The orthotics and prosthetics industry landscape has changed drastically in the post-pandemic climate. How O&Ps conduct business has undergone a seismic shift and it is business unusual. How do you revamp everyday operations to meet performance goals? There is a lot to unpack in this question. One key area to focus on would be the revenue cycle.
Revenue Cycle Management (RCM) is crucial for healthcare organizations to maintain a strong financial foundation and ensure efficient revenue flow. However, many RCM processes are manual and time-consuming, leading to potential revenue leakages and increased administrative burden. The integration of automation and artificial intelligence (AI) can significantly improve these areas, resulting in better financial outcomes and enhanced patient experiences.
Automating insurance verification can improve the accuracy and efficiency of gathering patient eligibility information. AI-driven software can access real-time data to authenticate patient benefits, deductibles, and coverage for medical equipment. By automating this process, DME organizations can reduce errors, prevent denials, and allocate staff to more patient-centric roles.
The prior authorization process is often time-consuming and manual, leading to delayed approvals and revenue losses. Implementing AI-driven software can streamline the authorization process by electronically identifying equipment requiring approval, collecting necessary information, and submitting requests in real-time to insurance payers. This significantly reduces administrative efforts and ensures quicker approvals.
Coding and billing for DME equipment can be complex, and staffing issues may lead to processing bottlenecks. Engaging a third-party billing partner with expertise and scalability can improve coding accuracy and claims processing efficiency. Automating these tasks can also help manage credit balances and improve contract performance for future negotiations.
Denied claims are a common issue in the healthcare industry and often go unchallenged, resulting in lost revenue. By leveraging AI technology with machine learning capabilities, denials can be effectively managed, and the revenue capture can be maximized. Automated denials management solutions prioritize and appeal denials in real-time, reducing surprise billings and improving patient satisfaction.
Unidentified insurance coverage can lead to uncollectible or charitable care balances. Implementing an insurance discovery system with data mining and probabilistic analytics can help identify undisclosed coverage for patients. This information is then used to update claims and improve the chances of successful reimbursement.
By leveraging technology to streamline administrative processes and improve accuracy, DME organizations can focus on providing better patient experiences and ensure their financial sustainability in the ever-changing healthcare landscape.