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clinical study

From Imaging Detection to Revenue Generation: The Economic Impact of Clinically Meaningful AI—Quantifying the Financial Impact of Automated PE Detection

Pulmonary Embolism, ROI

Materials & Methods
This study modeled the annualized economic and clinical impact of AI-guided incidental pulmonary embolism (iPE) detection within outpatient imaging workflows. Between July 2023 and March 2024, 11,700 outpatient CT scans were analyzed using a commercial AI algorithm (Aidoc, Tel Aviv, Israel). All radiologist-confirmed iPE cases were reviewed for downstream care pathways including Emergency Department (ED) transfers, inpatient admissions, anticoagulation initiation and interventional procedures. A financial model extrapolated nine months of data to annualized projections using standard reimbursement values (ED visits, DRG-based admissions, anticoagulant therapy).

Results
Among 11,700 reviewed studies, 80 iPE cases were identified and annualized to an estimated 107 cases per year. Operational outcomes included ED transfers in 29% of cases (31/107), inpatient admissions in 78% of transferred patients (24/31) and initiation of anticoagulation therapy in 83% of admitted patients (20/24). The annualized revenue impact was estimated at $219,073, driven by emergency department visits, inpatient admissions and anticoagulant therapy. Additional downstream imaging, specialist consultations and interventional cardiology procedures are expected to further amplify return on investment.

Conclusions
AI-enabled incidental PE detection generates measurable clinical and financial value by driving timely care escalation, reducing missed diagnoses and increasing capture of appropriately reimbursed services. Radiologists play a central role in creating downstream clinical and economic impact, reinforcing AI’s dual value as both a quality enhancer and a revenue generator.

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