Healthcare Payer Analytics: 5 Forms of Analysis that Uncover Revenue Recovery Opportunities

“Why did the spreadsheet go on a diet? It wanted to reduce its cells.”

Corny dad jokes aside, nobody knows better than healthcare revenue cycle managers how bloated spreadsheets can get. 

When rows, columns, and even tabs run into the thousands, making sense of the numbers escapes even the most dedicated. Hospitals already produce 50 petabytes of data per year, and by 2025, each patient will generate 36 percent more data than the year before. That means that, going forward, every three years or less the data for each patient will double. Are you up for juggling and analyzing all those data points via spreadsheets? 

And yet, it’s data that reveals what’s going on in healthcare facilities and provides the best back-up for decisions. 

To avoid a data crush and derive the best insights from data goldmines, more healthcare organizations are turning data management over to healthcare payer analytics. Market.us reports that the global healthcare analytics market will grow from USD $36.4 billion in 2023 to USD $249.3 billion by 2032, a nearly seven-fold increase in just nine years. 

Most likely, your organization has already begun your analytics journey. Today, it reveals your top payers, your most denied CPT codes, how underpayments are growing or diminishing according to payer, billing and coding errors, patient volume forecasting, and much more. 

Still, understanding all the nuances of healthcare payer analytics takes some study. Here we cover the five styles of analytics and how they can derive insights from every step in your revenue cycle from patient registration to denials. 

What is healthcare payer analytics?

Healthcare payer analytics assesses data coming in from payers including reimbursement rates, denials, approvals, and the reasons behind all. Effective analytics derive insights into prevailing payer reimbursement and restriction patterns. With these insights, providers can make more accurate decisions about payer mix, medical services offered, and operational and financial changes. Today it takes data to support decisions. 

Healthcare payer analytics fall into five categories: descriptive, diagnostic, predictive, prescriptive, and discovery analytics. Each type of analytics serves a unique purpose in healthcare, helping organizations understand past performance, diagnose issues, predict future trends, prescribe actions, and discover new insights. Together, they form a comprehensive approach to data-driven decision-making in healthcare.

Not all RCM software companies offer all analytics types, but providers benefit from using all five. 

5 types of healthcare payer analytics 

Most steps of the revenue cycle benefit from more than one of the following analytics styles. When discussing new software with vendors, you can ask them whether the analytics they provide are descriptive or diagnostic, predictive or prescriptive, and actually understand how each one matters. You can also express your interest in discovery analytics, which has the power to deliver ideas you may never have considered. 

1. Descriptive healthcare payer analytics - key in claims and denials analysis

• Definition: Descriptive analytics, which involves summarizing and visualizing historical data, helps managers understand what has happened in the past and identify areas for improvement.
• Healthcare applications: Descriptive analytics helps monitor KPIs such as days in accounts receivable (A/R), denial rates, collection rates, and patient payment patterns. Managers use dashboards and reports to regularly track these KPIs, enabling them to identify trends, set benchmarks, and evaluate performance against industry standards.
•  Example:  Monthly reports on denials received in the past quarter help provide insights on the CPT codes your staff may be using incorrectly or failing to upload the preferred documentation for. On the other hand, this problematic CPT code could simply indicate a service that’s not worth your practice’s effort. Use it to create interventions that improve claims approvals. 

2. Diagnostic healthcare payer analytics - key in claims and denials analysis

Where descriptive analytics focuses on summarizing and presenting historical data, diagnostic analytics delves deeper to analyze and interpret why those events occurred. 

• Definition: Diagnostic analytics uses techniques such as drill-down, data discovery, and correlations to identify root causes of payer denials. Fueled by the expertise brought by the software developers, diagnostic analytics can carry out reliable oversight. 
• Healthcare applications:  This type of analytics finds the issues behind denials, which may include coding errors, missing information, and breaches of payer-specific rules. It can also identify errors on the payer end. 
• Example:  For instance, a high frequency of denials from a particular payer for specific procedures can surface a variety of root causes. When a provider can rule out coding errors and missed documentation requirements on their end, the issue can be raised to the payer, suggesting their error on their end. Providers aren’t the only ones mis-keying and forgetting key steps, after all.  

