Transforming healthcare through data science
Use of large amounts of data has changed the healthcare landscape in numerous ways. Whether it’s strengthening the operational efficiency of a healthcare provider or leveraging large-scale information for deeper clinical insights, data science has firmly established its place in the field.
In this article, we’ll explore how the explosion of health data from wearables and clinical systems is both a massive opportunity and a challenge, how hospitals are using data to improve operations and patient care, and what it takes to responsibly manage sensitive health information. We’ll also walk through an example job posting to give you a clearer picture of what data science work looks like in practice within this space.
Data boom
With the growth in popularity of health apps and wearable devices like smart watches, there is now an immense amount of available data. The Wall Street Journal states that around 60% of households use a wearable device, with the overwhelming majority of those wearers taking advantage of access to health metrics.
While large amounts of data are useful, data science must be aware of potential pitfalls. First, clinical data is often unstructured, meaning that it can be difficult to extract actionable insights without manual effort or the use of natural language processing models. Second, achieving integration between different information systems (i.e. between different hospitals) is challenging.
Lastly, for practitioners to be able to achieve actionable insights, the data must be high quality and structured, so that the appropriate data processing tools can make well-informed observations and recommendations.
Administrative benefits
Artificial Intelligence tools that leverage this data are transforming the administrative side of healthcare. With some studies showing that providers in hospital ICU units spend anywhere as low as 15-30% of their time with patients, these tools can make an impact by allowing providers to offload or optimize tasks like chart documentation, appointment scheduling, and patient follow-up communication—freeing up more time for direct patient interaction.
There is also evidence that AI tools can prove sufficient at answering online patient questions. They can also improve how hospitals manage patient flow.
For instance, hospitals can make better use of room space and offer a greater number of services per day by analyzing the average time it takes for different procedures to be completed.
Another way in which data is leveraged is predicting staffing needs based on environmental factors. If air quality is low in the area, then the number of respiratory cases would increase as a result, thus requiring a greater number of medical staff on site. Data is even capable of predicting the rate of “no shows” for appointments, allowing hospitals to increase the number of patients seen per day.
Leveraging data for insights
One of the greatest uses of data is to allow connections to be drawn where they might not have been previously visible, or even reducing the amount of work that goes into finding those connections.
One example is improved tracking of adverse drug reactions, a vital step in launching a pharmaceutical drug. With the use of data, it is easier to link adverse events and identify potential issues for patients.
A second example is determining risk factors for different conditions or medical outcomes. For instance, by examining large data sets, researchers were better equipped to find a number of indicators for risk during and after childbirth. Once those risks are established, providers are able to help patients watch for potential complications.
Data security
Patient data is some of the most sensitive information out there, and protecting it is central to building trust in healthcare data science.
Anonymizing records is one line of defense, but is not perfect, especially with large datasets where small details can be stitched together to re-identify individuals. That’s why newer approaches to anonymization focus on preserving data usage while improving privacy, which offers more flexibility than traditional models.
Cloud-based systems are also becoming more common, allowing different healthcare providers to share data securely. These systems typically security methods like use encryption, one-time passwords, and two-factor authentication to limit access to only those who need it.
Fraud detection is another growing area. By analyzing patterns in patient records, claims, or care plans, data scientists can flag suspicious behavior—whether it’s billing irregularities or care patterns that don’t align with typical treatment paths.
Healthcare data science isn’t just about building models, it’s also about building trust by protecting the people behind the data.
Sample job posting
To get a clearer picture of what data science work can look like in healthcare, here’s a real example of a full-time Data Scientist role focused on improving clinical care and hospital operations through data. (Some of the original wording has been adjusted for anonymity.)
- Collaborates with business, clinical, and engineering stakeholders to identify key challenges and deliver data-driven solutions that impact patient care and institutional efficiency.
- Cleans and prepares healthcare datasets—including Electronic Health Records (EHRs)—and builds robust pipelines that support repeatable, high-quality analysis.
- Uses exploratory data analysis and statistical modeling to extract insights and support evidence-based decision-making.
- Applies machine learning techniques when appropriate, while ensuring that models are interpretable and clinically relevant.
- Shares results clearly with a range of stakeholders and supports the integration of data science outputs into clinical platforms and workflows.
This role is a good example of how data science shows up in healthcare. The work focuses on structuring data, building models, and sharing results clearly—skills that are central to many data science jobs, not just in this field. While some machine learning is involved, the day-to-day work is about using data to solve real problems in patient care and hospital operations.
Conclusion
Healthcare is a complex and evolving space where data science can make a real difference. From helping hospitals run more efficiently to giving patients better information and care, the opportunities are broad and meaningful.
For students thinking about a career in this area, it’s worth digging into how technical skills and healthcare context come together. If you’re curious about where to start, or how your data science background might apply, consider connecting with CAPD—we’re here to help you explore your next steps.
