Healthcare Providers Must Leverage Automation And Analytics To Optimize A/R Recovery

Medicine doctor and stethoscope in hand touching icon medical network connection with modern virtual screen interface, medical technology network concept

By Navaneeth Nair

For providers, 2020’s COVID- and payer-related challenges accelerated in 2021 and will continue to threaten revenue in 2022. Staffing shortages, increases in payer denials, and increases in days in A/R are not diminishing anytime soon. 

Advertisement

Of these pressures, the struggle to recover accounts receivable is the most lamentable. Accounts that go past their filing limits turn into write-offs, threatening business viability. Providers that adopt an approach to A/R recovery that can scale with volume and succeed independently of staffing fluctuations have the best opportunities to optimize revenues. First, let’s cover the common habits that are impacting reimbursement and cash flow. 

Three habits revenue cycle leaders must abandon in 2022 

We typically find three areas that hold providers back from recouping as much revenue as possible.

  1. Reliance on EMR to address A/R

Many practices and providers believe their electronic medical records systems sufficiently address A/R recovery. And yet, despite their dedicated use of their EMR, they are always chasing money. An EMR does not offer either a robust, comprehensive data source or the analytics necessary to derive useful conclusions from that data. 

Instead, EMR systems are structured with too many separate data silos when having one universal data source renders the most accurate answers. Of course, it takes analytics to get these answers. 

Additionally, EMRs provide basic packaged reports that don’t allow flexible analytics capabilities. For deep dives, teams have to manually download and manipulate data in Excel — by which time, it’s out of date. 

  1. Lack of A/R strategy 

When organizations don’t have a documented, evidence-based A/R strategy, providers arbitrarily make important decisions as to which accounts to pursue. The strategies often focus on a simple priority, for instance, high-dollar filing. These decisions result in manual, time-consuming work. Worse, they aren’t accurate because they look past critical metrics. 

A useful A/R strategy depends on you being able to reliably predict what A/R is recoverable as well as how to prioritize tasks. Simple rule-based analyses cannot effectively make these predictions that depend on dozens of variables, including complexity and probability of recovery You also want to be able to prioritize your tasks, which requires being able to predict net recovery either by agent or by resource. Machine learning is an important tool here for developing and testing useful metrics. 

Manual analyses like these depend on subjective, even biased knowledge of issues rather than a comprehensive 360-degree view of data. When providers lack the tools to apply enough dimensions to the data, they execute inconsistent, poorly-informed A/R tactics and leave money on the table. 

  1. Assigning staff to repetitive and time-consuming tasks 

Backoffice staff can only pursue a limited number of claims. The more time staff has to spend gathering and uploading data, checking insurance portals, following up on appeals, and getting put on hold by a payer, the less time they have for other important tasks. When automation handles the above tasks — often in seconds — staff can focus on the highest value work.  

Optimizing A/R recovery for maximum revenue recovery 

Without good data and analytics, closely examining operations for day-to-day management is a challenge and aging A/R tends to pile up. Always playing catch up becomes nerve-wracking. How do you avoid this? 

Leverage the key insights analytics can deliver

We argue that the backbone of your strategy should be sophisticated machine learning-based analytics so you can understand what the specific factors are driving your challenges and where you should focus your energies.  For example, with eligibility-related denials, which payers are you having eligibility issues with? Are there specific demographics that are impacted? Good analytics should be able to predict what A/R is recoverable, as well as prioritize tasks.

When you examine your analytics, start by benchmarking your existing numbers and developing metrics that measure your success against the industry. 

The most revealing metrics include: 

  • Net collection ratios – This is a clearer view of the actual A/R being collected than cross collection ratios.  
  • Aging – How much of aging A/R is in 0-30, 30-60, and 60-90 day buckets? What trends are you seeing contributing to aging in these buckets? 
  • Denials – By tracking denials, providers avoid having denials underlie A/R issues. What are the key causes of denials?  
  • Overtime rates – Is staff successfully overturning denials that are preventable during overtime? If not, why not and how can you solve this issue?
  • Staff productivity – Using metrics to measure the effectiveness of your A/R recovery methods will help maximize revenues. Measuring productivity against amount recovered will allow for continuous improvement. For example, how many phone calls or total time did a particular claim require?