3. Predictive Analytics - key in contract modeling, net revenue forecasting

Where diagnostic analytics focuses on understanding the causes of past events, predictive analytics takes the historical data and applies statistical models to forecast future outcomes. Diagnostic analytics answers “why did this happen,” and predictive analytics answers, “what will happen next?” 

• Definition: Predictive analytics uses statistical models and machine learning techniques to predict future outcomes.

• Healthcare applications:  Predictive analytics processes data from past payer interactions and provider revenue outcomes to estimate revenue impact given a proposed contract change. 

Healthcare organizational revenue forecasting software is also predictive analytics. Enabling “net revenue forecasting,” it helps healthcare leaders model and weigh different hypothetical scenarios, revealing which will deliver the best opportunity. With predictive analytics fueling net revenue forecasting, leaders make more accurate and powerful business decisions, mitigating uncertainty.  

• Example:  Predictive analytics features in software can forecast how an increase to a group of CPT codes will improve or diminish revenue. It can also forecast how a proposed carve-out can lead to increased underpayments and to what extent. Predictive models identify claims likely to be denied, facilitating preemptive actions to correct issues before submission, thereby improving claims acceptance rates and reducing delays in revenue collection Finally, it can deliver several potential scenarios based on variables fed into it. With outcomes delineated clearly, net revenue forecasting empowers healthcare leaders to make the best data-driven decisions. 

Rendering the most powerful insights for net revenue forecasting, predictive analytics also anticipates patient no-shows, allowing for proactive scheduling adjustments and improved resource utilization. Overall, predictive analytics leads to more efficient processes, better financial performance, and reduced financial risk for healthcare organizations. 

4. Prescriptive Analytics - Net revenue forecasting, contract modeling

Prescriptive analytics goes beyond predicting future outcomes by recommending specific actions to address these predictions. 

• Definition: Prescriptive analytics combines predictive analytics with optimization techniques to suggest the best course of action. 
• Healthcare applications: Prescriptives analytics’ suggestions for improving billing accuracy, managing denials, allocating resources efficiently, and negotiating better contracts provide the data that back up key leader decisions. 
• Example:  Prescriptive analytics can be used to optimize payer contract negotiations by providing actionable recommendations based on historical data and predictive models. This involves analyzing past contract performance, payer behaviors, and market trends to determine the best negotiation strategies and terms. It can generate a set of recommendations for negotiation, including the best terms to propose and potential concessions to make.

5. Discovery Analytics - claims and denials analytics

While all other analytics types either evaluate or predict, discovery analytics steps into the speculative space by making actionable recommendations untethered from the variables you may want to load into it.

•  Definition: Discovery analytics, also known as exploratory data analysis, involves uncovering hidden patterns, relationships, and insights from data without having predefined hypotheses. It uses data visualization and data mining techniques to explore data sets. Brace yourself: its findings may surprise you! 
•  Healthcare applications:  By exploring datasets without predefined hypotheses, revenue cycle managers can uncover new insights that lead to improved financial performance and operational efficiency.
•  Example: For instance, sometimes a healthcare provider notices inconsistencies in revenue collection but cannot pinpoint the exact cause. Discovery analytics reveals that a significant portion of delayed payments is linked to a particular type of service and a specific payer’s stringent documentation requirements. Further analysis uncovers that these issues predominantly occur with a subset of patients who have similar demographic characteristics. By implementing targeted training for staff on documentation requirements for these services and negotiating with the payer to streamline the process, reducing delays and increasing revenue collection efficiency.

Using discovery analytics, healthcare revenue cycle managers can uncover hidden issues. This approach helps to identify and address previously unrecognized inefficiencies, thereby enhancing overall revenue cycle performance.

Each type of analytics serves a unique purpose in healthcare, helping organizations to understand past performance, diagnose issues, predict future trends, prescribe actions, and discover new insights. Together, they form a comprehensive approach to data-driven decision-making in healthcare.

