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Remote patient monitoring for cardiac rehabilitation

Chanl Health is a cutting-edge digital health platform striving to transform the way rehabilitation is delivered to patients. The platform provides a comprehensive remote patient monitoring solution dedicated to patients with heart diseases.
Virtual care
Remote patient monitoring


Chanl Health Inc.


Healthcare, Virtual care




Project Based


Mobile App, Web App

Team Size

2 Developers, QA, UI/UX, PM


6 months


6 years (ongoing)
Project description

Chanl Health provides a comprehensive suite of tools and services that help patients manage their health more effectively, including telemedicine consultations, remote monitoring, and personalized health coaching. The platform also offers a range of wellness services to help patients improve their overall health and wellbeing.

The platform is designed to be easy to use and accessible to patients of all ages and backgrounds. It is built on a secure, HIPAA-compliant infrastructure, ensuring that patient data is protected at all times. The solution is implemented in over 50 hospitals across the USA.

Business objective: integrating a remote rehabilitation program with a network of clinics, using a comprehensive software platform.

Project results


Number of patients that participate in rehab


Engagement rate at 8 weeks


Improved 10% in exercise capacity


Gained control of body pressure (<130/80)
About the problem

The traditional cardiac rehabilitation, conducted at the time of the project's start back in 2017, involved all appointments being conducted physically in clinics. Patients had to either stay in hospitals or commute to them for rehabilitation sessions.

Consequently, the primary goal of the product's MVP phase was to conduct tests of the program on patients to demonstrate the effectiveness of remote rehabilitation compared to traditional solutions.

From the patients' perspective, the opportunity to undergo such rehabilitation remotely: 

  • improves quality of life,
  • eliminates difficulties associated with transportation to medical centers,
  • reduces out-of-pocket cost,
  • lowers risk of complications, hospitalization, and death
Project scope
Step 1
Document Digitalization

The first step involved converting scanned documents into digital formats using OCR technology. This phase was crucial due to the diverse nature and quality of the scanned documents. Advanced OCR solutions were employed, capable of handling various text formats, handwriting, and even low-quality scans, ensuring high accuracy in digitization.

Step 2
Document Categorization

Once digitized, the documents were categorized into predefined classes such as medical reports, lab tests, and billing documents. This categorization was facilitated by a machine learning model trained on a large dataset of annotated healthcare documents. The model was fine-tuned to recognize and categorize documents accurately, even when the formats and templates varied significantly.

Step 3
Key Facts Retrieval

The extraction of key facts from the categorized documents was the next critical step. Using natural language processing (NLP) and machine learning algorithms, the system identified and extracted pertinent information such as patient names, birthdates, addresses, ICD codes, and details of medical procedures. The AI model was trained to understand the context and semantics of the healthcare domain, ensuring a high level of precision in fact retrieval.

Step 4
Medical Summary and Report Generation

The final step involved synthesizing the extracted information into coherent medical summaries and reports. Generative AI models, trained on a vast corpus of medical texts, were employed to generate summaries that were both accurate and easily comprehensible. These summaries provided a consolidated view of the patient's medical history and current claims, significantly aiding in the decision-making process.

Key features
HIPAA Compliance
Integration with Fitbit
Chat with doctor
Parameters tracking
Analitycal dashboard
Rehab programms
Knowledge Base
live chat
with doctor
Project timeline
6 months
Development & Release

Our client came with a concept and the initial version of the app. We developed the product from the backend perspective and practically rewrote the entire frontend. By creating the MVP, we were able to release the application for initial testing on patients.

2 months
Product Discovery
Scaling Stage

With this phase, we transitioned into product discovery with a specific focus on optimizing the application for the patient's experience. This involved conducting a comprehensive redesign and delving deeper into the areas of user registration and daily task management.

3 years
Software Development
Mobile & Web

This stage involves the creation of two distinct management units: a mobile patient app and a web hospital unit dashboard. Our aim was to seamlessly integrate all virtual management frameworks, processes, and training into a single comprehensive software platform.

3 years (ongoing)
Support & Maintenance
+ Further Scaling

This ongoing stage involves adapting to legal, non-legal, and regulatory requirements while expanding the application to accommodate additional medical units. The application is also being functionally personalized to meet the specific needs of hospitals.

Project tech stack


Mobile Development


Frontend Development




Backend Development


Backend Development

Mongo DB

Project tool stack

Jira Cloud

Project Management

Confluence Cloud

Project Documentation

Atlassian Cloud

Project Management


Project Communication


Project Documentation


UI/UX Design


Video Calls Integration

Google Maps



E-mails Integration
Technical description

Chanl Health is a HIPAA-compliant platform hosted on AWS with a separate mobile application for patients developed using hybrid technology with Ionic, as well as a dashboard for employees created using Angular. We also utilize Twilio for chatting with patients via SMS within the web interface.