Claims denials may significantly affect the revenue stream of healthcare practices, from paying extra costs for reworking a claim to facing direct income loss. Modern healthcare organizations should reduce such denials, which is vital for maximizing their bottom line. Therefore, using healthcare claims data analytics solutions is perfect to make the desired things happen.
Meanwhile, that also requires relevant skills and knowledge to deal with medical claims data and an appropriate understanding of how this information works. So, your healthcare practice can ensure more informed decision-making and save costs in claims processing. In this blog post, we will explore the potential of claims data analytics and its impact on your company’s short- and long-term objectives.
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“At SPsoft, we take pride in our profound understanding of healthcare claims data analytics. With years of dedicated experience, our team has crafted innovative solutions that empower healthcare providers to derive meaningful insights. We are committed to driving efficiency and accuracy in the ever-evolving healthcare data landscape, ensuring our clients receive the highest standard of analytics to make informed decisions and enhance patient outcomes.”
Mike Lazor
CEO, SPsoft
I am thrilled to work in a team that excels in developing cutting-edge healthcare claim data predictive analytics software. Our commitment to innovation and precision is evident in every product we deliver. By leveraging our industry knowledge, we strive to create solutions that exceed the evolving needs of healthcare organizations. At SPsoft, we provide our clients with the tools they need to navigate the complexities of claims data and drive meaningful impact.
Romaniya Mykyta
Head of Product Management, SPsoft
The Rising Demand for Healthcare Claims Data Analytics
Claims analytics in healthcare utilizes medical information to analyze relevant claims and associated results, including rejections, denials, and acceptances. Nowadays, many analytical procedures depend on specific tech solutions that cover basic software tools such as databases and fully automated platforms. But what is the current situation within the healthcare claims data analytics domain? Let’s check.
According to Revcycle Intelligence, healthcare organizations have struggled with an increase of 20% in claim denial rates during the last five years. At the same time, the survey conducted by Harmony Healthcare shows that 33% of respondents deal with 10%+ average denial rates. It means such healthcare providers are in great danger. After all, some medical companies suffer from incredibly high denial rates of about 80%.
When it comes to real money, a modern hospital can lose almost $5 million annually because of unresolved healthcare claim denials. For many facilities, it can be 5% of net revenue, as the Journal of the American Health Information Management Association (AHIMA) reports. Thus, paying attention to these tendencies, the most effective approach to reducing such a negative financial influence is preventing a claim denial. That is where healthcare claims data analytics comes in handy since it can help define trends in denials and reduce or even avoid them.
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Key Applications of Claims Analytics in Healthcare
Despite the constant growth of claims denial in the medical sector, all healthcare practices and health systems can minimize relevant denials. With a proper quality claims analytics platform, your organization may address the issue on each level as you get an opportunity to find and understand the existing trends in denials better. Thus, below are the most critical use cases of healthcare claims data analytics.
Identifying Issues
The absence of claims data analytics means all denials are in a particular vacuum. For example, the patient has coverage denied for the magnetic resonance imaging (MRI). If you have no healthcare claims data analytics software, the only thing you may know is the cause of the denial. Besides, you do not understand the frequency of this issue, and you cannot realize the most common coverage details within such denied claims.
Fortunately, an analytics solution assists in transforming denials into valuable data your practice can utilize to learn about the current patterns. So, you can see the crucial problem rather than analyzing all denials as stand-alone issues. This step allows your organization to make more informed decisions regarding further actions.
Of course, the patient’s denial can be incredibly unique, and you may have no previous experience in dealing with it. But you do not need to implement something new and transformative in such a situation. However, if the issue has been repeated in the last months, you should look closer. Thus, healthcare claims data analytics is essential for identifying and prioritizing the problems that provoke denials.
Analyzing the CPT Code
One of the most effective approaches to resolving claim denials is to use the current procedural terminology (CPT) code. With healthcare claims data analytics software, you can connect the CPT code billing with relevant payment data and denial rates. That helps your organization to learn from this process, particularly about the frequency of billing a specific CPT code.
In turn, you can identify which CPT code or modifier has the largest number of associated claims denials and underpayments. Also, if some codes show disproportionate denial rates, you should explore them in more detail to realize other common critical points such claims can have.
Organizing Denials by Payer
With robust claims analytics in healthcare, you can organize and manage denials by payer more effectively. Besides, you can determine whether a payer returns a disproportionate amount of denials. You can even trace the relevant problems back to the claim requirements of your payers. At the same time, performing a basic review of such requirements and educating billing specialists if needed can help decrease your denial rates.
A detailed payer analysis may also benefit your healthcare organization in terms of payer contract negotiation. For instance, managed care services can leverage denial information to change contracts for revenue protection. Moreover, such data is often vital for negotiating new contracts successfully. After all, by understanding the reasons and how some issues with your existing payers happen, you can prevent future ones from them.