Read on to learn how each style of analytics can recover the most revenue from each step of the revenue cycle

The best healthcare payer analytics type at each revenue cycle step

Patient registration  

RCM research reveals that patient ineligibility is a top reason for payer denials. When providers rush patients into examining rooms before establishing eligibility, the cost of their care falls on the provider. When you analyze eligibility errors and track down and fix their root causes, your denials diminish. Most healthcare leaders share that the majority of denials are preventable, if only they had the staff, expertise, and time to explore and understand them. Healthcare payer analytics shoulders much of this work. 

Use descriptive analytics to clean up patient information

 Descriptive analytics analyzes historical data to identify common errors and inefficiencies in the registration process. Managers use it to develop standardized procedures and checklists that ensure consistent and accurate data entry, reducing the likelihood of errors that lead to claim denials.

Use diagnostic analytics to reduce errors and improve patient information quality

Diagnostic analytics investigates past registration errors to determine their root causes. It uncovers the factors that contribute to incorrect or incomplete patient information.

When issues like training gaps or systems issues reveal themselves, managers can implement targeted interventions and improve data accuracy.

Use predictive analytics to avoid registration bottlenecks

Predictive analytics forecasts periods of high patient volume and potential registration bottlenecks based on historical trends and patterns. Predictive models can help allocate staff resources more effectively, ensuring that sufficient personnel are available during peak times to reduce wait times and improve patient satisfaction.

Predictive analytics has the most significant impact on patient registration. By forecasting patient volumes and identifying trends, predictive analytics allows for proactive management of resources and processes, ensuring a smoother and more efficient registration experience for patients.

Use prescriptive analytics to optimize registration workflows

Prescriptive analytics can optimize registration workflows by suggesting specific changes to registration processes. It may propose implementing new technologies or reconfiguring staff schedules to enhance efficiency and accuracy.

Use discovery analytics to uncover hidden registration insights

 Discovery analytics can reveal unexpected trends and correlations, such as demographic factors that influence registration errors, guiding the development of targeted strategies to address these issues.

By integrating these analytical approaches, healthcare organizations can optimize their patient registration processes, leading to improved data accuracy, reduced wait times, and enhanced patient satisfaction, all of which contribute to a more effective revenue cycle.

Eligibility verification

Accurate and dependable eligibility verification reduces claim denials and improves the revenue cycle's overall efficiency. Here’s how each type of analytics reveals and offers solutions for eligibility verification. 

Use descriptive analytics to automate eligibility checks

Descriptive analytics summarizes historical data to identify trends in eligibility verification errors and successes. This information helps in refining the eligibility checking processes and ensuring compliance with payer requirements. Managers can create automated systems that cross-check patient information with payer databases to confirm eligibility in real-time, reducing manual errors and administrative workload.

Take a quick tour of how you can automate eligibility verification and estimate generation here:

Use diagnostic analytics to identify eligibility patterns and anomalies

Diagnostic analytics reviews past data to diagnose reasons for frequent eligibility-related claim denials. This information helps in understanding common issues so you can develop strategies to address them. Implement targeted improvements to the verification process to reduce your denials – which start in eligibility 42 percent of the time! (second only to prior authorizations.) 

Use predictive analytics to predict eligibility verification pitfalls

Predictive analytics finds potential eligibility issues before they occur. It can forecast which patients are most likely to have eligibility issues based on their history, so that   staff can prioritize and address these cases promptly to avoid delays and denials.

Predictive analytics has the most significant impact on eligibility verification. By forecasting eligibility problems, predictive analytics enables proactive intervention, which gets staff correctly verifying patients for coverage, reducing the risk of denied claims and improving the overall efficiency of the revenue cycle.

Use prescriptive analytics to find ways to improve eligibility workflows and accuracy

Prescriptive analytics recommends the best actions for handling eligibility issues. By analyzing past and real-time data, prescriptive analytics can predict potential eligibility problems and suggest preventive measures to address them before they disrupt healthcare services. This approach not only improves the accuracy of eligibility determinations but also optimizes the workflow, reducing delays and administrative burdens. For example, it can advise on when to update patient records or re-verify eligibility due to anticipated changes in insurance policies or coverage.  

