Optimize Revenue Cycle Performance Using the Right Metrics

By Alexander P. Garrison, Brian B. Sanderson, and Daniel Wallace
| 9/12/2017
Optimize Revenue Cycle Performance

Healthcare organizations today are drowning in data. Data metrics help organizations assess their revenue cycle performance, but the weight of too much data – coupled with a lack of clear direction for how best to interpret and use that data – quickly can overwhelm and even paralyze organizational leadership.

Knowing which data elements to home in on and how to drill down to obtain the most actionable information is crucial to optimizing revenue cycle performance. By following a step-by-step process, organizational leadership can determine what metrics they’re evaluating, assess the integrity of those metrics, and make adjustments so that the organization is working from a single source of data truth – with all information aligned.

Step 1: Take Inventory of Data Tracking

To assess whether it is effectively tracking and using data, an organization first needs to know what data it is already tracking. To determine this, organizations must inventory the current data sources they use to produce key performance indicators (KPIs).

Healthcare organizations track data and KPIs through their patient accounting systems (PASs), and most also use 835 and 837 electronic data interchange (EDI) data. Linking these three data sources at the account level allows for unique analytics and root cause analyses, particularly of denials and self-pay after insurance (SPAI) performance.

Step 2: Assess Data Integrity

The organization may track myriad metrics across numerous functional areas (patient access, finance, managed care, and others). But do those metrics have integrity? Organizations must evaluate the data source of the metrics. If different data sources are delivering similar KPIs – for example, if the finance and revenue cycle departments are using separate data sources – then the organization must investigate to determine if the metrics are accurate and have integrity.

An example of a potential data integrity issue is when an organization tracks final denial write-offs and uses this KPI to measure denial resolution performance. Leadership at the organization should consider whether the collections team is using its write-off codes appropriately. If representatives frequently, or even occasionally, use contractual transaction codes rather than the correct final denial write-off codes to close out denied accounts that were not successfully resolved, then this KPI will not be an accurate measure of the organization’s denial resolution performance.

Another way of evaluating a metric’s integrity is assessing whether the level at which that metric is evaluated accurately captures strengths or weaknesses in that revenue cycle performance area. Take, for example, another common revenue cycle KPI: point-of-service (POS) patient cash collections. Often, the metrics related to patient collections are assessed at a macro level and evaluated based on high-level financial information, such as net revenue and total patient cash collected. Assessing metrics from this high level can hide successes or areas in need of improvement. To get a better idea of what might be causing specific problems, providers should consider evaluating POS patient cash collections by department. For SPAI performance, providers might use 835 EDI patient responsibility as the denominator for SPAI point-of-service KPIs.

Step 3: Improve Current Metrics

If evaluation reveals that some of the organization’s source data does not have integrity, the organization should develop a single source of truth for the data sources in question. Then, once the organization has established its single source of data truth and corresponding KPIs, the final step is verifying that the KPIs selected align with the organization’s goals and mission and effectively measure performance down to the department level.

Having analytic and drill-down capability for each KPI selected is critical, as this allows stakeholders to perform root cause analyses and target errors to implement corrections and improve performance. For example, consider the initial denial rate KPI, which is measured as a percentage of gross patient services revenue. Understanding that an organization has a high initial denial rate when compared to past performance or industry peers can be helpful in measuring revenue cycle processes. However, performing a root cause analysis based on standardized categories, enabled through the account-level linking of PAS and 835 and 837 EDI data, could show that the increase is from a particular payer and concentrated in a certain service location or even with an attending physician. This type of root cause analysis is valuable, as it reveals opportunity areas and, ultimately, contributes to improved performance, particularly when evaluating complex processes such as denial prevention and resolution.

To assess a provider’s overall revenue cycle performance and target top areas for improvement, executives should consider using a weighted scoring mechanism to measure performance and spot trends. Weighting the most valuable revenue cycle KPIs to create an overall score can enable provider executives to better monitor overall performance and help their management teams focus improvement efforts on the measures and underlying processes that most closely tie to the organization’s success.

An Overall Scoring Mechanism for Better Insights

Healthcare executives can be overwhelmed by the large amount of data and KPIs delivered to their inboxes. Sifting through reports filled with numerous metrics and understanding incremental changes and the operational significance of KPI trends can be challenging for leaders, particularly those overseeing large and complex organizations.

By using validated and detailed data, healthcare organizations can gain real insight into revenue cycle challenges, identify priorities, and move ahead with performance improvements.

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Brian B. Sanderson
Daniel Wallace