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A Comprehensive Healthcare Solution Built For a Healthtech Company Using AI, Python and React JS

About Project

A health tech based in Arizona is using an EMR agnostic solution to manage patient medical history and basic demographic information. They wanted to create a system that could manage all types of patient medical history (social, personal, surgical, and family), current symptoms, and chief complaints. They also wanted to create a workflow that would streamline the process of booking appointments, complete consultation, follow ups and checking out for the convenience of patients and medical professionals. To achieve this, they needed a long-term IT partner with expertise in Python who could use the EMR system’s features to create specialized workflows.


The Zehntech team has been assisting the company with the design, customization, and maintenance of their EMR system for over a year. We have helped them to create a system that meets their specific needs and provides a high level of service to their patients.

Webapp for Healthcare company1
Healthcare Soltuion
Webapp for Healthcare company


1 Year+

Release date


Working Hours

  2000+ Hours

Zehntech Career

Business Challenge

The client was already using an electronic medical record (EMR) system, but they were facing challenges in utilizing its features. They wanted to build an intermediate system that could map their system information to different EMRs and then map it back to their system information when the patient visited the provider for a second time.

The existing EMR system had a poor user interface and required additional installations, which hindered the efficient use of artificial intelligence (AI) capabilities. It was also very difficult for patients to fill out all of the required details without the help of a healthcare assistant.

Scheduling appointments, managing EMRs, and ensuring post-care follow-up were all complicated processes with many points of friction. There was a need for a comprehensive solution that could seamlessly integrate with various EMRs while allowing doctors to focus on diagnosing diseases and providing treatment.

Business Solution

We began the project with comprehensive research and analysis. We identified the pain points and devised effective solutions to address them. We then proceeded to the development phase. We created an end-to-end solution that addressed these challenges head-on. We integrated client’s system with various EMRs in the background using secure web solutions, eliminating the need for any additional installations or downloads. Additionally, we used artificial intelligence solutions to improve healthcare professional-patient interactions, allowing them to spend more quality time with patients.


1. We designed a workflow to manage the process from booking an appointment to checking out, using Lucidchart. We implemented this workflow using React JS and Python and hosted the system on AWS Cloud for easy access.


2. We developed two different applications: one for doctors and one for patients. Both applications use React JS, and the REST APIs are built in Django. We also developed an interface to map information from the client’s system to the EMRs and vice versa. This was done using Celery Task Runner with RabbitMQ as the communication broker. The mapping of the client system’s symptom IDs to the EMR symptom IDs was saved in the Postgres system.


3. In the Patient app, patients can fill out all of their information, such as basic demographic information, medical history, and current symptoms. They have to go through OTP authentication process to secure their personal data. The application also has HIPAA compliance that follows data security standards.


4. The system was built using AI functionality, where the review of symptoms varies based on the chief complaints.


5. In the Doctor app, doctors can view patient data and consult with them on the proper treatment. This may include meditation, exercise, medication prescriptions, imaging, lab tests, procedures, and things to do or not to do.


6. To communicate between different applications, we used REST APIs, data brokers, and web sockets. And we used Postgres and S3 buckets for data storage.


7. We used Datadog to monitor the performance and logs of the applications, and to troubleshoot issues in production.


Applied Technologies

Python Logo

Our Role in Client Success

The implementation of our solution yielded remarkable results for our client. The intuitive user interface and seamless integration with electronic medical records (EMRs) streamlined the entire patient-healthcare professional experience, reducing friction points and enhancing efficiency throughout the healthcare system. Scheduling appointments is now seamless, patient data is securely stored in existing EMRs, and post-care follow-up is more effective even during different visits of the same patients. The integration with various EMRs ensures that healthcare providers can access and manage patient information effortlessly, leading to more personalized care and improved patient outcomes.

Related Work

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