An Artificial Intelligence-Driven Digital Health Solution to Support Clinical Management of Patients With Long COVID-19: Protocol for a Prospective Multicenter Observational Study

Aïna Fuster-Casanovas, Luis Fernandez-Luque, Francisco J Nuñez-Benjumea, Alberto Moreno Conde, Luis G Luque-Romero, Ioannis Bilionis, Cristina Rubio Escudero, Irene Alice Chicchi Giglioli, Josep Vidal-Alaball, Aïna Fuster-Casanovas, Luis Fernandez-Luque, Francisco J Nuñez-Benjumea, Alberto Moreno Conde, Luis G Luque-Romero, Ioannis Bilionis, Cristina Rubio Escudero, Irene Alice Chicchi Giglioli, Josep Vidal-Alaball

Abstract

Background: COVID-19 pandemic has revealed the weaknesses of most health systems around the world, collapsing them and depleting their available health care resources. Fortunately, the development and enforcement of specific public health policies, such as vaccination, mask wearing, and social distancing, among others, has reduced the prevalence and complications associated with COVID-19 in its acute phase. However, the aftermath of the global pandemic has called for an efficient approach to manage patients with long COVID-19. This is a great opportunity to leverage on innovative digital health solutions to provide exhausted health care systems with the most cost-effective and efficient tools available to support the clinical management of this population. In this context, the SENSING-AI project is focused on the research toward the implementation of an artificial intelligence-driven digital health solution that supports both the adaptive self-management of people living with long COVID-19 and the health care staff in charge of the management and follow-up of this population.

Objective: The objective of this protocol is the prospective collection of psychometric and biometric data from 10 patients for training algorithms and prediction models to complement the SENSING-AI cohort.

Methods: Publicly available health and lifestyle data registries will be consulted and complemented with a retrospective cohort of anonymized data collected from clinical information of patients diagnosed with long COVID-19. Furthermore, a prospective patient-generated data set will be captured using wearable devices and validated patient-reported outcomes questionnaires to complement the retrospective cohort. Finally, the 'Findability, Accessibility, Interoperability, and Reuse' guiding principles for scientific data management and stewardship will be applied to the resulting data set to encourage the continuous process of discovery, evaluation, and reuse of information for the research community at large.

Results: The SENSING-AI cohort is expected to be completed during 2022. It is expected that sufficient data will be obtained to generate artificial intelligence models based on behavior change and mental well-being techniques to improve patients' self-management, while providing useful and timely clinical decision support services to health care professionals based on risk stratification models and early detection of exacerbations.

Conclusions: SENSING-AI focuses on obtaining high-quality data of patients with long COVID-19 during their daily life. Supporting these patients is of paramount importance in the current pandemic situation, including supporting their health care professionals in a cost-effective and efficient management of long COVID-19.

Trial registration: Clinicaltrials.gov NCT05204615; https://ichgcp.net/clinical-trials-registry/NCT05204615.

International registered report identifier (irrid): DERR1-10.2196/37704.

Keywords: AI; COVID-19; COVID-19 syndrome; Postacute COVID-19 syndrome; artificial intelligence; clinical decision support; clinical information; digital health solution; health care; health care resource; health system; long COVID-19; mHealth; patient; primary health care; public health policy.

Conflict of interest statement

Conflicts of Interest: LFL, IB, and IACG are employees of Adhera Health Inc.

©Aïna Fuster-Casanovas, Luis Fernandez-Luque, Francisco J Nuñez-Benjumea, Alberto Moreno Conde, Luis G Luque-Romero, Ioannis Bilionis, Cristina Rubio Escudero, Irene Alice Chicchi Giglioli, Josep Vidal-Alaball. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 14.10.2022.

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