Healthcare: Revenue Growth and Opti mization through ML2 min read
Outside of patient diagnostics, AI can also have a positive impact on the business side of healthcare. Through implementing machine learning solutions, there are a number of ways that you can better manage revenue growth and cost optimization.
Limit Patient Absences
In various healthcare provider settings, patient no-shows are a constant source of lost revenue and wasted time. On average, no-show rates for patients can be anywhere from 5% to 55%, and are a constant source of lost revenue for healthcare organizations.
Using machine learning, healthcare organizations can actually decrease the rate of patient appointment absences in order to decrease lost revenue. Machine learning can do this through calculating the probability of the absence based on the patient’s demographic, plan, diagnosis, and previous history. From there, it can apply a specific follow-up action plan, from automated reminders through SMS, email, and applications to personal follow-ups made over the phone.
Decrease Claim Rejection Rate
Machine learning can also decrease claim rejection rates by allowing healthcare organizations to better process claims. A machine learning solution can suggest claim structure, missing information, and the probability of a required prior authorization.
These AI systems can also take a key part in revamping the payment process to ensure payment is completed and that the claim rejection rate is decreased over time.
Implementing CPT code modifiers is another way to implement AI into the patient process and speed along the payment process. CPT code modifiers enable the possibility for procedures to get payer-specific reimbursement on the first run through the billing cycle. This cuts down on lost or delayed payments and creates an easy way to log them for the provider.
To explore more about machine learning, AI, big data, and how they can affect your healthcare organization, read our white paper, “AI, ML, and Healthcare: Beyond Existing Applications.”.