Let us look at the topics that we will be going through in this blog on applications of Data Science in healthcare:
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Why do we use Data Science in Healthcare?
According to a study, a human body can generate around 2 terabytes of data every day. This data includes activities of the brain, stress level, heart rate, sugar level, and many more. To handle such a large amount of data, now, we have more advanced technologies and one of them is Data Science. It helps monitor patients’ health using recorded data.
With the help of the Data Science application in healthcare, it has now become possible to detect the symptoms of a disease at a very early stage. Also, with the advent of various innovative tools and technologies, doctors are able to monitor patients’ conditions from remote locations.
In earlier days, doctors and hospital management were not able to handle multiple numbers of patients at the same time. And due to the lack of proper treatment, the patient’s conditions used to get worse.
However, now, the scenario has changed. With the help of Data Science and Machine Learning applications, doctors can be notified about the health conditions of the patients through wearable devices. Then, hospital management can send their junior doctors, assistants, or nurses to these patients’ homes.
Hospitals can further install various equipment and devices for the diagnosis of these patients. These devices built on data science principles are capable of collecting data from the patients such as their heart rate, blood pressure, body temperature, etc. Doctors get this real-time data on patients’ health through updates and notifications in mobile applications. They can then diagnose the conditions and assist the junior doctors or nurses to give specific treatments to the patients at home. This is how Data Science helps in caring for patients using technology.
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Benefits of Data Science in Healthcare
Data Science helps in advancing healthcare facilities and processes. It helps boost productivity in diagnosis and treatment and enhances the workflow of healthcare systems. The ultimate goals of the healthcare system are as follows:
- To ease the workflow of the healthcare system
- To reduce the risk of treatment failure
- To provide proper treatment on time
- To avoid unnecessary emergencies due to the non-availability of doctors
- To reduce the waiting time of patients
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How to Become a Healthcare Data Scientist?
Becoming a healthcare data scientist necessitates the integration of education, skills, and experience. Here are the recommended steps to pursue a career in this field:
- Develop a strong foundation in mathematics and statistics: To embark on a solid foundation in mathematics and statistics, start by attaining a comprehensive comprehension of these subjects, as they form the essential underpinnings of data science. Focus your studies on key areas including calculus, linear algebra, probability, and statistical inference.
- Attain a bachelor’s degree: To establish a sturdy academic base and gain essential foundational knowledge, consider enrolling in a bachelor’s degree program in a field closely tied to data science. Suitable disciplines include computer science, statistics, mathematics, or healthcare-related fields. This educational path will equip you with a solid academic background that is pertinent to your pursuit of becoming a healthcare data scientist.
- Gain proficiency in programming: To enhance your skill set, focus on acquiring proficiency in programming languages commonly utilized in data science, such as Python or R. These languages are extensively employed for various data-related tasks, including data manipulation, analysis, and modeling. Additionally, familiarize yourself with healthcare-specific libraries and frameworks like TensorFlow or PyTorch, as they are specifically designed to cater to data science needs in the healthcare domain.
- Familiarize yourself with healthcare domain knowledge: Learn about the healthcare industry, including its terminology, regulations, and data sources. Develop an understanding of electronic health records (EHRs), medical coding systems, clinical trials, and healthcare analytics.
- Pursue advanced education: Consider pursuing a master’s degree or a Ph.D. in fields such as health informatics, biomedical informatics, data science, or a related area. Advanced degrees provide specialized knowledge and research opportunities in healthcare data science.
- Gain practical skills through projects: Immerse yourself in practical projects that entail the analysis of healthcare data. This may involve working with publicly accessible healthcare datasets, conducting research studies, or collaborating with healthcare organizations. By undertaking such hands-on initiatives, you can construct a portfolio that not only demonstrates your proficiency but also exhibits your capability to proficiently handle healthcare data.
Remember, continuous learning and adaptation are crucial to becoming a successful healthcare data scientist. Stay updated with industry trends, participate in conferences, join professional organizations, and pursue online courses and certifications to expand your knowledge and skills. By following these steps and maintaining a commitment to ongoing learning, you can enhance your prospects of thriving in this dynamic and rewarding field.
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Applications of Data Science in Healthcare
Now, let’s discuss the major data science applications in healthcare.
Medical Image Analysis
Medicine and healthcare together form a promising
field for utilizing technological advancements. The
healthcare sector is acquiring new heights due to the advancements in Data
Science. It is helping in various aspects, and one of them is the analysis
of medical images. It is one of the most interesting areas of study in image
recognition technology.
