What You Should Know
- The Launch: Qventus has launched the Care Gap and Coding Automation Suite, an AI-powered platform embedded directly into the EHR to identify missed diagnoses, orchestrate clinical interventions, and complete documentation in real time.
- The First Module: The suite is debuting with Malnutrition Care Automation. Severe malnutrition is frequently overlooked in acute settings, resulting in longer hospital stays, higher readmission risks, and an average loss of $10,000 per patient in reimbursement.
- The Paradigm Shift: Traditional Clinical Documentation Improvement (CDI) happens 24 to 48 hours after care is delivered. Qventus shifts this from a retrospective audit to a proactive, real-time workflow, automatically pre-populating consult orders and prompting diagnosis documentation before the care window closes.
- The ROI: At one southern academic medical center, the malnutrition module generated over $350,000 in additional reimbursements in just three months (a $1.4M annualized run rate).
The “Malnutrition” Use Case
To prove the platform’s efficacy, Qventus is launching with a highly specific, highly lucrative first module: Malnutrition Care Automation. Severe malnutrition is a perfect example of a chronic, highly intervenable condition that gets lost in the shuffle. When a patient is admitted for a heart attack or a complex surgery, busy care teams focus entirely on the acute issue. The patient’s underlying severe malnutrition goes undocumented.
The consequences are twofold:
- Clinical: The patient stays hospitalized an average of two additional days and faces twice the risk of readmission because their nutritional deficits weren’t addressed.
- Financial: The hospital forfeits an average of $10,000 per patient in reimbursement because the patient’s “Major Complication or Comorbidity” (MCC/CC) was never captured. It also artificially suppresses the hospital’s public quality ratings, as the patient’s severity of illness is understated.
“Hospitals don’t need another audit tool, more CDI resources, or another AI point solution firing alerts at overburdened providers,” said Jason Cohen, MD, Chief Medical Officer for Inpatient at Qventus. “They need proactive identification and closure of care and coding gaps earlier.”
Moving from “Alerts” to “Action”
The fatal flaw of early clinical AI was alert fatigue. Algorithms would flag a patient as “at risk” for malnutrition and send a pop-up to a nurse who was already managing four other critical patients.
Qventus is bypassing the alert layer and moving directly into workflow orchestration. Using real-time chart mining, the AI identifies the at-risk patient. But instead of just pinging the doctor, the system automatically pre-populates a nutrition consult order and prompts the exact diagnosis documentation needed for MCC/CC capture directly within the provider’s native EHR workflow.
It does the administrative “below-license” work so the clinician can simply review, approve, and deliver care. The early results are compelling: one southern academic medical center saw an annualized reimbursement bump of $1.4 million just three months after deploying the malnutrition module.
