In this Age of Information, data is power. It is a strategic asset that entities and individuals use to gain insights, make decisions, and drive growth and innovation. While some believe that data, which is actually a collection of raw information (unprocessed figures, facts, symbols, commands, or observations), is specific to the tech or finance sectors, this is not true. Data is widely used in the healthcare industry to improve patient outcomes, attain operational efficiency, enhance financial performance, and achieve a competitive advantage. According to the statistics compiled by Market.us Media:
- 80% of healthcare organizations believe that data analytics, especially the use of big data, will improve patient outcomes and operational efficiency.
- 70% of healthcare providers are already using predictive analytics to identify high-risk patients and potential health issues.
- 60% of healthcare executives have admitted that data analytics helped them save costs and improve the practice’s financial performance.
See how the data speaks for itself?
Now, the real question is, how can we use healthcare claims data analytics to optimize a practice’s revenue cycle? But before that, we must understand what healthcare claims data is and how it can be recorded, stored, and analyzed for effective decision-making and strategy implementation. So, let’s start.
Key Components of a Healthcare Claim
Have you ever had the chance to consciously ponder over the amount of data that is reported in a single claim form? Okay, let us simplify this for you. Generally, the key components of a standardized healthcare/medical claim include:
- Patient information
- Provider information
- Payer information
- Patient’s diagnosis (ICD-10 codes)
- Service date
- Service codes (CPT or HCPCS codes and modifiers)
- Service charges (itemized and on separate claim lines)
- Pre-authorization details (if applicable)
Apart from the information listed above, healthcare practitioners must also provide supplemental information in the form of supporting documents, such as lab test reports, referral letters, treatment charts, and clinical/operative/therapy notes.
So, can you recognize the wealth of information a single insurance claim carries? This is known as healthcare claims data. And when we continue to analyze this data over a period of time, we can observe patterns and trends that can be used for informed decision-making.
In fact, auditors often use this process to spot fraudulent billing activities (e.g., overbilling or unbundling) or to detect inaccuracies and inefficiencies in the billing workflow. So now, let’s understand how healthcare claims analytics works.
How Claims Analytics Works?
Healthcare claims data analysts use a variety of methods to analyze data presented in medical insurance claims and supporting documentation. For example, they use data mining, artificial intelligence (AI), and machine learning to identify trends, patterns, and risks, helping them formulate proactive strategies to reduce insurance fraud and claim denials.
Read further to understand how data analytics and reporting work in healthcare practices.
Data Aggregation and Processing
The first step of the healthcare claims analytics process is to gather all data from various channels and consolidate it in a single place for evaluation. Most likely, this place will be a Cloud storage platform with stringent security protocols to prevent data breaches.
The data that analysts usually procure from claim forms, electronic health records (EHRs), front-desk databases, and accounting ledgers is stored in the Cloud hub and gradually sorted and organized into digital folders for big data processing.
AI and Machine Learning Application
This is the processing stage of ‘claims analytics’. Here, analysts use advanced algorithms to analyze all data stored on the Cloud and generate patterns that often go undetected during manual reviews. The methods used for healthcare claims data analytics at this stage include:
1. AI-Driven Task Automation
Analysts use AI tools and software applications to give prompts for task automation. For example, they can use open-source AI applications like MedGemma by Google or subscription-based tools to extract structured data from lab reports, create classifications, and generate models.
It allows them to cut down the hours spent on data review, analysis, and reporting. How? AI algorithms can process data in milliseconds to microseconds; thousands of times faster than humans!
2. Data Clustering for Pattern Detection
Algorithms have the ability to group similar data points together (clustering) without prior labelling. This allows analysts to see hidden structures and patterns, which would otherwise have taken a very long time to become noticeable. So, based on these patterns, analysts can detect billing errors (e.g., inconsistent details) and fraudulent activities, like constant overbilling and claim duplication.
3. Machine Learning for Predictive Analytics
Machine learning (ML) is a branch of AI where computers learn patterns from data and make predictions or decisions without explicit human programming. In claims analytics, analysts use ML to create models for claim complexities, predict potential litigation, estimate revenue losses from unpaid claims, and forecast the practice’s financial performance.
In simple terms, using ML for healthcare claims data analytics allows practices to see where they went wrong, what mistakes they are continuing to make, and the impact it will have on the financial future of their clinic/hospital.
Workflow Optimization
Once analysts can observe patterns and pinpoint problem areas in the revenue cycle, they can rectify errors and improve processes to make the entire workflow more efficient. This is known as workflow optimization and may involve implementing solutions as simple as accurate patient verification, documentation maintenance, reviewing CPT codes, etc.
Remember, this step of claims analytics reduces bottlenecks and guarantees sustained cash inflow.
Actionable Insights
Many analysts are now using dashboards for healthcare claims analytics. These graphically appealing dashboards present key data and insights in a digestible manner (graphs, charts, or icons), so healthcare providers and other stakeholders can make informed decisions and take the necessary steps.
For example, if on the dashboard, the provider can see a consistently high claim denial rate and identify the reason as missing documentation, they can instruct physicians and billing teams to ensure that complete records are submitted with the claim form.
