Data science plays an important role in improving healthcare services and technology. It has widespread applications in the medical field, benefiting patients, practitioners, researchers, and those working in administrative positions. Learning about data science in health care can help you understand its applications, make better use of it, or decide if a career in data science and medicine is right for you.
In this article, we explain what data science in the healthcare industry is and cover nine examples of how to apply it in healthcare.
What is data science in health care?
Data science in health care is a field of study that uses the scientific method to interpret data patterns and gain knowledge that applies to the medical field. In data science, researchers use statistics, informatics, and various methods of data analysis to collect information, process it, and draw conclusions. A data scientist has the skills to design algorithms and other computer software systems to collect and interpret unstructured sets of data for specific functions. In health care, data science has diverse applications that benefit patients, providers, policymakers, and administrative managers.
Benefits of data science in health care
Below are some of the benefits of data science in health care:
Reduce failures in treatments: Data science can improve treatments and patient care by making more accurate predictions about the effects of prescriptions. This includes identifying potential causes of illnesses and improving the accuracy of diagnoses to reduce the failure rates of treatments.
Lower health care expenses: Since data science can track the history of patients through electronic health records (EHR), the patients are less likely to spend money on treatments they’ve already tried and avoid hospitalization because of incorrect treatments. Since they don’t repeat treatments and avoid hospital stays, they have lower healthcare expenses.
Improve drug development: Case studies and lab reports can both reduce the amount of time it takes to test and develop drugs. This means medical practitioners can create and test drugs more effectively because they have access to more big data about previous tests and clinical trials.
Increased specialization: Data scientists in health care can develop specialized knowledge of treatments and illnesses, allowing them to create better diagnostic programs and treatments for patients.
How health care uses data science
Here are nine ways that the healthcare industry uses data science:
1. Medical imaging
The first application of data science in health care is medical imaging. Doctors often rely on the results of scans like X-rays, magnetic resonance images (MRIs) and computerized tomography (CAT) scans to visualize internal body parts so they can make diagnoses. Even with extensive training, it can be difficult to identify microscopic details that signal an injury, condition, or illness. For example, a doctor may have difficulty finding a hairline fracture in a bone when viewing an X-ray, so they may rely on technologies developed using data science to analyze scanned images. These programs can segment images and scan them for abnormalities.
2. Pharmaceutical development
Developing new medications requires a complex analysis of chemical and biological processes. Data science can support the efforts of pharmaceutical researchers by providing detailed analyses and predictions of how different chemical compounds can affect the human body. These analyses may take less time to process, limit the potential for human error and efficiently work through extensive sets of data. Based on the calculations of data science software, pharmaceutical researchers may design more effective and accurate medications with greater efficiency than they can without this technology.
For example, researchers can use pharmaceutical development software to compile historical information about a drug’s effectiveness and compare those results to a prediction of how well a new drug may work to treat the same condition. The results may show that the previous drug works well for certain groups of people, while the new one may work better for another clinical population. Based on this information, the pharmaceutical team can make decisions about how to market the drug and advise doctors on which medication to prescribe based on their patient’s individual needs.
3. Predictive modeling and analytics
Predictive analytics is an essential area of healthcare research. It allows medical researchers to find patterns in human genetics, environments, symptoms, and conditions so they can better understand the causes of illness and the effectiveness of different treatments. Data scientists can work with health researchers to design programs that predict the short-term and long-term health effects of certain variables to identify at-risk populations, determine preventative measures, identify long-term care needs, and support other medical decision-making based on data analytics.
For example, a data scientist can work with a public health researcher to collect information about the overall health of a certain community. They may design computer software or artificial intelligence specifically to analyze the health risks that the community faces using machine learning. Based on the results, they may recommend the community implement certain policies that reduce health risks, or that they invest in building more health care facilities to treat the most common medical conditions in their area.
4. Disease tracking and prevention
Predictive analytics also apply to tracking and preventing the spread of diseases. Data scientists have developed systems that track and model how infectious diseases spread within communities, and these models can help them predict the long-term impact of these illnesses. By predicting the spread of disease, data scientists and health researchers can design interventions to stop, slow, or manage the infection at varying levels.
For example, these researchers can predict how widespread an illness may become within a certain time frame by determining the rate of infection and calculating other factors that can either contribute to or inhibit its spread. If a researcher identifies an outbreak of an infectious illness in a community, they can calculate the risk of it spreading to neighboring regions and develop interventions to reduce its impact.
5. Patient health record maintenance
Another application of data science in health care is managing patient health records. Most patient medical records are electronic, which allows medical providers to share, store, update, and secure patient information easily. Data scientists can work with health informatics specialists to design these systems. Additionally, as telehealth care services become more widespread and accessible to the public, more patients use online systems to manage their medical appointments and prescriptions. Data scientists use their design expertise to develop telehealth services that benefit patients and practitioners.
6. Virtual assistance
Data scientists can design applications that provide virtual healthcare assistance to patients and clinicians. For example, they’ve developed apps that allow patients to input their symptoms to learn what health conditions may cause them. Virtual assistant applications can also help patients track their medications and symptoms, remind them of upcoming appointments, and alert them when they receive test results. In addition to assisting patients, virtual assistant applications can help providers by tracking their scheduled appointments and helping them compile patient information to make diagnoses and plan treatments.
7. Digital Security
Digital security is an important application of data science in health care. Ensuring that patient medical records remain confidential, preventing fraud, and keeping patient insurance and billing information private are essential aspects of protecting patients and their information. Data scientists prioritize security when they design systems for health informatics, and they constantly update these technologies to increase security. In addition to designing systems that prevent unverified access to patient information, data scientists track suspicious activity and analyze security trends to keep medical systems up-to-date and well-protected.
8. Vaccine development
Data science has also helped scientists develop vaccines for deadly diseases. By studying data, including what the symptoms are and how the diseases affect people, the scientists were able to complete the vaccines faster than previous attempts at vaccine development.
9. Patient engagement
The healthcare industry can implement data science to engage patients and encourage compliance with treatment plans. Similar to virtual assistant applications, there are digital applications that help patients track and manage their appointments, medications, treatments, and lab work. Digital applications can also make it more convenient for patients to contact their healthcare providers with questions or to schedule appointments.
Other patient engagement applications help patients learn about medical conditions by providing interactive platforms. These programs let patients explore information about their conditions at their own pace or in the style that works best for them, like through simple games or short, informative videos. These applications can benefit children by presenting health information to them in a simple format.
Primary responsibilities of a data scientist in healthcare
Data scientists in health care are responsible for diagnosing illnesses correctly using their data. They can do this by collecting more data from a wide variety of sources and using that data to write reports that other healthcare practitioners such as pharmacists and doctors can use to treat patients. They may also study specific illnesses to collect a large amount of data about that illness so doctors, pharmacists, and psychologists can approach treatment with the most important information for treatment.
Becoming a healthcare data scientist
There are a few tasks you can do to become a healthcare data scientist. First, improve your analytical skills. This can help you identify data patterns and help you inform other medical practitioners of what data represents in terms of illnesses, diagnostics, and treatments. You can also develop communication and writing skills to help you spread awareness about the data you collect.