Semantic Health

Introduction

Have you ever wondered how artificial intelligence (AI) and machine learning (ML) technology is transforming healthcare systems? At Semantic Health, our team is working with leading hospitals to introduce AI into the revenue cycle. By improving current coding and auditing processes to drive staff efficiency, identify data quality opportunities, and uncover missed reimbursements for hospitals and health systems, we are building the foundation for future AI initiatives that will impact clinical care.

Identifying the Impact of the Resource Gap on Reimbursements

As a doctor, I realized that my patients needed more help than my medical care could provide: better treatments, shorter wait times, and preventative care. I believe that delivering these outcomes requires empowering clinical practice with strong, high-quality data generated by health information management (HIM) and revenue cycle management (RCM) teams.

Billions of dollars are spent each year to mitigate issues around high denial rates or missed reimbursements in hospitals across the US. Medical coding audits play a pivotal role in the US health data pipeline, but RCM teams remain overworked, coded data remains underutilized, hospital reimbursements are missed, and significant claims are denied when they should not be.

At most hospitals and health systems, auditing processes require specialists to comb through hundreds, and sometimes thousands, of pages of complex clinical documentation to identify relevant information, extract key clinical events, and appropriate code diagnoses and procedures to classification systems. The complex and inefficient workflow, coupled with increasing demands to code faster and more concurrently, puts medical coders and auditors under significant pressure.

Technology must be used to support the current workflow and streamline the current audit process. What if there was a way to make the job of RCM staff easier while uncovering missed reimbursements for hospitals?

That idea was how Semantic Health was born. My team built our software, the Semantic Auditor, powered by proprietary AI, to automate the secondary reviews of all coded and claims data with reference to the source clinical documentation. The platform compares the coded data with the clinical documentation and finds the gaps: missed, underspecified, and unsupported codes. In doing so, we have focused revenue cycle staff on the most important opportunities first, confirmed hundreds of data quality opportunities per site, and prevented missed reimbursements at scale while significantly improving data quality that can feed into various analytic use-cases.

Building AI Auditing Software for Rev Cycle Teams

By employing advanced AI technology, our Semantic Auditor can flag high-value data quality opportunities in the documentation and coding for review as they arise. With specific reference to clinical documentation, and the capability to dive into the medical record, Semantic Auditor accelerates the review of what is often the most time-consuming aspect of ensuring data quality improvements. In doing so, Semantic Auditor is able to improve reimbursement accuracy in leading hospitals. And, most importantly, it improves the accuracy of pre-billing to prevent revenue leakage before it happens.

At Semantic Health, our team is working with leading hospitals to introduce AI into the revenue cycle

The Future of the Revenue Cycle with Semantic Health

In action, Semantic Auditor uses AI to review 100% of data across major hospital sites. The software identified an average of 450 total data quality opportunities per 15,000 inpatient charts reviewed. These results among leading hospitals indicate there is a significant opportunity available to healthcare organizations looking to close the data quality gap, save time for HIM and RCM team members, uncover reimbursements, and pave the way toward the future of medical auditing. And, we believe, it all starts with Semantic Health.