Data Science Strategies in Stratified and Precision Medicine Data Science Strategies in Stratified and Precision Medicine
Healthcare is an industry ripe for innovation and one that has undergone times of rapid change in the last few decades.... Data Science Strategies in Stratified and Precision Medicine

Healthcare is an industry ripe for innovation and one that has undergone times of rapid change in the last few decades. An increasing amount of data is now available, allowing medical professionals to reach new heights in patient care and deliver better treatment options.

Technology empowers doctors, nurses, and other health care specialists by enabling them to harness the power of big data. Big data is trending in most industries, and many businesses are adopting data solutions integrated with machine learning (ML) and artificial intelligence (AI) to improve their operations. Health care companies are no exception.

Various data science applications can be game-changers for the health care sector, especially in stratified health care and precision medicine. Learn more about these two types of health care approaches and why data science will become increasingly useful in these areas.

Understanding Stratified Health Care and Precision Medicine

Before diving in, it’s important to understand the meaning of stratified health care and precision medicine. Stratified health care or stratified medicine is sometimes called personalized health care or medicine.

Essentially, stratified medicine is an approach that groups together patients based on their risk of disease or how they respond to therapy. This approach uses diagnostic tests or techniques to analyze patients within the group. Patients and their health care providers benefit from targeted, more effective treatments in stratified health care. The industry also benefits from the market expansion of efficient treatment development.

Precision medicine focuses more on the individual patient rather than groups of patients. It’s often used for disease prevention and treatment and takes an individual’s genes, environment and lifestyle into account. It’s a fairly modern approach in the current health care landscape. Rather than using a one-size-fits-all treatment plan, health care professionals tailor treatments to the individual based on the abovementioned aspects.

Stratified health care and precision medicine are not the same but are inextricably linked. Stratified health care reflects the effect of using big data to define disease patterns, whereas precision medicine reflects the clinical consequences, which is essentially better treatment for patients.

How Data Science Positively Impacts Health Care

So, where does data science fit into all of this? To pursue the development of effective treatments for a patient population or an individual patient, health care professionals must gather as much data as possible to drive their decision-making.

For years, health care facilities leveraging a data-driven approach have contributed significantly to improving patient outcomes and adopting value-based care. It’s estimated that approximately 30% of the world’s data is generated by the health care sector alone.

Additionally, the compound annual growth rate (CAGR) for health care data is higher than that of the manufacturing, entertainment, and financial services sectors, meaning that the volume of data generated will continue to increase rapidly.

A major benefit of using data science strategies in health care is using past health issues to better predict future ones. Consider how much data is being collected regarding the COVID-19 pandemic — one positive takeaway is how this valuable information will help global health care systems prevent and treat patients if a future pandemic were to occur.

Data Science in Stratified Health Care and Precision Medicine

Health care facilities have implemented various data solutions to streamline their operations, improve patient care and manage staffing needs. For example, consider the types of healthcare software assisting the healthcare sector:

  • Electronic health records (EHRs)
  • Electronic medical records (EMRs)
  • Health care management software (HMS)
  • Enterprise resource planning software (ERPs)

These types of health care software offer many critical benefits, including better financial control, clean data, enhanced patient services, and improved operational metrics and cycle performance.

When a health care facility implements data-oriented technologies, they can gain more insight into patient populations and individual patients, supporting precision medicine and stratified health care. Data must be at the foundation for these two approaches to be effective.

Big data solutions can even help health care workers understand health inequities, such as the impact of chronic disease in rural communities. Health care IT and big data can identify individual patients or populations requiring extra health services, allowing facilities to allocate resources better and serve underrepresented groups.

Chiba University, located in Japan, established the Center for Artificial Intelligence Research in Therapeutics (CAIRT) in 2018. CAIRT hosts around 50 researchers working on projects to develop data-driven health care approaches. The ultimate goal at the CAIRT is to stratify disease and develop novel treatment options which can support a precision medicine approach.

Data Science: Powering the Development of Modern Health Care Approaches

Data science and its sub-niches will grow in popularity among many sectors, including health care. Because the healthcare industry is so critical in maintaining a high-functioning society, it’s vital for healthcare facilities to use all resources available to improve treatments, personalize patient care and achieve better outcomes.

The convergence of data science and health care will likely become more prevalent in the next few years. Effective precision medicine strategies and fostering a stratified health care system will depend upon the widespread use of big data and data analytics.

Shannon Flynn

Shannon Flynn

About the author: Shannon Flynn is a tech writer and Managing Editor for ReHack.com. She covers topics in biztech, IoT, and entertainment. Visit ReHack.com or follow ReHack on Twitter or to see more of Shannon’s posts.