Optimizing patient management in the emergency department with navify® Analytics

Success story

Front view of Ospedale San Raffaele in Milan, Italy.

Ospedale San Raffaele

  • University hospital in Milan, Italy

  • 1,300+ beds, 50+ clinical specialties

  • Performs 7.5 million lab tests annually

Overview

Improving emergency department processes through diagnostic workflow optimization with navify Analytics for Core Lab

Ospedale San Raffaele, a major academic hospital in Milan, partnered with Roche Healthcare Consulting to implement navify Analytics for Core Lab and improve diagnostic workflows in its emergency department (ED). With a focus on high-sensitivity troponin testing for acute myocardial infarction, the project aimed to reduce turnaround times, optimize pre-analytical and analytical processes and enhance communication between the lab and ED teams. The result was a 68% reduction in pre-analytical turnaround time and a 45% overall improvement in test execution time – delivering faster diagnoses.1

Blurred motion image of people walking through a hospital corridor with patients seated in a busy waiting area, indicating a fast-paced healthcare environment.
Situation

Long ED stays hinder diagnosis and can negatively impact patient outcomes

Ospedale San Raffaele’s emergency department faced challenges with patient overcrowding, long wait times and delays in receiving laboratory test results. These inefficiencies affected clinical decision-making, prolonged patient length of stay and risked negative health outcomes. Timely diagnostics, particularly for acute myocardial infarction, were essential but hindered by slow pre-analytical and analytical processes.

Close-up of medical professionals in blue protective gear attending to a patient in a hospital bed, suggesting an urgent care or surgical setting.
Solution

Optimizing diagnostic workflow with navify Analytics in emergency cardiac care

To address diagnostic delays and emergency department congestion, Ospedale San Raffaele implemented navify Analytics in partnership with Roche Healthcare Consulting. The team focused on redesigning sample flows, introducing pneumatic transport and integrating STAT methods for high-sensitivity troponin testing. By leveraging data analytics to track turnaround times and optimize each phase – pre-analytical, analytical and post-analytical – the lab reduced execution time for acute myocardial infarction testing by 45% and significantly improved clinical decision-making efficiency.

The evidence collected made it possible to reduce the execution times of a clinically strategic test for the diagnosis of Acute Myocardial Infarction by 45%.

Dr Massimo Locatelli, Head of Laboratory Medicine

Redesigning lab-ED workflows to accelerate troponin test results

Ospedale San Raffaele re-engineered its emergency testing workflow using navify Analytics to monitor and refine each step. Urgent samples were rerouted through a pneumatic system directly to the integrated pre-analytical station and analyzers. STAT testing methodology was introduced, supported by weekly performance reports and continuous collaboration between lab and emergency department teams. These changes enabled faster diagnostics for suspected acute myocardial infarction patients.

Results

Measurable gains in diagnostic speed and efficiency transform emergency care at Ospedale San Raffaele

68%reduction in pre-analytical TAT

Turnaround time for urgent samples dropped from 50 to 16 minutes.

45%reduction in test execution time

Average overall test TAT decreased from 80 to 44 minutes.

FAQs

Frequently asked questions

Explore common questions about how navify Analytics helped Ospedale San Raffaele enhance diagnostic workflows and improve emergency care through data-driven process optimization.

References and notes
  1. F. Hoffmann-La Roche Ltd. Data from navify Analytics for Core lab Case Study: Data analytics in a Clinical Laboratory for patient path optimization: the Emergency Department example, Ospedale San Raffaele in Milan, Italy. MC--11281 (2023 May). Available from here
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