Data Science in Healthcare: How It Can Help Save Lives

In this post, we’ll discuss the different ways data science is being used in healthcare and some of the challenges that come with it. 

Data science is an interdisciplinary field that uses scientific methods, algorithms, and computation to extract knowledge and insights from data. This process can be applied across various industries, but today we’re going to focus on one industry in particular: healthcare.

But first, a short introduction before we dive into data science in healthcare

Data science combines multiple fields, including statistics, scientific methods, machine learning, artificial intelligence (AI), and data analysis, to extract value from data. The simplest definition of data science is the extraction of actionable insights from raw data.

Data scientists use various techniques to analyze data, including data mining, machine learning, and predictive modeling.

I like to think that data science is about combining programming, statistics, machine learning, and AI, computer science, to find interesting insights from large data sets. Then, package it and present it nicely to various colleagues and management to move from insights to actions.

If you want to learn more about Data Science, then check out or post: Data Science: Complete Introduction to Data Science to get a great introduction to data science.

Healthcare is one of the most important industries in the world. There are a lot of exciting possibilities for data science in healthcare, and the field is only going to grow in importance. 

Data science has the potential to improve patient care, increase efficiency within health systems, and reduce costs.

Data scientists are able to use big data to identify patterns and trends in healthcare data. This information can be used to develop predictive models that can help healthcare professionals to better manage patients’ health. Data science can also be used to improve the accuracy of diagnoses and to find new treatments for diseases.

In the healthcare industry, data science can be used to improve patient outcomes, reduce costs, and improve the quality of care. For example, data science can be used to improve the accuracy of diagnoses, develop new treatments, and predict patient outcomes.

Let’s look a bit closer on that. One way data science is being used in healthcare is by predicting patient outcomes. This involves using data from past patients to create models that can predict how a patient will respond to a certain type of treatment. This can help doctors choose the best treatment for each patient.

The healthcare sector gets excellent benefits from data science applications. The healthcare industry generates large datasets of valuable information on patient demography, treatment plans, results of medical examinations, data from connected devices (IoT) etc. 

5 benefits and use cases for data science in healthcare

The application of data science techniques to better understand diseases has the potential to revolutionize the field of medical research. By analyzing data collected from patients, electronic health records (EHRs), medical images, and other sources, data scientists can gain a better understanding of diseases and  build models that predict the development of diseases and test new therapies.

Data science can also help to improve our understanding of how diseases progress. By studying large amounts of data, scientists can identify patterns that would be difficult to spot by studying individual patients. This information can help to improve our understanding of diseases and lead to the development of new treatments.

Precision medicine is a new way of thinking about disease that takes into account each person’s genes, environment, and lifestyle. 

Data science can be used to develop precision medicine treatments for individual patients. In other words, we use data science to identify which treatments will work best for which patients. 

Precision medicine has a number of benefits, such as: 

• Improved treatment efficiency
• Reduced unfavourable effects of treatment
• Lower costs for patient and providers
• Earlier diagnosis using biomarkers (biomarker is a measurable indicator of some biological state or condition)
• Better prognosis estimation

Predictive models are used to identify patterns and relationships in data in order to make predictions about future events. A predictive model uses historical data, learns from it, finds patterns and generates predictions from it. 

Predictive modeling is a powerful tool for data scientists, as it allows them to identify trends and correlations that may not be apparent from simply looking at data. 

This information can be used to develop predictive models that can identify patients who are at risk for developing certain diseases or conditions, or who are likely to respond to a particular type of treatment. 

Hospitals may use data science to forecast the deterioration of a patient’s health and give preventative measures and early treatment to help reduce the likelihood of further deterioration of the patient’s health

Predictive models are created by training a model on a data set that, for example, includes information about patients who have already been diagnosed with the disease in question. The model is then used to make predictions about patients who have not yet been diagnosed. The accuracy of predictive models can be improved by including more data in the training

Medical image analysis is the process of applying mathematical and statistical methods to medical images in order to extract information about their content. The purpose of medical image analysis is to improve diagnosis, prognosis and treatment of medical conditions.

The main application areas are medical diagnosis, prognosis, treatment planning and follow-up. Medical image analysis is a rapidly growing field with a large number of potential applications in both clinical and research settings.

