Revenue cycle management maintains and operates the financial health of a dental practice and the challenges of running a healthy revenue cycle can be plenty. From accurately entering claim data and information to securing reimbursement and payments, several steps are involved in managing the revenue cycle of a practice. Inefficient revenue cycle management can put your practice at risk, as it not only hinders your practice’s growth but also results in a negative patient experience. Finding a solution for your RCM-related worries can be like attempting to find a needle in a haystack if a thorough understanding of where those problems occur is not achieved. Practices need to dig deeper into their revenue management if they truly want to fix their RCM and get back on the track of profitability and security.
This is where data analytics comes in; it offers you an insight into how precisely your RCM is running and where the difficulties lie. It also provides insights into everyday operations and projections of future trends and functions. This helps to make data-driven decisions and correct issues even before they occur. Such data-driven insights are required on a variety of topics, from profitability to the intricate analysis of lost revenue.
Data can be analyzed in three ways:
Descriptive analysis basically gives your practice the answer to the question, “What happened?” This is the threshold that practices should begin with as it is the simplest to perform. It mainly involves gathering and analyzing historical data about your practice. This provides you with information on potential revenue leaks caused by problems with insurance carriers, internal operations and the like.
Diagnostic analysis helps answer the question, “Why did it happen?” It basically unearths links between all the data collected and helps your practice pinpoint what led to the problem in the first place. Identifying the cause of the problem and any connections with other problems will help you take action accordingly.
Predictive analysis, on the other hand, provides your practice with an answer to the question, “What could possibly happen?” It takes the historical data collected, analyzes the patterns found, and then predicts future trends in the revenue flow of the organization. The practice can adjust its present revenue cycle billing method to reflect the trends identified from this analysis. This is also the type of analytics that a lot of practices use machine learning or artificial intelligence to perform.
Prescriptive analysis is the most advanced form of analysis and it answers the question, “what should the practice do?” It makes one or more recommendations based on collected data, allowing the user to consider the potential results of each suggested course of action. The accuracy of prescriptive analysis depends heavily on the accuracy of the earlier steps and hence can be the most difficult to perform. The end result, however, is of high import and is undeniably a powerful tool for RCM.
From improving your practice revenue to creating a better patient experience, data analytics can benefit your practice in a lot of ways.
Some of the main benefits include:
Reducing minor errors
Data analytics helps validate the data collected during the revenue cycle management process. It scrubs your data for any minor errors, and compares the collected data to the information from the insurance provider.
Understanding your RCM better
Data analytics helps your practice identify and breakdown its revenue cycle process, thus letting you understand your RCM better. It also gives your practice a clear picture of each step involved in RCM, thereby making it easier to identify any one that is not performed accurately. With this, you can finally assess and benchmark your RCM processes. You can now create appealing reports that provide a complete picture of the actual state of your revenue cycle. It becomes much easier to improve your practice’s profitability when you can identify the main cause of a trend that results in decreased profitability.
Determining key performance indicators
KPIs provide essential data on healthcare revenue. They keep a close eye on the claims processing to spot any errors and also assess the denial rates for improved reimbursements. KPIs are very important for the growth of your revenue cycle, and these KPIs can only be identified through proper data analysis. Only through a thorough data analysis can the trends in the revenue cycle and the areas that need improvement be identified.
Improving patient experience
With proper data analysis, patient payment collection can be made a lot easier, giving you more time to focus on patient care and treatment. Your patient experience can improve exponentially when you have sufficient data collected through data analysis and understand how payment collection can be improved without causing any inconvenience to them.
You can quickly identify patients and insurance companies that have frequent denials and rejections by incorporating data analytics into your revenue cycle management. This helps your practice improve its revenue flow by lowering its denial rate. Additionally, with proper data analysis, you can easily identify the frequent trends in denials, thereby making sure that you don’t endure similar denials or rejections in the future. Thus increasing your claim acceptance rate.
Understanding the performance of your revenue cycle is crucial for the steady growth of your practice. Remember, it is vital to remain on top of trends and benefit from utilizing data analysis in its entirety. Data analysis can provide insights into your practice that can help you make better decisions, increase revenue, operate more efficiently, and improve patient care.