The diverse amplification of big data in all spheres of life, from commerce to transportation makes us realize how indispensable it is in our daily lives. In the same way, big data has been transforming the medical sector, fundamentally changing how the most basic procedures of health monitoring are conducted, and that too by shaping and mapping unstructured information. No wonder 3.5 billion US dollars were invested in digital health startups in 2017, allowing the companies to fulfill their ambitions of revolutionizing the world’s general perceptions of health. So, let us learn about the importance of data science in healthcare.
We will also provide insights about how to pursue a career in data science in healthcare, and how a Data Science certified course can help you achieve your dreams of how to become a healthcare scientist.
Why Do We Use Data Science in Healthcare?
A number of factors make data science indispensable to healthcare in the present day, the most important of them being the competitive demand for valuable information in the health market. The collection of patient data through proper channels can help provide improved quality healthcare to consumers. From doctors to health insurance providers to institutions, all of them rely on the collection of factual data and its accurate analysis to make well-informed decisions about patients' health situations.
Nowadays, diseases can be predicted at the earliest stage with the help of data science in healthcare, that too remotely with innovative appliances powered by Machine learning. Mobile applications and smart devices constantly collect data about heartbeat rates, blood pressure, sugar, and so on, transferring this data to the doctors as real-time updates, who can then devise treatments accordingly.
Application and Use Cases of Data Science in Healthcare
The healthcare industry is revolutionizing by applying advanced-level machine learning and data analytics. Be it patient care or operations and pharmaceuticals, data science applications in healthcare are far-reaching, and here are some of the top f the top data science use cases in the healthcare sector.
1. Discovering drugs
The major contribution of data science in the pharmaceutical industry is to provide the groundwork for the synthesis of drugs using Artificial Intelligence. Mutation profiling and the metadata of the patients are used to develop compounds that address the statistical correlation between the attributes.
2. Virtual assistance
Nowadays, chatbots and AI platforms are designed by data scientists to help people get a better idea of their health by putting in certain health information about themselves and getting an accurate diagnosis. Furthermore, these platforms also assist consumers with health insurance policies and better lifestyle guides.
The present-day phenomenon of the Internet of Things (IoT), which ensure maximum connectivity, is a blessing to data science. Now when this technology is applied to the medical field, it can help monitor patient health. Nowadays, physical fitness monitors and smartwatches are used by people to track and manage their health. Furthermore, these wearable sensor devices can be tracked by a doctor if given access, and in chronicle cases, the doctor can remotely provide solutions to the patient.
4. Tracking Patient Health
Did you know that the human body generates 2TB of data daily? Data scientists for public health have developed wearable devices that allow doctors to collect most of this data like heart rate, sleep patterns, blood glucose, stress levels, and even brain activity. With the help of data science tools and machine learning algorithms, doctors can detect and track common conditions, like cardiac or respiratory diseases.
Data Science tech can also detect the slightest changes in the patient's health indicators and predict possible disorders. Various wearable and home devices as a part of an IoT network use real-time analytics to predict if a patient will face any problem based on their present condition.
An integral part of medical services, diagnosis can be made easier and quicker by data science applications in healthcare. Not only does the patient’s data analysis facilitate early detection of health issues, but medical heatmaps pertaining to demographic patterns of ailments can also be prepared.
6. Predictive Analytics in Healthcare
A predictive analytical model utilizes historical data, finds patterns from the data, and generates accurate predictions. The data could entail anything from a patient's blood pressure and body temperature to sugar level.
Predictive models in Data Science correlate and associate every data point to symptoms, habits, and diseases. This enables the identification of a disease's stage, the extent of damage, and an appropriate treatment measure. Predictive analytics in healthcare also helps:
- Manage chronic diseases
- Monitor and analyze the demand for pharmaceutical logistics
- Predict future patient crisis
- Deliver faster hospital data documentation
7. Medical Image Analysis
Healthcare professionals often use various imaging techniques like X-Ray, MRI, and CT Scan to visualize your body's internal systems and organs. Deep learning and image recognition technologies in health Data Science allow detection of minute deformities in these scanned images, helping doctors plan an effective treatment strategy. Some of the commonly used machine learning algorithms include:
- Image processing algorithm: For image analysis, enhancement and denoising.
- Anomaly detection algorithm: For bone fracture and displacement detection.
- Descriptive image recognition algorithm: Data extraction and interpretation from images and merging several images to form a bigger picture.
Furthermore, health data scientists are constantly working on developing more advanced techniques to enhance image analysis. For example, in a recent publication in Towards Data Science, the Azure Machine Learning platform can be used to train and optimize a model to detect the presence of three common brain tumors, Glioma tumors, Meningioma tumors, and Pituitary tumors.
Role of a Data Scientist in Healthcare
As a data scientist in the pharmaceutical industry, you have to employ your analytical skills to diagnose illnesses accurately and save lives. The immense amount of data that is sourced from the healthcare sector, from patient information to records stored by government bodies, requires a skilled analyst to manage it all.
The Covid-19 pandemic has recently shown how vital data science in healthcare can be. Not only has data science improved the collection and sampling of information but it also demonstrated global patterns in the spread of the infection, predicting the next region where Covid would spread and how government policies could be designed to combat the contagious disease effectively.
Regarding national-level healthcare, data scientists could help monitor the spread of the disease within the country and accordingly coordinate with the authorities to send resources to the most impacted areas.
