How is data science used in healthcare?

At its most basic level, data science in healthcare is similar to data science in other industries in that the ultimate goal is to extract meaningful and actionable insights from collected data. However, the healthcare industry offers more diverse opportunities for data scientists than many other businesses, ranging from assisting specific hospitals in becoming more efficient to influencing diagnosis and treatment processes to monitor the spread of a pandemic. The impact of a healthcare data scientist’s work can often be felt far and wide, potentially affecting millions of people.

  • One of the best examples of data scientists making a significant difference on a global scale is in the COVID-19 pandemic response, where they improved data collection, provided ongoing and accurate estimates of infection spread and health system demand, and assessed the effectiveness of government policies.
  • Data scientists are in a unique position to collaborate with other experts to find answers to pressing problems like:
  1. How will the virus spread?
  2. In comparison to a country-wide quarantine, how successful is social distancing?
  3. Is there a higher danger of contracting the virus if you watch a football match in a pub rather than a stadium?

Data scientists can assist medical professionals use information like electrocardiograms (ECGs) or medical imaging to make more accurate diagnoses and design-focused treatment regimens on a more local level.

Researchers from Stanford University, for example, developed a model that can predict abnormal heart rhythms from single-lead ECGs better than a cardiologist, saving time and minimizing the frequency of misdiagnoses by collecting and annotating existing ECG datasets. Similarly, employing a model in which the AI classifies photos of skin lesions as benign markings or dangerous skin cancers, researchers were able to construct artificial intelligence that can identify skin malignancies.

Data scientists have utilized machine learning to help doctors, nurses, and organization leaders reduce readmissions by parsing through data to forecast risk and guide clinical actions at some hospitals and treatment centers. After collaborating with data scientists and machine learning, the University of Kansas Health System observed a 50 percent reduction in hospital readmissions.