3 Steps To Leveraging Analytics For Revenue Cycle Improvements
By Jeff Wood, vice president of product management, Navicure
A recent report projects the business intelligence (BI) and data analytics market will reach $16.9 billion this year, a healthy increase of 5.2 percent over 2015. In healthcare IT specifically, one-third of respondents in a recent annual Health IT Industry Outlook Survey cited BI and analytics as the biggest topic in healthcare technology. In addition, 37 percent of respondents also indicated their organizations lacked the resources to complete the requested initiatives.
Now more than ever, healthcare organizations are realizing the benefits of implementing a robust BI and data analytics program. To start, with revenue cycle optimization, analytics can increase and accelerate revenue, as well as eliminate revenue cycle inefficiencies. A strong BI program can also contribute to an organization’s quest to prepare for value-based reimbursement; however, before realizing any of these benefits, organizations must develop a plan specific to its needs and positioned to attain near- and long-term results. These three areas should be evaluated to ensure you’re creating the optimal analytics program:
- Leverage High-Impact KPIs First
As you develop your analytics program, it’s helpful to review revenue cycle key performance indicators (KPIs), so you can determine which are most beneficial to measure initially. A few core KPIs to evaluate include:
- First Pass Rate — Also called clean claims rate, this KPI shows your staff’s proficiency in successfully submitting claims to the payer the first time. A high first pass rate points to greater productivity, as your staff will need to spend less time correcting and resubmitting claims. A first pass rate can provide insight into several important factors such as staff productivity, a shorter revenue cycle and better cash flow.
- Denial Rate — For most organizations, denials represent one of the greatest revenue cycle bottlenecks; therefore, denial rate is also an effective KPI for the first phase of your data analytics program. A high number of denials increases the length of your revenue cycle and delays payer reimbursement. By tracking the percentage of denied claims by payer and provider, you can identify and correct issues the first time — and prevent them from reoccurring and impacting cash flow.
- Charge Lag — Charge lag represents the amount of time between the date of service and the date the claim was submitted electronically. By monitoring charge lag, you can ensure your staff is submitting claims in a timely fashion. Minimizing this metric is especially important since this is one part of the revenue cycle that is completely within your organization’s control.
While you may want to address additional revenue cycle metrics to make sure you have a full picture of your organization’s financial health, a BI program in its initial stages can benefit from focus and starting small. These three KPIs all provide significant insight, so selecting two or three high-impact metrics enables you to conduct meaningful analysis in the early stages.
- Use Insights For Optimal Efficiency
Once you’re ready to add additional KPIs to your analytics program, you can leverage the insights you’ve already gleaned to make additions. For instance, if you have a high denial rate, you can dig deeper into this area and add other denial-related KPIs to your analytics program:
- Denial Trends — Provide a historic performance over a set period of time, which can tell you if current performance (and current problem areas) is an anomaly or reflective of a more severe problem.
- Denial Totals — Shows you which claims are currently denied by the payer and require immediate action. In addition, this metric can show you the financial impact of your current denials, thus giving you the insights needed to make immediate corrections and adjust processes.
By reviewing these KPIs in conjunction with your denial rate, you’ll get a more complete picture of your team’s performance related to denials. As a result, you’ll be able to optimize processes, coach staff accordingly and promote continuous improvements.
- Make Data Actionable
When asked to name their greatest complaint regarding BI programs, many healthcare leaders will cite a lack of actionable data. Generally, this complaint stems from two scenarios: Either the respondent receives slick dashboards with no ability to drill down into the underlying data, or they receive an overwhelming amount of data and can’t sift through it to achieve meaningful results.
Making data actionable requires two steps: a disciplined project approach with clear expectations, and technology that can support your program’s goals. Monitor the KPIs you previously identified weekly, monthly, quarterly and annually to identify trends. Set timeline-specific KPI goals and use milestones such as percentages. This degree of specificity will allow you to monitor goals more closely and will enable you to keep analytics aligned with particular actions and milestones.
Regarding technology, make sure you are working with a vendor partner who can provide meaningful, customizable reports. For example, having the ability to customize KPI reports by grouping and filtering can help identify areas of workflow improvement for staff, as well as overall areas of financial improvement. Even more specifically, if your analytics program can be used in conjunction with your clearinghouse, further enhancements can be made to the core KPIs you are tracking.
Leveraging Data To Help Win The Race For Value
As healthcare organizations are tasked to further improve value, BI and data analytics programs will play an increasingly important role. Implementing a plan with specific, measurable goals will facilitate ongoing revenue cycle improvements, help your organization achieve greater financial health and position you for future success.
About The Author
Jeff Wood is vice president of product management at Navicure, a cloud-based healthcare claims management and patient payment solutions provider.