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How analytics can help address revenue cycle challenges


In order to better anticipate and respond to future obstacles, providers must implement data analytics tools that will allow them to quickly and reliably identify potential weaknesses and opportunities for improvement.

How analytics can help address revenue cycle challenges

The long-term financial stability of any healthcare organization has always been directly correlated with the efficacy of its revenue cycle. Unfortunately, although healthcare providers have always faced challenges in maintaining an efficient and optimized financial operation, the COVID-19 pandemic has posed a particularly tumultuous period for the industry.

In order to better anticipate and respond to future obstacles, providers must implement data analytics tools that will allow them to quickly and reliably identify potential weaknesses and opportunities for improvement. In this whitepaper, we will evaluate some of the greatest revenue cycle challenges currently facing the healthcare industry and how data analytics can help.

No single version of the truth

Although the healthcare industry has a vast ocean of data at its disposal to leverage in garnering valuable insights, much of this is unusable due to poor organization and discrepancies between multiple sources. In order to foster collaboration and enable data-driven decision making, organizations need to ensure that all users are looking at the same information.

Consistent and reliable information is necessary for generating trust and fostering a data-friendly work environment. Access to a single and reliable platform increases user engagement and reduces the costs associated with training, and in turn, increases company-wide adoption. To put it simply, employees who trust their data use their data.

This is where a single, encompassing analytics platform becomes relevant. End-to-end solutions consolidate data from multiple heterogenous channels into one source, providing a usable and accountable resource for company-wide decision making.

Information isn’t democratized across the organization

In order to maximize your return on investment, you have to ensure your data is ending up in the right hands and is being used by the right people. Often, organizations limit their data applications to a select few departments or don’t take the steps necessary to encourage deeper adoption throughout the organization. Every level of a business presents opportunities for improvement that can benefit the entire company, but recognizing these opportunities requires staff who actually use the data in their decision-making.

One of the key factors behind democratizing your data is the implementation of software with self-service capabilities. Self-service analytics allows end-users without a technical background or in-depth understanding of analytics to access and use data. According to Howard Dresner’s 2021 Self-Service Business Intelligence Market Study, self-service analytics was ranked sixth out of 44 topics for the most important BI technologies and initiatives. With these features, you can provide staff from all departments with resources necessary to garner data-driven results.

Many companies are also held back by their own executives and administrators, who neglect the importance of fostering a data-friendly environment and generating support from other departments. Mid-level managers and other team leaders need to be inspired by their leadership in implementing data in their day-to-day operations.

Incomplete, missing, and denied charges

One of the biggest obstacles facing healthcare revenue cycles is missing and denied charges. According to a report by the Kaiser Family Foundation, 17% of in-network claims in 2019 and about 14% of in-network claims in 2018 were denied. Due to the urgent nature of healthcare, denied charges can significantly impede operational efficiency and lead to increased administrative time and wasted resources.

Healthcare organizations need a way to quickly identify possible red flags in their revenue cycle and preemptively address any faulty claims before they’re submitted. Using data analytics, businesses can not only monitor the state of in-process claims, but also diagnose other roadblocks and inefficiencies in revenue cycle performance. By evaluating claims against pre-set conditions, analytics platforms can quickly alert staff to at-risk charges and allow organizations to make any necessary adjustments prior to submission. Ensuring that claims are accepted the first time around protects businesses from unnecessary time and resource expenditures further down the road.

The transition to value-based care reimbursement

The healthcare industry as a whole has been struggling to transition to value-based care amidst major payment reforms from the Centers for Medicare & Medicaid Services (CMS). Prior to these reforms, many healthcare organizations relied on fee-for-service payment systems for their revenue cycles.

Now, the push for value-based-care has led to the development of new systems like bundled payment contracts and accountable care organizations, making financial operations increasingly complicated. Hiccups in patient processing can leave valuable funds tied up and unusable while also making it more difficult to properly evaluate the state of the revenue cycle.

However, with the implementation of the right tools, organizations can drastically reduce the pressure induced by a clumsy and burdensome transition. Data analytics platforms can leverage revenue cycle and outcomes-focused information in the same system, allowing you to manage the transition intelligently and responsibly from fee for service to value-based payment.

Furthermore, by combining population health management with analytics, organizations can adopt a cross-functional perspective that takes into account the day-to-day measures of clinical and operational teams. Now that frontline metrics play such a significant role in evaluating claims, organizations have to utilize data in adopting a cross-functional perspective.

Low-quality data and poor data-governance

At the end of the day, data is only as good as the analytics platform you use to manage it. Many healthcare organizations fail to garner any meaningful insights from their data due to poor governance and a lack of any strong foundation. Without proper management and analytic capabilities, your data’s potential will never be realized.

The necessity for high quality and curated data has only become more apparent as businesses increasingly look to employ fact-based decision making in their revenue cycle operations. Opportunities for improvement and potential bottlenecks only become apparent when you can dive deep into the data and evaluate underlying trends.

This is where the advantages of an end-to-end analytics solution become evident. One comprehensive platform conducting integration, rules management, and analytics provides your organization with the resources necessary to optimize operations and substantially increases return on investment.

The way forward

Fortunately, many of the challenges facing the healthcare revenue cycle can be remedied through the adoption of a data-friendly work environment and support from leadership. With proper education and a realistic yet ambitious business intelligence strategy, many providers can drive meaningful changes within their organization. A commitment to changing company culture and how data is viewed at all levels of operation is paramount to improving patient outcomes and increasing ROI.

Moving forward, providers must take a deliberate look at what their long-term objectives are and what pathways they need to forge to get there. Data analytics by itself won’t magically fix all the issues impeding revenue cycle goals, but when accompanied by a committed leadership and a willingness to learn, organizations can make substantial strides in growing and optimizing their enterprise.

George Dealy is vice president of healthcare solutions at Dimensional Insight, an enterprise analytics company based in Burlington, Mass. You can follow Dimensional Insight on LinkedIn or on Twitter @DI_tweet.

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