Executive Summary
Revenue cycle management (RCM) is critical for clinical laboratory financial performance but plagued with inefficiencies. Common issues include:
- Manual processes prone to error and delay
- Difficulties with coding, claim denials, patient collections
- Lack of insights into physician productivity
These factors lead to significant missed revenue opportunities.
Artificial intelligence and machine learning can optimize many aspects of RCM through:
- Automated coding, claim scrubbing, denial prevention
- Advanced analytics for trend detection, staffing optimization
- Improved collections via patient risk analysis
AI adoption is still low but expected to rise. Research indicates AI could increase lab revenue 25%+ through automation, data insights, and improved staff productivity. Investing in AI-driven RCM makes labs more efficient and profitable.
The Importance of RCM for Laboratories
Revenue cycle management (RCM) is a crucial component of running an efficient and profitable clinical laboratory business. However, laboratories face mounting obstacles in managing their revenue cycle metrics - negatively impacting cash flow and the bottom line.
- 91% of healthcare leaders agree that RCM is crucial, yet only 35% are happy with their current solutions (Ingenious Med).
- Common pain points include inefficient processes, coding errors leading to denied claims, missed charges, collecting from patients, and resolving underpayments.
- These issues result in organizations leaving over 15% of potential revenue uncollected (Ingenious Med).
Laboratories tend to rely on manual workflows and spreadsheets to manage billing and collections. But this makes RCM prone to human error and lag time.
What does automation in healthcare look like?
- McKinsey's research suggests that automation, AI, and digital tools like revenue cycle dashboards could save 20-30% of manual work time. This enables us to achieve significant potential cost savings that we can redirect to more value-adding tasks.
- Nearly half report issues with missed charges; 40% experience charge capture delays; and one-third have problems with inaccurate coding (Ingenious Med).
- Confirming insurance eligibility and obtaining authorizations is time-consuming for staff.
- The high rate of denied claims also takes its toll – 61% of leaders cite denial rates as a top KPI.
Together, these inefficiencies lead to significant revenue leakage.
Staffing shortages compound the cost of manual RCM. According to Robin DeShields of AtlantiCare, “The biggest problem is human resources for everybody, everywhere.” Less experienced new hires struggle with tasks like following up on denied claims and collecting patient payments. This widens the revenue gap.
6 Key Applications of AI/ML
Artificial intelligence and machine learning offer innovative solutions to modernize RCM and address these myriad issues. Six key applications of AI/ML to help with revenue cycle automation include:
1. Automated Coding
Natural language processing AI can read a lab test order and the resulting report. It then automatically selects the most accurate billing codes for the services rendered. This eliminates slow and error-prone manual coding that often leads to unnecessary claim denials. The AI continues to improve by analyzing large datasets to optimize code selection and reimbursement levels.
2. Claim Scrubbing
AI tools can scan claims prior to submission. They catch errors or missing information like inaccurate patient details or lack of pre-authorizations. This step prevents the rejection of claims and avoids lost revenue - and the necessity to resubmit corrected claims.
[A study conducted by Olive AI](https://hitconsultant.net/2020/12/03/olive-acquires-verata-health-prior-authorization/#:~:text=Olive is now able to reduce write-offs by,80%25%2C ultimately offering hospitals %243.5 million in savings.) found that automating prior authorization workflows using machine learning reduced processing time by 80%. This resulted in faster submissions and reimbursement.
3. Denial Prevention
AI reads the text in denial rationale letters to understand the exact reasons for denials. It then takes smart corrective actions, such as fixing incorrect procedure or diagnostic codes or filling in any missing claim information. This enables fast creation and submission of corrected, compliant claims to rapidly recoup denied revenues. Learn more about preventing denials with revenue cycle analytics.
4. Trend Analysis
By establishing baseline patterns and comparing new data each day, machine learning algorithms can identify emerging issues with reimbursement by payer, test type or other factors. For example, AI may spot that a certain payer has suddenly stopped covering a common test. Rapid insight allows the lab to intervene with the payer and prevent prolonged revenue disruption.
5. Physician Productivity Insights
AI analytics provide real-time tracking of number of samples processed, staff hours worked, and services billed for each provider. This allows laboratories to optimize staffing levels and identify potential under-coding for follow-up. According to one survey, only 26% of healthcare organizations currently have adequate insights into physician productivity. Check out 9 critical KPIs to track in your revenue cycle dashboard.
6. Patient Collections
Machine learning tools can predict patients at highest risk for not paying bills, allowing outreach for payment plans or financial assistance applications. AI can also optimize the timing and types of collection messages for improved outcomes.
AI Solutions Increase Revenue
Over 80% of healthcare leaders believe AI will play a moderate to significant role in optimizing revenue cycle management in the future. Adoption is still low – only 16% currently use AI for RCM – but expected to rise steadily. Current AI users perceive improved financial performance and greater efficiency as the top benefits.
While implementing AI solutions requires investment, the research indicates it could increase lab revenue by over 25% on average. Using AI for revenue cycle management automation and gaining data insights can greatly improve revenue capture for labs with tight budgets. Partnering with experienced revenue cycle management vendors speeds integration of AI tools and allows labs to realize benefits more quickly. In the revenue cycle healthcare domain, AI offers a promising path to relieve burden on staff and boost the bottom line.
Frequently Asked Questions about AI/ML RCMs
How long does it take to implement AI-driven RCM solutions? What does it involve?
The time it takes to implement varies depending on the size. However, basic automated tasks such as coding and denial prevention typically take 6-12 weeks to set up.
To simplify the text, we need to do several things. First, we need to connect the AI platform to billing systems. Then, we need to set up rules and workflows.
After that, we need to train algorithms with past data. Finally, we need to conduct testing. Dedicated project management and vendor support help streamline the process. Extending AI to other revenue cycle use cases is iterative based on priority areas.
What factors should a lab consider when selecting an AI revenue cycle vendor?
Key selection criteria include:
- ease of integration with current systems
- AI model accuracy
- solution breadth and scalability
- healthcare domain expertise
- transparency of AI
- project management and support services
- total cost of ownership
- demonstrable results
I recommend prioritizing solutions purpose-built for healthcare over-generalized AI. Comparing vendors on these factors helps identify the optimal partnership.
How can labs prepare their data for maximum effectiveness of AI-driven RCM?
High-quality, well-structured data is crucial for training AI algorithms.
Labs should ensure billing and LIMS data is complete, consistent, and standardized. Removing duplicates and outliers helps as well.
Investing in data integration, governance, and warehousing accelerates AI success. Ongoing data hygiene processes to address issues also enable the AI to improve continuously. Data preparation is essential to maximize the return on investment in AI.