📄 medicine

TeleFAST: Pooling Remote Physician Capacity Across Emergency Departments to Improve On-Site Flow and System Resilience

This multicentre observational study demonstrates that TeleFAST, a centralized telemedicine fast-track model pooling remote physician capacity across multiple emergency departments in Portugal, significantly reduces length of stay and buffers on-site workload variability for low-acuity patients while maintaining safety and receiving positive acceptance from healthcare professionals.

Original authors: Micaela Seemann Monteiro, Filipa Lourenço, Débora Conceição, Susana Rafael, Eduarda Oliosi, Ana Rita Santos, Kimia Bajelan, Federico Guede-Fernández, Ana Paula Giordano, Ana Rita Londral

Published 2026-08-14

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Micaela Seemann Monteiro, Filipa Lourenço, Débora Conceição, Susana Rafael, Eduarda Oliosi, Ana Rita Santos, Kimia Bajelan, Federico Guede-Fernández, Ana Paula Giordano, Ana Rita Londral

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine the emergency department (ED) as a bustling, chaotic airport terminal. Every day, thousands of travelers (patients) arrive, but not everyone needs a pilot to fly them across the ocean. Some just need a quick map, a snack, or a ticket change—low-acuity issues that don't require a full medical team. In many places, these travelers get stuck in the same long security line as the people with broken bones or heart attacks, causing massive delays for everyone. This is the problem of "ED crowding."

To fix this, hospitals often build a "Fast Track" lane, a special express line for the travelers with minor problems. But here's the catch: traditional Fast Tracks are like having a single, local security guard who can only work at one specific gate. If that guard is busy, the line stops, even if there are plenty of guards sitting idle at other gates nearby. The system is rigid; it can't share resources. This paper explores a new idea: what if we could pool all the guards from different airports into one giant, remote team? By using video calls and digital tools, this "TeleFAST" model tries to create a flexible workforce that can jump between different hospitals instantly, smoothing out the chaos and keeping the lines moving, even when demand spikes unexpectedly.

The TeleFAST Experiment: A Remote Rescue for Busy Hospitals

In this study, researchers looked at four hospitals in Portugal to see if this "remote guard" idea actually works. They set up a system called TeleFAST, where a central team of doctors, sitting far away from the emergency rooms, could treat patients via video call. These remote doctors were supported by "Telemedicine Assistants" (TMAs) on-site—think of them as the friendly ground crew who hold up the camera, check the patient's throat with a digital tool, or listen to their heart while the doctor watches on a screen.

The study compared two groups of patients with minor, non-urgent complaints (like a sore throat or a mild rash). One group went through the usual "Standard of Care" (SoC) line, waiting for a doctor physically present in the hospital. The other group was sent to the TeleFAST line, where they were treated by the remote team.

The Results: Faster, Lighter, and Smarter

The findings were pretty exciting. The TeleFAST group didn't just move faster; they moved much faster.

Time Saved: Patients in the TeleFAST group waited 9 minutes less to see a doctor for the first time.
Total Time: Their entire stay in the emergency department was cut by 43 minutes. While the standard group stayed for a median of 117 minutes, the TeleFAST group was out in just 74 minutes.
Fewer Tests: The remote team used fewer resources. They ordered fewer lab tests and gave fewer treatments, yet they still managed to solve the problem for 96% of the patients without needing to send them to a physical doctor.

Crucially, the study checked if this speed came at the cost of safety. They looked at how many patients had to come back within 72 hours or 7 days. The answer? The rates were almost identical for both groups. This suggests that treating patients remotely didn't make them sicker or cause them to miss a serious diagnosis.

The "Resilience" Magic

Here is the most clever part of the paper. The researchers wanted to know if this system could handle the "ups and downs" of a busy hospital. Imagine demand as a wave. In a normal hospital, if a wave of patients hits, the on-site staff gets overwhelmed, and the line gets huge.

The study introduced a "Resilience Index" to measure this. They found that while the total number of patients arriving at the hospitals fluctuated wildly (like a stormy sea), the workload for the on-site staff stayed much calmer. The TeleFAST system acted like a shock absorber. Because the remote doctors could help multiple hospitals at once, they could soak up the extra patients when one hospital was swamped and another was quiet. The study calculated a Resilience Index of 0.42, suggesting that the system successfully buffered about 42% of the demand fluctuations, preventing them from crashing the on-site workflow.

What the People Who Work There Think

The researchers also asked the doctors, nurses, and assistants who used the system what they thought. The feedback was generally positive.

Attitude: The staff had a very positive attitude toward the system (scoring 4.33 out of 5) and wanted to keep using it.
Usability: They found the technology easy to use, giving it a score of 71.8 out of 100 on a standard "System Usability Scale."
Challenges: However, it wasn't perfect. Some staff mentioned that communication could be tricky and that technology glitches sometimes happened. They also noted that getting everyone on board with the new way of working took some effort.

The Bottom Line

This paper suggests that pooling remote doctors across multiple hospitals is a viable way to make emergency departments run smoother and faster for minor issues. It shows that you can cut wait times by nearly an hour and a half without hurting patient safety. While the study was observational (meaning they watched what happened rather than forcing a random assignment), the data strongly points to a model that is not only efficient but also "resilient"—able to bend without breaking when the pressure gets high. It's a promising step toward a future where emergency care isn't stuck in one building, but flows freely across a network, ready to help wherever the need is greatest.

