As organizations realize the importance of AI for saving time and costs, its applications are expanding dramatically, especially in the healthcare sector. According to a survey conducted by Change Healthcare in 2020, more than four-fifths of healthcare organizations worldwide are reaping the benefits of AI.
Revenue Cycle Management (RCM) is one of the critical processes in the healthcare sector that AI can transform. The survey conducted by Change Healthcare reported that nearly two-thirds of healthcare organizations are already utilizing AI in revenue cycle management. What’s more, nearly 98% of decision-makers surveyed in the healthcare system stated they plan to use AI in revenue cycle management by 2023.
Here are four ways in which AI is likely to be improving revenue cycle in healthcare:
Rapid prior authorizations
Doctors, physicians, and other healthcare staff spend countless hours using Prior Authorization (PA) from payers every year. According to a survey conducted by American Medical Associations (AMA) on PA in 2021,
- More than one-third of surveyed physicians reported that delays in PA led to adverse events such as hospitalizations, death, disability and permanent bodily damage, among others
- Eighty-eight per cent of physicians consider PA a burden
- Nearly 40% of physicians have dedicated staff for carrying out PA activities
AI-driven PA software streamlines workflows, obtains rapid prior authorizations and enables healthcare staff to focus on patient care, avoiding any kind of adverse events.
Error-free medical billing
Manual billing is a cumbersome, error-prone, expensive and time-consuming process. Sending accurate billing information to payers avoids discrepancies in claim approvals, reimbursements and patient collections. According to the 2021 CAQH Index, the U.S. healthcare industry can save approximately $20 billion every year by implementing electronic administrative transactions. Implementing artificial intelligence in revenue cycle management can also save time, reduce costs for healthcare and insurance providers and improve accuracy. AI learns and extracts data through the patient-submitted documentation to prepare and send error-free billing information.
Submitting correct coding information
According to a report by Forbes on Medical Coding, nearly 140,000 classification codes, including CPT codes, ICD codes, HCPCS codes, and modifiers, exist in the updated version of the International Classification of Disease (ICD-10). Sorting, finding and assigning correct codes manually can be tiresome and error prone. Submitting incorrect codes to insurance providers incurs additional time delays and costs. The AI system can examine, interpret, extract and validate correct codes without errors and speed up the claim submission and reimbursement processes.
Extensive denial analysis
In another survey Change Healthcare survey, it was found that nearly 50–65% of the total denied claims are never reworked. This is because the costs incurred in reworking those claims manually are higher than the costs they generate. AI could play an important role in analyzing and determining the reasons for denial, such as incorrect coding, inaccurate billing information, etc. AI can play the instrumental role of offering recommendations to fill in missing patient information, mark errors and inaccurate codes or address prior authorization issues. It’s safe to say then that AI in revenue cycle management can offer predictions, lower denial rates, and boost reimbursement. This will save a lot of time not just for healthcare organizations but for other payers too.
Given growing awareness among healthcare organizations, the role of AI in improving revenue cycle in healthcare is undeniably huge. As the industry as a whole takes stride towards applying AI in revenue management, challenges, questions and opportunities alike are bound to crop up the implementation and operational phases. HelioNext is here to help—get in touch today!