It is no mystery that revenue cycle management has gotten more challenging. Increased patient financial responsibility, convoluted payer contracts, and the shift to value-based payments are just a few of the factors that make RCM more difficult. AI is no longer just a buzzword in the revenue cycle; it is a strategy leveraged to enhance the efficiency of RCM and the associated outcomes. By simplifying archaic processes and providing new intelligence to the claims lifecycle, Artificial Intelligence (AI) is revolutionizing the healthcare industry.

From spiraling claim volumes to more stringent payer requirements and increasing reporting obligations, today’s health practitioners face higher challenges than ever. As a result, diagnostics providers across the country are effectively looking for new revenue streams, improved payer relations, cost-cutting opportunities, and more predictable reimbursement rates. For example, high-quality and carefully designed data sets facilitate improved analytics, and the use of Artificial Intelligence (AI) can expedite the Revenue Cycle Management (RCM) process while also enhancing financial and operational performance.

The American Medical Association (AMA) polled 1,000 practicing physicians and found that 86 percent of doctors rate the burden of prior authorizations as high or extremely high. Almost the same percentage (88%) indicated the strain has increased in the last five years. According to the AMA survey, physicians and their staff spend over two full business days per week processing prior authorizations. More than one-third of physicians have personnel dedicated only to this job. The process of Prior authorizations is appropriate for automation due to its transactional nature. Council for Affordable Quality Healthcare, Inc. (CAQH) used data from medical plans representing nearly half of the US insured population to find that electronic prior authorizations adoption rose by just one percentage point to 13 percent from 2018 to 2019.

Automated revenue cycle processes can help in automating medical codes, eligibility verifications, and the like. For instance, automated medical coding platforms have been helping deliver accurate claims, fewer refusals, and higher compensation. Further, RCM automated platforms also minimize the administrative burden on physicians while improving patient care quality.

The fact that automation and AI can revolutionize the execution of the RCM process is evident, with more healthcare organizations setting AI as a top priority. Recent surveys cite that 75% of medical institutions are implementing or planning to implement an AI strategy, with 43% indicating that automating business activities such as revenue cycle management could be their first move.

Artificial Intelligence (AI) is not a new concept in healthcare. For years, visionary healthcare providers have been using technology to improve care for people suffering from sleep problems, eye illness, cancer, and now, even COVID-19. This technology has sparked interest in clinical care, with promises of speedier illness detection, expanded access to care in underprivileged or emerging areas, reduced EHR use burden, and much more. The use of AI in revenue cycle management, on the other hand, could perhaps be the technology’s massive breakthrough in healthcare.

Although the market for AI in healthcare revenue cycle management is still developing, providers can already reap the benefits of the cutting-edge technology. Identifying use cases is an excellent place to start with for AI investments. With AI, providers can discover plenty of avenues to automate processes that will help realize benefits in the long run and not just stop-gap solutions.

Organizations aim to continue to improve their revenue cycle processes by adding more AI-powered revenue cycle management technologies and including AI as-a-service. In the revenue cycle management arena, AI could be the next big step. The solution enables healthcare providers to outsource AI technology, allowing them to experiment with AI for revenue cycle management without incurring large expenditures upfront.