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Python in Healthcare: Advancing Management and Data Science

It’s no secret that healthcare organizations deal with massive amounts of data from their patients and facilities daily. According to Statista, there is an explosive growth rate in the amount of global healthcare data from about 153 exabytes in 2013 to over 2,300 exabytes and growing in 2020. With global challenges such as the COVID-19 pandemic, we’ve come to realize how important efficient data management and timely data analysis are on every level from a single patient to the entire world.

Today I’m talking about Python, why healthtech companies love it, and the undisputed potential of the increasingly popular programming language for efficient data management, patient care, and data science in the healthcare sector.

What is Python?

Python is an object-oriented, high-level dynamic programming language that has been widely used across a plethora of different industries for nearly 30 years now. Python is general-purpose, which means you can effectively use it to build anything from simple web and desktop apps to powerful data management systems to even complex machine learning and artificial intelligence tools. Today Python is the fourth most commonly used programming language in the world, praised by software development engineers worldwide for its ease of use and versatility. But more than any other language, Python’s incredible flexibility and unique features have proven their worth in the healthcare sector in recent years.

Why healthtech developers prefer Python

Here are the main reasons Python has topped the healthcare software development charts over other programming languages such as Java and C++ in recent years:

  • Increased productivity

Python’s clear syntax, clean object-oriented designs, and code reusability enable software developers to write code faster and add complex features more efficiently than using other languages.

  • Dynamic semantics

Python is a dynamically-typed language with shorter design cycles and reduced volume of source code. This brings more flexibility to incremental development and saves healthcare projects a lot of time. They just code, test, and deploy to production.

  • Open source

Python can be used and distributed freely even for commercial purposes. This also means a large, vibrant community of contributors who share knowledge and provide solutions that may help your healthcare project development.

  • Available libraries and frameworks

The abundance of high-quality libraries and flexible frameworks that cover nearly any use case eliminate the need for software architects to reinvent the wheel. Healthcare projects don’t have to write new code for every single feature on the list of requirements.

  • Test-driven development

Python’s great code readability and testing frameworks make TDD simple and very effective. This means healthcare projects can minimize the time spent fixing bugs and greatly reduce the cost of product maintenance.

  • Interoperability

With the help of certain frameworks, Python offers great interoperability with other programming languages and technologies, which enables seamless integration of Python-based solutions with practically any platform.

  • Versatility

Python is general-purpose which allows healthcare projects to reduce overall project complexity and resources spent on development by using a single language to cover their every project development goal.

  • HIPAA compliance

There are numerous free and readily available Python packages for data security and cryptography that make it easy for your healthcare development project to tick every box on the HIPAA compliance checklist.

Python use cases in healthcare

Hospital and patient care management

Hospitals and clinics often suffer from serious resource constraints that make efficient budget and personnel allocation a big issue. On the one hand, overstaffing on a shift with low admission will lead to unnecessary labor costs. On the other hand, a busy day with limited staff available can result in fatal outcomes. And even with optimal staffing, you can’t always be sure that appointments, diagnostics facilities, and treatment are all scheduled and managed properly.

With machine learning libraries like TensorFlow, Pytorch, and Scikit-Learn, Python can improve healthcare providers’ management and cost control capabilities, helping them become more sustainable. Using Python, healthtech companies can create Deep Neural Network solutions that will analyze patient admission rates, EHRs, and other hospital data to catch relevant patterns that can be used to optimize operations management. Thus, things like staffing, inventory management, emergency care, and patient waiting times will become significantly more predictable and cost-efficient. Industry analysts believe that such applications can potentially create $150 billion in annual savings for the US healthcare economy by 2026.


Medical errors affect one in 10 patients worldwide. It’s a very serious factor in healthcare as even one misdiagnosis or wrong prescription can have lethal consequences. In the United States alone, approximately 10% of patient deaths are caused by diagnostic errors while hundreds of thousands of other patients experience adverse reactions to prescribed medicine.

Python machine learning capabilities can be effectively applied in building powerful image recognition and medical data processing tools. These tools will be able to learn from patient EHR data, identify specific patterns, and provide personalized diagnostic and medication insights at very high accuracy. This includes the analysis of electrocardiograms, ultrasound imaging, magnetic resonance imaging, diffusion tensor imaging, and computerized tomography.

With Python-powered machine learning tools, doctors can combine these pieces of data into a single diagnostic outcome with a more efficient, accurate treatment strategy. This kind of advanced diagnostic technology will be especially helpful to oncologists and pathologists.


There are numerous patient-specific factors that can affect the course of the disease. This requires doctors to process inconceivable amounts of data to be able to estimate the patient’s condition and potential for complications. With so many similar signs and symptoms related to different diseases, it’s very hard for even the best and most experienced doctors to predict the expected duration and outcome of the disease for each individual patient.

With Python-based frameworks, machine learning libraries, and recent breakthroughs in predictive analytics, the language can be used to create reliable predictive disease prognosis tools. Such Python-powered tech is less time-consuming to implement and more cost-efficient to maintain than existing prognosis methods. These tools can process heaps of patient EHR data, analyzing current diagnosis, duration of the disease, and treatment for each patient.

The system will compare the course of the disease, given treatment plans, prescribed medicine, and additional patient-specific factors to analyze efficiency and identify what could happen next before any serious complications show themselves. Such solutions can also be applied to palliative care, predicting the mortality of patients with terminal illnesses more accurately and in a timelier manner.

Data science

Python-based data management and analysis solutions may very well become a huge driver of scientific advancements in healthcare. In addition to efficient statistical computing, Python can be used to connect different databases, platforms, and external APIs to pull in data directly into data science pipelines.

By means of powerful, cost-efficient Python algorithms and systems, healthcare organizations will be able to process exabytes of structural and unstructured healthcare data to aid the research, development, and testing of new treatment methods, discover preventive care solutions, improve epidemics control, etc.

Healthcare startups that use Python

  1. DrChrono
    An innovative end-to-end healthcare management platform with customizable workflows that simplifies EHR management, patient care scheduling, medical billing, and revenue cycle management.
  2. Qventus
    An AI-based software platform that provides real-time predictive care solutions to help hospitals optimize patient flow and resource allocation across emergency departments and inpatient units.
  3. AiCure
    An advanced data analytics solution that uses clinically-validated AI and computer vision technologies to analyze how patients respond to treatments and provide dosing support, medication adherence, and insights.

Fathom Health
A medical coding automation platform that leverages deep learning and natural language processing models to accelerate medical reimbursement by structuring and analyzing EHR datasets.



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