[Telephone follow-up of SARS-CoV-2 positive patients at the Oncology Department of Lausanne University Hospital]. / Suivi téléphonique des patients testés positifs au SARS-CoV-2 au Département d'oncologie du CHUV.

Compared with the general population, oncology patients face a higher morbidity and mortality caused by the COVID-19 pandemic. As a result, health systems had to quickly adapt cancer care in order to maintain the best quality and patient safety. From March to May and from October to December 2020, 254 patients diagnosed with cancer and tested positive for SARS-CoV-2 benefited from a tele-health monitoring at the Oncology Department at CHUV. This article describes the key points of the development, implementation and operation of this tele-health monitoring, enabled by an interdisciplinary and inter-professional collaboration between different units and healthcare professionals.

En comparaison de la population générale, les patients oncologiques font face à une augmentation de leur morbimortalité en lien avec la pandémie de Covid-19. Par conséquent, les systèmes de santé ont dû s'adapter rapidement dans ce contexte instable afin de poursuivre des soins de qualité tout en assurant la sécurité des patients. De mars à mai ainsi que d'octobre à décembre 2020, un total de 254 patients oncologiques testés positifs au SARS-CoV-2 ont bénéficié d'un suivi téléphonique au Département d'oncologie du CHUV. Cet article décrit les points clés de l'implantation et du fonctionnement de ce télésuivi, grâce à la collaboration entre différentes unités et une équipe interprofessionnelle.

A differential process mining analysis of COVID-19 management for cancer patients.

During the acute phase of the COVID-19 pandemic, hospitals faced a challenge to manage patients, especially those with other comorbidities and medical needs, such as cancer patients. Here, we use Process Mining to analyze real-world therapeutic pathways in a cohort of 1182 cancer patients of the Lausanne University Hospital following COVID-19 infection. The algorithm builds trees representing sequences of coarse-grained events such as Home, Hospitalization, Intensive Care and Death. The same trees can also show probability of death or time-to-event statistics in each node. We introduce a new tool, called Differential Process Mining, which enables comparison of two patient strata in each node of the tree, in terms of hits and death rate, together with a statistical significance test. We thus compare management of COVID-19 patients with an active cancer in the first vs. second COVID-19 waves to quantify hospital adaptation to the pandemic. We also compare patients having undergone systemic therapy within 1 year to the rest of the cohort to understand the impact of an active cancer and/or its treatment on COVID-19 outcome. This study demonstrates the value of Process Mining to analyze complex event-based real-world data and generate hypotheses on hospital resource management or on clinical patient care.