Learn why healthcare organizations use data analytics and why Python is considered one of the best options for it.
Modern healthcare is based on large volumes of digital data from millions of patients. Researchers expect the CAGR of data for healthcare to reach thirty-six percent by 2025. Traditional tools that help store, process, and analyze information cannot cope with the scale of this data. This is what the big data analytics software was created for. Medical providers and other healthcare professionals use healthcare data analysis to identify trends based on historical data and to predict the future.
What is Data Analytics Used for in Healthcare?
The first obvious answer is: to improve patient care. Big data analysis is based on patients’ electronic health records (EHR). It is able to identify patterns and create machine learning (ML) or predictive models. These models check a specific disease at both the individual patient and population levels and predict whether it will rise or depreciate over time.
Providers can personalize treatment plans using data analytics algorithms. Patient info appears in a medical database in real time. An analytics system with integrated artificial intelligence (AI) scans it and predicts what diagnosis the patient may have. If the diagnosis needs to be confirmed, the analytics software is able to suggest additional tests. If the diagnosis is confirmed, analytics algorithms offer a treatment plan based on the individual needs and conditions of the patient. Big data analytics tools also help find patients at risk of getting the wrong treatment.
Data analytics system is able to support other healthcare areas. Providers and researchers use it to detect diseases early or prevent them. The provider studies the reports and suggests prevention or necessary interventions based on them. It helps to reduce the risk that a certain group of the population (for example, pregnant women or the elderly) will experience an outbreak of a disease.
Big medical data analytics software also helps to optimize healthcare resources. Analytics reports can contain info about expired drugs or devices in stock at the healthcare facility. The healthcare provider can plan stocks and distribute the workload across the organization thanks to the reports.
Which Programming Language is Best for Healthcare Data Analytics?
Dmitry Baraishuk, the CINO at the software development company Belitsoft with twenty years of expertise in digital healthcare, explains the advantages of the Python programming language with examples.
Many programmers believe it is better to start working with Python because in their opinion it is the simplest programming language. Python code is easy to read.
Developers of Python in healthcare often use a framework based on it – Django – to create medical applications. Creating web apps with Django is much easier. Because this framework reduces the amount of code the developer needs to write.
Since Python is an open-source language, it has strong community support. Experts from health informatics, bioinformatics, and medicine represent these communities. They can advise you if questions arise.
Python has a rich ecosystem of libraries specifically for health data analysis. Examples of libraries include Pandas, SciPy, scikit-learn, and NumPy. Machine learning libraries are also available. Machine learning methods are useful for various tasks, e.g., medical data classification. Statistical analysis and numerical computations are very important for patient data. Python code works effectively for such processes.
What is Data Analysis for Healthcare with Python?
It is the process in which Python-based software collects, cleans, transforms, and analyzes healthcare-related data. It helps analysts find patterns, extract important insights, and inform decisions. The software enhances data analytics in many areas of healthcare. Some of them are briefly mentioned above. But there are several major areas of data analysis in Python that are worth highlighting.
Medical Diagnostics
Malawi and Zambia are countries with weak infrastructure. There, babies with human immunodeficiency virus (HIV) are born and, unfortunately, don’t receive qualified medical care in time. UNICEF has implemented a special project to solve this problem. The developers have created two mobile apps for a free and open source code-base on Python. One of the apps allows faster delivery of the results of HIV lab tests taken from babies to doctors in rural clinics. Another app allows local health workers to quickly register a baby and assign its mother to postnatal observation.
Also deep learning (DL) models in Python helped radiologists distinguish pneumonia from COVID-19. The average diagnostic accuracy was 91.69%.
Drug Development
Many diseases occur because DNA or proteins in the human body are «broken». Python-based PyMOL software shows scientists the smallest details of broken proteins and DNA in 3D. With this level of detail, scientists can identify a weak spot (target) where they need to send the right drug molecule. They then send molecules of different drugs to this target and study how each molecule affects the target. PyMOL offers up to twelve stereo visualization modes. This allows scientists to efficiently and easily highlight different important features in the structure of targets.
Python is used to solve this problem for a reason. As mentioned above, it is a relatively easy programming language. Various plugins improve the functionality of Python for drug selection calculations. They can be used to solve both simple and complex problems. For example, plugins help edit molecules in situ or analyze the interaction of drugs and targets.
Complex Diseases Prediction
As the saying goes, there is no smoke without fire. ML algorithms in Python help analyze genetic data and find the «fire» – the reason or reasons why an individual or focus group got sick. Based on the same data, analysts can study how an individual’s genetics are associated with the risk that this person will develop a certain disease.
Next-generation sequencing (NGS) is one of the important developments in the field of predictions that run on Python. NGS testing is different from traditional sequencing methods. It analyzes not one fragment of DNA at a time, but millions of fragments. It compiles a report on the genome quickly and accurately.
NGS in Python plays a very important role in oncology. Algorithms detect genetic mutations that a person has inherited. For example, analytical reports predict that these mutations are able to lead to oncology. So the provider plans early intervention and prevention. NGS not only identifies hereditary disorders but also studies the genetic structure of viruses and bacteria. It helps track disease outbreaks and also plan control and prevention measures.
In the End
Today digital robots can perform operations. So Python is like «an assistant» in surgery. Three-dimensional cameras make records in special format. Python helps convert this format to more popular ones, e.g. «.bag» into «mp4». There are many tools and programming languages available. However, Python has become a strong ally for health researchers and medical professionals.