Here we have outlined the essential responsibilities of a healthcare data scientist.
- Management of substantial volumes of data: In other words, you have to take a huge quantity of data, compress, sort, and convert it into smaller packets that are easier for non-technical colleagues to grasp.
- Preparing reports and dashboards and presenting the findings to the authorities in charge of the overall management of the organization.
- Management of database. Not only do you have to retrieve data by locating it from the storage provisions, but also keep it secure by guarding it against potential breaches or intrusions.
- Understanding the role of data science in health care is also a vital responsibility for a healthcare data scientist. It involves modifying collected data to align with the goals and objectives of the organization.
Benefits Of Data Science in Healthcare
We have listed the top benefits of data science in healthcare that one can avail of.
1. Reduced failure in treatments
Perhaps the most vital use of data science in healthcare is to minimize errors in treatment through accurate predictions and prescriptions. Since a substantial amount of data about the patient’s medical history is collected by the data scientists, that stored data can be used to identify symptoms of illnesses and provide an accurate diagnosis. Since treatment options can now be personalized, and care is more informed, mortality rates are much lessened.
2. Facilitates drug development
The development of medicines requires time and intensive research. However, both time and effort can be reduced by medical data science. Through the usage of case study reports, lab testing outcomes, prior medical data, and the effect of the drugs in clinical trials, machine learning algorithms can predict whether the drug is going to produce the desired effect on the human body.
3. Develops a specialized skill set
For quality treatment to be administered, it is imperative to develop skillsets that can provide an accurate diagnosis. Using predictive analytics, one can predict which patients are at a higher risk and how to intervene early to prevent serious damage. Furthermore, the huge quantity of data needs to be handled skillfully to prevent errors in administration, for which data science can be a solution.
4. Minimizes healthcare expenses
Electronic Health Records(EHRs) can be used by specialists of data science in the medical field to identify the health patterns of patients and prevents unnecessary treatment or hospitalization, thus reducing costs.
Data Science Healthcare Projects
There is no better way to learn the use of data science in medicine than to work on projects implementing both. Artificial intelligence and machine learning have promoted the initiation of many data science projects in healthcare where aspiring scientists can not only gain knowledge but also retain their knowledge, sharpen their skills, and act upon real-life problems. Below we have listed a few projects that will help you best demonstrate your problem-solving approach to data science in healthcare challenges.
1. Identifying patient risks
As a data scientist, you can create machine learning algorithms to combine variables like socioeconomic background, lab values, and other individual information to create results of a person’s health condition.
2. Medical imaging
Perhaps the most common project of data science in the healthcare industry is medical imaging for detecting internal issues. The X-Rays, MRIs, and CT scans which are so widely used technology are all types of medical imaging for scanning the internal organs of the human body. Doctors can now provide a far more accurate diagnosis of the patient's conditions with medical data science than they could with traditional methods.
The role of data science in healthcare also involves mapping and analyzing genome sequences. Data scientists working on genomics analyze strands of genes and attempt to find anomalies.
4. Predicting strokes
Using support vector machines, a scientist can solve the problem of predicting stroke patterns. One of the vital data science use cases in healthcare requires extensive analysis of exploratory information and the application of algorithms.
How to Become Healthcare Data Scientist in 2023
To become a healthcare data scientist, you have to proceed with a specific approach to healthcare data. At the postgraduate level, many educational institutions offer a degree in medical data science. The curriculum comprises training the student in quantitative research, machine learning, statistical analysis, inference computation, and methodological approach to epidemiology. You will get all the information you need at the insight Data Science Bootcamp.
Apart from the rigorous training that one gets for a Master's in Data Science, it is strongly necessary to prepare oneself beforehand for a career in this field. Since high specialization is an absolute must, one must get the necessary educational qualifications required to succeed in the field.
- Step 1: The first step to getting data science jobs in healthcare is to get a bachelor’s degree in computer science, information technology, physics, maths, or any other associated discipline. Simultaneously, you can also learn the basics from an authentic data science course like KnowledgeHut’s Data Science certified course.
- Step 2: Now enroll in a Master's degree program that offers education in medical data science or data science in general.
- Step 3: Work on developing vital skills required for data science in healthcare. A good grasp of quantitative research practices and the ability to communicate the findings of your research to the stakeholders in your work is especially solicited. It involves communicating with research colleagues, clinicians, patients as well as the general public.
As per the findings of a March 2019 article, the organizations offering employment in data science in the medical field require you to be adept in statistics, machine learning, Python programming language, and narrative strategies to bag the job.
Besides acquiring the necessary educational qualifications, you can work as a research associate on projects and internships to get more acquainted with the specifics of the healthcare industry and develop practical experience.
Future of Data Science in Healthcare
The 21st century is making heavy use of data science in the healthcare industry to optimize surgeries, operations, and patient recovery procedures. Apart from technological advancement and the increased digitization of lifestyles, data science will also help reduce healthcare expenses, making quality medical amenities accessible to all.
The aforementioned data science in healthcare examples show that in the near future, big data will be the key player in the medical industry, and how the data is stored and managed will impact how people’s health will be governed in the long run. Of course, such a huge potential comes with its risks, such as lack of standardized data and ineffective policies of data management. However, such challenges can be overcome by setting some protocols for data science applications in healthcare, such as ensuring privacy, maintaining security, and working towards augmenting the implements used for data analytics in healthcare to make them as error-free as possible.