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For healthcare organizations looking to succeed in the transformation to value-based care delivery models, ForeSee Medical™ has developed ForeSee ESP™, a specialized software platform for accurate risk adjustment. Using proprietary disease algorithms and machine learned natural language processing, the software rationalizes your patient data across the healthcare system. Then using artificial intelligence (AI), diseases are discovered from the text notes and EHR data you already have. The result is insightful HCC risk adjustment support, integrated seamlessly with your EHR.
“Our EHR-agnostic solution aggregates data from sources across the healthcare system to create accurate and evidence-based HCC coding,” says Dr. Sol Lizerbram, Co-Founder and Executive Chairman.
It always feels great to do something right the first time. Prospective decision support eliminates clerical bottlenecks by coding correctly at the point of care or pre-visit. If your workflow relies more on retrospective review, the system supports those processes with powerful reports and insightful dashboards. For recapturing previously coded conditions, the system considers the likelihood of recapture based on disease persistence ratings and alerts the user if there is no current evidence to support a recapture opportunity.
Jonathan Flam, Co-Founder and CFO reports that, “Inaccuracies in HCC coding result in tens of millions of dollars per year in mispayment.”
ForeSee Medical has adopted the integration and interoperability standards used by the overwhelming majority of majorEHRs, so integration is easy. The system has the ability to simplify confusing codes like SNOMED to ICD10 and process your patient data in just about any format, including the latest FHIR APIs.
It’s no secret that all providers don’t “speak the same way”, accordingly, the engineering team at ForeSee built their natural language processing engine on a machine learning platform to understand more than 20,000 complex medical terms, accommodating a wide variation of terminology. The negation engine understands 500 forms of negation at 97% accuracy. Your clinical notes (unstructured data) are “read” by the NLP engine which extracts the patient chart information, codifies it (structured data), and creates a longitudinal patient record.
Physicians don’t perceive their patients as ICD codes or HCC risk categories, instead they are trained to understand and diagnose specific diseases that may be impacting the lives of their patients. The ForeSee Medical Disease Translator maps the 9,500+ HCC ICDs to hundreds of risk adjustment related disease concepts within the 80+ HCC categories. The proprietary Disease Detector powered by 50,000 rules is matched against every patient’s chart to accurately identify the patient’s disease burden. Since providers typically think in terms of disease concepts, not codes and categories, ForeSee Medical’s Disease Translator and Disease Detector make HCC clinical decision support easy.
Something to also consider, as the software processes patient data, it’s designed to follow, and not break, the HCC rules. Risk adjustment rules like hierarchical logic, interactions and counts are all built-in and incorporated into the automated RAF scoring calculations. When the NLP engine processes clinical notes, M.E.A.T. section headers are recognized and data extractions are weighted accordingly. The ICD coding support uses machine learned artificial intelligence, so the system is able to display what codes are commonly used by other Medicare providers for a specific disease.
“I can attribute our company’s determined 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.
