Prioritizing Surgical Care During COVID-19 Using the SWALIS 2020 Model

As the COVID-19 pandemic shows no evidence of slowing down, many cancer surgeons worldwide have begun to devise models to aid with the prioritization of canceled (backlog) elective and upcoming cancer surgeries. Researchers have also strongly advised conducting COVID-19 testing at various time points before and after any surgical procedures with the aim of minimizing the risk of COVID-19—related mortality peri- and postoperatively, particularly in high-risk patients undergoing major surgeries. Thus, in most ways, it can be argued that elective surgeries on non-COVID patients are burdened similarly to those with COVID-19, by increasing waiting times and costs.


The SWALIS 2020 Model 

In 2009, the SWALIS prioritization model was developed “to implicit urgency criteria, time-based priority-based scheduling, and waiting list measuring indexes.” During the 2020 European Society of Surgical Oncology (ESSO)’s annual meeting, speaker and scientist, Roberto Valente, MD, Ph.D., who was involved in modifying this model, shared their findings from implementing it in Spring 2020 to prioritize elective surgery throughout and beyond the COVID-19 pandemic in Italy.

The SWALIS 2020 is a ‘software-aided inter-hospital multidisciplinary pathway’ that dynamically reorders the surgical waiting list for cancer patients from least to most urgent in real-time, thereby ensuring priority-based surgical access and scheduling for past, present, and incoming patients. There are three main stages to this dynamic scheduling:

  • Clinical triage assessed using patient clinical history, pre-administrative information, clinical urgency, and demand modulation
  • Prioritization and selection calculated by dividing the maximum by the actual waiting time for that patient
  • Timely allocation of the surgery computed by the expected priority and weekly forecasted capacity demand

SWALIS 2020 uses these three steps to automatically allocate resources on the basis of oncological demand and significantly reduces the timeframe from consult to surgery by autonomously putting together an MDT for the patient, allowing hospitals adopting SWALIS 2020 to rapidly respond to the patients’ needs depending on the level of surgical urgency. Data shows that these three urgency categories were most commonly used to classify tumor progression when using the SWALIS 2020 model during the pandemic: A1 – 15 days (certain rapid disease progression), A2 – 21 days (probably progression), and A3 – 30 days (potential progression).

The results following the SWALIS 2020 feasibility phase (n=55 patients) showed that 240 referrals were successfully prioritized using this model without any major criticalities. Additionally, when researchers monitored the waiting time data for patients and operation theatre allocation for surgeons, they found that 222 cancer patients underwent surgery without related complications or delayed discharges. These results allowed Dr. Valente and his team to objectively quantify the effectiveness of the SWALIS 2020 model—it was 88.7% effective in week 1, then persistently 100% effective during the remainder of the phase. Even at a 30% or lower capacity, this model can consistently and smoothly manage active and backlogged waiting lists, making it one of the first autonomic models for dynamic patient scheduling. Dr. Valente and his team concluded that the SWALIS 2020 model for dynamic patient scheduling was effectively optimizing oncological surgeries based on urgency. The success of this early pilot study paves the way for future collaboration to create an effective dynamic waiting list and surgical scheduling platform during the next phase of the pandemic.



While health care professionals may argue that the change in cancer surgical oncology catalyzed by the ongoing COVID-19 pandemic is disruptive, scientists and researchers strongly believe that this reshaping will vastly improve the future of cancer care and strengthen the health economy. One could make the argument that this restructuring is necessary for improving patient outcomes and facilitating patient care. Consequently, many automated models similar to SWALIS 2020, which create waiting lists and guide elective prioritization, are being developed to fundamentally alter the field of surgical oncology for the better.


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