Embracing data as a strategic advantage is crucial for medical labs. Leveraging healthcare claims data analytics can improve claim quality, monitor financial metrics, and navigate payer agreements & policies. By understanding patients, processes, and finances, labs can optimize operations and enhance their bottom line.
As obvious as this article subject may sound, I often see healthcare executives roll their eyes when I talk about data. But you see, healthcare organizations - especially medical laboratories - are actually technology companies, and their product is data. Data comes in from patients' samples, and data goes out to inform medical diagnosis. And the labs that come to terms with this reality will evolve; those who don’t will cease to exist.
So, putting that aside, we first need to know that embracing data as a strategic advantage is essential for medical labs. Data is the key to understanding patients, processes, and finances. By collecting and analyzing data, labs can identify areas of improvement, optimize operations, and improve their bottom line. One way to achieve this is by utilizing healthcare claims data analytics.
Using Your Data to Improve Claim Quality
Gistia RevenueIQ has analyzed millions of claims, and nearly 70% of denied claims are caused by the quality of the claim. Simply put, it's usually a process error, not substantive.
Healthcare claims data analytics can help to identify patterns in claims that are likely to be denied. It can also track the performance of the claims submission process and identify areas where improvement is necessary. This information can be used to make changes to the processes that are often the cause of denials - such as order or billing workflows.
For example, data can be used to identify common reasons for claim denials, such as missing or incorrect information. These revelations can then be used to train staff and avoid future errors.
Some common claim errors include:
- Incorrect patient information
- Missing or incomplete documentation
- Coding errors, such as CPT code mistakes
- Billing errors
There are a number of ways to identify and prevent these errors. One way is to use claims analytics to review your medical claims data for potential errors. You can also implement quality assurance programs to review claims before they are submitted. Additionally, you can ensure your staff is up-to-date on proper coding and billing procedures.
By taking these steps, you can significantly reduce the number of claim errors you experience. Naturally, doing so will help you improve your revenue cycle performance and get paid more quickly for the services you provide.
Here are some additional tips for preventing common claim errors:
- Make sure all patient information is accurate and up-to-date
- Complete all required documentation for each claim
- Use the correct CPT codes for the services you provide
- Review claims carefully before submitting them
- Train your staff on proper coding and billing procedures
Most of these prevention techniques are manual - however - in this blog we will also discuss methods for automation. (Make sure to subscribe!)
Monitoring Financial Metrics
Once we understand what is driving denials, we should have a real-time way to monitor what’s going on. We need the instrumentation a plane needs to fly.
When discussing monitoring metrics, you may think of reports and other documentation. However, using the plane analogy, imagine a pilot running static reports in the middle of a storm when visibility is zero. A pilot needs instruments that respond to real-time inputs. In the same way, a VP of Revenue Cycle needs to keep an eye on real-time key performance indicators - proactively identifying and addressing issues.
We recommend the following KPIs, which are standard in Gistia RevenueIQ:
- Average payment rate: This indicates how much of your revenue you are actually collecting. A low average payment rate may be a sign that you are having trouble collecting payments from patients or insurance companies.
- Denial rate: This is the percentage of claims that are denied by insurance companies. A high denial rate may be a sign that you are not submitting claims correctly or that your coding is inaccurate.
- First pass yield: This is the percentage of claims that are paid on the first submission. A low first pass yield may be a sign that you need to improve your claims processing procedures.
- Cash trends: This is a measure of how quickly you are collecting cash from patients and insurance companies. Slow cash flow may indicate a problem with collecting payments. It could also mean that accounts receivable is not being managed properly.
- Charge volume: This is the total amount of money that you are billing for services. A decrease in charge volume may be a sign that you are seeing fewer patients or that your prices are too low.
- Claim volume: This is the total number of claims that you are submitting. An increase in claim volume may be a sign that you are seeing more patients or that you are billing for more services.
- Denial resolution rate: This is the percentage of denied claims that you are able to get paid. A low denial resolution rate may be a sign that you are not effectively appealing denied claims.
- Late charges: This is the amount of money that you are losing due to late payments. A high amount of late charges may be a sign that you need to improve your collection procedures.
- Total charge lag days: This is the average number of days it takes to collect payment for a charge. A long total charge lag day may be a sign that you are having trouble collecting payments or that your accounts receivable is not being managed effectively.
Monitor Payer Agreements and Payer Policy
And lastly, reimbursement requirements depend on a macro picture. The above two strategies are very ‘micro’, meaning we are thinking of the minute details. But we must keep an eye on the big picture. This is where being aware of what’s going on with CMS policy, and payer agreements is critical.
By using data to analyze payer policy, bulletins, and other sources of information, providers can identify trends, track changes, and understand the implications of new regulations. This information can be used to develop strategies to avoid denials, adjust prices accordingly, and improve compliance. Additionally, data can be used to identify potential risks and develop mitigation strategies, which can help providers reduce their exposure to financial and operational risks. By understanding the competitive landscape and developing effective strategies, providers can increase their market share.
Here are some additional benefits of using data to navigate the regulatory environment:
- Improved compliance: By tracking changes in regulations and understanding their implications, providers can avoid costly compliance errors.
- Reduced risk: By identifying potential risks and developing mitigation strategies, providers can reduce their exposure to financial and operational risks.
- Increased market share: By understanding the competitive landscape and developing effective strategies, providers can increase their market share.
- Improved contractual terms: By having proof of good practices, providers can use a data-driven approach to obtaining better contractual terms.
So there you have it - three actionable ways data can be operationalized for better revenue cycle performance. Embrace data as a strategic advantage and watch your medical lab thrive in the ever-changing healthcare landscape. By leveraging electronic health records, medical records, and health claims data, labs can identify trends and use predictive analytics to optimize their operations. This can lead to better collaboration with health plans, improved high-risk patient management, and overall enhanced healthcare delivery.
Frequently Asked Questions
How can healthcare claims data analytics help with staffing and resource allocation in medical labs?
Healthcare claims data analytics can provide insights into trends and patterns related to patient volume, claim submission, and service demand. Medical labs can use data analysis to optimize staffing and resource allocation. This ensures they can meet patient needs, maintain efficiency, and control costs. It can lead to improved patient satisfaction, reduced wait times, and increased operational effectiveness.
Can healthcare claims data analytics assist in predicting future trends and challenges in the medical lab industry?
Analyzing historical data and identifying patterns can help medical labs predict future trends and potential challenges. Healthcare claims data analytics are the tools used to do this. Labs can proactively adapt their strategies, invest in the necessary technology or resources, and stay ahead of the competition. Predictive analytics can also aid in identifying potential growth opportunities and areas for expansion.
How can healthcare claims data analytics improve patient outcomes?
Healthcare claims data analytics can aid medical labs. They can reveal patterns and trends in patient care. For example, detecting common diagnostic errors and treatment inefficiencies.
By addressing these issues, labs can improve the accuracy and efficiency of their services, leading to better patient outcomes. They can also help identify areas to invest in new technology or techniques. Data analytics are the future for labs.