Real World Implementation of AI-assisted PERT-Workflow and its Association with Care Processes and Budget Impact: a Retrospective Cohort Study

Clicks: 7
ID: 333504
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #77 of 80 articles by views in European Heart Journal - Digital Health

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Background Pulmonary embolism (PE) remains a major cause of morbidity and mortality. In intermediate-high and high-risk PE, timely escalation beyond anticoagulation is critical, yet real-world pathways are often delayed. Pulmonary Embolism Response Teams (PERTs) improve decision-making, but activation remains inconsistent. Whether artificial intelligence (AI)-enabled triage strengthens PERT and improves acute PE care has not been elucidated so far. Methods We retrospectively screened 2500 patients presenting to our university hospital with a symptomatic central or segmental PE between 08/2022 and 07/2024. Clinical and economic outcomes were compared during a one year timeframe before and after implementation of an AI algorithm that analyses CT scans, flags suspected PE, and supports PERT triage. Results 424 patients met inclusion criteria. AI-supported triage increased PERT activation from 9.2% to 84.5% (p < 0.001). Time from CT completion to PE diagnosis decreased from 2.1 to 1.3 hours (p < 0.01). Time from PE diagnosis to interventional therapy was reduced by 50% (p < 0.05). Hospital/ICU length-of-stay and 30-day mortality showed non-significant reductions. Model-based economic analysis showed lower 1-year per-patient costs and annual savings associated with AI-supported PERT. Conclusions Implementation of an AI-supported PERT workflow was associated with significant improvements in PE care processes, including higher PERT activation rates and shorter diagnostic and treatment intervals. Numerical reductions in mortality, hospital/ICU length-of-stay did not reach statistical significance. The exploratory budget impact model suggested potential cost savings, mainly driven by trends in reduced resource utilization; however, these findings require prospective validation using adequately powered clinical endpoints.
Reference Key
openalex_W7223202084 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Daniel Messiha, Felix Nensa, Johannes Haubold, David Pinsdorf, Michael Forsting, Friederike Engel, Fadi Al‐Rashid, Olga Petrikhovich, Ramtin Knuschke, Julia Lortz, Tienush Rassaf, Christos Rammos
Are you one of these authors? Claim your profile
Journal European Heart Journal - Digital Health
Year 2026
DOI
10.1093/ehjdh/ztag165
URL
https://doi.org/10.1093/ehjdh/ztag165
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.