Digital and Personalized Healthcare System for COVID-19 and Future Pandemics

It has become clear over the decade that our health is not that deterministic. Clinicians must measure various nucleotides to decide on our care and incorporate a growing number of health data generated and tracked by people. The chapter addresses important aspects of personalizing and digital health diagnostics for incorporating data in the health care system, so that informed choices can be made, as the idea of ‘one pill for all’ does not encompass all. The benefits of using predictive analytics are the same as many digital health categories: better care and lower costs. The difference is that the path to achieving these benefits is only possible through the use of these technologies—through personalized attention. It is essential and real that emphasis is reduced to a set of algorithmically generated probabilities. But it is also the dream. Predictive analytics is the learning process from historical data to make (or any unknown) predictions for the future. For health care, the predictive analysis will allow the right decisions to be made so that each patient can receive personalized care. The chapter will discuss how digital health and personalized analytics directly affect patient care and prepare us for future COVID-19-like pandemics. Big data and the development of algorithms have generated interest and excitement in predictive analysis. Fortunately, a wave of modern technology, including many open-source technologies, has emerged in the past few years to 56process and manage all these healthcare data. The importance of having a history around an individual’s historical data is so crucial, particularly in health care, when we still know what is “healthy” or “standard.” Recommendations often provide ranges for each patient that are incorporated in clinical alert systems without any context. Predictive analytics can be used to improve predictive certainty. Personalized treatment can result from high-trust algorithms that can predict workable interventions to improve long-term health outcomes of individuals affected with COVID-19 and prepare the healthcare system for future pandemics. Nevertheless, it is not an easy task to build digital models that can detect symptoms in patients and choose personalized treatment for COVID-19 like pandemics; however, it should involve numerous factors including the ability to comprise new types and sources of information, accurate, robust predictive models, timely data, prediction accuracy, simplicity, and contextual recommendations. Hence, healthcare should be highly personalized and individual-centric for therapeutic purposes. The prescribed medication to ensure adequate control depends on the severity of the condition and the frequency of the causes at individual levels and should not be prescribed blindly. COVID-19 is one example that requires disease management and also requires complete personalization of treatment in the context of causes and vulnerabilities. This being the main reason, leads to active involvement of various stakeholders such as researchers, clinicians, digital, and IoT experts, and pharmaceutical experts to collaborate and build a digitized and personalized model for detecting and treating the pandemic. “...can empower consumers to make better-informed decisions about their own health and provide new options for facilitating prevention, early diagnosis of life-threatening diseases, and management of chronic conditions outside of traditional care settings.” —Digital Health Definition, United States Food and Drug Administration