The COVID-19 pandemic put significant strain on societies and their resources, with the healthcare system and workers being particularly affected. Artificial Intelligence (AI) offers the unique possibility of improving the response to a pandemic as it emerges and evolves. Here, we utilize the WHO framework of a pandemic evolution to analyze the various AI applications. Specifically, we analyzed AI from the perspective of all five domains of the WHO pandemic response. To effectively review the current scattered literature, we organized a sample of relevant literature from various professional and popular resources. The article concludes with a consideration of AI’s weaknesses as key factors affecting AI in future pandemic preparedness and response.

A 24/7 telemedicine respiratory therapist (eRT) service was set up as part of the established University of Pennsylvania teleICU (PENN E-LERT^®) service during the COVID-19 pandemic, serving five hospitals and 320 critical care beds to deliver effective remote care in lieu of a unit-based RT. The eRT interventions were components of an evidence-based care bundle and included ventilator liberation protocols, low tidal volume protocols, tube patency, and an extubation checklist. In addition, the proactive rounding of patients, including ventilator checks, was included. A standardized data collection sheet was used to facilitate the review of medical records, direct audio–visual inspection, or direct interactions with staff. In May 2020, a total of 1548 interventions took place, 93.86% of which were coded as “routine” based on established workflows, 4.71% as “urgent”, 0.26% “emergent”, and 1.17% were missing descriptors. Based on the number of coded interventions, we tracked the number of COVID-19 patients in the system. The average intervention took 6.1 ± 3.79 min. In 16% of all the interactions, no communication with the bedside team took place. The eRT connected with the in-house respiratory therapist (RT) in 66.6% of all the interventions, followed by house staff (9.8%), advanced practice providers (APP; 2.8%), and RN (2.6%). Most of the interaction took place over the telephone (88%), secure text message (16%), or audio-video telemedicine ICU platform (1.7%). A total of 5115 minutes were spent on tasks that a bedside clinician would have otherwise executed, reducing their exposure to COVID-19. The eRT service was instrumental in several emergent and urgent critical interventions. This study shows that an eRT service can support the bedside RT providers, effectively monitor best practice bundles, and carry out patient–ventilator assessments. It was effective in certain emergent situations and reduced the exposure of RTs to COVID-19. We plan to continue the service as part of an integrated RT service and hope to provide a framework for developing similar services in other facilities.

Incorporating the advanced practice provider (APP) in the delivery of tele critical care medicine (teleCCM) addresses the critical care provider shortage. However, the current literature lacks details of potential workflows, deployment difficulties and implementation outcomes while suggesting that expanding teleCCM service may be difficult. Here, we demonstrate the implementation of a telemedicine APP (eAPP) pilot service within an existing teleCCM program with the objective of determining the feasibility and ease of deployment. The goal is to augment an existing tele-ICU system with a balanced APP service to assess the feasibility and potential impact on the ICU performance in several hospitals affiliated within a large academic center. A REDCap survey was used to assess eAPP workflows, expediency of interventions, duration of tasks, and types of assignments within different service locations. Between 02/01/2021 and 08/31/2021, 204 interventions (across 133 12-h shift) were recorded by eAPP (n_routine = 109 (53.4%); n_urgent = 82 (40.2%); n_emergent = 13 (6.4%). The average task duration was 10.9 ± 6.22 min, but there was a significant difference based on the expediency of the task (F [2; 202] = 3.89; p < 0.022) and type of tasks (F [7; 220] = 6.69; p < 0.001). Furthermore, the eAPP task type and expediency varied depending upon the unit engaged and timeframe since implementation. The eAPP interventions were effectively communicated with bedside staff with only 0.5% of suggestions rejected. Only in 2% cases did the eAPP report distress. In summary, the eAPP can be rapidly deployed in existing teleCCM settings, providing adaptable and valuable care that addresses the specific needs of different ICUs while simultaneously enhancing the delivery of ICU care. Further studies are needed to quantify the input more robustly.