By understanding how much time and effort is put into each claim, as opposed to averaging against the total, you’ll have a much better view across the board. This will inform you on which A/R to focus on in the future.

Analytics can empower A/R specialists with a data-informed workflow

Data can also be used to construct an informed workflow for A/R specialists. With analytics-based prioritizations, these specialists don’t have to waste time on claims that are unlikely to return revenue. Moreover, a powerful, less subjective analytics structure uses many more dimensions than the typical A/R recovery process that simply prioritizes high-dollar claims without a realistic ROI calculation. 

  1. Machine learning and RPA can alleviate A/R specialists’ workload

One aspect of A/R automation specialists appreciate the most is its ability to relieve them of tedious A/R tasks. After all, keeping guidelines and requirements from hundreds of payers straight is nearly impossible. When this information is compiled by machine learning in an external database, staff can rely on the software rather than their memory. Robot process automation can minimize tedious status checks and appeals, saving staff hours on the phone and insurance portals.  

In addition to including payer requirements, software can contain the state regulations that pertain to payer and provider actions. For instance, when the software notifies the specialist that the account is overdue, it informs the specialist that their state mandates that any payer accounts more than 40 days overdue are subject to fines and interest. (Alert: today, more insurers are taking more than 90 days before paying claims.) 

Automation also improves the overall patient experience. Answers about eligibility and coverage arrive quickly, often in seconds. Always-current EOBs result in fewer inaccuracies and delays. 

  1.  An outside partner can help you scale rapidly to maximize collections 

Despite the time- and cost-savings it can provide, technology only goes so far in terms of solving A/R challenges. For providers currently facing significant aging A/R, bringing on a partner to scale your revenue collection efforts will maximize the amount you can collect. 

Your greatest leverage will come from a two-pronged approach – technology will give you the insights into where your denials are coming from and which claims to prioritize while a partner can provide you with the people power to realize revenue. Experienced A/R staff who understand payer guidelines and can deal with payers and help resolve A/R are essential to your revenue recovery. In this market, these professionals are hard to find or train quickly enough to prevent write-offs. 

A secondary benefit of working with the partner is the ability to easily scale up and down during times of growth and volume fluctuations, a key feature of the pandemic-era healthcare revenue environment.

If you consider an external partner, verify that they are experienced working with analytics and what they can offer in terms of expertise in your specialty and best practices to optimize your existing workflow. 

  1.  Proactively manage self-pay patients 

Finally, consider your approach to self-pay patients, a population that has risen in recent years. Because collecting from these patients can be harder than collecting from the insurance company, providers need a unique plan for educating and collecting payment. 

The first step is to provide the patient with a preservice patient pay estimation and a formal, written payment policy. Machine learning can help facilitate this by generating an estimation based on payer policies and previous data with a high degree of accuracy. 

Providers have many tools to recover A/R in 2022 

In 2022, healthcare providers must recognize that they cannot depend on bringing on additional staff to tackle their aging A/R. Many candidates have left for retail or remote opportunities that offer comparable pay, less stress, and more flexible schedules. Experts anticipate that they won’t come back. This virus has changed the work landscape dramatically and, most likely, permanently. Healthcare providers will need to make similarly dramatic changes, and a new approach to A/R recovery is one of the most critical. 

Modern machine learning-based analytics and AI can help you prioritize your A/R strategy using data-driven predictions of the real ROI on your claims as well as providing intelligence on the major sources of denials. Robot process automation can support claim submissions and status checks in tandem with partners who can help realize this revenue with maximum efficiency. 

Shifting conditions will continue to create revenue instability and staffing challenges in 2022 but also offer an opportunity to redesign your A/R collections process to be more effective than ever. 

————

Navaneeth Nair is Chief Product Officer at Infinx Healthcare which provides leading-edge AI-assisted end-to-end solutions across the payment lifecycle, including patient access, prior authorization, and revenue optimization. Navaneeth has over 20 years of experience in healthcare, where he has specialized in leading large-scale technology product and solutions developments.

Healthcare Business Today is a leading online publication that covers the business of healthcare. Our stories are written from those who are entrenched in this field and helping to shape the future of this industry. Healthcare Business Today offers readers access to fresh developments in health, medicine, science, and technology as well as the latest in patient news, with an emphasis on how these developments affect our lives.