Why Should We Use Python for E-health?
Never before has digitising healthcare been as hotly discussed as it is now, following more than a year of lockdowns, vaccine trials, and novel research on coronavirus strains.
Perhaps the nature of “e-health” apps being discussed right now — everything from track and trace apps to the highly controversial “vaccine passports” — has accelerated innovation in healthcare by large.
However, for our team at Thorgate that is experienced in building healthtech software for the last 10 years, we knew that innovative digital products in healthcare are crucial for saving lives and advancing the medical sector to make it more accessible, and cost-effective.
Building a healthtech software is complex and expensive, therefore, it is vital to understand the choice of technologies when building the product. A bad choice could obviously cost a lot in both time and money, and with experience, we at Thorgate have found a tech stack with Python (with Django framework and React) at core to be the ideal solution.
Python is the secure choice for healthtech product development
Regardless of the type of healthtech application being developed — whether a wearable medical device, health passport, telemedicine app, or anything else — the prime concern in healthtech is security. The theft of Personal Health Information is a megabucks industry, meaning that any e-health app developed must be utterly impenetrable.
When talking about security in software development, Python is the logical choice.
In a recent study, Python was named as the most secure programming language out of seven popular languages tested. The study discovered that Python had the lowest number of high-severity vulnerabilities in open source projects tested than any of the other languages considered!
Of course, the quality of the programming also plays a role, but if the language itself is flawed, the statistical probability of writing flawed code is commensurately increased.
As our CEO often points out, programmers should not be overburdened with mundane, repetitive tasks that can be taken care of by automated tools. Because Python is so robust, programmers can focus their attention on functionality instead of getting bogged down too much into security. By simply following industry best standards for coding, security is automatically baked in.
Machine learning and Python in healthcare
Whether being used to correlate data to assist in diagnosis, or to sift through Big Data to find potential cures for rare diseases, machine learning is a crucial aspect of e-health.
Machine Learning is currently used in all sorts of e-health applications, including:
- Improving medical record storage
- Predicting illness
- Medical imaging and diagnostics
- Computer-assisted MRIs
- Rapid drug development
- Hospital operations
- AI-driven genomic studies
- Predictive prognoses
- And so on.
When it comes to efficiency in Machine Learning, Python is again the natural answer.
Just one example of Python’s suitability for machine-learning programming is in the area of Natural Language Processing (NLP) to gather, consolidate and understand massive amounts of textual data from patient records. Python’s open-source Natural Language Toolkit (NLTK) is a “plug-and-play” solution to implementing NLP in any machine-learning app without having to develop anything from scratch.
As for machine learning itself, Tensorflow is a natural choice and allows you to implement machine learning into your app, again, without having to write anything from scratch.
There’s also the Python Scikit-learn for data mining and data analysis.
Python is unbelievably flexible
Probably the greatest selling point for Python, and the reason it is the world’s fastest-growing programming language, is its sheer flexibility.
Python can integrate easily with other languages, and data-type conversion and error-checking can be automatically taken care of using SWIG (Simplified Wrapper and Interface Generator).
Extension modules can wrap C-code, thereby allowing it to integrate seamlessly with Python. In this way, C/C++ coders can work side-by-side with Python programmers on a project.
Python is also incredibly easy to learn, which means that you can bring in a team of programmers specialised in different languages, and quickly have them all coding in Python.
Bonus Tip: React Native — e-health mobile app development made simpler
No discussion of e-health app development would be complete without a bonus hat-tip to React Native.
Going back to the element of security in e-health, maintaining separate codebases only adds to potential security flaws that can exist due to the inherent complexities of maintaining multiple projects.
React Native solves that problem.
There are three primary benefits to building healthtech apps in React Native:
React Native uses native components that compile directly to the device’s own machine language.
There are multiple ways of speeding up development, such as interfacing with third-party plugins for using the device’s own functionality (e.g. accelerometer and gyroscope).
- Community Support.
React Native was the second-highest contributed-to project on GitHub in 2018. (Incidentally, Tensorflow was the third.) React Native has a tremendous amount of community backing behind it.
I'd like to thank my colleague and Thorgate tech lead Johan Viirok for contributing to this article and providing technical insights on why we use Python in E-health.
Thorgate specialises in healthtech, Python and React Native development projects. If you'd like to discuss how we can assist you in your own healthtech project, please feel free to write to me at firstname.lastname@example.org