Python in Healthcare

Python is featured among the most popular programming languages in the world. However, is Python programming a good idea for healthcare software development?

Contents

Python overtook other backend programming languages, according to the Stack Overflow Developer Survey

Python overtook PHP

Healthtech startups that use Python

Aledade

Aledade is not a traditional startup, but it still feels like one when it comes to innovation and its heavy use of technology. Python plays a big role in what they do. So, they fit right in on a list of companies using Python in healthcare.

Aledade is a healthcare company that partners with independent primary care physicians to create and manage Accountable Care Organizations (ACOs). Their platform offers data analytics, guided workflows, healthcare policy expertise, and payer relationships to support practices through value-based care models.

Aledade has raised a total funding of $780M over 11 rounds from 17 investors.

Senior software engineers at Aledade use Python to maintain, improve, and expand their web applications and data pipelines. They are responsible for API design and development, building backend services, developing serverless functions, automating cloud workflows, and managing infrastructure. They also work with server-side web technologies, including Python (along with Java, Scala, C#, C++, and Go), and web frameworks like Django, FastAPI, and Flask, in combination with SQL and NoSQL databases (e.g., Postgres, Databricks, Snowflake). Their work ensures scalable systems, optimized query performance, and cleanly abstracted interfaces to support web applications, business analytics, and artificial intelligence data consumers.

They integrate and process cloud-based data pipelines, design, build, and optimize ETL processes, work with data storage technologies, manage data ingestion systems, and optimize large-scale data processing performance.

Python is also used to build and improve the AI/ML infrastructure for developing, training, and deploying models like LLMs and other generative AI. After analyzing their software product usage, they make decisions on where generative AI can be better implemented and create APIs to integrate these models into healthcare systems.

Their staff AI researchers rely on Python to work with large healthcare datasets, including Electronic Health Records (EHRs), clinical data, and other medical data collected from millions of patients. They address data quality issues like incomplete or mislabeled data and use Python's statistical packages alongside R and SAS for data analysis.

They also design feature engineering pipelines (data processing, feature extraction, and transformation to optimize model performance), fine-tune generative models to specific healthcare domain tasks, build custom machine learning systems, and design and implement deep learning architectures with major frameworks (e.g., PyTorch, TensorFlow, Keras). All these efforts lead to the development of working prototypes and the delivery of Proof of Concept (POC) solutions for healthcare.

Their engineering teams that maintain innovative patient outreach platforms use Python for developing server-side components and building APIs for SMS and calling communication channels. They rely on Python to integrate with SMS gateways and calling APIs, handle high-volume communication traffic, develop caching mechanisms, optimize database queries, and manage asynchronous communication and load balancing for SMS and calling systems.

Their staff security engineers (application security architects) leverage Python to design and deploy security controls that protect networks, systems, and applications. Python is often used alongside Terraform and CloudFormation for automated security testing and validation, as well as for identifying, evaluating, and triaging vulnerabilities through Static and Dynamic Application Security Testing (SAST/DAST) tools. In cloud-native environments (AWS, Azure, GCP), Python supports secure application deployment, and workflow automation. They also use Python—alongside other languages—to secure AI/LLM and machine learning architectures. Python helps address OWASP Top 10 vulnerabilities and implement security measures in web-based SaaS applications, such as API security and WAF configurations.

Their senior security engineers develop and deploy Python-based automation scripts to perform advanced log analysis, detect threats, security incidents across cloud environments, and anomalous behaviors, and support incident response workflows. They also use Python to automate vulnerability scanning processes, perform data security checks, and facilitate proactive threat hunting by correlating data from various sources (network, endpoint, application) to identify potential security threats that may bypass traditional detection systems.

Their network security engineers may use Python with SIEM systems like Sumo Logic to automate log parsing, alert generation, and network incident handling, as well as to automate packet analysis workflows and tasks for forensic investigations.

Python contributes to securing PHI/PII by automating encryption key management, configuring access control policies, and generating compliance reports. It's also commonly used to create interactive, real-time dashboards for visualizing security metrics.