Data Science helps in the recognition of scanned images to figure out the defects in a human body to help doctors make an effective treatment strategy. These medical image tests include X-rays, sonography, MRI (Magnetic Resonance Imaging), CT scans, and many more. Proper analysis of the images of these tests helps gain valuable insights for the doctors to provide the patients with better treatment.
These are the general imaging techniques. But, the involvement of Data Science has made these imaging techniques further revolutionize the healthcare industry. There are various methods in Data Science that find the differences between the states of image and resolution and check the orthogonality. Data Scientists are working on creating more advanced techniques to improve the quality of the image analysis so that the patient’s data from an image is extracted efficiently.
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There is a recent study published by Google AI on diagnosing skin diseases using Deep Learning. The Deep Learning model is created in such a way that it can diagnose 26 diseases related to skin with an accuracy of 97 percent. The diagnosis is performed using deep neural networks, Machine Learning, and Data Science. Now, let us look at the three common algorithms used in medical image analysis:
- Anomaly detection algorithm: This algorithm helps in identifying conditions such as bone fracture and displacements.
- Image processing algorithm: The image processing algorithm helps in analyzing images and enhancing and denoising them.
- Descriptive image recognition algorithm: It visualizes and extracts data from images, interprets it and makes use of it to form a bigger picture (for example, merging the images of the brain scan and designating them accordingly).
These algorithms are successfully implemented by using supervised and unsupervised learning algorithms.
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Predictive Analytics in Healthcare
In today’s world, information is one of the important factors in healthcare analytics. Due to the lack of proper information about a patient, the condition can get worse. Thus, information or data about the patient must be collected efficiently. This data can be anything from the patient’s blood pressure, body temperature, or sugar level. After collecting the patient’s data, it is analyzed to search for patterns and correlations in it. This process tries to identify the symptoms of a disease, the stages of the disease, the extent of damage, and many more.
Then, the predictive analytics model built on top of
Data Science makes predictions on the condition of the patient. Also, it helps in making
strategies for the appropriate treatment that should be given to the patient.
Therefore,
predictive analytics is a very useful technique and it plays a major role in
the healthcare industry.
The major benefits of predictive analytics in healthcare are given below:
- It helps in the management of chronic diseases.
- It efficiently monitors and analyzes the demand for pharmaceutical logistics.
- It predicts a patient’s condition and suggests preventive measures.
- It provides faster documentation of hospital data.
- It helps in efficiently utilizing doctors and other resources for the benefit of the maximum number of patients.
- It predicts the future medical condition of a patient.
Thus, the application of Data Science in healthcare in the form of predictive analytics is proving itself to be of great use.
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Drug Research
As the world’s population is growing, there are many issues in the human body emerging every now and then. This may be due to the lack of proper food, anxiety disorder, pollution, physical illnesses, etc. It has now become a challenge for medical research institutes to find medicines or vaccines for diseases in a short time. To find a formula for a medicine, the researchers have to understand the characteristics of the causative agent, it may require millions of test cases to do this. Then, after finding a formula, the researchers have to perform further tests on the formula.
To go through the data of the millions of test cases mentioned above, earlier it required 10–12 years. But, now, with the help of various applications of Data Science in healthcare, it has become a much easier task. The data from millions of test cases can be processed within months or maybe in weeks. It helps in evaluating the efficiency of the drug through data analysis. Hence, a successfully tested vaccine or medicine can be launched in less than a year. This is all possible with the help of Data Science and Machine Learning. Both have revolutionized the research and development sectors of the medicinal drug industry.
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Data Science in Genomics
Genomics is one of the interesting areas of study in medical science. It is the study of the sequencing and examination of genomes that consist of genes and DNAs of living beings. The research on the genes of organisms facilitates high-level treatments. The aim of studying genomics is to find the characteristics and irregularities in DNAs. Also, it helps find the correlation between disease, symptoms, and the health condition of the person affected. Further, the study of genomics includes the analysis of drug response for a particular type of DNA.
Earlier, before the emergence of powerful data analysis techniques, the study of genomics was a redundant and time-consuming task. This is due to the presence of millions of pairs of DNA cells in the human body. But, now, the applications of Data Science in healthcare and genomics have made this task easier. With the help of various Data Science and Big Data tools, we can analyze human genes with less effort and time. These tools facilitate researchers to find specific genetic issues and the drug that responds best for a specific type of gene.
