Dr. Sol Lizerbram, Co-Founder & Executive ChairmanValue-Based Reimbursement (VBR) models reward insurers and providers that document disease burden and close gaps in care for their patient populations. Many providers now participate in VBR models because of the growing adoption of Medicare Advantage (MA) plans and ACOs. VBR models seek to transfer financial risk from insurers to providers and align the incentives of the provider with the insurer and the patient. Each constituent succeeds when the disease burden (risk profile) of their patient population is accurately documented/coded and when every gap in care is closed. CMS has devised two essential methodologies to help understand patient risk profile and care gaps.
The first is the Hierarchical Condition Category (HCC) coding system which adjusts payments based on the risk profile of patients (Risk Adjustment). Risk Adjustment assures health plans and providers that there are adequate resources to care for patients that are very ill, but it depends on providers following a complex set of rules.
The second is the HEDIS Clinical Quality Measures (CQMs), a set of measures that aim to discover gaps in care. Insurers’ Medicare STAR ratings improve when their patient populations perform well based on the HEDIS CQM guidelines. CMS pays more premium to insurers with high STAR ratings and some of that “reward” is passed on to providers in the form of “quality bonuses.”
Traditionally, tracking and managing HCC and HEDIS CQM data for a patient population is challenging, complex and a time-consuming administrative burden. Jonathan Flam, Co-Founder and CFO reports that, “This burden leads to a failure to optimize the HCC coding and HEDIS CQM systems, resulting in tens of millions of dollars per year in underpayment.”
The team at ForeSee Medical are addressing this challenge, with a solution that uses the latest interoperability standards, AI, ML and NLP to help providers and insurers with the HCC coding and HEDIS CQM systems thereby facilitating proper reimbursement and high quality of care.
ForeSee Medical Software utilizes structured and unstructured data from various sources, such as Electronic Medical Records, laboratory, pharmacy, hospital data, consults and radiology reports. The software’s NLP capability can automatically read unstructured data form documents such as PDFs, interpret important information and process it through the software’s proprietary algorithm, to identify potential HCC coding and care gap opportunities. The software then offers real-time Clinical Decision Support (CDS)at the time of the patient encounter. “Our EHR-agnostic solution aggregates data from sources across the healthcare system and applies our proprietary clinical algorithms and NLP to create risk stratification cohorts, optimizes HCC coding, and identify gaps in care,” says Dr. Sol Lizerbram, Co-Founder and Executive Chairman.
Our EHR-agnostic solution aggregates data from sources across the healthcare system and applies our proprietary clinical algorithms and natural language processing to create risk stratification cohorts, optimizes HCC coding, and identify gaps in care
“I can attribute our company’s keen attention to accuracy to our creative team of engineers who boast of backgrounds in data science, coding and user interfaces. As physicians we bring to the table extensive experience in the medical field, and I am encouraged by the positive response we see in the marketplace,” says Dr. Seth Flam, Co-Founder and CEO.
The software uses state of the art technologies to act as a “virtual assistant”optimizing payment. When the proper amount of resources are allocated to patient care the health of patient populations improve – a win for patients, providers and insurers!