AI-assisted patient intake for a healthtech company

This AI healthcare solution was built for a health tech company based in Arizona. The company uses an EMR agnostic solution to manage patient medical history and basic demographic information. First, they wanted a system that could manage all types of patient medical history (social, personal, surgical, and family), current symptoms, and chief complaints. Second, they wanted a workflow that streamlines booking appointments, consultation, follow ups and checking out. This helps both 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. As a result, the system now meets their specific needs and gives their patients a high level of service.

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. This system would map their own information to different EMRs. Then it would map the data back when the patient visited the provider for a second time.

The existing EMR system had a poor user interface and required additional installations. This hindered the efficient use of artificial intelligence (AI) capabilities. Also, it was 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. Therefore, the client needed a comprehensive solution. It had to integrate with various EMRs and let doctors focus on diagnosing diseases and providing treatment.

The project began with comprehensive research and analysis. Our team identified the pain points and devised effective solutions to address them. Then came the development phase, where we created an end-to-end solution that addressed these challenges head-on. In the background, the client’s system connects with various EMRs through secure web solutions, so nobody needs additional installations or downloads. Additionally, artificial intelligence improves healthcare professional-patient interactions, so doctors can spend more quality time with patients. See our AI product engineering services.

1. First, we designed a workflow in Lucidchart to manage the process from booking an appointment to checking out. Then we implemented it with React JS and Python. We hosted the system on AWS Cloud for easy access.

2. Next, we developed two different applications: one for doctors and one for patients. Both use React JS, and the REST APIs are built in Django. We also developed an interface that maps information from the client’s system to the EMRs and back. For this, we used Celery Task Runner with RabbitMQ as the communication broker. Postgres stores the mapping of the client system’s symptom IDs to the EMR symptom IDs.

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

4. Moreover, the system uses AI functionality, so 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 connect the different applications, we used REST APIs, data brokers, and web sockets. Finally, we used Postgres and S3 buckets for data storage.

7. For monitoring, Datadog tracks the performance and logs of the applications, and Sentry.io to troubleshoot issues in production.

Our solution brought clear benefits to the 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. For example, 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. Because of the integration with various EMRs, healthcare providers can access and manage patient information effortlessly, leading to more personalized care and improved patient outcomes.