US-based healthcare technology company Edifecs has launched Point of Care Suspects, its risk adjustment solution designed to identify potential conditions at the point of care.
Point of Care Suspects aims to boost financial performance and operational efficiency by delivering potentially undocumented conditions to clinicians.
The risk adjustment solution enhances risk adjustment code capture and strengthens collaboration between healthcare providers and payers.
It employs advanced clinical AI, natural language processing (NLP), and machine learning to analyse both structured and unstructured data.
The approach offers actionable insights into patients’ known and suspected conditions throughout the risk adjustment lifecycle, prospective, concurrent, and retrospective.
The Point of Care Suspects feature integrates these insights into existing clinical workflows, enabling real-time care and coding gap management without disrupting care delivery.
Edifecs chief medical officer Summerpal Kahlon said: “Healthcare organisations face mounting challenges from risk adjustment coding complexity, impacting both patient care and revenue accuracy.
“Point of Care Suspects builds on our market-proven, best-in-class clinical AI to transform this process.
“By seamlessly integrating with existing workflows, we’re improving the provider-payer relationship while reducing provider burnout and compliance risk – exactly the kind of win-win healthcare needs.”
According to customer feedback, Integrated Delivery Networks (IDNs) capture 30% more Hierarchical Condition Categories (HCCs) with unstructured data, compared to structured data alone.
The Point of Care Suspects solution allows providers to gain a more comprehensive view of potential conditions, reducing manual effort and improving documentation accuracy.
The integration within clinical workflows reduces labour intensity and enhances efficiency.
Also, the Point of Care Suspects solution aligns coding and documentation, reducing compliance risks and easing audit defence workloads.
For payers, the solution ensures risk adjustment scores accurately reflect patient complexity, maximising revenue and reducing discrepancies that could lead to audits or penalties.
It improves quality metrics to enhance bonus incentives and market reputation, while identifying underreporting patterns guides targeted quality improvement efforts.
Edifecs customer said: “The more complete and relevant we can be in surfacing both recapture and net new conditions for clinician review, the better the overall patient care and provider experience.
“Minimising irrelevant or inaccurate conditions improves the quality of hierarchical condition category (HCC) capture with less provider abrasion.”
