What is Revenue Cycle Analytics?
Revenue cycle analytics is the use of data to analyze, track, and optimize healthcare revenue cycle management. This includes patient registration, insurance and benefit verification, charge capture and claims submission, payment posting, and accounts receivable follow-up. The goal of revenue cycle analytics is to improve efficiency, reduce costs, increase profitability, and enhance patient satisfaction.
Types of Healthcare Revenue Cycle Analytics
Here are some types of revenue cycle analytics:
Contract and Payer Analytics
This type of analytics helps organizations understand the financial performance of their payer contracts. It helps identify problematic contracts, underpayments, and trends to negotiate more favorable terms.
This type of analytics uses statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. In the context of revenue cycle management, predictive analytics might be used to anticipate payment delays or identify which patients are most likely to have trouble paying their bills.
Prescriptive analytics uses advanced tools and technologies to recommend various courses of actions. It helps in making informed decisions about the processes involved in revenue cycle management. For example, it can help in identifying the best way to approach a patient about an unpaid bill or how to optimize the claims submission process.
This is the simplest class of analytics which deals with historical data to draw conclusions about the past. For example, healthcare providers may look at the number of denied claims in the past year to identify common reasons for denials and find ways to address these issues.
Diagnostic analytics uses the data and analyzes it to answer the question “Why did it happen?”. In terms of revenue cycle management, this could involve drilling down into the details of why certain claims were denied or why a particular month had lower revenue than usual.
This involves analyzing data in real-time as it enters the system. For example, as new claims are filed, real-time analytics could be used to flag potential issues before the claim is submitted to the insurer.
Comparative analytics involves comparing a healthcare organization's revenue cycle performance to that of other similar organizations. This can help identify areas where the organization may be underperforming and highlight opportunities for improvement.
By leveraging these types of revenue cycle analytics, healthcare organizations can more effectively manage their revenue cycle, identify issues and opportunities, and make data-driven decisions that enhance their financial health.
What Are the Insights that Can Be Gained from RCM Analytics?
Revenue Cycle Management (RCM) analytics can provide a wealth of information to healthcare providers. This data not only helps them understand their financial performance but also allows them to streamline operations and enhance patient services. Here's a comprehensive look at the different types of data and insights that can be gained through RCM analytics:
Patient Financial Data
This includes information related to patient payments, insurance coverage, and outstanding balances. Analytics can provide insights into trends in patient payment behavior and the effectiveness of collection strategies.
An analysis might show that a significant number of patients delay payments beyond 30 days. As a response, the healthcare provider could introduce early payment incentives to improve collection times.
Claim Denial and Underpayment Data
Data from claims analytics can provide information on common reasons for denied claims, which departments or procedures are most affected, and the financial impact of these denials. Additionally, claims analytics can provide detail on what claims are underpaid.
If data reveals that a high percentage of orthopedic surgery claims are denied due to coding errors, the hospital might initiate additional training for the coding team or even consider adopting a computer-assisted coding solution.
Payer Performance Data
This encompasses data related to the performance of different insurance payers, such as denial rates, payment timeliness, underpayments, and contract adherence.
If an insurance company consistently underpays compared to the contracted rate, a hospital could use this data to renegotiate the contract or decide whether to continue partnering with that payer.
Coding and Billing Data
Data on coding accuracy, charge capture, and billing practices can be used to identify areas of revenue leakage or inefficiencies in the billing process.
If an analysis at a hospital uncovers recurrent missed charges in the emergency department, the organization could implement a charge capture tool to ensure all services rendered are billed appropriately.
This includes data on the efficiency of different revenue cycle processes, such as the time taken for claims processing, collection times, and staff productivity.
If the data shows that the average days in accounts receivable (A/R) is rising, the billing department might need to re-evaluate its follow-up processes with payers.
Revenue and Profitability Data
High-level financial data such as revenue, costs, profit margins, and key financial ratios can provide an overall picture of the organization's financial health.
If a certain specialty consistently generates lower margins than expected, the healthcare provider might consider investing in equipment or staff to increase its profitability.
Patient Access Data
This involves data related to patient registration, insurance verification, and benefit eligibility. This can help identify issues in the patient access process that may lead to claim denials or delays in payment.
If a significant number of claims are denied due to insurance eligibility issues, the organization might automate the insurance verification process at the time of patient registration.
Contract Management Data
Data on payer contracts can help organizations understand the financial implications of different contracts and identify opportunities for renegotiation.