Managing Denials
KFF reports that 10% of claims denials in marketplace plans result from the lack of referral and prior authorization. Besides, an additional 16% happens due to excluding claimed services within the patient’s plan. About 1.7% of non-behavioral health claims result in denials for medical necessity reasons. All the mentioned insights are impossible without modern healthcare claims data analytics software. However, they can provide your healthcare company with the correct direction by showing you the most common problems.
Here, an ophthalmology practice is one of the best examples. Because of the complicated coding requirements in this field, coding and modifier issues provoke numerous denials. That makes healthcare claims data analytics a vital instrument in collecting precise information specific to your healthcare practice. The appropriate analytics software allows for allocating resources to the problems that directly affect your company’s bottom line.
How Healthcare Providers Can Reach the Full Potential of Claims Analytics
In today’s digital era, claims analytics in healthcare may promote significant changes. The more robust and advanced your system, the more data your medical organization has and the more efficient strategies you implement. Here are two critical ways to ensure the optimal platform.
Utilize an Automated Tool
Adopting automated healthcare claims data analytics software is the most effective option, especially regarding speed and costs. Such a tool frees your company from manual data collection and organization, which often leaves too much space for unintentional human errors. An up-to-date automated solution points your practice toward issues you may need help to see. So, you can consider the existing patterns regarding payer underpayment and denials that you may ignore in other cases. That allows you to focus on resolving them as you will have valuable data.
Furthermore, the right claims analytics platform will even assist you in managing relevant denial investigation processes. These enable all your stakeholders to stay informed and help monitor the success of changes in policies, procedures, and contracts.
Hire a Department or Professional to Work on Claims Analytics
If your healthcare practice is small and you deal with simple billing tools, you can provide the manual performance of healthcare claims data analytics. In this scenario, a particular specialist or department can manage the process with spreadsheets and visual scanning for patterns.
However, manual tools will not come in handy if your organization is larger and you use more complicated billing systems. That is because you may face challenges when organizing the code, as your company should handle advanced procedures and numerous billing codes. So, you must establish a large team with the required expertise to achieve the desired results.
Ultimately, the most intelligent solution is to apply an automated platform and hire a specialist or a small department to ensure efficient management. Since these tools are much faster than manual work, you can utilize the data more effectively and create a more substantial impact.
The Advantages of Using Healthcare Claims Data Analytics Software
A quality software platform enables your organization to do more critical things with its information faster. However, simultaneously, the system’s value depends on your further steps regarding using obtained data. With healthcare claims data analytics software, you will spend fewer hours dealing with numbers but can dedicate time to making informed solutions. Below are the most crucial benefits developed directly from healthcare data insights.
Identified Potential Revenue Flows
According to the Journal of AHIMA, modern healthcare providers may recover up to 66% of denied claims. But at the same time, “as many as 60 percent of returned claims are never resubmitted.” With an advanced claims analytics tool, reworking and appealing more denials will become much easier for your healthcare practice.
Such solutions allow for automatically detecting patterns of each denial and underpayment from payers. The relevant patterns demonstrate where to pay more attention and spend resources. Besides, a software platform can indicate emerging errors and define whether they occur on your organization’s or the payer’s side. For instance, you may establish a specific repeatable procedure for contractual errors made by payers to highlight them and request further reviews.
Meanwhile, when the issue lies in coding, your claims analytics software will also showcase where it appears the most frequently. Thus, your coding team can use the particular information to begin troubleshooting activities.
Higher Clean Claims Rate (CCR)
Apart from enhancing your practice’s bottom line, the successful rework of denied claims also assists in increasing the clean claims rate. The latter is the proportion of claims you do not need to edit before submitting them to payers.
Thanks to robust healthcare claims data analytics platforms, all administrative errors that require edits will be highlighted. But also, you must train your billing and coding experts to double-check such errors. That is vital for strengthening your CCR and reducing the risk of potential denials.
Discover the key pillars of effective healthcare data management, from seamless collection to secure storage and robust integration. Unlock the benefits here!
Reduced Days in Accounts Receivable (AR)
The delays associated with accounts receivable can undoubtedly affect the healthcare practice’s profits. For example, the American Journal of Roentgenology reports that “the probability of collecting a delinquent account drops from 73% at 90 days to 50% at 180 days” in AR.
However, the research also claims that the acceptable maximum is about 60 days, varying by the type of healthcare services. For family physicians, claims should stay in accounts receivable for 30-40 days at most.
With healthcare claims data analytics, you can decrease days in accounts receivable significantly by reducing issues regarding submitted claims. After all, cleaner claims are processed faster, so your healthcare practice will get payments sooner.
Cost Savings Due to Minimized Appealing Claims
The study conducted by HFMA shows that “the cost of reworking denied claims is as high as 20% of an organization’s revenue cycle expenses.” That is because such claims “are four times more expensive to process than the initial claim.” Meanwhile, the calculated rework costs for healthcare practices or systems are about $118 per denied claim. Fortunately, your organization can address issues faster with claims analytics software. Also, by highlighting the most common errors, you can prevent or correct them more efficiently.
