Savonia Article: Revolutionalising Remote Healthcare: Unleashing the Power of Open-Source Innovation

Remote areas, particularly low-to-middle-income countries like India, grapple with significant healthcare delivery challenges. The World Health Organisation (WHO) reports a shortfall of 4.3 million healthcare workers globally, impacting these regions the most. The aggravating factors include a shortage of staff and medical professionals and the unavailability of expert doctors in rural areas (Kumar 2023). To address these inadequacies, Padvi’s (2024) thesis aims to offer potential and viable innovative healthcare solutions by developing two prototype models using freely available open-source technology. These prototypes, namely electrocardiography (ECG) interpretation and remote monitoring applications, are designed to assist medical professionals in providing better care with limited resources. This approach exemplifies how technology can bridge gaps in healthcare delivery.


This research, rooted in experience within India’s public healthcare sector, focuses on developing cost-effective healthcare solutions for resource-constrained remote areas. The research methodology relies on the concepts of action-based and applied research. Action-based research involves identifying remote healthcare and ageing challenges as problems, analysing them, and developing solutions. The solution was to create or develop applications that could assist healthcare staff in interpreting ECGs, develop remote monitoring applications for older adults and physically challenged individuals who want to live independently and enable healthcare professionals to monitor them.

The project involved exploring the recent technological advancements in ECG interpretation and understanding regulations pertaining to software as medical devices (SaMD) and guidelines set out by the International Medical Device Regulators Forum (IMDRF). Medical devices are instruments, apparatus, machines or software designed for diagnosing, treating, preventing, or mitigating medical conditions used for medical purposes such as monitoring health parameters, administering treatments, or aiding in diagnosing diseases. SaMD is software specifically created for medical applications, functioning autonomously without integration into any medical hardware apparatus. (IMDRF.) Furthermore, developing a prototype from scratch entailed obtaining a deeper understanding of the heart’s anatomy and electrophysiology to develop a design suitable for a knowledge-based system. For the practical implementation of these prototypes, which formed the applied research part of this thesis, investigating and understanding knowledge-based systems, computer vision and OpenCV, open-source software and hardware, and Unity Engine was necessary. Collaborating with an IT consultant was pivotal for coding the prototypes, ensuring they functioned as intended through dynamic feedback and iterative refinement. The ECG interpretation prototype is successfully developed using the Unity Engine and guides users through the steps required to interpret an ECG. The preliminary tests with the remote monitoring prototype based on Raspberry Pi technology showed successful facial recognition. This research advocates using open-source technologies to develop innovative healthcare solutions, as demonstrated in these prototypes.


The ECG interpretation application uses a knowledge-based system (KBS) framework, utilising computer programs to solve problems or make decisions by mimicking human intelligence. This system stores expert knowledge in a knowledge base and uses reasoning to draw inferences and reach conclusions (Russel & Norvig 2003). The system integrates expert knowledge from extensive ECG interpretation literature in this ECG interpretation prototype, forming a comprehensive knowledge base for reasoning and drawing inferences. Due to resource constraints, direct access to cardiologists for expert knowledge was not feasible. The inference engine is the system’s reasoning component that processes information from the knowledge base and applies it to specific situations or problems. The ECG prototype’s inference engine uses propositional logic based on true or false statements and standard connectives. The developed prototype uses the JSON (JavaScript Object Notation) format for encoding rules due to its ease of use, interoperability and compatibility with human and machine processing. The user interaction with the knowledge base is facilitated through a user interface employing finite state machines (FSM). All design aspects, data structures, and components were implemented using the Unity Engine, ensuring a robust and user-friendly application.

The app guides the user from its initial disclaimer notification, the basics of ECG, taking a picture of an ECG to be interpreted, to guiding through the essential basic ECG components necessary to interpret the ECG and ending with results in the form of a summarised report of the conclusions. This application is in a very early stage of development. It will require significant additions to its knowledge base and rules to increase its domain coverage. It has yet to be tested or evaluated by experts from the medical fraternity or regulatory authorities. Since it is a potential prototype for diagnosing medical conditions, such as cardiovascular illness, it falls under software as a medical device (SaMD). Compliance with IMDFR regulations is necessary for market release. Therefore, the users are advised to heed the disclaimer, which clarifies that the app does not provide a medical diagnosis at this stage. The difficulties in creating this prototype involved technical restrictions, resource constraints, and the necessity to concentrate on a specific portion of the ECG diagnosis domain. While features like automatic curve detection are desirable for enhanced functionality, they require significant IT expertise and funding. The current prototype lays the groundwork for future enhancements, including more sophisticated algorithms to broaden diagnostic capabilities in cardiology.


The second aspect of the thesis investigated remote monitoring applications, utilising open-source hardware like the Raspberry Pi and Raspberry Pi camera and the open-source software OpenCV. The idea was inspired by a paper by Moreira et al. (2020). The experiment successfully identified the trained test subject, aligning with the definition of the SaMD since its purpose is remote monitoring.

The challenges encountered included variable accuracy due to lighting and facial expressions, highlighting the need for algorithm refinement. The Internet quality also emerged as crucial for real-time monitoring, alongside privacy considerations for patient data. Future improvements include enhancing facial recognition accuracy, integrating additional sensors for more comprehensive health monitoring, and applying machine learning for predictive analysis of patient health. Addressing system performance in low-bandwidth conditions is also a key area for development.


This research thesis aims to demonstrate how we can leverage open-source technologies to develop healthcare solutions that tackle challenges faced in remote areas. The ECG interpretation and remote monitoring application prototypes developed during the thesis are practical demonstrations of the use of these technologies. Open-source technologies are freely available to use, modify, and distribute. Such technology is crucial in healthcare settings where resources are limited, allowing for broader implementation of essential healthcare solutions. One of the most significant advantages of open-source technologies is their cost-effectiveness. They reduce the need for expensive proprietary software, making it feasible for healthcare providers in low-income areas to adopt advanced technological solutions. Open-source allows for customisation to meet specific local needs. This advantage is crucial in healthcare, where tailored solutions are necessary for unique medical and environmental conditions. The open-source community provides robust support and continuous improvement of technologies. (Wenzel 2023.) This collaborative environment fosters innovation and rapid problem-solving, vital in healthcare technology development. Open-source solutions are often more sustainable in the long term, as they are not dependent on a single vendor or proprietary restrictions. They are a dependable choice for healthcare facilities situated in remote regions.


To summarise, developing and implementing the ECG interpretation app and remote monitoring system as this thesis project (Padvi 2024) illustrates the potential of open-source technologies in addressing healthcare challenges in remote areas, showcasing their efficiency and cost-effectiveness. These innovations, born from a collaboration between the medical and IT sectors, highlight the feasibility of developing impactful solutions. They exemplify a significant step towards more equitable healthcare by bridging global health disparities.


Pritee Padvi, Master of Global Public Health, Savonia University of Applied Sciences

Maria Luojus, Principal lecturer, Savonia University of Applied Sciences


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