No industry is safe from buzzwords. Blockchain buzz has energized the computer industry. Self-driving vehicles are driving the auto industry wild. And in healthcare, there’s no shortage of enthusiasts evangelizing analytics as the silver bullet to optimizing the revenue cycle. Ask yourself, how many times you have heard the words actionable intelligence in the past year? There’s no disputing that analytics have brought us to the cusp of a new era in better understanding and better managing the revenue cycle. But beware of the one-list-fits-all approach that tends to be the message around KPIs (key performance indicators). Instead, start with your organization’s business objectives and map those back to the metrics that matter most.
There’s a common set of business needs that are universal to the revenue cycle. These are best summarized as the need to quickly secure accurate payment for services rendered, at optimal cost, working across a mix of teams, and with minimal errors. But the root cause elements that should be acted on to deliver on those needs vary from health system to health system. You may track a list of KPIs, but can your team name the top three to watch?
KPIs need to be tracked
For every provider, there’s a short list of KPIs that should be used to track the most pressing needs for ongoing process correction, risk mitigation, and other initiatives. This list can and should change over time as priorities are realigned. Thresholds and resources vary. The team can consume more time or deploy more people to collect and improve accuracy.
But this drives up administrative costs. On the other hand, the team can push bills out quickly, but that may leave money on the table or introduce errors that require rework. A tight filter process to “perfect” claims prior to submission requires more resources and time, but a looser filter speeds the process at risk of missing charges or other information for payment. Each organization has to decide the right balance to monitor.
Consider a hospital that has an incredible 0.1% denial rate. They’re collecting nearly every penny they’re entitled to, but deployed a small (and costly) army of 40 people as well as a third-party collection agency to get there. They are not focused on industry-leading cost to collect. This isn’t possible for organizations with limited resources that must collect as much as they can by focusing on claims that deliver the most impact as opposed to trying to work them all.
Monitoring KPIs covering every stage and influence on the revenue cycle is certainly possible, but it’s not realistic to think an organization can optimize all metrics simultaneously. Some KPIs are in conflict. For example, an organization can have stellar cost-to-collect metrics but poor A/R days if staffed too leanly. Again, the choice of which KPIs to include depends on the organization’s strategy (i.e., its goals and priorities for the period) and realistic self-assessment of areas that can drive meaningful gains.
Accurate payment as a concept
Consider the concept of accurate payment. This is based on contracted rates with payers and agreements with patients. If a facility isn’t vigilant, it leaves money on the table from payer underpayments and inefficient consumer collections. Also, how much time does it take to collect? What’s acceptable? How much effort is being used to collect? How many people? At what cost?
Rather, focusing on the overall patient reimbursement rate will influence how other metrics are used. How does the organization work the reimbursement rate? It might drive up the cost to collect. That might lead to a decision to cap that expense or the number of billers employed.
Patient mix and service mix vary widely between organizations, so the cost and time to collect will also vary, as will the reimbursement rate. These variables are intertwined. Avoid getting lost in detail. A team can monitor dozens of KPIs and track everything from patient registration accuracy and efficiency to services rendered, billing efficiencies, reimbursements, denials, payer relations, and more. But which KPIs align best to helping the organization meet its current challenges? And is there one metric that tends to move others through a ripple effect?
Meta KPIs
To get started, consider these four “meta KPIs.”
- If the organization is struggling with reimbursement accuracy, reimbursement rate looks at how effectively the hospital is being paid at contracted rates and collecting from patients. This important metric is often elevated at hospitals engaged in complex alternative payment models, such as bundled payment. The slightest shifts in the reimbursement rate can impact the bottom line.
- If the issue is timely collections, payment velocity reports how quickly the organization is being paid. A/R days is the standard metric. Increases usually indicate a process problem, such getting claims out the door in a timely manner. Finding the root cause of the slowdown and having easy-to-interpret velocity data to share with stakeholders is crucial to making improvements that speed payment.
- When a drop in payment velocity is suspected to be due to internal factors, quality and productivity metrics, such as Days Not Final Billed reveals how long it takes to get a claim out the door. This KPI is also likely to light up early due to improper integration between the clinical and financial systems. That’s why it’s crucial to establish integration points between the systems.
- If the organization is experiencing a high rate of denials, look to claim quality to see what is influencing everything downstream. Something is amiss when denials climb. Zeroing in early on the cause and having data to support appeals can help get the errant processes back on track and denials under control. Establish alerts for timely filing thresholds to ensure your team doesn’t miss deadlines.
There’s no shortage of cool, granular metrics for revenue cycle leaders to eyeball and consider. But a catalog of metrics isn’t the answer. The metrics that matter are. And those vary from hospital to hospital. Start with the problems the team is trying to solve and then choose the metrics that matter to drive activities that make the most difference.
