Account Intelligence
Guided Agents Drive AR Gains
Experience with AR follow up and denial transactions across 20 million account touches on leading practice management systems leaves us continually driving for higher reimbursements.

The Ineffective AR Strategy


Modern practice management systems contain a trove of information for generating higher reimbursements from the flow of claims.

Unfortunately, little of that information is available to an agent following up on billed accounts.

The industry seems to attribute poor reimbursement to inflexible systems and weak skills. Although we sympathize with our clients’ staff. Beyond obvious targets like high-dollar accounts and accounts approaching timely filing limits, system queues provide little differentiation to increase cash flow. We don’t, unfortunately, always work on the most promising accounts.

And the PMS provides little guidance on what tactics successfully resolved similar accounts, leaving staff mired in long comments fields and reaching deeply into their own experience for clues on a promising approach. Newer agents are left awash in ineffective tactics like the Refer to Manager button or "change something reasonable and hit the Resubmit button."

Our agents feel like lab rats tagging a bar to dispense a pellet, and trying to figure out what to do with that undifferentiated account before the next account flies down the chute. We’ve found this to be an ineffective strategy for lowering AR and terribly demotivating to our staff.


Managers and team leaders at Vee Healthtek tackled the need for a more effective AR process. Their solution started with account prioritization and scaled by mapping accounts to scenarios that guide agents through high probability solutions.

We imprinted an account priority model and process scenarios on our Process Management Information System. Vee Healthtek built the ProMISe platform to scale clients' production transaction processes.

Setting account priority is akin to emergency triage. With good intelligence, some accounts need immediate attention to have a chance of getting paid. We can ignore others as likely to be paid without further attention.

The key insight for setting account priority came from history-driven inventory categories. We’ve examined the effects of payer liquidity rates, denial reasons, physician specialties, and billing errors on the timeliness of reimbursement.

With a client’s aged trial balance and 837s/835s, we accumulate a production history to forecast results and adjust model parameters. We create virtual queues of target accounts to implement daily priorities.

Our process managers carefully craft root cause error categories into ProMISe solution scenarios. Each scenario leads the agent through a research path using discriminating questions, decision trees, payer clarifications, and recommended actions to get reimbursed with the fewest touches.

ProMISe serves the agent as a coach through each account encounter to offer high-probability paths for a successful transaction. Agents increase skills through micro-events, as they’re offered data to support a resolution.

Following the ProMISe priority algorithm, agents address accounts each day that are most likely to need intervention to increase payment results. Working process scenarios at scale, we experience a 20% increase in monthly reimbursements from working the right accounts with the right scenarios at the right time. While continually improving our agents’ skills and experience, these gains have been driven primarily by inventory priority control.

Inventory Control Drives Reimbursements

From process scenarios and accumulated history data, ProMISe feeds a dashboard to help our managers and clients deeply understand account inventory and control resource priorities.

Experience in managing root causes of payment failures led us to reengineer the entire concept of a dashboard.

On the management dashboard, need meets function. It's designed to provoke questions and unearth insights. Our managers and clients use it to understand inventory factors like account age, financial categories, error categories, payer dynamics, and scenario volumes.

By drilling down from categories to sub-categories, and then to account details, a manager builds an intuitive feel for the account inventory, how it's changing, and results driven by current priority strategies.

Our dashboard highlights inventory structure in graphic dimensions like:

  • AR by Age – Account values categorized by financial class, denial categories and scenario placement.
  • Inventory Status – Exception categories like DNFB, High Value and Approaching TFL
  • Resolution Detail – by account age, resolution volumes and inflow
  • Collections by Month – Total reimbursement and account volume by month
  • AR Trends – Payments and adjustments for the current and prior periods
  • Payer Denials – Denials by payer, with drill-down on causes

Drilling down in each graph gives insights on structure and causes. For instance, drilling into AR by Age shows Financial Classes for that age. Following a Financial Class shows the Denial Categories. Under each Denial Category, the Payer Inventory for that denial is exposed. Ultimately the detail for individual accounts can be accessed.

With insights from quickly exploring different data paths, our managers develop intuition for the inventory and prospective improvement actions. The dashboard becomes indispensible to production reviews by focusing attention on the largest opportunities.

For a client’s inventory, the dashboard highlights impending problems and helps them explore optimization strategies. Critically, we find that solid performance data measures the effect of recent decisions and helps formalize initiatives for continuing improvement.

For any mix of resources, Vee Healthtek's clients enjoy better control of billing inventory and higher reimbursements from our ProMISe.


Meet the Author

Randall Davis - Senior Director Client Operations

Randall Davis' entire career in complex systems has been focused on solving practical problems in service development and operations. Through two decades in software engineering, finance, and healthcare, he deployed critical infrastructure for process performance and reliability on a collapsing cost curve. Since 2013, Randall has dramatically improved healthcare revenue efficiency through technology and process innovation.