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The tools used in the research of genomics are:
- MapReduce: MapReduce helps in processing huge amounts of genetic data. With the help of MapReduce, the genetic sequences can be processed in lesser time.
- SQL: SQL helps in the retrieval of genomic data from various databases and also helps in the computation of this data.
- Galaxy: It is a GUI-based application used for biomedical research. To perform research on genomes, we can do specific operations using Galaxy.
- Bioconductor: Bioconductors are used for the analysis of genetic data.
Having knowledge of how DNA cells respond to a particular drug for a patient, doctors can perform the treatment efficiently. The useful insights into the genetic structure help them make effective strategies to cure a disease for a particular patient.
Virtual Assistance
The applications that are built using virtual assistance are a great example of the utilization of Data Science. Data Scientists have built comprehensive platforms that give personalized experiences to patients. Medical applications that use Data Science assist a patient in identifying the disease by analyzing the symptoms. The patient just needs to enter his/her symptoms and the application will predict the disease and condition of the patient. It will suggest precautions, medication, and the treatment required as per the condition of the patient.
Further, the application analyzes the data of the patient
and creates a checklist of the treatment processes that need to follow. Then, it regularly notifies
the patient for taking medicines. This helps in avoiding the situation of
negligence that might make the condition worse.
Virtual assistance has also proved to be useful for patients who suffer from Alzheimer’s, anxiety, depression, and other psychological disorders. The treatment of these patients becomes productive as the application regularly notifies them of taking required measures. These measures include proper medication, exercise, and food intake. One of the examples of virtual assistance is Woebot developed by Stanford University. It is a chatbot that helps patients with psychological disorders in improving their mental health with the proper treatment.
Wearables
The modern engineering marvel Internet of Things (IoT), facilitating unparalleled connectivity, greatly enriches the field of data science. When harnessed in healthcare, IoT becomes a valuable tool for continuous patient health monitoring. Presently, individuals employ wearable devices like fitness monitors and smartwatches to manage their well-being proactively. These wearable sensors can be remotely accessed by healthcare professionals, allowing for real-time monitoring and intervention.
For instance, devices such as Fitbit and Apple Watch exemplify the potential of data science applications in healthcare through IoT, offering smooth health tracking and remote patient care.
Tracking Patient’s Health
As discussed above, in the domain of public health, data scientists have pioneered wearable devices based on IoT that empower healthcare professionals to capture a wealth of vital information. This information includes heart rate, sleep patterns, blood glucose levels, stress indicators, and even brain activity. Utilizing cutting-edge data science tools and machine learning algorithms, physicians can now swiftly identify and monitor prevalent conditions such as cardiac or respiratory diseases.
Furthermore, this technology enables the detection of the most subtle changes in a patient’s health metrics, facilitating the prediction of potential disorders. Various wearable and in-home devices, integrated into an IoT network, employ real-time analytics to forecast potential health issues based on a patient’s current state, exemplifying the remarkable applications of data science in healthcare.
Diagnosis of diseases
In the domain of healthcare, data science applications play a key role in streamlining and expediting the diagnostic process. These applications not only analyze patient data to aids in the early detection of health issues but also enable the creation of medical heatmaps that highlight demographic patterns of various ailments.
For example, wearables like smartwatches and fitness trackers continuously collect and transmit vital health data, allowing for real-time monitoring and early intervention. Also, medical imaging devices such as MRI machines employ data-driven algorithms to enhance the accuracy of diagnoses and treatment plans.
By looking at all these applications of Data Science in healthcare, we can say that Data Science is one of the wonderful creations by humans.
The Role of a Data Scientist in Healthcare
The role of a Data Scientist is to implement all techniques of Data Science for integrating it into healthcare software. The Data Scientist extracts useful insights from the data to make predictive models. Overall, the responsibilities of a Data Scientist in healthcare are as follows:
- Collecting data from hospitals and pharma companies
- Analyzing the needs of hospitals for the management of equipment
- Structuring and sorting the data for use
- Performing Data Analytics using various tools
- Implementing algorithms on the data to extract insights
- Building predictive models with the development team
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Healthcare Companies Hiring Data Scientists
Numerous healthcare organizations are actively seeking data scientists to harness data-driven insights and enhance healthcare outcomes. Below are some prominent healthcare companies that regularly recruit data scientists:
- Johnson & Johnson
- Pfizer
- NovaSignal
- Novartis
- Merck & Co.