Use discovery analytics to uncover opportunities in eligibility

Discovery analytics can reveal unexpected correlations and trends that may not be immediately apparent, guiding revenue cycle managers to implement innovative solutions.

By attacking eligibility verification from these five analytical approaches, you can diminish claim denials, win faster reimbursements, and more respect from your payers. 

Contract management and modeling

By using various types of analytics, healthcare revenue cycle managers can get:

•  the data to support their contract demands
•  the critical insights into payer performance as compared to other payers
•  identify opportunities for negotiation
• streamline contract management processes
• generate revenue scenarios based on proposed payer changes. 

This last contract tool affords providers the power to base their decisions on real potential outcomes rather than payer promises. It gives you ammunition to counter payer claims of revenue impact. 

Use descriptive analytics to clarify and analyze historical contract performance

Descriptive analytics summarizes past performance data to evaluate how well current contracts are performing. This information establishes trends and payment rates so you do not have to depend on the payer for this information. Managers generate reports on metrics such as reimbursement rates, denial rates, and payment timeliness to identify high-performing and underperforming contracts. 

Use diagnostic analytics to identify root causes of contractual issues

Diagnostic analytics evaluates past data to understand the reasons behind contractual issues like denials and delayed payments. It can uncover specific problems with payer contracts, such as ambiguous terms or non-compliance with agreed-upon rates, allowing for targeted renegotiation strategies. 

Use predictive analytics to forecast future contract performance

Predictive analytics uses historical data to forecast the impact of proposed contract changes on revenue, enabling managers to use data-driven insights to make their cases for better rates and terms. Use it to simulate hundreds of reimbursement models based on the rate and volume variables you choose. 

Use prescriptive analytics to win better contract terms

Prescriptive analytics provides actionable recommendations for optimizing contract terms and negotiation strategies based on performance data. Leveraging the contract negotiation intelligence coded in by contract experts and software developers, it can suggest specific adjustments to contract terms, such as modifying fee schedules, adding performance guarantees, or including clauses that reduce administrative burdens. 

Prescriptive analytics has the most significant impact on optimizing payer contracts. By providing actionable recommendations it helps ensure that contracts are not only competitive but also aligned with the organization's financial goals and operational needs.

Take a quick, self-guided tour through a powerful contract management and underpayments recovery tool:

Use discovery analytics to discover new opportunities

You don’t know what you don’t know, but discovery analytics does!  Discovery analytics explores large datasets to uncover new opportunities for improving payer contracts, such as identifying emerging trends or underutilized payer relationships. It locates payer behaviors that correlate with higher reimbursements or lower denial rates, guiding strategic negotiations. 

By integrating these types of analytics, healthcare organizations can significantly enhance their contract management processes, leading to better payer relationships, higher reimbursement rates, and improved financial performance.

Coding accuracy

Coding accuracy can make or break a practice or physician group.  By leveraging various types of analytics, healthcare leaders can identify errors, optimize coding practices, and ensure compliance with coding standards.

 Use descriptive analytics to identify coding errors

 Descriptive analytics summarizes historical coding data to identify common errors and patterns in coding practices. This helps in recognizing areas where mistakes frequently occur. Healthcare leaders can then generate reports that highlight frequently occurring coding errors, allowing for targeted training and process improvements to reduce these mistakes.

 Use diagnostic analytics to find root causes of coding issues

Diagnostic analytics examines past data to understand why coding errors occur. This approach involves investigating the underlying causes of discrepancies and inaccuracies in coding. Coding errors, such as insufficient training or ambiguous documentation need corrective measures. 

Diagnostic analytics has the most significant impact on coding accuracy. By thoroughly analyzing the reasons behind coding errors, diagnostic analytics enables revenue cycle managers to identify the root causes of these issues and implement targeted solutions to prevent them. This type of analysis helps in understanding the specific factors contributing to coding inaccuracies, allowing for more effective interventions and continuous improvement in coding practices.

