Therefore, it is crucial that we start a healthy discussion on issues such as the reliability of data during COVID & other medical emergencies, the recommendations of the experts in the field; the national academy of medicine, the gaps in data science in healthcare, and the future of data science. Let’s elaborate on these issues…

What is the reliability of data during uncertainty and COVID period?

Reliable data helps in the decision-making process. COVID crisis showed the importance of data science and artificial intelligence in healthcare. But what is equally important is how we collect and use this data. It’s dangerous medically and financially to lower the quality of research to get the most possible quantity. Sure, sometimes, we have to make decisions before getting the complete data. However, the usage of emergency data science can be fast but never rushed.

As Matthew Robinson (2021), an assistant professor of medicine in the Johns Hopkins School of Medicine, said in his article (Data-driven COVID-19 care A new algorithm created by Johns Hopkins scientists predicts which COVID-19 patients will become gravely ill): “IT makes it easier for clinicians to anticipate what will happen to patients and helps them focus on patients who are the sickest”. 

He and his colleagues at the Johns Hopkins University School of Medicine and the Bloomberg School of Public Health innovated SCARP, Severe COVID-19 Adaptive Risk Predictor, a computer algorithm program that alerts clinicians to those who need urgent treatment.

What are the recommendations of the National Academy of Medicine?

The National Academy of Medicine (NAM) is an American Institute of Medicine founded in 1970 and focuses on four strategic action domains—informatics, evidence, financing, and culture. 

To know more about NAM, visit their site: https://nam.edu/

Matheny et al (2019) wrote the NAM key recommendations to successfully execute data science in the healthcare system in his article (Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril):

1. IT governance must manage AI applications and must set the standards of processing and implementing AI. They must enrol in healthcare system leadership and must analyze the financial benefits and risks of implementing AI. 
2. The healthcare administrative leadership must define the short and long-term goals of applying AI to improve the healthcare system. They must be sceptical about the strength and advancement of research.
3. The stakeholders such as governments, patients, consumers and the public must assess the transparency of the healthcare system, and assess the expectation, before operating AI. For example, assessing the intercultural resistance and workflow challenges.

What is the present gap in data science in healthcare?

Data science is a cornerstone in healthcare, but the problem that has been raised many times before, including in K. Rodolfa et al (2021)Taking Our Medicine: Standardizing Data Science Education With Practice at the Core is that there are very few programmed degrees that combine medical training with data science knowledge. It is important to understand that there is a difference between data science, epidemiology and biostatistics. Data scientists have more experience in computer science and informatics, while epidemiologists have a better working knowledge of study design and causal inference.

What is the future of data science in healthcare?

Focusing on training young students in the healthcare industry with specialized health data science degrees can be the current hope.

Over the years, institutions have been innovating to bridge the current gap, and one such developed program that integrates computer science, bioscience, and bioinformatics is innovated by Johns Hopkins University the Krieger School of Arts and Sciences and the Whiting School of Engineering.

Another area to improve in health data science is to create a trusted, flexible rapid response network [E.Kolaczyk et al (2021)]. This network will target three areas:

1. People: the team consists of data scientists, policymakers, emergency leaders and community stakeholders who work together on the same goal.
2. Science: the rapid response science should be based on standards, ethics and coordination
3. Translation: the results of data science must be translated to policies and guidelines with an action plan.

What is the take-home action and solution? 

If you want to know more or have a new idea that needs planning to be implemented, get in touch with us by visiting our Healthcare Analytics page and book your free consultation.