- Eli Lilly and Company
- Roche Holding AG
- AstraZeneca
- UnitedHealth Group
- Cigna
- Anthem, Inc.
These companies recognize the importance of data analytics in improving patient care, optimizing healthcare operations, and driving innovation. Keep in mind that the demand for data scientists in the healthcare industry extends beyond pharmaceutical companies to hospitals, research institutions, insurance providers, and healthcare technology firms.
It’s advisable to visit the career pages of these companies or utilize job search platforms to explore current job openings and specific requirements for data scientist roles. Additionally, networking within the healthcare industry and attending industry events can provide valuable opportunities to connect with potential employers.
Impact of Data Science on Healthcare
Having observed the demand for data scientists across various companies in the preceding section, let us now shift our focus to the impact of data science in the healthcare sector:
- Patent Foramen Ovale (PFO):
A PFO is a hole in the septum, the wall that separates the left and right atria of the heart. PFOs are common, occurring in about 25% of the population. However, they can increase the risk of stroke, especially in people who have had deep vein thrombosis (DVT).
Diagnostic Advancements: Data science techniques have empowered the creation of predictive models that identify individuals at a higher risk of PFO-related complications, like stroke. These models meticulously analyze patient characteristics, medical history, and imaging data, delivering more precise risk assessments.
Treatment Approaches: NovaSignal, a company focussing on monitoring and improving the blood flow in the body through data worked on it and created the NovaGuide Intelligent Ultrasound which uses a technique called transcranial Doppler (TCD) to identify PFOs. TCD is a non-invasive procedure that uses sound waves to measure blood flow through the brain. During a TCD exam, a small probe is placed on the scalp, and ultrasound waves are directed into the brain. The ultrasound waves bounce off of blood cells and create an image of the blood flow.
The NovaGuide Intelligent Ultrasound uses artificial intelligence (AI) to automatically identify PFOs in TCD images. The AI algorithm is trained on a large dataset of TCD images, and it can identify PFOs with a high degree of accuracy.
Enhanced Long-term Outcomes: Data science contributes to tracking the long-term outcomes of PFO interventions. By collecting and analyzing data from patients who have undergone PFO closure, researchers gain valuable insights into the efficacy and durability of different treatment options, leading to improved patient outcomes.
- COVID-19:
COVID-19, also known as the Coronavirus Disease 2019, is a highly contagious respiratory illness caused by the SARS-CoV-2 virus. To prevent from this disease, Pfizer and BioNTech jointly developed the mRNA-based COVID-19 vaccine, which was one of the first vaccines authorized for emergency use in various countries. In response to the COVID-19 pandemic, data science has played a crucial role in various aspects related to the disease:
Disease Surveillance and Predictive Modeling: Data science has been instrumental in monitoring the spread of COVID-19. Through the analysis of data from diverse sources such as confirmed cases, hospitalizations, and mobility patterns, data scientists have developed models to predict disease outbreaks, identify hotspots, and support public health decision-making.
Patient Risk Stratification: Data science techniques have facilitated the development of models to predict the risk of severe outcomes in COVID-19 patients. By analyzing patient demographics, comorbidities, and biomarkers, these models identify individuals who are more likely to experience severe symptoms, enabling healthcare providers to allocate resources and provide appropriate care.
Treatment Optimization and Drug Repurposing: Data science has expedited the analysis of extensive clinical and genomic datasets to identify potential treatments and repurpose existing drugs for COVID-19. Machine learning algorithms have been deployed to screen and prioritize potential drug candidates, expediting the discovery and development process.
Future of Data Science in Healthcare
The future of data science in healthcare is promising and transformative. Data science techniques and technologies have the potential to revolutionize healthcare delivery, improve patient outcomes, and drive medical advancements.
With the increasing availability of healthcare data, including electronic health records, genomics, wearables, and medical imaging, data science can enable personalized medicine, predictive analytics, early disease detection, precision diagnostics, and treatment optimization.
Machine learning algorithms and artificial intelligence can assist in clinical decision-making, drug discovery, and medical research. Additionally, data-driven approaches can enhance healthcare operations, resource allocation, and population health management.
The integration of data science into healthcare holds the potential to enhance efficiency, effectiveness, and patient-centric care, leading to significant advancements in the field.
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