Eliminated Manual Spreadsheet Tasks
Of course, you may fear the up-front costs of adopting healthcare claims data analytics automation. On the other hand, imagine the amount of time required to perform the same tasks manually. Such spreadsheet work needs many hours of attention your experts can dedicate to more critical operations that drive direct revenue.
Instead, an automated claims analytics platform can collect, sort, and process the information for your practice, improving its overall productivity. It provides valuable data insights with visual formats, allowing for quickly identifying and acting on issues.
Assessed Medication Compliance
By utilizing healthcare claim data predictive analytics, healthcare providers can ensure adequate assessment of medication compliance, as claims data often cover key details about medications. They may see all prescription fills and refills, along with relevant dates of such events. The correct analysis of this information helps healthcare practices identify patients who ignore taking the required medications and act accordingly to resolve the issue.
Explore the world of data collection in healthcare, from understanding the importance of accurate information to navigating challenges and embracing future trends!
Driving Claims Data Analytics to Promote Public Health Initiatives
Today’s healthcare practices utilize healthcare claims data analytics to achieve some critical objectives regarding population health initiatives. Such goals often cover the following:
Monitor Medication Adherence
Medical companies can utilize healthcare claims data analytics to track medication adherence. They can do that properly by determining which medications patients have been prescribed. Then, they analyze whether the individuals have filled and refilled relevant prescriptions. Such data is vital for healthcare organizations to realize patients who may struggle with medication adherence and create specifically targeted interventions for them.
Report Patient History
Healthcare providers consider medical claims information a valuable asset that involves various data types collected from many organizations. That enables medical practices to obtain a bigger picture of the patient’s history details, such as previous procedures, diagnoses, and treatment approaches. Therefore, they can guarantee better decision-making related to care options.
Utilize Resources Effectively
Finally, one of the medical companies’ most essential ways of utilizing healthcare claims data analytics lies in tracking the expenses associated with the particular system’s performance. Such data helps define the key areas of money overspending and the opportunities for cost savings. After all, the detailed analysis of this information can assist healthcare providers in understanding how their patients use the available medical services.
Popular Tools for Healthcare Claim Data Predictive Analytics
Nowadays, you can find numerous software applications for healthcare claims data analytics. The up-to-date solutions allow for visualizing the information in various ways, identifying patterns, and forecasting future trends and tendencies. With these tools, organizations can dive deeper into the claims information and understand how to use it appropriately to improve patient outcomes.
Healthcare Cost Containment (HCC) Models
When it comes to risk-scoring algorithms, adopting HCC coding is a widely used approach. The relevant risk-scoring models are well-known for assigning single numbers to individuals that describe their risks. Meanwhile, such numbers are crucial for determining the services patients should get. Thus, HCC coding can help your healthcare organization leverage claims data to find high-risk patients and prevent them from potential health issues.
Clinical-Based Models
Industry knowledge and expertise are necessary to use medical claims information for predictive analytics successfully. That also covers working with different data types, analyzing them correctly, and understanding what such information means.
If your healthcare organization has the required expertise, you can create clinical-based models that depend on your clinical knowledge. They use data about your patient’s medical history and previously received treatments, which is essential for predicting potential events. Although establishing clinical-based models is often time-consuming, the particular efforts will benefit your healthcare practice.
Delve deeper into the nuances of medical data standards, metrics, and tools with our comprehensive guide and elevate your industry game!
Event Groupers
Being among the most valuable tools in healthcare, event groupers assist in visualizing the relationships between multiple events. Such data may help you explore the connection between these events, realize the existing patterns, and analyze the influence of these events on your patients’ health outcomes. You can find several event groupers in the market, and all of them have their advantages and drawbacks. However, you should select the one that perfectly matches your healthcare practice’s requirements.
Final Thoughts
To conclude, implementing healthcare claims data analytics software is becoming more popular and crucial in a particular domain. The proper analysis and understanding of such information enable healthcare practices to provide targeted interventions and enhance the patient’s health outcomes. Ultimately, advanced software will allow your organization to make billing and claims management more transparent and get revenue advantages because of fewer denials.
Uncover the potential of healthcare claims data analytics by building platforms and transform raw information into actionable insights!
FAQ
What is claims analytics in healthcare?
Claims analytics in the healthcare domain uses information for the detailed analysis of medical claims and their further results. The latter covers rejections, denials, or acceptances. With the appropriate healthcare claims data analytics, medical practices can understand the existing trends and tendencies in claims processing.
What are claims metrics in healthcare?
Although there are many claims metrics in the medical domain, the most popular ones involve claim denial rate, days in accounts receivable, clean claim rate, and claim submission accuracy.
Why use healthcare claims data analytics?
Implementing claims analytics tools in the medical industry frees time for organizations to focus on making more informed solutions. For instance, their key advantages include:
– Higher clean claims rate
– Identification of potential revenue flow
– Minimization of days in accounts receivable
– Cost savings because of reduced appealing claims
– Elimination of manual spreadsheet tasks
– Assessment of medication compliance
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