In the ever-evolving healthcare market, both researchers and practitioners are increasingly adapting innovative methods to advance the quality of patient care and improve clinical outcomes. The healthcare systems that earlier focused on medical interventions driven by episodic interaction with the patients, are now recognizing the need to fully understand exogenous factors (such as genomics, behavior, and social and environmental) in order to deliver continual care. The holistic approach to healthcare system has been highlighted below, which briefs about the role of real world data in decision making.
This data landscape is continuously changing and the capacity for rapid data accumulation and interpretation is also advancing exponentially. A myriad of innovative techniques, such as computer learning and natural language processing (NLP), and the evolution of electronic health records (EHRs) are revolutionizing the availability and potential use of real world data sources to improve healthcare outcomes. These techniques are significantly contributing towards the development of COVID vaccine and understanding the novel virus.
A Real World Approach to COVID-19
According to USFDA, power of real world evidence uptake is “silver lining” of the COVID-19 response. In the long term, real-world data will assist in understanding the way virus impairs the body, new health risks it creates and emerging long term complications. The real-world evidence gathered from longitudinal studies of COVID-19 patients and vaccine recipients is likely to play a significant role in achieving these goals. As it has been observed by scientists about the heterogenous nature of the virus, the insights from the real world populations are critical. The data will certainly help in understanding the virus and further, assist the healthcare professionals to plan the specific treatment as well as minimize the risks of serious complications. In addition, real world data is likely to present the results of certain population categories, which have been excluded from the clinical trials. These categories include pregnant women, old age people as well as patients with existing comorbidities.
Earlier in 2014, during outbreak of Ebola, the forecasters used the combination of real world data of population and their mobility along with their rigorous random models of disease transmission. This further assist in the prediction of the status of spread of the disease, globally. In the similar manner, tracking models can be adopted to fight any upcoming COVID-19 outbreaks. As predicted by scientists, it can take more than one year for the COVID vaccine to reach the market. Therefore, in such situations, real world evidence can be proved to become an essential tool to suppress the COVID-19.
As mentioned earlier, data can be generated from various networks and further analyzed by the healthcare organizations. This will assist them in monitoring and controlling the real-time disease. In addition, the adoption of technology in such areas, will lead towards the automation and analyzing, which is the utmost requirement for the big data.
Further, technological advancements have made it possible in aggregating the data from various online platforms. These platforms include traditional reporting tools and mobile applications. There are various government-backed applications, which are analyzing the personal level information to group individuals in the form of color coded categories. This information can directly be utilized to check upon the status of health as well as risk to contract the virus.
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