Evidence-based clinical research is traditionally- proctored protocols, with systematic and time-tested methodologies and phased trials culminating in regulatory approvals, and therefore, by design and intent, a time-consuming process. While this approach holds well for most scenarios, global outbreaks of pandemics demand a robust, rapid and responsive approach to identification, classification, diagnosis and prognostication of the disease to help achieve control and prevention of spread, mitigation and eventually cure.
In situations such as the current Novel CoVid 19 pandemic, rapid and global sourcing of clinical, epidemiological and lab data to document and understand patterns, modes of manifestation, degrees of severity and responses to different types of treatment is of paramount importance. It is also imperative for multidisciplinary teams to network seamlessly in a coordinated manner. Practical protocols and diagnostic guidelines that can be easily developed, complied and consistently deployed on a global scale have to be widely circulated and universally adhered to. Careful considerations must encompass sterile patient and sample handling, triaging, treatment of unforeseen complications, resourceful management of medical supplies etc.
The role of Telemedicine in such situations of having a wide and remote outreach, while applying the principles of social distancing and thereby saving time and costs effective to both the treated and treating parties is of dire need in highly contagious pandemic situations. The restriction of movement in lockdown events can be adhered to with online consultations and prescriptions and preventing panic crowding at the hospitals. Tele-triaging of patients, communication of epidemiological information and door-to-door sample collection-drug delivery systems can largely restrict community transmission of these highly contagious diseases. Rapid and systemic dissemination of knowledge of the evolving disease trends, experimental drug trials and tracking of patients’ recovery responses can be conveniently communicated within the clinical network.
Added to this, the powerful number-crunching abilities of artificial intelligence, trend predictions, classification matrices and systematic deductions from vast probabilities is a much-needed tool in escalated research scenarios. Complex curated algorithms, efficient machine learning and rapidly expedited deep learning programs with natural language processing can be a tremendous contribution to applied implications such as contagion spread rates, mortality and morbidity projections, recovery rates etc. As humanity gets embroiled in newer and more complex disease processes posing unprecedented challenges to the scale and scope of its diagnosis and treatment, a synergistic approach between medicine and technology is the need of the hour.
