There has been a lot of attention on the patient experience and consumer-centered initiatives in healthcare recently. Hospitals, outpatient facilities, doctors, and DME providers understand that when they can provide a stand-out experience — by ushering patients through the administrative requirements in the most effective, informative, and coherent way possible — this not only improves the patient experience but also clinical outcomes.

One category, Durable Medical Equipment, Prosthetics, Orthotics, and Supplies (DMEPOS), impacts a significant number of patients while also attracting a lot of scrutiny from insurance payers through the prior authorization process and denied claims.

As reported by Joe McTernan, director of coding and reimbursement at the American Orthotic & Prosthetic Association (AOPA), “The [prior authorization] process, as a concept, is not one that AOPA is necessarily against, but we have been and remain concerned that if it is not done effectively and fairly, that patients will, ultimately, be the ones who suffer through reduced access to medically necessary, high-quality clinical care.”

A Better Patient Experience Through Automation

Unique to healthcare, third-party financial responsibility adds a layer of complexity that is often confusing and deliberately complicated. Through Artificial Intelligence (AI) and machine learning, hospitals, outpatient facilities, doctors, and DME providers can now proactively engage insurance payers and meet them on a more level playing field.

Through automation and AI-driven software, insurance plan requirements can be updated and maintained with data accessible and actionable in real-time, creating up-to-the-minute results that positively impact a patient’s ability to acquire needed medical equipment, such as oxygen, mobility and pressure-reducing support surface devices, or CPAP machines.

Setting the Foundation – Insurance Verification

It is critical to gather and confirm precise patient eligibility information, including co-pays, co-insurance, deductibles met and remaining, and out-of-pocket maximums. While this automated process has more industry-wide acceptance with, on average, according to the 2020 CAQH Index, 84% of organizations using at least partially automated workflow, there are still efficiencies to be gained.

Concurrently, using an integrated, automated, and comprehensive platform solution would allow patient eligibility details to also inform patient pay estimates and prior authorization requirements.

Prior Authorizations for DME

The single most significant area in need of modernization is utilization review and prior authorizations. The 2020 CAQH Index states that on average, only 21% of healthcare providers have fully adopted an electronic solution for managing prior authorizations which means that countless hours are being spent on hold with insurance payers trying to obtain approvals or following up on rejected requests.

Through today’s technology and a unified workflow, automation and AI-driven software can enable hospitals, doctors, and DME providers to manage the prior authorization process in real-time electronically. From submission to follow-up and appeals, prior authorizations can be digitally managed, and when completed, can be routed to the appropriate equipment provider in real-time.

A/R and Claims Denials

Last, the revenue cycle concludes with the management of claim denials. By leveraging AI and machine learning technology, denials can be managed effectively, and revenue captured that may currently be abandoned entirely. Hospitals alone, lose on average, $262 billion per year on denied claims.

When claims are denied, they are often passed through to the patient and are a major source of “surprise” billings. Utilizing an automated AI-driven denials management solution, hospital and provider organizations can significantly reduce the high number of claims that are returned or rejected, further reducing the reported “surprise” billings that rightfully upset patients.

Insurance Discovery

Nobody wants it to happen, but sometimes balances have to be written off as uncollectible or charitable care. But what if there were a way to look for undisclosed insurance coverage for patients? We’re talking about coverage that they don’t even know exists like secondary coverages and Medicaid.

Once the patient’s information is downloaded into a proprietary insurance discovery system, the software completes deep data mining and probabilistic analytics to glean, check, and double-check the data. Access to this information comes from a sophisticated network of clearinghouses, direct payer connections, and is supplemented through a network of public and private databases.

Collectively, this information is then used to identify undisclosed coverage and socio-demographic identifiers to correct and update claims so that they can be processed by a team of highly qualified billing experts and plans can begin to pay.

In Summary

Changes in healthcare are going to continue, and being prepared by utilizing the available technology to improve reimbursement as well as the patient’s experience will position your organization for the future. By utilizing AI and machine learning technology, your DME organization will be able to streamline administrative functions freeing up people (the human intelligence) to educate and guide patients and improve their satisfaction and loyalty.

Find out more about leveraging automated technology to improve your patients’ experience.