Hospitals continue to lose billions of dollars of income they deserve and desperately need to provide care for their communities. The average hospital leaves up to $22 million on the table, according to research.
What cannot be measured cannot be managed. Hospitals leave money on the table because they cannot identify revenue leaks effectively. Most hospitals rely solely on traditional revenue recovery approaches
Healthcare is a highly data rich function. Advances in Data Sciences, Artificial Intelligence (AI), and Machine Learning (ML) can identify revenue opportunities that humans and traditional approaches miss.
Artificial Intelligence to the rescue:
- 60% of healthcare executives forecast that predictive analytics will save their organization 15% or more over the next five years.
- 87% of executives say predictive analytics is important to the future of their business.
How AI and ML can fix revenue leaks:
Traditionally, revenue cycle performance is assessed using KPIs against industry benchmarks and historical performance. Financially savvy hospitals are able to see beyond the traditional KPIs, while laggards are not. Financially savvy hospitals can identify the revenue leaks and their root case.
AI and ML can supercharge efforts to measure and analyze denial reviews, underpayments and more.
Predictive analytics helps make decisions using both historical and real time data. This capability enables the organizations to focus on “what should be done”. For example, Predictive analytics use the historic revenue performance and predict future revenue and project risk by assessing Revenue at Risk (RAR)
RAR = Revenue denied + Revenue underpaid + Self pay revenue
According to industry research and HealthSigma desk research,
- Denials cost $118 dollars per claim to appeal. This is a $8.6 billion administrative costs to the healthcare industry.
- 1–5% of self-pay accounts written off as bad debt have billable insurance unknown to the provider. This is equivalent to leaving money on the table.
A data savvy hospital knows where to focus their recovery efforts with precision.
AI and ML combined can predict patient accounts at risk for non-payment. Analytics can quantify the registration errors, insufficient documentation, and unclean claims. These represent areas that directly influence non-payment and claim delays.
AI and ML is essentially helping decision makers focus squarely on cases which will yield additional revenues for the hospital. Data savvy hospital are able to scale this focus across the enterprise with the help of AI and ML. The laggards rely on human efforts solely.
Health Sigma Case Study 1:
AI Driven Analytics helps Orthopaedic surgery practice avoid leaving money on the table
For a multi-location, Texas based Orthopaedic surgery and sports medicine practice, Health Sigma’s AI Driven Analytics identified underpayments of approx. $500,000 across an annual revenue of approx. $15M.
To start with, the AI engine is trained not to overly focus on small amounts, but on amounts that can collectively move the needle for underpayments recovery.
A small percentage of claims, 2 – 4% met the minimum threshold and delivered a meaningful impact to the bottom-line. We observed that underpayments ranged from 2.5% (for BCBS) to 3.84% (for Aetna).
The AI engine can continue to learn and work almost autonomously for lower amounts. However, there is always an informed balance to be struck between efforts and potential underpayments recovery.
Health Sigma Case Study 2:
Health Sigma’s AI Driven Analytics discovers $500,000 of underpayments for cash starved hospital
The context is a 50 bed hospital with approx. $160M in billed charges and approx. $100M in revenues across Inpatient, Outpatient and Clinic.
Health Sigma’s AI Driven Analytics poured over 2018 Medicare IP Claims which accounted for revenues of $11.63M.
The AI powered analytics smartly identified approx. $700K of potential underpayments. That is a potential 6%+ impact to the bottom-line of a cash starved facility. From this consideration set, the AI predicted with a high degree of confidence that approx. $500K will be recovered.
AI and ML to analyze revenue cycle margin opportunities:
With the help of analytic tools, AI, and ML, providers can identify and categorize the root causes of revenue leakages from historical data and new patterns in real-time. These categorizations can then be represented as a Value Matrix. The Value Matrix guides recover opportunities with the best mix of efforts needed and RoI.
The Value Matrix for denials: Source HFM
AI and automation to improve revenue margins:
Revenue cycle is associated with time-consuming and repetitive processes that are tedious and subject to human error.
Leading industry research and HealthSigma internal research predicts the cost to collect will decrease between 25% to 50% over the next 5 to 10 years due to automation and advances in Data Sciences like AI and ML.
AI and ML can help in identifying the right codes the first time to prevent re-work and associated costs for the appeals. This further reduces the days for claim submissions, thus reducing the risk of untimely fillings.
AI can generate the data that categorizes the accounts handling and helps in strategic talent allocation, assigning specific accounts to the right staff members to maximize their expertise.
The most common reasons for denials were authorization-related denials due to changes in service and incomplete information. AI and ML can bring these datasets together from various source systems to evaluate line-item level denials with high precision, scale and speeds humans alone cannot match.
Every hospital is prone to revenue leaks in revenue cycles. Analytics and advanced data science capabilities differentiate the financially savvy hospitals from the rest. Turning raw data into a reliable, scalable decision-making tool is non-trivial. The path to effectively using data sciences starts with the desire and ambition to think beyond traditional approaches. The journey also involves selecting the right partner with a proven track record. A partner that deeply understands that hospitals deserve every dollar that is owed to them, so that they can continue to keep out communities healthy and productive.