It’s as simple as this: Managing your credits effectively is the difference between dollars coming in or going out the door. 

Managing accounts receivable, including credits and debits, has become significantly more challenging in recent years, Many healthcare organizations struggle to maintain efficient processes in the face of rising costs, increasing payor denials, and reduced staffing and financial resources. But there is a bright light at the end of the tunnel: the growing sophistication of technologies like data science and automation. This means you now have more power to streamline how you resolve credit and debit patient accounts with the assistance of data science and machine learning. 

Remind me again … What are data science and machine learning? 

In healthcare and nearly all other industries, talk of “machine learning,” “data science,” “artificial intelligence,” and “automation,” are pervasive. But what do they all mean, and how do they relate to your revenue cycle operations? 

Data science models, which can operate within your existing workflows, use automation to help revenue cycle teams identify and reconcile financial anomalies in credit and debit balance patient accounts. These insights can help you and your team deliver continuous improvements in your revenue cycle systems and processes. 

Machine learning is a type of AI and computer science that uses data and algorithms to replicate how humans learn. Machine learning models can leverage historical patient accounting system data to analyze patterns and predict how accounts might be resolved, providing insights on future accounts with similar patterns. Automation allows machine learning models to home in on targeted segments of credit or debit accounts. That provides revenue cycle teams with high-confidence predictions about credit or debit accounts that might not be possible with manual methods. More traditional methods, in which staff members search for patterns manually, are time-consuming and more limited in scope. 

Why automation? 

Adding automation to your credit balance and debit workflows not only saves valuable time and resources but greatly increases the accuracy of reserve modeling. That’s especially true when your automated solutions are grounded in data—real, accurate patient accounting transaction data. 

Automation is a buzzword within healthcare and other industries. But not every automated solution is grounded in the meaningful data needed to give you the most actionable insights into your credit and debit balance patient accounts. Consider another buzzword: “bots.” Despite what you might have heard, bots “running in the background” can’t replace your organization’s own valuable data. 

At the foundation of Kodiak’s data science and machine learning methods is the data already housed within your organization. Using your patient accounting transaction data allows you to know, with confidence, that the analysis the models discussed above generate is accurately portraying what your credit and debit populations have been like historically, what they look like today, and what they most likely will look like in the future. 

Other benefits of incorporating data science, machine learning, and automation into your account resolution processes include: 

• Identifying hidden financial benefits. Automation can help identify adjustment errors, such as false credits, and help revenue cycle teams prioritize which accounts to focus on to have the most financial impact for the organization. 
  
  
• Resolving credits in bulk. Resolving credits is incredibly time-consuming, and in our experience, can take up to 15 minutes per account to resolve. Automation speeds things up exponentially, allowing staff to focus on higher priority tasks. 
  
  
• Applying historical data to make more accurate predictions. Machine learning and data science can allow you to predict with confidence the resolution on an account based on a historical transactional pattern. 
  
   
• Improving patient satisfaction. If you owe patients money, they appreciate you returning it to them as soon as possible. By identifying refunds faster, you can get patients their cash back sooner. 
  
  
• Increasing job satisfaction. Kodiak’s machine learning models help make better use of staff members’ often-limited time by taking accounts that can be resolved through automation off their plates and steering staff toward those accounts that yield the highest cash value. Knowing that they’re working on these valuable accounts can greatly improve their job satisfaction. 
  
  

Seeing success: Automation yields impressive results  

The following case studies, based on real Kodiak customer results, highlight the benefits of augmenting your accounts receivable management processes with machine learning, data science, and automation. By using unique algorithms to identify and reconcile financial anomalies within customers’ patient accounting systems, Kodiak was able to help these health systems save time, resources, and dollars. All results occurred over an approximately two-year period. 

30-hospital nonprofit health system 

Kodiak: 

Provided: Weekly automated results across hospital credits and debits and physician credits 

Created: KPIs to give leadership additional insight into their accounts receivable, including expected and historical realization rate analysis and estimated controllable loss on denied claims 

Results: More than $68 million in A/R resolutions to date on nearly 183,000 accounts and a savings of 11 FTEs’ worth of manual work annually 

20-hospital, 300+-clinic health system 

Kodiak: 

Provided: Weekly automated results across hospital credits and debits and physician credits 

Created: Kodiak worked with the customer to unlock previously unused patient accounting system functionality to improve back-end efficiency through work queue and account routing automation and leveraging claims data to increase account resolution visibility. 

Results: More than $57 million in A/R resolutions on nearly 200,000 accounts and a savings of 6 FTEs’ worth of manual work annually 

50-hospital, 900+-clinic health system 

Kodiak: 

Provided: Weekly automated results across hospital and physician credits 

Created: Worked with the customer to ensure continuous improvement and maintain buy-in through incorporation of machine learning and expected pay logic and ad hoc credit clean-ups based on payor trends and customer need 

Results: More than $152 million in A/R resolutions on nearly 820,000 accounts and a savings of 34 FTEs’ worth of manual work annually 

A tailored approach to your receivables management 

Using automation to resolve patient accounts can save you big dollars. You also generate opportunities to save your staff valuable time.  

Kodiak’s team of experienced and knowledgeable revenue cycle experts can help your organization clean up large volumes of debit and credit populations to better target accounts with cash opportunities. We’ll help you streamline your workflows to optimize outcomes and increase cash coming in—and decrease cash going out—the door. Kodiak’s data science methods are proven and market-tested, and we combine them with our experts’ deep understanding and experience in account resolution and automation. 

Find out more about our customized, automated receivables management approach, designed to meet your organization’s unique needs. Contact our experts today.