A Scoping Review on the Clinical Decision Support Systems in COVID-19 and the Exigent Need to Develop and Accelerate its Implementation in Long COVID-19

Abstract

Introduction: The Coronavirus Disease-2019 (COVID-19) pandemic has caused global disruption, putting health experts and healthcare systems at risk. However, the ultimate goal of medical systems to offer precise medical care in a holistic manner for the benefit of patients cannot be compromised. With the massive advancements in digital technology, it is now possible for healthcare systems and medical practitioners to handle and utilise enormous amounts of patient data to provide appropriate medical assistance with minimal error. Integrated with Electronic Health Records (EHR), Clinical Decision Support Systems (CDSS) are digital programs that analyse patient data and assist medical professionals in making decisions and recommendations, thereby enhancing patient care. CDSS have played a critical role in maximising care during the pandemic by helping clinicians offer evidence-based medical care using patient data, paving the way for a more accurate and personalised healthcare delivery. However, their extended usability to manage post-COVID-19 conditions remains unexplored. Aim: This scoping review seeks to outline the use of these CDSS in the management of COVID-19 and their potential usability in risk assessment, severity prediction, and treatment goals for patients experiencing long COVID-19 symptoms. Materials and Methods: A thorough literature search was conducted on Google Scholar and PubMed using key search terms to identify relevant articles that support the objective of this study. A total of 3,010 records were available, of which 13 articles were chosen for inclusion in this review after extensive screening in accordance with the eligibility standards set up. The supporting data were meticulously extracted and charted to provide a clear outline of the utility of CDSS. This scoping review also features a conceptual framework for the extended usability of CDSS in long COVID-19 management. Results: After the methodological search and selection process, data from 13 articles were analysed. Most of the included studies were conducted in the United States. The majority of the CDSS were designed to assess the severity of COVID-19. These CDSS predominantly analysed blood investigations, COVID-19 symptoms, and radiological findings of patients to make appropriate clinical decisions for managing the disease. There was a lack of scientific literature supporting the use of CDSS in long COVID-19 management. Conclusion: With healthcare systems dealing with massive amounts of patient data, especially during the pandemic and postpandemic crisis, appropriate methods to manage and handle this information are critical to delivering patient-centered medical care. CDSS have been widely utilised in this regard to enhance the health outcomes of patients by guiding health professionals to make the right treatment choices in the most evidence-based manner using patients’ health data. Thus, given the future of healthcare systems with Artificial Intelligence (AI), a greater emphasis on expanding the usability of these CDSS beyond the scope of COVID-19 is essential.

Keywords

• artificial intelligence
• digital technologies
• evidence-based medicine
• long-term patient care
• personalised healthcare
• precision medicine
• tracking