Advanced AI Creating New Avenues of Healthcare Efficiencies
First, there was artificial intelligence (AI) which made machines capable of following mathematical directions to capture and store intelligence. Then came machine learning (ML), which advanced machine intelligence to use collected data to make predictive solutions. We now have deep learning, a more intelligent subset of ML that is based on the human brain. As found on Medium, "Artificial Intelligence would be the bigger, encapsulating circle that contains Machine and Deep Learning".
With deep learning, we can now scale business practices with automated processes including the revenue cycle. As the Medium article describes the advanced intelligence of AI, "deep learning was inspired by the structure and function of the brain, namely the interconnecting of many neurons. Neural Networks are algorithms that mimic the biological structure of the brain." For Healthcare, deep learning is opening up exciting new avenues for treating patients and efficiently running practice management processes.
The Challenges of Revenue Cycle Management (RCM)
The patient cost responsibility continues to grow as the high-deductible health plans (HDHPs) continue to capitalize on the insurance market, as found in Medical Economics. As reported, the HDHPs are becoming "a burden for both patients and healthcare organizations." The out-of-pocket expenses, associated with HDHPs, are causing medical practices to have increasing, uncollected debt.
New Avenues of AtRCM Operational Efficiency
It is a relief to know that artificial intelligence advanced learning is opening up a new avenue for healthcare leaders "to optimize their revenue cycle functions." It's all about merging workflows into "one comprehensive process."
- Medical organizations need to have the capability "to view, manage, and address obstacles" that hinder medical billing collections.
- Unique billing information, per payer contracts, needs to be integrated for accurate coding of services rendered to increase quick invoice to payment turnaround.
- "Critical components of payer-provider relationship can be proactively streamlined for improved workflows and increased revenue."
Two Critical Components to Increase Medical Billing Collections
The only two ways to collect payments for services rendered is either through insurance or the patient. Focusing on these two critical components, today's advanced AI can help streamline your collection process.
Efficient Preparation of Clean Claims and Insurance Denials
Submitting clean claims for insurance submission is vital for healthcare practices in every sector. Keeping up with ever-changing payer guidelines is part of a never-ceasing challenge for all healthcare systems. The time spent, and money lost, on denied claim work-up continues to plague many healthcare providers.
With today's AI technology, you can identify potential denials and fix them before they go out the first time. Along with those claims that may be in question, "AI and robotic automation can be used to allow providers or RCM partner's to auto-correct the claims and prepare any supporting documents in advance."
Anyone in medical billing knows the challenges and headaches working through claim denials. With advanced AI RCM, the handling of claim denials can now be electronically grouped to tackle claims with similar rejections for faster turnaround.
Through the advancement of AI solutions, healthcare practices "can reduce the number of denied or underpaid claims." Not only can you get your invoices out cleaner, but you will also realize increased ROI from AI-enhanced, billing efficiencies.
Patient Out-of-Pocket Responsibility
The Medical Economics' article reports some alarming stats regarding collections from patients. "Overall, practices collect just 12 percent of outstanding balances at the time of service and collect nothing 67 percent of the time." Part of the problem is the "decreased price transparency" of HDHPs. And, patients are usually at a loss for what their insurance will pay.
You can now fast-track patient eligibility verification, co-pay, and deductible data, and offer the information to your patients before appointment dates. Helping patients understand their financial responsibility is key to successfully capturing payments at the time of the visits and improving the revenue cycle.
As the Medical Economics' article concludes, "innovation is the only answer for adapting to the ever-changing healthcare industry," you can find out how to increase patient financial responsibility with today's advanced technology.
The MedicsRCM Has the Artificial Intelligence to Maximize Your RCM Process
With decades of innovative software advances for the healthcare industry, Advanced Data Systems Corporation is proud to offer our MedicsRCM, your complete end-to-end revenue cycle management services. Contact us for an exciting demonstration that will help you increased your operational efficiencies while increasing your return on investment.