Benefits of Claims Data Analytics for Providers
The following are some of the benefits that healthcare providers can reap when they invest time and money in claims analytics.
Enhanced Revenue Cycle Management (RCM)
Through healthcare claims analytics, providers and their billing teams can pick up on the common reasons for claim denials and delays.
For example, by recording and charting the frequently received denial codes, they can identify the root causes of denials. Similarly, they may notice instances where the payers paid them less than the contracted rates. All of which will enable them to fix the errors in the billing workflow and streamline the revenue cycle.
Operational Efficiency and Resource Allocation
Claims analytics gives providers a bird’s eye view of how their practice is functioning on a day-to-day basis. So, for example, they can use the data to identify clinical or administrative processes that are creating bottlenecks in the revenue stream and offer targeted training to their staff for improvement.
Moreover, they can make data-driven decisions for resource allocation. For example, if the practice determines that medical coding is its weak suit, they can either hire a certified professional coder instead of retaining three junior medical coders or outsource medical coding to third-party medical billing companies.
Improved Quality of Care and Population Health
Sure, healthcare claims data can give us a massive amount of financial information, but let’s not forget that patients’ conditions, symptoms, diagnoses, and treatment procedures are all part of clinical information. And this information is used by medical experts to identify high-risk populations and ensure early intervention to reduce the burden on the healthcare system (e.g., in case of epidemics or pandemics).
Therefore, healthcare claims data analytics ensures better patient outcomes by identifying gaps in care.
Strategic Growth and Competitive Benchmarking
As we have discussed before, another benefit of performing claims analytics is that healthcare providers can recognize the specialties and services that are most profit-generating and invest more in them for greater returns. Similarly, they can use the denial trends to determine the payers or procedures that frequently trigger a denial and minimize their dealings with that payer or cut down on offering those procedures.
So, real-time data insights help providers make informed financial decisions for their practice’s sustainable future.
Regulatory Compliance and Audit Readiness
Remember how we mentioned earlier that even auditors use healthcare claims data analytics to detect fraud and non-compliance? Now, let’s build upon that point. When you use data analytics and reporting for internal audits and consistency checks, it helps you catch billing errors and non-compliance before a large volume of claims is submitted to the payer.
So, instead of the payer detecting frequent instances of upcoding or unbundling, auditing your workflows, and imposing penalties on you, you will serve as the internal spotchecker and compliance officer.
Healthcare Claims Analytics Management Challenges
Now that you are familiar with the benefits of employing claims analytics in healthcare practices, another burning question remains unanswered. Does it always go as smoothly as anticipated? The answer is NO. Healthcare practices often encounter challenges when using claims data for informed decision-making and performance tracking. Some of those challenges are discussed below.
AI Slop
In-house billing teams often rely on free and open-source AI applications and tools to analyze data and monitor trends. While this can speed up work, these platforms are still a work in progress and learning and evolving with each human interaction. This leads to the problem known as AI slop, where inaccuracies and imperfections are clearly visible in AI-generated responses. So, it may be that 30% of the data fed into the AI application was incorrectly processed, which led to inaccurate findings.
Cybersecurity Issues
Another problem that sometimes arises at practices performing in-house claims analytics is data leaks. As discussed before, all data obtained from claims and supporting documents is stored in a centralized database, often a Cloud platform. But if proper security measures are not taken, hackers can access this database and steal patients’ health information (PHI) and other sensitive details like their social security numbers. With this information in their hands, they can perform identity theft and cause significant financial losses.
Financial Constraints
Small healthcare practices have limited budgets and resources. They may not always have the capital to invest in advanced technologies like healthcare claims data analytics and reporting dashboards. Moreover, if the day-to-day claims volume is low (e.g., 10 claims per day), they may find it more cost-effective to analyze data manually.
Lack of Trained Staff
You must have heard that it takes specialized personnel to operate technology. The same is the case with claims analytics systems. Practices must hire skilled data analysts or at least provide sufficient training to the existing workforce to ensure effective results. Unfortunately, both hiring and training staff are expensive and time-consuming; that’s why some practices completely skip healthcare claims data analytics.
Interoperability Gap
The last but one of the most tiresome claims analytics challenges is unstructured or fragmented data. When claims data is obtained from multiple channels or disconnected systems instead of a centralized database, it creates information gaps and inconsistencies, which obviously leads to skewed results/findings.
So, when practices do not organize all data in one place and maintain separate records in EHRs, front-desk systems, labs databases, and pharmacies, what you get is “dirty data,” which is useless for predictive analytics.
Make Data-Driven Decisions with MediBillMD
A vast majority of healthcare providers are using healthcare claims data analytics and reporting to improve their practice’s financial performance and attain better patient outcomes, so why are you lagging behind? Right, we get it. Analyzing big data is not as easy as it sounds, especially if your core RCM processes, like denial management, rely on it.
The good thing is, you are not alone in this journey. At MediBillMD, we offer end-to-end healthcare revenue cycle management services, including claim analytics and reporting, so you can make data-driven decisions to boost your practice’s financial health.
