If you’re passionate about helping people, and want to become a data scientist, you might be interested in data science careers in healthcare and medicine. These career paths can vary drastically, from managing financial data for a hospital, to helping to develop new diagnostic tools. Either way, the work that you do in the healthcare field helps to save lives.
Below, we’ve outlined some of the possible roles you might find as a data scientist working in healthcare. As you read, consider whether you would prefer to be involved in more research, or logistics-oriented projects.
One of the biggest chunks of data that healthcare providers process is related to filing insurance claims. In this kind of role, you might analyze data related to claims acceptances and rejections, financial trends, and demographic insurance data. You might use Machine Learning models to identify patterns, and predict potential problems. This work will enable the healthcare organization to process claims quickly, earn the money they deserve, and help patients more efficiently.
Today, doctors can monitor much more about their patients’ lives. Apps and wearable monitoring devices make tons of data available, but it may be hard for doctors to figure out what to do with that data. As a data scientist, you can analyze this data to detect patterns in symptoms and evaluate health factors. This valuable information helps doctors to develop individualized treatment plans.
Medical imaging is one area where data scientists can make a direct intervention into a patient’s wellbeing. Within medical research, data scientists are helping to make imaging more accurate and clear. They are also helping to diagnose scans. When doctors use imaging such as X-rays, MRIs, and CT scans, they typically diagnose problems by manually looking at the results. Sometimes, however, a simple visual check can miss minor anomalies. Deep Learning techniques, on the other hand, can evaluate what imaging should look like and detect small irregularities. This can lead to the earlier detection of serious health issues.
Data scientists are responsible for developing diagnostic models beyond medical imaging as well. Machine Learning and Predictive Analytics can sometimes diagnose health issues more quickly than doctors, and detect issues that human eyes might miss. Examples include a model to diagnose heart arrhythmia, and an AI model for classifying skin lesions as either benign or malignant. These models are often being developed by university-based research groups. Data scientists working as a member of these teams are helping to diagnose serious health issues more quickly, and save lives.
Hospitals have to manage major operational and logistical details, which data scientists can help with. Working for a hospital, you might use Predictive Analytics to plan staffing, evaluate the availability of hospital beds at different times, or improve emergency room operations. All of this work helps hospitals to make the most of their resources, and helps patients to receive care more quickly.
Developing new pharmaceutical drugs, and testing them through clinical trials is extremely money and time intensive. Data Science can help to expedite these processes. Mark Ramsey, Chief Data Officer at GSK, noted that using AI and computer simulation can reduce the drug discovery process to less than two years. In the early stages of development, data scientists can use large amounts of patient metadata, and information from biobanks to better understand genetic mutations. Once drugs move into the trial phase, algorithms can simulate how drugs will perform before testing in human subjects. These tools help to make new drugs safer, and accelerate the development process.
More and more companies are developing virtual tools to improve and monitor health. These include wearable devices, and apps. Popular healthcare apps offer virtual consultations with a doctor or therapist, while others create a platform for tracking individual symptoms. There is still a very active market in developing new apps, and in using the data they generate to evaluate larger patterns.
There is no single Data Science role within the healthcare industry. Organizations ranging from hospitals, to insurance providers, to biotech companies, to pharmaceutical researchers are hiring data scientists. Some of these roles will be quite challenging, developing new models to create new medical research tools, or create new healthcare solutions. This is a very rewarding career, however, for data scientists who want to save lives. You might not see your impact as directly as doctors and nurses do, but you’ll know that you’re helping to create better treatments, and improve patients’ quality of life. For some people, this kind of impact might create too much stressful pressure. But for others, it might be the perfect equation for a meaningful Data Science career.
Today, only a small percentage of data scientists work within the healthcare and hospital industries. But there’s a lot of room for expansion in this field. Especially for data scientists who are interested in medicine, and want to contribute directly to other people’s welfare, careers in healthcare may be a fulfilling choice.