Cedars-Sinai cuts PE intervention time by 40% and saves $500K annually with Aidoc AI-powered PERT workflow

Cedars-Sinai deployed Machine Learning & Predictive Analytics for Patient Flow & Hospital Operations in Hospital & Health System. As reported by www.aidoc.com: $500,000+ annual cost savings.

Maintained by Peter Korpak, Lead EditorHow evidence is checked
$500,000+Annual Cost Savings
40% (~7 hours)Time to Intervention Reduction
3 days per patientICU Stay Reduction

Source-reported figures (cited source: www.aidoc.com)

Cedars-Sinai
Metric Before After Impact
ICU Cost per Patient $45,000 $35,000 $10,000 reduction (22% savings)
Time to Intervention ~17.5 hours ~10.5 hours 40% reduction (~7 hours)
ICU Stay Duration per Patient — Reduced by 3 days 3 days shorter per patient

What Cedars-Sinai was trying to fix

Managing pulmonary embolism (PE) patients involved a nine-step process averaging 17 hours from imaging to thrombectomy. The PERT team lacked real-time awareness of patient flow and volume, leading to delayed triage and fragmented multidisciplinary coordination.

What Cedars-Sinai deployed

Cedars-Sinai implemented Aidoc's AI solution, which alerts radiologists and the PERT team immediately after identifying a suspected PE. Multiple workflow steps that previously occurred sequentially now happen simultaneously, enabling faster triage, multidisciplinary assessment, and treatment coordination.

Results

Time to intervention was reduced by 40% (approximately seven hours). ICU stays shortened by three days per patient. ICU care costs dropped from $45,000 to $35,000 per patient, resulting in more than $500,000 in annual savings for the institution.

Key Takeaways

  • Parallelizing workflow steps through AI alerts can dramatically compress time-to-treatment for time-sensitive conditions like PE.
  • Clinical AI adoption can deliver simultaneous operational and financial benefits, not just clinical outcome improvements.
  • Real-time AI-driven notifications enable effective multidisciplinary team coordination without adding manual coordination overhead.

Evidence for Cedars-Sinai's Patient Flow & Hospital Operations deployment

Reported outcome metrics
3 cited below
Cited source
www.aidoc.com
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