Technical Summary: TeleFAST – Pooling Remote Physician Capacity to Improve ED Flow and System Resilience

Problem Statement
Emergency Department (ED) overcrowding is a global challenge driven significantly by low-acuity visits that consume physician capacity and extend wait times. In Portugal, nearly 40% of ED visits are low-acuity (triaged as Manchester Triage System II "blue" or "green"). While conventional Fast-Track Models (FTMs) have been implemented to address this, they suffer from a structural limitation: their throughput is bounded by the availability of on-site physicians. This dependency prevents multi-site health systems from pooling capacity to absorb demand peaks or buffer against workforce fluctuations, thereby limiting system resilience. Furthermore, existing evidence on telemedicine in FTMs is often limited to models operating outside routine ED workflows, lacking real-world multicentre data on operational performance and workforce flexibility.

Methodology
The study employed a multicentre observational design across four EDs within the CUF Hospital Network in Portugal, covering the period from November 2024 to January 2026. The research comprised two complementary components:

Retrospective Cohort Comparison: The study analyzed 7,046 low-acuity adult ED visits (MTS II blue or green). Patients were allocated to either the TeleFAST pathway (a centralised telemedicine FTM) or the Standard of Care (SoC) pathway (on-site physician) based on the first available physician in a shared queue. TeleFAST eligibility was determined by predefined criteria (age 18–65, specific presenting complaints, no "dizziness" discriminator, etc.).
- Intervention: TeleFAST utilizes synchronous video consultations where remote physicians assess patients supported by on-site Telemedicine Assistants (TMAs) who perform digital physical examinations (e.g., otoscopy, auscultation).
- Outcomes Measured: ED Length of Stay (LOS), time to first observation, resource utilisation (diagnostic/therapeutic interventions), short-term reattendance (72 hours and 7 days), and a novel Resilience Index (RI) calculated as $1 - (SD_{SoC} / SD_{Total Demand})$ to quantify demand buffering.
- Statistical Analysis: Comparisons used Wilcoxon rank-sum, chi-square, and Fisher's exact tests. Mixed-effects models adjusted for MTS II flowcharts assessed operational outcomes, while logistic regression assessed reattendance.
Cross-Sectional Survey: A survey was administered to 24 Healthcare Professionals (HCPs) involved in TeleFAST (remote physicians, nurses, TMAs). The instrument utilized validated scales: Technology Acceptance Model (TAM), Organizational Readiness for Implementing Change (ORIC), and System Usability Scale (SUS), alongside open-ended questions for thematic analysis.

Key Contributions

Operationalizing System Resilience: The study introduces and applies a Resilience Index (RI) to quantify the capacity of a centralised telemedicine model to decouple total low-acuity demand fluctuations from on-site workload variability.
Multicentre Real-World Evidence: It provides rare multicentre data on a telemedicine FTM integrated directly into standard ED workflows, moving beyond isolated virtual urgent care studies.
Workforce Pooling Model: The paper demonstrates a "geographically agnostic" physician pool that dynamically redistributes capacity across multiple sites, addressing the structural dependency of conventional FTMs on local staffing.

Results

Efficiency Gains: TeleFAST significantly reduced the median ED LOS by 43 minutes (74 minutes vs. 117 minutes in SoC; $p < 0.001$ ). After adjusting for MTS II flowcharts, the reduction remained at 34.8 minutes. Time to first clinical observation was reduced by 9 minutes ( $p < 0.001$ ).
Resource Utilisation: TeleFAST patients required fewer therapeutic interventions and diagnostic tests compared to SoC ( $p < 0.001$ ). Notably, 95.9% of TeleFAST patients were managed entirely remotely without escalation to on-site care.
Safety: Short-term safety outcomes were comparable. Reattendance rates at 72 hours (1.3% vs. 1.3%) and 7 days (2.2% vs. 2.0%) showed no statistically significant difference between groups ( $p > 0.05$ ).
Resilience: The total low-acuity demand variability (SD = 11.7) exceeded the variability of the on-site SoC workload (SD = 6.7), yielding an RI of 0.42. This indicates that the centralised model successfully buffered demand fluctuations, preventing them from fully translating into on-site workload volatility.
HCP Experience: Survey respondents reported positive attitudes (mean 4.33/5) and strong intention to continue using the system (mean 4.29/5). Usability was rated as acceptable (SUS score 71.8/100). Organisational readiness scores were moderate (3.52–3.79). Qualitative feedback highlighted reduced waiting times as the primary benefit, with communication barriers and technological limitations cited as main challenges.

Significance and Claims
The paper claims that TeleFAST represents a scalable, resilient workforce model for managing low-acuity demand in multi-site emergency care systems. By pooling remote physician capacity, the model addresses the structural limitations of conventional FTMs, improving operational efficiency and maintaining short-term safety without increasing resource consumption. The study posits that centralised telemedicine FTMs can absorb non-synchronous demand peaks across a network, thereby enhancing system resilience. The authors conclude that while the model shows promise, its transferability may be limited in resource-constrained settings due to dependencies on reliable connectivity and digital infrastructure. The findings suggest that such models warrant further validation, including economic evaluations and longer-term safety assessments.

Technical Summary: TeleFAST – Pooling Remote Physician Capacity to Improve ED Flow and System Resilience

More like this