Gain New Insights With Analytics, AI to Accelerate RCM Workflow

Today’s healthcare leaders face more challenges than ever ― from increasing claim volumes to more rigorous payor requirements and more demanding reporting obligations. As a result, diagnostics providers nationwide are actively seeking innovative approaches that can lead to new revenue streams, improved payor relations, opportunities for cost reduction, and more predictable reimbursements.

As an example, high-quality and specially structured data sets enable advanced analytics and the application of artificial intelligence (AI), which can accelerate the revenue cycle management (RCM) process and improve financial and operational performance.

While laboratories, pathology practices, molecular diagnostic providers, and hospital outreach programs process millions of claims worth billions of dollars each year, many are still using spreadsheets, or legacy data management tools, to undertake the financial and operational analysis of their business performance. Consequently, key financial and operational insights remain untapped, ultimately impacting diagnostic business performance, profitability, and market competitiveness.

Great Business Intelligence Requires the Right Data Models

Healthcare leaders must implement enterprise-grade business intelligence (BI) capabilities to more deeply understand and be able to improve financial and operational performance. In particular, laboratories will need access to an enterprise data warehouse and BI capabilities that are built using a healthcare financial management-specific data model with referential integrity. This ensures that the laboratory or ancillary service can produce accurate, meaningful results that are compliant with Generally Accepted Accounting Principles (GAAP) and Sarbanes-Oxley, and that also conform to Financial Accounting Standards Board (FASB) reporting requirements.

Extending enterprise-grade BI with more advanced analytics and visualization capabilities allows laboratories to garner actionable and timely insights in previously unobtainable ways, thereby transforming their executive, business, and operational decisions. The key features to look for when adding power to your BI infrastructure include:

• Executive-level consultative analytics that visually direct a viewer’s attention toward areas of comparative focus, problem areas or opportunities across clients, denial codes, exceptions, payors, pricing trends, and procedures
• Key performance indicators (KPIs) that provide insight on reimbursements, denials, price per accession, price per unit, paid units, throughput, and write-offs
• Pre-built visualizations and charts that can be easily modified using point-and-click interfaces
• Subject-specific datasets, such as accession lifecycle and preconfigured visualizations that enable detailed data exploration and easily digestible dashboards for financial trending, reimbursements, and zero balance analysis
• The ability to integrate or federate other data sources such a product, sales, or operational information, for purposes of trend spotting and to identify growth and performance improvement opportunities

Improve Financial Predictions with AI and Machine Learning Applications

Increasingly, laboratories are turning to practical AI solutions and more automation to transform their RCM process. AI and machine learning have moved beyond theory, and the diagnostics industry already has practical applications available that can directly impact the laboratory’s bottom line.

Applying AI, machine learning, or other advanced statistical methodologies to high-quality, properly organized data models is proving a successful endeavor. With specific inputs, AI can provide highly accurate predictions of important elements of the RCM process, such as expected payor pricing, reimbursement likelihood, team member productivity or effectiveness, and revenue.

Many diagnostics claims begin the RCM process with errors that need correction before being submitted to the appropriate payor. In the absence of an automated workflow, this creates the need for costly manual intervention; otherwise, millions of dollars may never be collected. By pairing advanced analytics and AI with workflow automation, labs can streamline their RCM process. AI can identify opportunities to improve workflow automation based on routing claims to team members that are most adept at specific denials codes rather than a team member that processes the most claims. 

Multifaceted Workflow Automation for Reduced Denials and More Successful Appeals

In its most advanced state, workflow automation should provide configuration for multiple levels, including facility, client, payor, and payor plan, and should also handle both individual and batch claims submissions and status requests. Real-time connectivity and error correction functionality, including integrated patient demographic or insurance discovery capabilities, enable missing or incorrect information to be quickly identified and corrected so that the clean claim can be submitted without human intervention. The result is fewer denials and more timely reimbursements, at a lower cost.

One of the most labor-intensive and thus costly parts of the claims handling process has always been managing denials and appeals. Being able to automate much of the denials and appeals management process is a significant advantage, especially when paired with the ability to attach additional documentation such as medical necessity or prior authorization documentation and to individually or in bulk generate appeal letters. This leaves only the most complicated claims to be handled by exception, providing a dramatic reduction in the total cost of billing.

The latest business intelligence analytics, AI, and workflow automation capabilities can truly help healthcare leaders improve operational and financial performance while responding to challenging market demands, patient expectations, and reimbursement challenges.

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About the Sponsor

XIFIN is a San Diego, California-based health information technology company with solutions (XIFIN RPM, XIFIN LIS, XIFIN ProNet, and VisualStrata) that span the diagnostic RCM, laboratory information systems (LIS), precision medicine informatics, and digital pathology consultation spaces. XIFIN RPM is used by seven of the top 10 largest labs and supports four of the five largest integrated delivery networks in the United States.