There is nothing more precious than our health, a maxim given greater currency by the pandemic. The application of data science has helped in the research of new vaccines and led to large strides in improved healthcare, with much more to come on a local and global scale.
Healthcare is diverse – embracing medical imaging for comparative and predictive analysis, providers such as hospitals for short or long-term care, device-makers, suppliers like pharmaceutical companies that dispense drugs for conditions, viruses, and the like after years of gathering data for efficacy and safety.
There is also the regulatory and policy side of healthcare as administered by governments, which often delve into data in their efforts to determine whether certain age groups or areas of the country they serve are more at risk of a potential health threat. Insurance companies are also avid users of data science to find preventive measures and efficiencies to reduce costs while keeping their premiums competitive.
With all groups working towards the same goal, society can expect vast improvements in the health of its citizens and increasing data-focused job opportunities in healthcare.
1. Data science is driving healthcare forward
Data science is at the forefront of initiatives in all health sectors. At the heart of these developments is the computation of big data – large data sets – that are analysed to reveal patterns, trends and associations.
Artificial intelligence (AI) is saving lives and has the potential to multiply this into the millions every year worldwide. In The socio-economic impact of AI in healthcare, a report in October 2020, commissioned by MedTech Europe, Deloitte estimates that the lives saved in Europe could number around 400,000 annually. Deloitte focused on artificial intelligence applications, specifically wearables, imaging, laboratory applications, physiological monitoring, real-world data, virtual health assistants, personalised apps and robotics.
Deloitte calculates that €170.9bn-€212.4bn could be saved in just one year, largely from wearing AI applications and virtual health assistants, which themselves could free up 1,659–1,944m hours of healthcare professionals’ time annually.
Machine learning has been deployed extensively in the time-consuming process of drug discovery. Machine learning enables scientists to determine whether certain molecular combinations and reactions have the potential for further evaluation in the lab.
In the Artificial Intelligence Index Report 2021, published by Stanford University’s Human-Centered Artificial Intelligence Institute, an AI startup was reported to have used machine learning to accelerate COVID-19-related drug discovery. Its goal was to assess how easily compounds could be made using submissions of molecular designs from scientists. The company received more than 2,000 submissions and designed synthetic routes in less than 48 hours. The report says chemists would have taken three to four weeks to accomplish the same task.
Predictive analytics is seeing much growth in healthcare. Typically, a predictive model sifts through historical data to find patterns and make predictions, which is especially helpful in treating common diseases in terms of prevention and management.
Deep-learning technologies are employed in medical imaging, chiefly X-Rays, MRIs and CT Scans, which depict the inner human body. Usually, doctors would look at the images and make their own deductions, but with deep-learning technologies, microscopic deformities are routinely picked up, which was not always the case.
Genomics, the study of sequencing and analysing genomes, is also ripe for exploration. With the advances in data science tools, insights from studying the human gene and DNA can be made more quickly and less expensively. Genomic strands can be examined for irregularities, defects, connections made in genetics and the patient’s health. The field is wide and there are numerous opportunities for further research.
2. Health data science skills shortage is blocking progress
Despite the only too clear benefits of healthcare technology and data science, impediments are blocking wide-scale implementation. A lack of standardisation in data infrastructure and regulatory frameworks is one; corporate data literacy is another.
An academic study, The Data Literacy Index, The $500m Enterprise Value Opportunity, commissioned by software analytics company Qlik on behalf of the Data Literacy Project, found healthcare to have the lowest score in its Data Literacy Index. The index scores companies based on the data skills of their employees and the use of data for making decisions. A total of 604 global enterprise business decision-makers in 10 geographic areas were surveyed on their use of data and approach to data literacy. Healthcare’s 67.1 score was the lowest of the industries surveyed.
As healthcare organisations’ data skills are lacking, this may indicate insufficient marketing initiatives that target healthcare workers on the front line. With too few healthcare workers educated in data science, progress stalls.
3. Health industry data investment points to growth ahead
Research shows investment in data science is gathering speed. In its report, Transforming healthcare with AI: The impact on the workforce and organisations, McKinsey calculates that venture capitalists investing in AI companies in healthcare from 2015–2019 increased their investment five times in the US, 22 times in Europe, and 28 times in Asia.
More recently, in the Artificial Intelligence Index Report 2021, the category “drugs, cancer, molecular, drug discovery” was reported to have received the greatest amount of private AI investment in 2020, with more than $13.8bn, 4.5 times higher than 2019. The urgency in COVID-19 research clearly spiked expenditure, and although investment may decline when the pandemic is under control, indications are that the advances made by AI will help ensure investment doesn’t fall back to pre-COVID-19 levels.
4. Career opportunities for health professionals with data science skills
With the evolving nature of the health industry, skills in data science, big data analytics, machine learning, and AI certainly complement healthcare qualifications and experience. For example, if you can make sense of large health data sets to identify relevant/hidden trends and patterns, which then help stakeholders to make informed and timely decisions at all levels of healthcare, you will be a highly valued team member in any organisation. There will also be continuous opportunities for you to contribute to the diverse areas of health, all because you have the ability to understand and leverage data insight.
The need for data science professionals with a background in health is rapidly growing due to the amount of data created every day in the health industry and the historical data available to health organisations. As an employee, the combination of data and health knowledge will serve to help you broaden your prospects, increase your salary, and make you even more in-demand as digitalisation and analytics grow, taking healthcare forward with momentum.If you are a healthcare professional looking to take on more data-focused roles or tasks in the future, gaining practical data science skills will enable you to implement data-driven solutions and stand out in the job market. Click here to learn more about becoming an industry-certified data professional or schedule a call here.