Digital referral versus clinical reality: evaluating prehospital trauma allocation with IVENA eHealth

• Published in: European journal of trauma and emergency surgery (Munich : 2007) Vol. 51; no. 1; p. 278
• Main Authors: Faul, Philipp, Neubecker, Daniela, Schweigkofler, Uwe, Koch, Daniel, Hagebusch, Paul
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2025; Springer Nature B.V
• Subjects: Adult; Aged; Cardiac catheterization; Classification; Computer platforms; Critical Care Medicine; Datasets; Emergency medical care; Emergency Medical Services - methods; Emergency Medical Services - organization & administration; Emergency Medicine; Female; Fractures; Germany; Health care; Humans; Injury Severity Score; Intensive; Intensive care; Intubation; Male; Medical referrals; Medicine; Medicine & Public Health; Middle Aged; Original Article; Patients; Referral and Consultation; Retrospective Studies; Spinal cord injuries; Sports Medicine; Surgery; Surgical Orthopedics; Telemedicine; Trauma; Trauma Centers; Traumatic brain injury; Traumatic Surgery; Triage - methods; Wounds and Injuries - diagnosis; Wounds and Injuries - therapy; Germany; Hospital resource allocation; Digital referral systems; Trauma team activation; Overtriage; Field triage; Undertriage; Emergency medical services
• ISSN: 1863-9933; 1863-9941; 1863-9941
• DOI: 10.1007/s00068-025-02949-w

Background Providing effective emergency trauma care is a growing challenge for healthcare systems, particularly amid rising case numbers and limited hospital resources in Germany. Accurate prehospital assessment and appropriate hospital allocation significantly influence patient outcomes. IVENA eHealth® (Interdisziplinärer Versorgungsnachweis) is a nationwide digital platform that supports Emergency Medical Services (EMS) in coordinating emergency care and assigning patients based on real-time hospital capacity. However, its clinical accuracy has not been thoroughly evaluated and its triage categories are based on EMS estimation rather than a validated classification system. This study analyzes the concordance between prehospital trauma-patient allocation via IVENA and actual clinical treatment pathways. Methods In this retrospective single-center study, trauma referrals to a level-1-trauma-center in 2018 were analyzed. Included were patients triaged as severity category 1 (SC1, immediate) or severity category 2 (SC2, urgent) with suspected polytrauma, traumatic brain injury (TBI), thoracic or pelvic trauma. Prehospital SC was compared to hospital admission types: SC1 was assumed to require ICU care, SC2 a general ward. Patients triaged as severity category 3 (SC3, non-urgent) were not included in the analysis. Overtriage and undertriage rates were assessed, and prehospital suspected diagnoses were compared with discharge diagnoses. Sensitivity, specificity, and positive predictive value (PPV) were calculated. Logistic regression was used to identify influencing factors. Results Among 4,331 patients, 742 (17.1%) were SC1, 2,840 (65.6%) SC2, and 749 (17.3%) SC3. SC1 (245 included patients) triage accuracy was 64.08%, with 35.92% overtriaged. SC2 (446 included patients) had 55.61% overtriage and 2.24% undertriage, exceeding the ≤ 50% recommended threshold. SC1 classification showed 94.01% sensitivity and 64.08% PPV. Discrepancies between prehospital and discharge diagnoses were frequent, particularly for polytrauma, TBI, and thoracic trauma. Polytrauma with TBI had 82.35% triage accuracy, while polytrauma without TBI showed 45.45% overtriage. Closed TBI cases classified as SC1 were overtriaged in 41.67%, while SC2 patients had 51.33% overtriage and 2.66% undertriage. Conclusion The digital referral system IVENA eHealth® supports the structured transmission of EMS-assigned patient allocations, but the accuracy of SC1 and SC2 assignments varies considerably. The observed discrepancies between prehospital classifications and actual clinical care highlight the limitations of current prehospital assessment practices. SC1 classification resulted in ICU admission in only 64.08% of cases, indicating the need to reassess assumptions linking severity codes with clinical requirements. Despite these mismatches, the overall undertriage rate remained below 3%, suggesting a high level of patient safety within the current system. Optimizing prehospital assessment protocols, integrating real-time hospital capacity data, and strengthening EMS training may improve allocation accuracy. To better align digital referral systems with clinical realities, future research should focus on developing dynamic decision-support tools, implementing adaptive allocation algorithms, and evaluating the effects of updated clinical guidelines.