The main application areas of medical image analysis are:

• Detection and characterization of abnormalities in medical images (e.g. cancerous tumors)
• Measurement of anatomical features in medical images (e.g. tumor size)
• Determination of the physiological state of tissues in medical images (e.g. blood flow)

The use of medical images has increased dramatically in recent years due to the widespread availability of affordable and powerful imaging technology. Another aspect that is driving the development is that the manual interpretation of medical images by human experts is often time consuming and costly. 

Data science for drug/medical development is a process that uses data analytics to identify potential new drugs and to improve the development of existing drugs. The process begins by gathering data on the disease that is to be treated, including the genetic makeup of the disease and its effects on the body. This data is then used to create a model of the disease that can be used to test potential new drugs.

Drug development and innovation is a highly complex discipline. Pharmaceutical companies can leverage data from, for example, patient metadata, journals, and clinical research to develop models and find statistical relationships between the attributes. 

Data science can help design smarter trials, strengthen the scientific discoveries, shorten the time to develop new and safer medicals, and hopefully help more people

Read more about AstraZenecas work with Data Science here

Healthcare is one of the most complex and critical industries in the world. The data science challenges in healthcare are unique and multi-faceted. Hence, there are many challenges for data science in healthcare. 

One challenge is that there is a large and varied amount of data in healthcare. This data can come from Electronic Health Records (EHRs), medical images, genomic data, or data collected from sensors worn by patients, etc. 

In addition, another major challenge for data science in healthcare is ensuring that data is accurate and reliable. Although this issue is present in most of the data science projects and industries, in healthcare where lives are on the line, it is critical that data is correct.

Another challenge is that this data is often unstructured. This means that it is not in a format that is easy to process using traditional methods. Healthcare data also tends to be sensitive, so it needs to be protected from unauthorized access.

Finally, healthcare projects in data science face a challenge with data privacy. Healthcare data is some of the most sensitive data in the world and must be protected at all costs.

The field of data science is growing rapidly, and its impact can be seen in almost every industry. One area where data science is making a particularly significant impact is in the field of healthcare.

In fact, the McKinsey Global Institute has predicted that the application of data science in healthcare could result in over $300 billion in value creation by 2025.

There are many ways in which data science can be used to improve healthcare. Some of the most notable applications include the use of big data to improve patient care, the use of machine learning to predict patient outcomes and to identify patterns and relationships that would be difficult to spot with traditional methods

The rapid expansion of data in healthcare and its potential to improve patient care has led to a growing demand for professionals with data science skills in the healthcare industry. Data science skills are also in high demand outside of the traditional healthcare sector. 

Hospitals, pharmaceutical companies, and insurance providers are all looking for professionals with data science skills to help them analyze and make use of the large amount of data. 

Data scientists play an increasingly important role in helping hospitals, healthcare providers, medical researchers to identify patterns and trends that can result in life-saving policies and procedures

From now and up to the year 2025, the healthcare analytics market is expected to have  a compound annual growth rate (CAGR) of 23.55%, according to Valuates Report, with an estimated valuation of $40.78 billion by 2025—this means that both demand and opportunities for data scientists in healthcare are on the rise.

Healthcare is one of the most important industries in the world. Data science has the potential to improve patient care, increase efficiency within health systems, and reduce costs.

Benefits and Use cases for data science in healthcare include

• Improved Understanding of Diseases: By studying large amounts of data, scientists can identify patterns that would be difficult to spot by studying individual patients
• Precision Medicine: We use data science to identify which treatments will work best for which patients
• Predictive Models: Develop predictive models that can identify patients who are at risk for developing certain diseases or conditions, or who are likely to respond to a particular type of treatment
• Medical Image Analysis: The process of applying mathematical and statistical methods to medical images in order to extract information about their content
• Drug Development (Medical Supplies): A process that uses data analytics to identify potential new drugs and to improve the development of existing drugs

Finally, both demand and opportunities for data scientists in healthcare are on the rise.

Read More About Data Science Use Areas 

If you want to read more posts similar to this, have a look at our other posts on data science in various industries 

• Data Science in Finance
• Data Science in Healthcare
• Data Science in Manufacturing