How Data Science is Advancing Healthcare
- Wearable technology: With a rise in wearable technology comes a large amount of day-to-day health data that can be analyzed to help improve management of chronic diseases.
- Disease prevention: By applying data analysis, medical researchers open a new door to curing diseases. Some data scientists work exclusively in tracking and exploring ways to prevent diseases.
- Disease monitoring: Data analytics is used to better understand signs and symptoms of illness in an effort to help physicians treat and manage patients more effectively.
- Drug response: Advancing genomic technologies will enable data scientists to increase the amount of genomic data collected on study populations, contributing to a better understanding of the genetic base of drug response and disease.
- Descriptive analytics: Data scientists can help a health organization gain insight in a variety of ways, such as how many re-admissions it had in a year, the busiest hours in the ER, or how hours worked impacts employee health. Such data is followed up with predictive and prescriptive analytics, which helps them better understand patient and business outcomes, as well as revenue.
- Patient needs: Data can be analyzed to predict which patients are going to need the most care in the future.
Types of Healthcare Data Scientists
Clinical Health Data Scientists
Some data scientists analyze patient records, which might include medical images, lab results and doctors’ notes. Electronic Health Records are becoming standard practice, resulting in large sums of data that can be pooled and analyzed. In 2015, more than 83% of doctors had already adopted electronic healthcare record systems. And by 2020, medical data is expected to double every 73 days. Doctors use technology to record demographic information, view image results, place prescription orders and enter notes. Though not all of this data can be shared due to privacy concerns, data scientists are needed to analyze the data that is available.
Data is also a big part of clinical research trials. The IBM Watson Health initiative employs cognitive computing to analyze extremely large datasets to reveal health trends that were previously hidden. Health data scientists working on these systems leverage their experience in data preparation, machine learning and statistics to investigate complex problems. They then interpret rich data sources while managing large amounts of data, create visual aids to understand the data and present their findings to clients.
Pharmaceutical Data Science
Health data scientists also work for pharmaceutical companies to interpret data in effort to help develop new medication. These types of scientists might combine clinical, genomic and real-world data to identify subtle correlations, which reveal obesity sub-types for instance, or predict who might be at risk for diabetes or cardiovascular disease. Or, if a pharmaceutical company is developing a lupus drug, data scientists might analyze data to identify characteristics of lupus patients who are most likely to experience life-threatening disease flares.
Patient Behavior Data Scientists
According to ABI Research, by 2019 healthcare data analysis will grow to a $52 million market thanks to wearable technology. Health wearables do everything from monitoring heart rates, sleep patterns and steps, to tracking when and how patients take their medication and how asthmatics use their inhalers. As wearables become increasingly popular, so will the need for scientists to interpret all of this data in an effort to come up with better solutions for patient engagement and care.
MOVING TOWARDS THE FUTURE
Data analytics will contribute to vast improvements in the healthcare system well into the future and will be challenged with finding the most efficient and effective ways of mining data. With such large volumes of data scientists in high demand, if you are interested in meeting this demand, learn more about online degrees in data science. Or find out more information on careers in healthcare administration.