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​​How Increased Data Literacy Can Improve Patient Outcomes and Accelerate Clinical Breakthroughs

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     Dmitri Adler          
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        June 2022              
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data literacy clinical breakthrough

A long time in the making, the age of widespread digitization in healthcare is imminent. Healthcare and life sciences organizations are moving rapidly toward a full embrace of AI, ML, natural language processing (NLP), software as a service (SaaS), and cloud computing technologies. I believe this development positions data science at the center of a seismic industrial shift, leaving behind the era when data science was a marginal presence, confined to the most cutting-edge healthcare and life sciences organizations. The new reality is one in which the work of improving patient outcomes and accelerating clinical breakthroughs increasingly relies on sophisticated approaches to data. In this article, I will outline how research facilities, hospitals, and other patient care centers can unlock value for their stakeholders by equipping their workforces to adapt to today’s data-driven world.

abstract data-driven insights

Introduction

Due to its sheer volume and transformative potential, healthcare data is a valuable asset. The merits of big data are especially significant in an industry dedicated to uncovering accurate insights that impact the health and safety of populations. Further, this resource is multiplying at a dizzying rate, offering nearly boundless potential to revolutionize all areas of the healthcare and life sciences industries for generations to come. A study by McKinsey Global Institute predicted that by fully and effectively adopting data science methodologies, the healthcare industry in the United States could generate an additional $300 billion in additional value per year. Yet, a 2016 report by McKinsey found that the United States healthcare industry had leveraged only 10-20% of the data and analytics potential available, eclipsed by such sectors as retail and manufacturing. To capture the value of growing data resources and facilitate the rise of digitization, professionals in these fields will need enhanced data literacy that will help them effectively tackle such challenges as:

  •   Ensuring product quality and safety.
  •   Speeding drug development.
  •   Optimizing commercial effectiveness.
  •   Providing appropriate treatments to patients.
  •   Transforming and anonymizing healthcare data.
  •   Driving better health outcomes.

Healthcare: The Rewards and Challenges of Embracing Data and Digitization

I believe the moment will soon arrive when data fluency will be an essential skill in the toolset of healthcare professionals. The most successful practitioners in the field will have training in data science that will enable them to use data to address their patients’ medical needs proactively. Beyond guiding diagnosis and facilitating identification of more personalized treatment courses, data science is improving processes across healthcare functions.  

The application of machine learning (ML) and artificial intelligence (AI) capabilities in inpatient care settings has the power to increase operational efficiency and improve worker responsiveness. In addition, widespread digitization is ushering in an era of astounding progress in healthcare, changing the delivery and experience of services, including pharmacy visits, routine checkups, and healthcare financial support. As workers begin to incorporate data science technologies and migrate to digital systems, healthcare organizations must reskill their workforces accordingly to deliver high-level patient care. 

Given the complexity and sensitivity of current healthcare data, insufficiently trained workers can be vulnerable to misinterpreting and misapplying data-driven insights. For this reason, the promise of this data revolution carries with it the greater impetus for healthcare organizations to ensure that workers have appropriate training to help them avoid common pitfalls and make optimal use of their data assets.

data literacy abstract

Life Sciences: Making Groundbreaking Discoveries Through Data and Data Science Training

Today’s life sciences industry is likewise positioned to make a quantum leap with data science innovations. Life sciences organizations can derive inestimable value from AI-based solutions ranging from operations and commercialization to clinical trials design and drug discovery. For example, a Deloitte article describing the power of AI technology to improve the speed and accuracy of drug discovery predictions states that a 10% increase in prediction accuracy could yield savings of billions of dollars in drug development costs. 

health data DNA abstract image

AI and ML technologies are opening an expansive frontier of exploratory possibilities by offering researchers the ability to mine big data from varied sources efficiently. Life sciences organizations are leveraging vanguard technologies to remarkable effect, applying AI and ML to uncover previously imperceptible links between asymmetrical life science datasets. Capable of integrating and analyzing vast, complicated, multi-dimensional datasets, these tools drive discovery by revealing patterns and matches that might otherwise go undetected. 

This emerging field of data-driven and technology-powered research has led to incredible advances, and I have no doubt it will increasingly deliver revolutionary breakthroughs in years to come. However, to sustain this progress and propel the field toward new clinical epiphanies, organizations must invest in upskilling their workforces to meet the demands of the future. Life sciences professionals will need ongoing training to cultivate and maintain the data literacy skills that help them understand and extract insights from the complex datasets critical to their work. For life sciences organizations, meeting dynamic industry standards is tied to continued investment in data science training.    

Conclusion

Healthcare and life sciences organizations have taken impressive strides in response to—and anticipation of—rapidly evolving circumstances. While the catalysts for many of these advances have been unforeseen challenges, a mechanism these successful solutions have had in common is data science technology. As a result, the innovations that organizations have implemented have secured their place in the future of the healthcare and life sciences industries. However, reskilling healthcare and life sciences professionals with modern data science, AI, ML, natural language processing (NLP), software as a service (SaaS), and cloud computing knowledge is essential as organizations pivot into technology-first entities. The path forward will demand a commitment to workforce training and an investment in resources, such as new-age data science learning platforms that give workers the tools they need to unlock untapped value for their stakeholders.  

 

Toward a Healthcare Data Revolution

Whitepaper

With more data science applications in healthcare around the globe, it is clear the industry's transformation has already begun. However, achieving the data maturity required to leverage these capabilities effectively does not come without significant infrastructure, culture, and education challenges.

We'll explore how to start a data science movement that impacts:

  • Hospital Operations
  • Personalized Medicine
  • Patient Self-Care
  • Public Health

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