Epidemiology and Vaccines – Challenges and Solutions with Blythe Adamson

Introduction:  

Epidemics may come without a warning, but the speed at which a vaccine needs to be developed is sometimes astonishing. This was witnessed during the COVID pandemic with a striking contrast to the HIV vaccine, which has still not been developed. However, many epidemiological issues relate to the vaccine and how it is being deployed. Vaccine development is only the first step along a tedious path. From effective communication and efficacy requirements to increased accessibility, many factors contribute to the vaccine’s overall success.   

The Early Challenges of COVID:  

As COVID spread across the country, the initial reaction was baffling. Public health needed to be more organized, and the healthcare industry had just stumbled upon a big challenge. Communication regarding the origin of the virus was the initial challenge. Healthcare experts initially started applying past knowledge of HIV and Influenza epidemics, to gain an initial understanding of the disease. Another big challenge was the asymptomatic population, which was still contagious. There was no way to identify them during the initial days. Without enough testing kits, there was no way to predict or apply strategies to mitigate the speed at which the disease would spread.   

Lack of Systematic Data:  

 The recent pandemic displayed an unfortunate and massive lack of data in the healthcare industry. A deep understanding of probability and statistics is essential for the scale of decisions that must be made during an epidemic. In the early days, there was complete disarray regarding hospital data. It is a distinct revelation of our public health data infrastructure vulnerabilities. With the absence of technologies, such as electronic medical records (EMRs), the country needed to catch up and address the needs of the pandemic. However, COVID set an example to ensure that healthcare agencies invest in modernizing health data. In the long run, systematic and innovative methods of collecting health data can benefit national and economic security.    

The Evolution of the Virus:  

 As the COVID virus evolved and there were new strains, information had to be quickly modified according to new learning. Transmission protocols and contact tracing strategies changed almost overnight. Social distancing and contact tracing were fairly new concepts for the country to understand, and when it seemed like the healthcare industry was gaining a handle on it, the virus mutated, and there was a new strain. Environmental factors affecting the population’s health are increasingly coming under the microscope as significant factors that need to be studied and analyzed. Innovative technologies are being developed and applied to address this issue.   

Effective Vaccine Communication:  

 Once developed, a significant requirement of vaccine success is the communication that is given to the population. Nuances with understanding how the vaccine can protect individuals need to be carefully analyzed and communicated. Will the vaccine block an individual from getting infected, reduce the risk of getting infected for a stipulated period, or reduce the virulence of the disease in terms of avoiding hospitalization? All these factors need to be effectively communicated to the population. Apart from the general idea that the vaccine will protect our health, educating the population on specifics can play a role in larger acceptance. With COVID, the vaccines created were quite potent across most parameters and variants. mRNA vaccines have been designed with the purpose of quick adaptability.   

Technological Tools for Health Data Structuring:  

A major breakthrough in healthcare technology was unlocking unstructured components of medical records from EMRs through Natural Language Processing (NLP). This feature of AI development solutions is designed to comprehend, communicate, and liaise with human inputs and machine recommendations to offer holistic and reliable suggestive courses. Health insights are easily extractable; trends can be identified, and data can be structured – endless possibilities. The recent crisis has made us realize the urgency of accurate and efficient testing modules to face the challenges associated with the evolving nature of healthcare. Automated smart infrastructure reduced manual intervention to offer faster results.    

No technology can do it better than machine learning in terms of data organization and utilization. It lies at the core of most healthcare solutions and uses deep learning to extract meaningful insights from data, in a reasonably autonomous fashion. When embedded with superior automation and intuitive learning capabilities, the overall profitability toward healthcare outcomes is significant. Technologies such as these can be realistically applied to compare the effectiveness of two treatments or evaluate policies toward Medicaid expansion. Innovative technologies can be the game changer towards getting the right treatment from the right people at the right time. Healthcare data analytics identifies relative patterns and complex correlations by efficiently examining multiple data sets to predict and prevent outbreaks.  

Conclusion  

The current healthcare EMR systems could be more structured and time-consuming due to their poor designs and impractical functionality. Through constant improvisations in technology and mobile devices, how patients interact with doctors and physicians is poised to be transformed—robust technologies making disruptive breakthroughs in this domain. Engaging patients is now a reality that every healthcare organization can hope to achieve. To gain a competitive edge, healthcare agencies must adopt technologies that augment their EMR systems to engage patients, customize communication between patients and doctors, and optimize administrative tasks. Explore more interesting topics and listen to insightful podcasts related to healthcare only on CareTalk.   

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