Use predictive analytics to forecast potential coding problems

Predictive analytics uses historical data to predict future coding errors and identify high-risk areas, preventing errors before they occur.  Predictive models can forecast which types of claims or procedures are most likely to be coded inaccurately at your organization, enabling managers to focus on these areas and implement preventative measures. 

Use prescriptive analytics to optimize coding practices

Prescriptive analytics provides actionable recommendations for improving coding accuracy based on data analysis. This guidance can include suggestions for best practices, process changes, and training programs. It will lay out an argument for adopting new coding software, revising workflow processes, or providing targeted coder education to enhance coding accuracy and efficiency. Comparing these suggestions jumpstarts your revenue-optimization efforts. 

Use discovery analytics to find coding improvement opportunities

 Discovery analytics explores large datasets to uncover new insights into coding practices and areas for improvement. With it, you can identify unexpected trends, correlations, and unusual patterns in coding errors that might not be immediately apparent.

By unleashing healthcare payer analytics on your coding processes, you improve coding accuracy, leading to better compliance, fewer claim denials, and enhanced financial performance.

Charge capture optimization

Ineffective charge capture depletes healthcare organizations of significant revenue. Given the overwhelm healthcare staff experiences today, missed charge captures are on the rise. Analytics can track down the most prevalent sources of charge capture. 

 Use descriptive analytics to identify missing charges

 Descriptive analytics summarizes historical billing and charge data to identify trends and patterns in missing charges, recognizing areas where services may not be consistently captured. Healthcare leaders can use these descriptive reports to pinpoint departments or types of services with frequent missing charges, leading to targeted process reviews and corrections.

Use diagnostic analytics to uncover root causes of charge capture errors

Diagnostic analytics explores the underlying causes of missing or incorrect charges by analyzing past data. It reveals systemic issues, such as gaps in workflow or inadequate staff training, that lead to charge capture errors. Rectifying these root causes can significantly improve accuracy. 

Diagnostic analytics has the most significant potential impact on charge capture. By thoroughly analyzing the reasons behind missing or incorrect charges, diagnostic analytics enables revenue cycle managers to identify the root causes of these issues and implement targeted solutions to prevent them. It reveals factors contributing to charge capture inaccuracies, allowing for more effective interventions and continuous improvement in charge capture practices.

Use predictive analytics to forecast potential charge capture pitfalls

 Predictive analytics uses historical data to predict future instances of missing charges and identify high-risk areas to prevent charge capture issues. Predictive models can forecast which types of services or patient encounters are most likely to have charge capture issues, enabling managers to implement preventative measures and focus resources on high-risk areas. 

 Use prescriptive analytics to improve charge capture processes

Prescriptive analytics delivers actionable recommendations to optimize charge capture processes based on data analysis and predictive insights. These recommendations can include process improvements, technology implementations, and best practices. Prescriptive analytics might suggest implementing real-time charge capture systems or improving documentation practices to ensure that all services are accurately recorded and billed. 

Use discovery analytics to uncover missed charges

Discovery analytics helps identify unexpected trends and correlations that can inform better charge capture practices. For instance, it can reveal unusual patterns in service delivery and billing that may not be immediately apparent, guiding managers to investigate and address these anomalies. 

By applying all styles of analytics on charge capture, healthcare organizations can achieve better compliance, fewer missed charges, and enhanced financial performance.

Claims submission optimization

Use descriptive analytics to pinpoint and prevent claims errors 

 Descriptive analytics summarizes historical claims data to identify common errors and patterns in claims submissions. Managers can generate reports highlighting frequent coding errors, incomplete documentation, or missing patient information so that staff can take corrective attention to prevent these issues. 

Use diagnostic analytics to uncover root causes of claim denials

Diagnostic analytics analyzes past data to understand why claims are denied. Reasons for denials vary by provider. Yours may be incorrect coding or inadequate documentation, where another provider may be falling victim to payer errors. Should errors originate on your end, you can implement targeted training and process improvements to address and rectify them for long-term improvements. 