Health Catalyst

Health Catalyst is a healthcare performance improvement company focused on driving clinical, operational, and financial improvements for healthcare organizations in the United States.

Health Catalyst has raised a total funding of $392M over 10 rounds.

Their Migration (Data) Engineers in the Migration Team (Analytics Services) use Python to modernize legacy healthcare data warehousing platforms. These platforms are designed to collect and manage raw data from Electronic Medical Records (e.g., Epic, Cerner, NextGen), Financial Systems (e.g., EPSi, Peoplesoft, Lawson), Patient Satisfaction Systems (e.g., Press Ganey, NRC Picker), HR Systems (e.g., Lawson, Peoplesoft, Ultipro), Administrative Systems (e.g., API Time Tracking), and Claims Systems (e.g., Medicare, Private Payers). The data is standardized using common identifiers (e.g., patients, labs, encounters, diagnoses, medications, and more) and fed into Subject Area Data Marts, which are designed for specific analytical purposes.

Their Data Engineers use Python for migrating client data marts and reports, legacy data models, identifying and resolving issues during migration, enhancing automated tooling, reducing migration timelines, leading data validation tests to ensure consistent and accurate results, and writing and optimizing Databricks queries.

Their Software Engineers on the Technology (Platform) team use Python, along with C#, JavaScript, and SQL to develop cloud-based RESTful API analytics and ML platform solutions on Azure services. They are responsible for data governance of cloud storage and compute, auditing data access (including tenant and identity management), and event-based downstream process executions.

Innovaccer

Innovaccer is a healthcare technology company that uses advanced analytics, artificial intelligence (AI), and machine learning (ML) to help payers, providers, and life sciences companies make better decisions. Innovaccer's healthcare data cloud platform integrates data from multiple sources, enabling solutions for population health management, value-based care, and clinical analytics.

Innovaccer has raised a total funding of $379M over 7 rounds from 31 investors.

Python is used by backend software development engineers at Innovaccer to develop various AI products. It's widely used with Django and FastAPI for backend development, and MongoDB, PostgreSQL, Redis, Snowflake, AWS, and Azure are commonly integrated into their Python projects for data storage and cloud computing. Python also allows for developing efficient and optimized APIs and microservices to support various frontend applications and external integrations. It is also used to implement data models and database schemas.

Python is great for things like prototyping quickly, integrating backends, and automating testing and QA. It’s also used for working with BigData, distributed systems, and async programming, which makes it perfect for handling real-time data pipelines with tools like Kafka or RabbitMQ.

Innovaccer uses Python to build analytical models, including descriptive, predictive, and prescriptive models, and to develop automation scripts and tools for data preprocessing, model training, and evaluation workflows. Python works seamlessly with SQL/NoSQL databases and ETL processes.

Python is a preferred language for designing ML pipelines, managing model deployments, and developing monitoring solutions. Innovaccer uses Hugging Face for integrating model hubs and deploying or fine-tuning ML models, and AWS Bedrock to facilitate access to foundation models through its no-code platform and Python SDKs.

Python is used by Innovaccer's database reliability engineers for the end-to-end automation of database management of large-scale distributed systems in a multi-cloud environment (cloud managed databases and self hosted databases), including administration, observability, troubleshooting, configuration changes, upgrades, and migrations to ensuring the reliability, scalability, and performance of their database systems. It is also used for automating backup, replication, and failover tasks, scripting access controls, enforcing policies, and automating HIPAA data compliance checks and audits. Additionally, Python is employed for analyzing usage patterns to assist with capacity planning for large-scale database clusters. It is compatible with monitoring tools (New Relic, DataDog, PagerDuty) for custom monitoring and alerts. Python may enhance SQL operations and serve as a connector for syncing data between systems such as PostgreSQL and ElasticSearch or Snowflake and ElasticSearch.

Their software development engineers in test use Python to ensure the quality and performance of their software products by designing, developing, and maintaining automated test frameworks for UI, API, and database applications. They also develop and execute performance and load-testing scripts using tools and frameworks with strong Python support (Selenium, and Playwright).

