Why is accuracy important in healthcare reimbursement

Data can help inform more personalized, individualized care, coverage, and payment which can lead to enhanced loyalty. This is especially important as the industry recovers and rebuilds from the pandemic. It can also make all the difference when it comes to seeing patient visits return to pre-pandemic levels. The new normal will consider the patient, payer, and provider as equally important stakeholders in the process — and having accurate, actionable datasets will ensure desirable outcomes for all parties.

Jim Bohnsack is senior vice president and chief strategy officer at TransUnion Healthcare. May 9, This can be done at speeds and with precision unattainable through human means. In the pre-submission or pre-payment phase, machine learning helps the parties quickly understand whether a claim is valid or needs to be reviewed.

In the post-payment stage, machine learning gives payers information for possible outreach and education programs tailored for provider staff who code and submit claims. The result: reduced health care administrative costs; a less abrasive, improved provider-payer relationship; and an easier transition to value-based care.

The provider-payer dynamic morphs from abrasive to collaborative — from negative to positive. In the end, this more positive provider-payer dynamic improves consumer satisfaction. We all want to improve health care delivery and patient outcomes. Dave Cardelle is vice president of payment integrity solution management at Change Healthcare. Amy Larsson is vice president of clinical claims management solutions at Change Healthcare.

Morning Consult welcomes op-ed submissions on policy, politics and business strategy in our coverage areas. Updated submission guidelines can be found here. These may occur singly or together: — Pre-Submission : A newer check, before the provider submits a claim; — Pre-Payment: Before money is sent by the payer to the provider; — Post-Payment Audit and Recovery : The most common, when the payer verifies claim accuracy after reimbursement.

These are: — Cloud-based claims payment-and-verification information technology systems, which are standardized; — Artificial intelligence , which helps providers or payers analyze claims and find anomalies; — Machine learning, a form of AI, which looks within massive quantities of data for patterns that then can be leveraged to reduce costs.

Health plans must perform advanced statistical analysis and extensive medical record review in order to achieve the level of documentation needed to confidently request reimbursement with any likelihood of success. Client Center. Payment Accuracy solutions Unlock new value with integrated prospective and retrospective payment integrity solutions. In uncertain times, be certain you have the right payment integrity partner.

From the Cotiviti blog COVID claim editing resources for health plans Cotiviti has compiled a summary of the latest COVID guidelines as a resource for health plans to quickly find the most relevant information needed to ensure payment policies are up to date. Claim Editing. Learn more. Clinical Validation. Billing Accuracy. Contract Compliance. Payment Responsibility. Clinical Chart Validation.

Case study: Payment Policy Management. Case study: Coding Validation. Get more value faster from a proven leader in payment integrity Payment integrity leadership requires experience, scalability, innovation, and proven value. Greater breadth and depth of analytics s of payment rules, policies, and concepts spanning prospective and retrospective intervention points. A trusted partner 21 of the top 25 national payers are clients, with nearly unique payer clients total.

Case study: Clinical Chart Validation. Case study: Contract Compliance. Infographic: Maximize the retrospective value chain Download our infographic for a comprehensive breakdown of the critical questions health plans should consider when evaluating a payment vendor—and learn how Cotiviti is leading the way forward in payment accuracy with a one-stop shop approach.

View the infographic. Payment Policy Management Identify millions of dollars in prospective payment savings More. Coding Validation Reduce costs from clinically complex claims More. Save another 0. Dental Claim Accuracy Reduce the cost of inappropriate dental claims More. Save 0. Payment Data Validation Save medical costs by finding undetected billing compliance issues More.