If an analytics tool shows certain payer contracts consistently result in lower payments, the healthcare provider could use this data during contract renegotiation to argue for more favorable terms.
Patient Satisfaction Data
This includes feedback and survey data related to the patient financial experience. This can be used to improve communication and patient services.
If patient feedback indicates frustration over the billing process's complexity, the organization could invest in a more user-friendly patient portal and communication strategies.
Risk Management Data
Predictive analytics can provide data on potential future risks, such as anticipated claim denials or changes in payer behavior.
Predictive analytics might indicate an uptick in claim denials due to a forthcoming coding update. The organization could then proactively train its coding team on the new guidelines to minimize the impact.
Comparative analytics can provide data on how the organization's revenue cycle performance compares to that of similar organizations.
If a healthcare organization finds its claim denial rate is significantly higher than similar organizations, it might conduct a full-scale review of its revenue cycle processes to identify areas for improvement.
By leveraging this rich array of data provided by RCM analytics, healthcare organizations can track all necessary RCM metrics to measure success. This enables them to identify issues, make data-driven decisions, and ultimately enhance their financial performance and patient satisfaction.
Benefits of Revenue Cycle Analytics
Revenue cycle analytics provides valuable insights into the financial health of a healthcare organization. Here are some of the key benefits:
Revenue Leakage Identification
Revenue leakage occurs when services rendered are not fully paid for, leading to lost income. Analytics can pinpoint the sources of this leakage, whether it's due to under-coding, unpaid claims, or inefficient collection practices, and guide corrective action.
Improved Operational Efficiency
Through descriptive and diagnostic analytics, inefficiencies in the revenue cycle process, like claim denials or delayed payments, can be identified and addressed. This leads to streamlined operations and a faster, more efficient revenue cycle.
Enhanced Patient Satisfaction
By using analytics to understand patient behavior, healthcare providers can personalize communication and improve service delivery. For instance, predictive analytics could help identify patients likely to have trouble paying their bills, leading to proactive outreach with payment plans or financial counseling.
Strategic Decision Making
Prescriptive and comparative analytics can help organizations make strategic decisions, like negotiating more favorable contracts with payers or investing in technology to improve certain aspects of the revenue cycle.
Predictive analytics can also be used to identify potential risks before they become problems. For example, if the data indicates a high number of future claim denials, a healthcare organization can preemptively review its claim submission process to reduce these denials.
Comparative analytics allows organizations to compare their revenue cycle performance against that of similar organizations. This can help identify areas for improvement and drive performance enhancement initiatives.
In a nutshell, the benefits of revenue cycle analytics are manifold. They can drastically improve the financial health of a healthcare organization, streamline operations, enhance patient satisfaction, and support informed, strategic decision-making.
How to Implement RCM Analytics
There are several methods through which healthcare organizations can adopt revenue cycle analytics.
Spreadsheets are the simplest way to implement RCM analytics. They can be used to organize, track, and analyze revenue cycle data, providing valuable insights into operations.
Revenue Cycle Analytics Software
Specialized RCM analytics software offers more advanced capabilities than spreadsheets. These tools can automate data collection and analysis, provide real-time insights, and offer advanced analytics features like predictive and prescriptive analytics.
Outsourced RCM Services
Outsourcing RCM processes to a third party revenue cycle specialists can be another way to take advantage of revenue cycle analytics. These vendors often have expertise in RCM and can offer comprehensive analytics as part of their services.
Why Software Is the Best Way to Implement Revenue Cycle Analytics
While revenue cycle outsourcing can be a valid option for some organizations, there are several reasons why adopting revenue cycle analytics software may be more beneficial.
Using in-house software gives you more control over your data and analytics processes. You can customize the software to meet your organization's unique needs and maintain direct oversight over the entire revenue cycle.
While there's an upfront cost associated with purchasing and implementing software, it can be more cost-effective in the long run compared to the ongoing costs of outsourcing. Unlike some revenue cycle outsourcing agencies that charge a percentage of claims or revenue recovered, software is typically priced on volume of patient encounters. Large healthcare organizations who see a lot of patients will pay more than smaller organizations.
RCM software can be integrated with other systems like your electronic health records (EHR) or practice management systems, which can streamline operations and provide a more comprehensive view of the revenue cycle.
Get Visibility into Your Revenue Cycle Performance with MD Clarity
MD Clarity allows you to see how and where your organization is leaking revenue. From patient collections to payer contract performance, you can quickly identify opportunities for revenue recovery. Get a demo to see MD Clarity in action.