. Using the data, the hospital and physicians agreed to a new policy that included a penalty for noncompliance. Within three months the hospital reduced the number of days between discharge and getting the claim into the claim management system for processing from 26 to 13 days, thus helping improve their cash flow. 
A mid-sized government-managed hospital in the Northern Plains that was experiencing skyrocketing claims errors used data to isolate major gaps in the claims transmission process and common errors in claims submitted from its HIS. The hospital was able to cut its error rate over the next three months to less than 10 percent from 20 percent.
Healthcare providers can always benefit through the use of data analytics. With ICD-10 just around the corner, the need for increased transparency into the revenue cycle is more important than ever. With a fourfold increase in diagnostic codes, ICD-10 is predicted to double or even triple denial rates, increase accounts receivable by 20 percent to 40 percent and claim error rates by 6 percent to 10 percent.3 The transition will be managed best by those that analyze timely facts to determine where to focus or adjust their priorities.
By creating a data-driven culture that promotes periodically “stepping back” to gather, distribute and assess their data, healthcare providers can position their organizations to accelerate their operational performance, find new opportunities and thrive during the transition to ICD-10 and well beyond.
2 IBM Global Business Service Executive Report, The value of analytics in healthcare: From insights to outcomes James W. Cortada, Dan Gordon and Bill Lenihan; 2012
3 2011 HIMSS ICD-10 Transformation: Five Critical Risk Mitigation Strategies  

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