What Does a Data Scientist in Healthcare Do?

Learn more about the opportunities, responsibilities, and salaries of data scientists in the healthcare sector here.

data scientist healthcare

If there’s one industry that goes hand-in-hand with data science, it’s the healthcare sector. As the industry continues to face a deluge of data collected across all its verticals, data scientists play an increasingly important role in helping hospitals, healthcare providers, medical researchers, and federal and state agencies identify patterns and trends that can result in life-saving policies and procedures. Of all the career paths within data science, nowhere are the stakes higher, the impacts so strongly felt, or the opportunities as diverse as they are for healthcare data scientists. 

The healthcare industry is a playground for data scientists who are keen on putting their analytical skills toward improving, and even saving, lives. With around 30% of the world’s data storage residing in the healthcare sector alone—from patient information and insurance claims to government agency records—there’s a growing need for data scientists who can manage, analyze, and act on the trove of information to find efficiencies across the board.

How is data science used in healthcare?

At its core, data science in the healthcare industry isn’t too different from data science in other fields—the ultimate goal is to tease out meaningful and actionable insights from collected data. The healthcare sector presents data scientists with more varied opportunities than many other industries, though, from helping individual hospitals be more efficient, to influencing diagnostic and treatment processes, to mapping the spread of a pandemic. In many cases, the results of a healthcare data scientist’s work can be felt far and wide and potentially affect millions of lives.

One of the best examples of data scientists making a meaningful difference at a global level is in the response to the COVID-19 pandemic, where they have improved information collection, provided ongoing and accurate estimates of infection spread and health system demand, and assessed the effectiveness of government policies. In fact, through data collection and modeling, Dr. Sarah Callaghan, the editor-in-chief of research journal Patterns, says that data scientists are uniquely positioned to work with other researchers to answer pertinent questions, such as: How will the virus spread? How effective is social distancing in comparison with country-wide quarantine? And is the risk of catching the virus high if you watch a football match in a pub as compared with the stadium?

At a more local level, data scientists can help medical professionals leverage information such as electrocardiograms (ECGs) or medical imaging to make more accurate diagnoses and formulate targeted treatment plans. For example, by collecting and annotating existing ECG datasets, researchers from Stanford University developed a model that can diagnose irregular heart rhythms from single-lead ECGs better than a cardiologist—effectively saving time and reducing the number of misdiagnoses. Similarly, researchers were able to develop artificial intelligence that can diagnose skin cancers using a model in which the AI classifies images of skin lesions as benign marks or malignant skin cancers. 

At certain hospitals and treatment centers, data scientists have used machine learning to help doctors, nurses, and organization leaders reduce readmissions by parsing through data to predict risk and guide clinical interventions. The University of Kansas Health System, which previously struggled to meet its readmission targets, saw a 50% decline in hospital readmissions after it began working with data scientists and machine learning. 

Healthcare data scientist job roles/responsibilities

Most data scientists bring to the table technical skills such as knowledge of probability and statistics, data visualization, machine learning and AI, and proficiency with programming languages such as R, Python, and SQL. And while these skills might help a person parse through troves of information, healthcare data scientists must first and foremost be strong problem-solvers who understand the goals of their organization. 

When an organization hires a data scientist, they typically have problems or inefficiencies that they hope data analytics will help them address. A hospital might want a data scientist to help them find ways to improve quality of care while also lowering costs; a government health agency might want help tracking the spread of a virus; insurance firms might want to use data for more accurate risk calculations—each of these goals requires a different approach to data collection, analysis, and modeling. Because of this, job listings for healthcare data scientists commonly seek those who can understand what an organization wants to achieve and devise strategies specific to those needs.

Another common requirement in healthcare data science job listings is a strong grasp of quantitative data analysis. Due to the large volumes of data generated by hospitals and government agencies, data scientists need to be able to organize, manage, and analyze varied data sets without becoming overwhelmed. Once they’ve wrangled large amounts of information, healthcare data scientists are then expected to connect dots and identify solutions and suggestions that can help an organization reach its goals.

Other common responsibilities of healthcare data scientists include:

  • Converting data into digestible bites that non-technical members of an organization can understand
  • Understanding the functions and systems within hospitals and using data findings to support decision-making
  • Database management, including data collection, storage, retrieval, and security
  • Creating reports and dashboards so that findings are accessible to management
  • Being nimble enough to hop around different types of data sets, from operational, to clinical, to financial

Healthcare data scientist salary

Healthcare companies are spending big on data science and analytics because it helps them cut administration costs, reduce fraudulent payments, provide more accurate treatments and diagnostics, and improve organization-wide decision-making. Between now and 2025, the healthcare analytics market is expected to experience a compound annual growth rate of 23.55%, according to Valuates Report, with an estimated valuation of $40.78 billion by 2025—this means that both demand and opportunities for data scientists are on the rise.

The salary of a data scientist is typically determined by education, years of experience, location, and organization type. As of 2020, the average salary of an entry-level data scientist in the healthcare industry is around $93,202. The average salary of a senior-level data scientist in the healthcare industry is around $125,749.

Don't forget to check out Springboard's guide on how to become a data scientist with no experience.

Is data science the right career for you?

Springboard offers a comprehensive data science bootcamp. You’ll work with a one-on-one mentor to learn about data science, data wrangling, machine learning, and Python—and finish it all off with a portfolio-worthy capstone project.

Check out Springboard’s Data Science Career Track to see if you qualify.

Not quite ready to dive into a data science bootcamp?

Springboard now offers a Data Science Prep Course, where you can learn the foundational coding and statistics skills needed to start your career in data science.

Download our guide to data science jobs

Packed with insight from industry experts, this updated 60-page guide will teach you what you need to know to start your data science career.

Ready to learn more?

Browse our Career Tracks and find the perfect fit