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Artificial intelligence can alleviate revenue cycle management challenges


With constant changes in healthcare and how much patients are responsible to pay for their care, practices should turn to technology for assistance.

About 64 million adults in America struggle to pay their medical bills. In fact, one in five Americans is considered to have delinquent medical debt. 

As high-deductible health plans (HDHPs) have grown in popularity over the past couple of years, patient financial responsibility continues to increase, making it more crucial than ever for patients to thoroughly understand their financial obligations and for medical practices to adapt to the latest cutting-edge technology for efficient revenue cycle management (RCM). 

Despite the lower monthly premiums, HDHPs have become a burden for both patients and healthcare organizations, as unexpected medical emergencies and non-preventative health conditions can be costly for patients, leaving them ill-prepared for their out-of-pocket expenses and with unpaid medical bills. Consequently, practices are then left with millions of dollars in uncollected funds.

To alleviate this complex, systemic issue, healthcare organizations are turning to artificial intelligence and machine learning to optimize their revenue cycle management processes. 

Workflows can be merged into one comprehensive process, allowing organizations to view, manage, and address obstacles to ensure proper claim reimbursement. They can also compare documentation, coding, and services rendered to align with payer contracts, securing accurate reimbursement. Critical components of the payer-provider relationship can be proactively streamlined for improved workflows and increased revenue. These components include claim scrubbing or denial prediction, denial and underpayment management and patient payment estimations.

Claim scrubbing and denial prediction and management

Payer guidelines are constantly tweaked and reassessed, making it considerably more difficult for providers to guarantee their claims will be accepted. 

The healthcare industry spends upwards of $315 billion per year on claims processing, payment and reconciliation. Clinicians should do everything in their power to avoid denied claims and the costly follow-up work associated. Big data analytics and machine learning curated specifically for claim scrubbing and denial prediction can quickly identify payer patterns and denial reasons before claims are sent out. AI and robotic automation can be used to allow providers or RCM partners to auto-correct the claims and prepare any supporting documents needed in advance, and more notably, at the least expensive stage of the claim life cycle.

In the instance of a claim denial, denial management is typically conducted through collectors calling payers to follow up on denied claims individually. AI can alleviate this grueling process through grouping similar claims together, allowing collectors to adjust them all at once. 

With the integration of innovative, automated RCM infrastructures, practices can reduce the number of denied or underpaid claims and cut back on operational expenses through increased efficiency, protecting their bottom line.

Out-of-pocket estimation and upfront collections

Overall, practices collect just 12 percent of outstanding balances at the time of service and collect nothing 67 percent of the time. HDHPs have indirectly decreased price transparency, making it difficult to determine the exact price owed at the time of service, ultimately resulting in underpayment or no reimbursement at all.

Additionally, insurance plans constantly evolve, leaving patients confused about their coverage. Through automated insurance eligibility checks, healthcare organizations can minimize claim denials by obtaining medical eligibility verification, co-pay, and deductible data prior to the appointment. This way, practices can provide patients better estimates of their financial responsibility, allowing them to prepare for their payments or to set up suitable payment arrangements.

Once a practice determines what is owed by a patient, it must have flexible patient statement submission and retrieval tools and processes that help them easily retrieve their information on the web or phone with options to make partial payments or the entire balance in full.  Using machine learning, providers can determine the propensity to pay by a patient and offer payment options. Practices cannot send paper statements by mail and expect patients to send checks.

Innovation is the only answer for adapting to the ever-changing healthcare industry and the RCM complexities that come with it. Streamlined, reliable approaches integrated into in-house processes can assist healthcare organizations, big or small, in maximizing revenue performance and making collections a seamless component of everyday workflow.

Sunil Konda is vice president of products at SYNERGEN Health, a leader in complex revenue cycle optimization solutions for health care organizations.



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