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How Is Data Science Used in Healthcare? 3 Real-Life Examples
How Is Data Science Used in Healthcare? 3 Real-Life Examples

How Is Data Science Used in Healthcare? 3 Real-Life Examples

Innovative technologies have already transformed many industries, and now they’re entering healthcare. Now people can book doctor visits online or take advantage of remote care. Data science and its advantages are the next steps in changing the healthcare sector.  

Data scientists are now hired by healthcare service providers to extract and analyze raw patient data to drive value from them. So, what is data science in healthcare? How does it influence the industry? How do healthcare organizations leverage data analytics? Let’s look into the topic right now.

How Does Big Data Affect the Healthcare Industry?

Specialists are now adhering to a new data science approach to transform raw data aggregated in the healthcare sector into valuable information. Data science can change the healthcare industry in so many ways, but among the most important areas where data science is applicable and beneficial are: 

  • Workflow and process optimization
  • Data management
  • Patient care
  • Predictive analytics

In this chapter, we’ll look into how data analysis can improve medical research and patient care delivery and its quality management. 

Medical Imaging 

In recent years, artificial intelligence and machine learning have captivated the healthcare sector as the analytics tools become more accurate when it comes to diagnosing human conditions. Accuracy plays a key part in medical diagnostics in radiology.  

Here’s how data science tools help with medical image analysis:

  • With data analysis, medical images analysis is improved by the enhancement of modality difference, image size, and resolution.
  • BI imaging techniques allow doctors to conduct an in-depth analysis of human organs and diagnosis of diseases, including tumors, artery stenosis, etc. 
  • Routine common cases can be detected faster with AI automation. 

Medical researchers are now working on use cases that can help radiologists improve the quality and efficiency of patient care with AI tools. For example, radiologists from all practice sizes and locations are involved in the work of ACR DSI (Data Science Institute at the American College of Radiology). They contribute to use case development and create annotated image datasets to help developers train algorithms for clinical use.

Predictive Medicine

In medical treatment, it’s essential to prevent disease by identifying risks before they become a serious problem. Modern wearables can prevent different conditions and diseases such as heart attacks and mental disorders. Healthcare applications like migraine-prevention apps are getting popular among consumers helping with monitoring their health. Healthcare wearable devices are also used by healthcare specialists to keep track of health improvement expectancy, examine specific factors that influence patients’ condition, etc. But wearables are only one of the possible data science applications based on data science techniques.

Predictive analytics is becoming a cornerstone of personalized healthcare. Medical specialists working in any area can leverage historical patient data stored in EMR software to make predictions. With AI and machine learning, data scientists can build predictive models to forecast a patient’s response to certain treatments, the risks of disease development, etc.

So, what’s customized healthcare? This approach is based on the analysis of an individual’s medical history, social risk factors, and other characteristics that healthcare service providers can convert into valuable insights about a patient. Predictive analytics helps them shift from treating a patient as an average to treating as an individual, which improves patient care and as a result streamlines overall customer satisfaction. 

Virtual Assistants 

The modern principle of effective treatment is based on the idea that sometimes patients don’t need to visit doctors in person. For some tasks, an AI-enabled mobile app can be as effective as a consultation with a doctor. Chatbots integrated with mobile healthcare solutions can provide basic healthcare support. For example, chatbots can be trained to identify patients’ conditions based on the symptoms described or help you make an appointment with a doctor. 

This kind of patient support is based on machine learning algorithms and natural language processing to provide accurate information to people before visiting a doctor. They aim to simplify routine medical tasks while improving user experience.


Data Science Healthcare Apps in Action

There are numerous cases of how data science is applied in the healthcare industry. Below are the three remarkable examples of how healthcare organizations put data science methods into practice.

Preventing Diseases With Smartwatches

The researchers from Stanford Medicine have found out that smartwatches can signal physiological changes, including a change in a red blood cell count, early signs of dehydration, anemia, and illnesses such as the flu or a cold. The results are a breakthrough in the area of preventive medicine. Various wearables  — smartwatches, glucose monitors — enable people to be continuously informed about their condition and help doctors prevent certain diseases and conditions. 

The scientists went further and developed a smartwatch app alerting users when their bodies show signs of fighting an infection. A study of retrospective data showed that the app was able to identify the signs of COVID-19 before they arose.


Skin Cancer Detection with Deep Learning Algorithms

With the growing rate of skin cancer cases, high mortality rate, and expensive medical treatment, it’s vital to diagnose its symptoms as early as possible. Here are machine learning algorithms that can help detect skin cancer at early stages. As part of machine learning techniques, deep learning has revolutionized the entire landscape of machine learning in recent decades. Thus, researchers have developed different early detection techniques and frameworks based on the analysis of such lesion parameters as symmetry, color, size, shape, etc. Equipped with BI technologies, healthcare professionals can provide a comfortable, less expensive, and speedy diagnosis of cancer symptoms.


Transforming Raw EMRs into Valuable Insights 

Beth Israel Deaconess Medical Center (BIDMC) is a great example of how a healthcare organization can leverage data science in the medical field. The medical center adopted the technologies to leverage the huge amounts of electronic health records —  growing at a rate of 25 percent a year. They integrated data analytics tools and visualizations to enable decision making, quality management, and investigation. 

Here’s a recap of the organization’s approach to applying data science while delivering medical services:

  • ‘’Clinical Decision Support Systems’’. BI-based decision support for clinicians is employed across multiple departments and functions at BIDMC, working in cooperation with other databases hosted outside of BIDMC. All the collected electronic records are analyzed for responding to actions and events such as changes in medications, patient visits, new lab results, and newly discovered allergic reactions. 
  • ‘’Measuring Quality By Centralizing Big Data’’. Intelligent data analysis allows practitioners and other healthcare professionals at BIDMC to improve patients’ care. For this objective, BIDMC implemented a cloud-hosted EHR. As part of the organization’s internal regulations, all the clinicians send a standardized, structured summary of each visit to a single database for analysis. 
  • ‘’Sending The Questions To The Data’’. To support the comparative effectiveness of medical research, Clinical Query was created. It’s a web-based tool that allows for analysis and visualization of the clinical data collected by BIDMC.

Future of Data Science in Healthcare

The healthcare industry is changing under the impact of data analysis, but there’s still a lot to do. The sector is adopting innovation, but it takes time and costs that are much higher than expected. According to a study, 56 percent of hospitals have no strategies for data governance or analytics. 

But the situation is much better than it might be. Atul Butte, Chief Data Scientist at the University of California Health System, believes that in the near future, we’ll see more technological advancements in data-driven healthcare. 


As the recent report states, the global healthcare business intelligence market size was valued at USD 4.61 billion in 2018 and is projected to expand at a CAGR of 12.8% till 2025. Thus, in spite of the challenges that the healthcare industry is facing, data science has the capacity to revolutionize the way health care is delivered. Want to bring your healthcare organization to the upper level? Benefit from a data-driven approach by building your own reports and dashboards and improving data management within your institution. 


Contact us if you have an idea or need a consultation on how to organize data analytics practices in your organization. As a software development company with expertise in the healthcare industry, we offer data science services to healthcare organizations of any size. Feel free to explore our portfolio section to learn more about our expertise.

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