Study Confirms POCUS Program with AI Assistance Reduces Hospital Stay and Costs

A study published in JAMA confirms that a hospitalist POCUS workflow, heavily focused on lung ultrasound and assisted by AI, reduces hospital stay and costs. The collaborative model saved 246 hospital bed-days and $751,537 in direct costs. The study found that POCUS changed clinical management in 35% of cases and reduced expected hospital length of stay by 30%. The incremental cost-effectiveness ratio was $3,055 per hospital bed day saved.

A recent study published in The Journal of the American Medical Association (JAMA) has provided compelling evidence supporting the efficiency of AI-assisted Point-of-Care Ultrasound (POCUS) in reducing hospital stays and costs. The research, led by Dr. Partho Sengupta of Rutgers Robert Wood Johnson Medical School, found that integrating a hospitalist POCUS workflow, heavily focused on lung ultrasound and assisted by AI, resulted in significant improvements in patient care and hospital resource utilization.

The study, titled "POCUS for Cardiopulmonary Assessment and Resource Evaluation (POCUS-CARE)," evaluated a six-month stepped-wedge quality-improvement (QI) evaluation that reached 208 patients. The collaborative model, which included hospitalists with sonographer mentoring and remote cardiologist support, demonstrated a 246-bed day savings and $751,537 in direct cost savings. The study also found that POCUS changed clinical management in 35% of cases and reduced expected hospital length of stay by 30% [1].

The incremental cost-effectiveness ratio was $3,055 per hospital bed day saved, underscoring the economic value of integrating POCUS into inpatient care. Daily lung ultrasound (LUS) with B-line tracking was a key exam component, supporting management and speeding discharge.

"This study reflects Butterfly’s commitment to generating high-quality clinical evidence in support of scalable, sustainable POCUS integration across the healthcare continuum," said Dr. John Martin, Chief Medical Officer Emeritus of Butterfly Network. "By pairing hospitalist-led lung ultrasound with our AI Auto B-line Counter and cloud workflow, teams could standardize and speed congestion assessment, supporting earlier, better-informed decisions and hospital efficiency."

The findings of this study have significant implications for healthcare providers and investors alike. The ability to reduce hospital stays and costs through the use of AI-assisted POCUS can lead to improved patient outcomes and increased efficiency in healthcare delivery. As the healthcare industry continues to seek innovative solutions to these challenges, the adoption of AI-assisted POCUS technologies is likely to grow.

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[1] https://www.stocktitan.net/news/BFLY/rutgers-robert-wood-johnson-medical-school-study-published-in-jama-9b2fsk7qvn3x.html