Use predictive analytics to avoid likely claim submission issues

 Predictive analytics reviews historical data to identify claims likely to be denied based on patterns and trends. With these insights, managers can take preemptive actions to correct these claims before submission. 

Predictive analytics has the most significant impact on claims submissions. It enables revenue cycle managers to take proactive steps to ensure the accuracy and completeness of claims before they are submitted. This approach reduces the likelihood of denials, accelerates reimbursement, and improves overall cash flow.

Use prescriptive analytics to clean up claims submissions

Prescriptive analytics provides actionable recommendations for optimizing the claims submission process based on data analysis and predictive insights. It might suggest process changes, such as adopting automated claims submission tools, improving coding accuracy, and ensuring thorough documentation.

Use discovery analytics to uncover claims submission improvement opportunities

Discovery analytics identifies unexpected trends and correlations that can inform better practices. Unusual patterns in claim denials or delays guide managers to investigate and address anomalies. 

Payer denials limitation

Denials remain a top concern for healthcare leaders. Many denials can be prevented by achieving better accuracy during patient access points like registration and eligibility.  On the back end, healthcare revenue cycle managers use analytics to identify, analyze, and mitigate issues that lead to payer denials. By leveraging data, you can streamline processes, reduce errors, and enhance overall financial performance. Here’s how different types of analytics are applied to denials management:

Use descriptive analytics to identify patterns in denials

Descriptive analytics helps managers generate reports that highlight the most common reasons for denials, such as coding errors or missing information. 

Use diagnostic analytics to understand reasons behind denials

 Diagnostic analytics reveal underlying problems, such as inadequate documentation practices or specific payer requirements that are not being met, allowing for targeted training and process improvements. 

Use predictive analytics to forecast which claims are likely to be denied

 Predictive models can flag claims that are at high risk of denial, enabling staff to review and correct them before submission.

Predictive analytics has the most significant impact on improving payer denials because taking proactive steps to address potential issues before claims are submitted reduces denials rates, thus improving cash flow.

Use prescriptive analytics to optimize denial workflows 

Prescriptive analytics can suggest best practices for denial management, such as implementing automated workflows for denial resolution, improving documentation standards, or enhancing communication with payers to resolve issues promptly

Use discovery analytics to identify unexpected denial trends and correlations.

Discovery analytics can reveal hidden patterns in denials, such as specific payer policies that frequently lead to denials, guiding managers to renegotiate contract terms or adjust internal processes to comply with payer requirements. 

Payer analytics solutions have come a long way in 20 years

As with other technology trends in our space, it was the advent of the EHR that sparked healthcare analytics. Digitization of patient information led to some data analysis – but performed by algorithms unleashed on patient data. By the mid-2000s, some reporting features had emerged. Focused on patient data rather than revenue, however, the EHR did not have the sophistication or experience necessary to compile and analyze payer data accurately. 

For too long, deriving insights based on payer data took manual and fragmented methods carried out by staff. Manual payer data entry into spreadsheets was time-consuming and error-prone. 

Today, EHR analytics still falls short of the analytics that software developers who’ve built tools focused on payer (rather than patient) data from the ground up. Often, providers have to rely on outside consultants to derive trends and uncover revenue opportunities and leaks, and provide reports with recommendations – a costly approach. 

Get payer analytics expertise natively built into an automated software solution

You can understand not only the motivations behind proposed payer contract changes but also their impact on your revenue when you use robust healthcare payer analytics. Revenue cycle analytics tools really can even the playing field – and affordably at that. 

MD Clarity's RevFind helps practices, physician groups, and MSOs derive key insights from payer data and contracts by digitizing and centralizing all agreements in one place. This system examines each payment against the contract's stated terms, identifying discrepancies for staff to address with payers to correct improper reimbursements. It also compares your reimbursements with national benchmarks, including Medicare standards. RevFind provides essential insights for proactive negotiations and identifies costly trends for significant revenue recovery. By pinpointing systemic root causes, it helps prevent future underpayments.

Schedule a demo to see how RevFind sweeps in the net revenue you’ve already earned.

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