However, Python is not the only programming language Innovaccer uses. Their software development engineers develop and deliver some backend solutions (backend microservices, data, cloud, observability, etc.) using C# and .NET Core, where Python serves as a secondary language of choice.

For the architecture, design, and development of the frontend stack (including micro-frontends), they use TypeScript, modern frontend frameworks like React (with Redux), and the server-side JavaScript framework Node.js.

For their AI-powered medical scribe, software development engineers use Flutter and Dart to build the next generation of mobile applications for Android and iOS.

Qventus

Qventus is a healthcare operations platform designed to help hospitals and healthcare organizations automate and streamline their processes (patient flow, resource management, and clinical decision-making) by integrating data from electronic health records (EHR), staffing systems, and patient monitoring systems and using AI and machine learning.

Qventus has raised a total of $98.3 million over eight funding rounds.

Qventus engineers, including Senior Data Scientists, LLM Engineers, Data Engineers, and QA Automation Engineers, rely on Python as a foundational tool to build scalable innovative solutions in healthcare: AI-driven and data-intensive applications.

Engineers use Python for cleaning and preprocessing datasets, uncovering patterns, identifying anomalies, and extracting insights from complex healthcare data. Python’s libraries (e.g., Pandas, PySpark) enable analysis and preparation of data for machine learning and analytics workflows.

Python is used to design, build, and optimize scalable, reliable data pipelines that support AI and ML solutions. Engineers use Python to write scripts for data transformation pipelines. Python supports the discovery, evaluation, and integration of new datasets while enabling data modeling.

Python is a key tool for developing and fine-tuning machine learning models, including Generative AI (GANs, VAEs, Transformers) and LLM-based solutions such as Retrieval-Augmented Generation (RAG). Python frameworks like TensorFlow and PyTorch are employed to create intelligent agent-based systems capable of processing multimodal inputs (text, image, audio) and outputs.

Engineers use Python for rapid AI product development, supporting workflows from concept to deployment.

Python’s ecosystem supports integration with cloud platforms such as AWS (Glue, Lambda, S3, RDS), Databricks, and DBT to deliver modular, secure, and scalable data systems. Python supports workflows in high-regulation ecosystems like HIPAA, requiring maintaining data privacy and security standards.

Python integrates with tools like Looker and Tableau to enable actionable insights and facilitate cross-functional collaboration with clinical and product teams.

QA engineers use Python to build and maintain automated testing frameworks (e.g., Playwright, Cypress, Selenium), script test cases, and automate regression and API testing, ensuring product reliability.

AiCure

AiCure is a New York-based AI and advanced data analytics company that uses AI to understand how patients respond to treatment and provides real-time monitoring of patient dosing and behavior. AiCure has raised a total funding of $51.8M over 5 rounds from 15 investors.

Python is central to many of AiCure’s operations. The team uses Python for exploratory data analysis, statistical testing, and creating predictive models of patient behavior with machine learning techniques. Libraries such as Scikit-learn are commonly used, while frameworks like PyTorch or TensorFlow are applied for deep learning tasks when necessary. Python is used for data quality assessment and managing ETL processes with SQL for data extraction and transformation.

Python also supports data visualization, helping the team analyze complex biomarker data and assess model performance. Additionally, Python is critical for deploying machine learning models in cloud environments like AWS or GCP. Python is alo used to develop APIs.

Fathom Health

Fathom Health is a medical coding automation AI platform based on deep learning and Natural Language Processing (NLP), trained on over 400 million coded encounters (terabytes of clinician notes) contained within the EHRs of the world’s largest health systems. Its deep learning engine automates the translation of patient records into billing codes used for healthcare provider reimbursement, helping to prevent errors and denied claims.

Fathom has raised a total funding of $21.8M over 3 rounds from 19 investors.

