In healthcare, the process of receiving payment is unlike that of any other business. For most transactions in other industries, the consumer makes a full payment when a service is performed or product is received. Such is not the case in healthcare: Imagine going to Best Buy and saying, “I’ll take the TV home now, but I’ll pay you in 90 days, maybe.”
Meanwhile, revenue cycle management (RCM) in healthcare has remained mired in technology’s Bronze Age, defined by people-intensive, manual processes, and performed by disconnected operational systems. However, emerging technologies in the areas of automation and artificial intelligence (AI) have created a perfect storm that has finally reached RCM. By applying these technologies to RCM, healthcare providers can pave the way to long-term financial success, while also improving the healthcare experience for patients.
Why Change Is Necessary
The following data points highlight the inefficiencies and challenges caused by current-state RCM:
- Missed appointments account for $150 billion total costs incurred in healthcare.
- Just 32% of patients currently pay their share of the medical bill in full; this figure is expected to drop to 5% as more patients struggle with high deductibles and a greater share of payment responsibility.
- 60% of claim denials can be traced to front-office inefficiencies, including missing patient information.
- 94% of claims are submitted manually, with claim status checks taking five to 12 minutes and costing $9.79 per claim.
- 90% of denials are preventable, and it costs $25 on average to rework each denied claim.
- Up to 49% of patient responsibilities are written off as bad debt by hospitals.
Powering Healthcare with Digital RCM
Changes to the current state of RCM can be made by applying AI and automation to RCM – what we call digital RCM (dRCM). While dRCM is critical to long-term financial success for large and small healthcare organizations, its impact will also offer healthcare providers the opportunity to improve the patient experience.
By moving to a dRCM model, healthcare organizations can effectively address the inefficiencies and challenges of current RCM processes. For example, a smart wearable device could initiate a wellness or screening appointment based on the patient’s current physical condition. Once the appointment is scheduled, the patient’s smartphone could provide regular reminders about the upcoming appointment. This capability would reduce the risk of missed appointments and consequent revenue loss.
Before the appointment, the patient could be informed of the exact cost of care and how much is expected at the time of service, which would enhance the patient experience. On the day of the appointment, the patient could pay through frictionless payment mechanisms, such as virtual pay and mobile devices, helping with revenue realization for the provider.
Further, automating claims submission and claim status check processes would reduce operational costs. Using machine learning and AI to identify claims that could get denied vs. those with a higher likelihood of being successful would enable the most productive use of RCM staff.
A Three-Staged Move to dRCM
Moving to dRCM involves a three-part approach that moves from simpler to more sophisticated technology. It’s advisable to start dRCM with small steps: build on what exists, leverage existing data within the enterprise and then improve on it and add more complex technology over time.
The three phases include:
- Systems that do: Focus on automating routine tasks. Examples include applying robotic process automation (RPA) to the process of capturing consumer-driven information and RCM operations, such as verifying eligibility and checking claims status.
- Systems that act: Move to solutions with higher cognitive components driven by data science and predictive analytics, which can make decisions beyond rules-based logic. By applying machine learning to claims editing systems, organizations can leverage insights generated from payer contracts and claims denial histories and apply them to current claims to flag potential issues with a claim before it is submitted to the payer.
- Systems that think: Use digital technology that adapts its behavior based on insights captured directly from data without human intervention, leading to new knowledge and insights. These capabilities are driven by advanced cognitive technologies, such as neural networks, AI and machine learning. For example, an AI-based chatbot can anticipate customer needs and has relevant information available based on recent customer interactions, or it can assess customer emotions through voice analysis to deliver a seamless experience.
Moving to Digital RCM
No matter where an organization is on the dRCM timeline, these changes will take commitment, time, effort, and human and financial resources. Based on our experience, we’ve developed a handful of suggestions to improve the chances of a successful shift to dRCM:
- Evaluate the RCM value chain and identify functions that could yield a solid ROI to begin incorporating dRCM technologies, such as repetitive manual functions.
- Take small steps instead of revamping the entire technology stack, but start making changes now to improve RCM operations with small inroads into the realm of dRCM.
- Start with existing data to build AI solutions and keep adding data from outcomes achieved to improve learning and accuracy.
- Begin the transformation based on systems that do, and use what’s learned to progress toward the more technologically complex systems that act and then systems that think.
Digital technologies offer a new approach to RCM for healthcare organizations of all sizes and stages of maturity. By augmenting the human aspects of revenue management with AI and intelligent automation, healthcare organizations can ensure their long-term financial stability and deliver a superior patient experience.
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