As the final installment of this 4-part series, we’ll review the remaining two considerations for hospital and health system lab leaders when it comes to selecting the right RCM system for outreach and outpatient laboratory services. As discussed earlier in this series, too often health system leaders expect outreach laboratories to use the hospital’s enterprise revenue cycle management (RCM) module that is offered as part of the EMR/EHR software. While the health system may see this as the most cost-effective option, it fails to take into account the specific needs of the lab to maximize its revenue contribution and effectively manage compliance risk.
The purpose of this blog series is to highlight the advantages of investing in RCM solutions purpose-built for laboratories and designed to interoperate with enterprise systems.
Does Your RCM System Provide Robust Business Intelligence (BI)?
Hospital outreach and outpatient diagnostic laboratories have specific business intelligence needs that cannot be met by most enterprise RCM systems. Data on the efficiency and effectiveness of lab operations are essential. It is also critical to have fast access to reporting and analytics without impacting production system performance. Some of the most important BI components and capabilities for hospital laboratories include:
Does your RCM Leverage the Power of Analytics, Artificial Intelligence (AI), and Machine Learning (ML)?
Just as important as data visibility is the analytics to help make the data actionable. Analytics help an organization leverage its data assets to understand how the lab is operating, by payor, by specialty, by test, by territory, etc. Analytics also help lab leaders better understand the business they earn from each referring physician, thereby helping identify opportunities for increasing the value of the relationship. In addition, this data provides tremendous value to those outside the laboratory billing department, making it a treasure trove for analyzing and improving population health.
AI or ML approaches can be applied in powerful ways to create insight and actions in revenue cycle management. To truly unlock that value, it’s important to have an RCM technology partner that understands the hospital outreach or outpatient lab data, workflows, and the data science techniques to know when and how to apply the right methodology to the right problems to optimize results. The Lab RCM Business Intelligence area is the ripest for machine learning (ML) since it informs other ML or AI applications within the workflow logic and claim and billing process.
Machine learning also plays an important role in business process automation. AI or ML can be applied to clinical, diagnostic, and financial data to gain new insights, improve operations, support better decision making, and increase forecast accuracy. For example, how much a lab can expect to be reimbursed for a particular claim by the payor. With just a few specific inputs applied correctly, an AI-enabled RCM can provide a highly accurate prediction and automate expect pricing analysis to better forecast reimbursements.
Other potential RCM AI applications include:
- Recommending and automating decision rule creation to accelerate the reimbursement process
- Automatically routing error processing to those team members best suited to solve the problem based on the characteristics of the error
- Predicting which claims are at the highest risk of being denied
Lab-Specific RCM Solutions Provide More Value
As the healthcare industry continues to adopt precision medicine approaches, labs need informatics technology to integrate clinical, diagnostic, and financial data to enable care teams to gain better insight into the patient’s medical situation, make better diagnostic and therapeutic decisions, and improve outcomes. Frankly, this is something that most enterprise RCM systems just cannot do. They are not designed to improve the efficiency of patient care coordination, deliver data visibility or quality metrics reporting for reimbursement, nor to effectively discover actionable insights.
Advances in precision medicine also create more complex tests, thus more complex billing. This results in more rejected claims and more appeals. The appeal process can be challenging for labs using an enterprise RCM system since access to the specific data needed for a successful appeal is difficult to attach and is not automated. This creates a need for costly manual intervention and opens the laboratory up to new risks.
In short, enterprise RCM systems likely don’t have the many capabilities required for managing the billing complexities of hospital and health system laboratories. A lab-specific RCM system helps make the outreach laboratory a valuable, revenue-generating business. Using a purpose-built RCM system, whether in-house or outsourcing to an RCM partner, means that the enterprise system works more effectively for its users with bi-directional exchange and access to diagnostic information and even biomarker data. Any additional cost for a purpose-built RCM more than pays for itself through increased cash collection and reduced labor expenses and minimizing costly compliance risk.
We invite you to register for the upcoming webinar entitled “How Pairing a Purpose-Built Lab RCM System with Your Enterprise System Improves Health System Economics.” Guest speaker Mutaz Shegewi, Research Director from IDC, will discuss how a purpose-built RCM improves cash collections while enhancing enterprise system investments with bi-directional data exchange.