Their senior software engineers (infrastructure and backend/data), distributed across Toronto, San Francisco, and New York, ensure the stability, security, and performance of Fathom’s platform by developing internal tools using Python to make machine learning and software engineers more efficient. These tools support tasks such as infrastructure deployment, continuous integration, and testing. They are also responsible for building, controlling, and monitoring secure cloud-based platforms, leveraging Python’s compatibility with Google Cloud services.

They focus on developing data infrastructure using Python to ingest, sanitize, and normalize a wide range of medical data, including electronic health records, journals, established medical ontologies, crowd-sourced labeling, and other human inputs. They build performant and expressive interfaces to the data and create infrastructure to support scaling data ingestion and large-scale cloud-based machine learning workflows. Their responsibilities include developing backend systems, data pipelines, and integrations, where Python plays a central role, particularly in production settings.

Python Safety: is Python safe?

Data security (patient privacy) has become especially critical to the healthcare industry with the adoption of electronic health records (EHR). Is Python a safe language for building healthcare apps? Or maybe there are more secure programming languages?

This is the responsibility of the coder to know what can be done and what to avoid. An application becomes secure when the developer adopts the best practice and best security policies and techniques.

The more a programming language is popular, the more it is safe because the more security vulnerabilities are widely known and the more of them could be fixed by professional Python developers.

There were a lot of talks that Python 2 was not so secure, so even OWASP created a project pythonsecurity.org to highlight this issue. However, with the arrival of version 3 of Python, there is no need for this project anymore, and the website no longer gets updated or opens. Now, the Python team (Python Software Foundation) itself checks Python's security and lists potential vulnerabilities. Anyone who works with the Python code can apply appropriate solutions in advance. They can also report issues to the Python Software Foundation.

According to Reddit users, some corporate IT departments ban older Python versions. However, blocking older versions alone is not a complete solution. Not all 3rd-party modules support the latest Python version, but they may be critical for other systems to function correctly. Python is a free programming language, and some modules are developed by enthusiastic contributors in their spare time. So they simply do not have enough resources to update them. Therefore, custom modifications of outdated Python modules may be required sometimes.

Django, a Python-based framework, was released in July 2005 for the purpose of creating web applications, including medical apps. A good Django dev is a good Python dev. Django simplifies web application creation by reducing the amount of code that your developer needs to write. Instead of creating modules from scratch, Django offers a solid set of built-in blocks (such as packages for the admin interface, user authentication, chat functionality, etc.). Django also provides the protection against the three main types of web app attacks (SQL injection, XSS, and CSRF). Using Django security best practices, you can be sure of the safety of your healthcare app.

Flask is a Python-based microframework primarily used for building API. Though it can be extended to a full-stack framework with the help of existing extensions. While Flask has fewer users than Django and takes more time for the configuration, it is often preferred for building prototypes because you can get going much more quickly with it. Which framework is easier to secure? Django. 

Python / Django and HIPAA

As a rule, development of a medical software application is associated with handling medical data that needs to be protected under the HIPAA compliance requirements. Are Python and Django or other Python-based frameworks secure enough to be HIPAA compliant? In fact, the HIPAA is a checklist that does not depend on a programming language or a framework.

  • "You want to provide your clients the assurance that the information being presented is meeting the HIPAA requirements. This is not entirely a Django/Python implementation, but falls in line with the Database back-end support".
  • "HIPAA factors like "how you store your data" and "how often sysadmins review logs" and "what the access control policies at the data center are" will probably play a bigger role than which programming framework you use, so you need to make sure to have good answers to those questions".
  • "There are about 100 individual checkboxes that you'll need to hit to be HIPAA compliant. Approximately 90% of the requirements can be satisfied by having good engineering/risk management practices and documenting them. For example, one requirement is that you need to have a formal policy on use of patient information" (Patrick McKenzie).
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Written by
Deputy Business Development Director at Belitsoft
I am a customer’s advocate and a manager of several key accounts.
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Ray
2024-11-07 at 13:27
Thank you for raising the topic of Python's use in healthcare programming! Data security and HIPAA compliance are extremely critical for the medical industry. Luckily, frameworks like Django help meet these standards. Due to its security features, Python is an excellent choice for healthcare application development.
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