CASE STUDY Medical Devices

Medical Device Maker Connects Clinical Intel to Patient Care

This startup, which provides medical-grade sensors and data services for continuous health monitoring, needed Infostretch’s digital expertise to help build out the full promise of its data-as-a-service offering.

At inception, the company developed a Data-as-a-Service (DaaS) platform for remote patient care providing ongoing monitoring, predictive analytics, and algorithmic clinical insights. The offering combines wearable sensors, a mobile application and cloud service to collect and report real-time patient data such as skin temperature, heart rate, respiratory rate, fall detection, coughing, sneezing, vomiting, etc. for early detection of adverse trends.

  • Medical Device Company

    Founded in 2018

  • Medical grade monitoring and management tech for scalable remote care

    Medical grade monitoring
    and management for scalable remote care at home

  • Wearable Tech in Healthcare

    Single-use wearable devices with 30 & 90 day battery life

  • Data-as-a-Service (DaaS) platform for remote patient care

    Advanced data-as-a-service offering for early detection of adverse trends

Remote Monitoring Healthcare Tech Solution

As the company looked at getting its offering market-ready, it needed help on the development of the companion mobile application and the connectivity and interaction between the different solution elements. The company was familiar with related work Infostretch had done supporting other digital healthcare innovators such as Proteus and LifeScan.

The company started working with Infostretch in 2020 where they embarked on a phased approach to quickly develop an MVP that included a companion mobile application on Android and iOS which would communicate with the sensor device using BLE protocol and transmit the information to the cloud where it could be securely accessed by healthcare providers. The roadmap from there included SDK creation, UI enhancements to the mobile application, creation of a scalable cloud architecture, data ingestion framework and analytics around sensor performance.

the Results

Key Outcomes

Dramatically faster product cycle time
Dramatically faster
product cycle time

Through fail fast approach

Ability to monitor key device parameters
Ability to monitor
key device parameters

And take proactive steps for improved user satisfaction

Scalable, high-performance cloud architecture
Scalable, high-performance
cloud architecture

Through strong GCP cloud engineering and DevOps practices

Our methodology

how
we did it

Infostretch works with companies across the digital lifecycle.

Go Digital
Go Digital

Accelerating the delivery of new digital initiatives with confidence

Be digital
Be digital

Creating the infrastructure and foundation to scale digital initiatives

Evolve Digital
Evolve Digital

Leveraging data and analytics to continuously improve digital delivery processes

The challenge

To build remote monitoring for patient vital statistics

Personalized, predictive remote care is imperative to the future of healthcare. So, the promise of 24×7 remote monitoring of patient vital statistics was incredibly compelling to healthcare providers and others in the health ecosystem.

But it also presented critical technology challenges for the company to ensure efficacy and performance. Some of these included:

Developing a mobile application

Developing a mobile application

Building an engaging and intuitive companion mobile application to support both the patient and the care provider. The company did not have expertise in this area, but it was a critical aspect of the overall solution.

Transmitting real-time data

Transmitting real-time data

Doing this reliably on an ongoing basis between the sensors worn by the patients, the companion mobile application, and the cloud where the data was stored and accessed. This had to happen in the background so that it did not adversely impact the performance and battery usage of the sensors.

De-identification of patient data

De-identification of patient data

Handling the de-identification of patient data as it is transmitted and stored to ensure compliance with privacy regulations such as HIPAA and GDPR.

framework for collecting patient and device data points

Data framework

Creating a framework for collecting patient and device data points to be used by the company’s data science team.

remote monitoring sensor devices

Developing telemetry

For monitoring sensor devices to ensure high performance over long periods of time.

The Solution
Focusing on 5 core areas

As part of this engagement, Infostretch focused on five core areas:

Android and iOS mobile application development for patients and care providers
A Companion Mobile App

Infostretch started with a small mobile application team developing the app on Android and iOS for use by both patients and care providers to view the patient’s health information. The team leveraged a low-energy BLE protocol for communicating data between the sensor worn by the patient and the mobile application which then was transmitted to the cloud. The Infostretch team also integrated a health chat bot to monitor the health of patients. This enabled it to be used for the monitoring and contact tracing patients in Covid-19 quarantine scenarios.

system verification and validation, load testing and cloud performance testing
V&V and Production Support

With a focus on quality at the core, Infostretch started working on the development of an end-to-end QA strategy, system verification and validation, load testing and cloud performance testing. Infostretch used its Quality Automation Framework (QAF) for automating API and performance testing to identify threshold factors and scalability of the company’s backend systems. The team is also identifying opportunities for optimization in support of Live customers and setting up early monitoring alerts to flag any potential issues with devices in use.

Data Analytics, Data Science
Data Analytics, Data Science

Infostretch incorporated millions of patients’ data collected by the company to form the data foundation and expand the use of data science internally at the company. This involved developing a data ingestion framework which could work effectively on all unstructured data sources; creating a unified data model; de-identifying patient data; creating microservices API endpoints; and collecting performance data from all sensors and mobile devices including all alerts, crashes, hardware faults, watchdog resets, etc.

Cloud & DevOps
Cloud & DevOps

Infostretch aligned the right resources and skills to develop Python API’s which were used to serve data to front-end mobile apps, device analytics, and device telemetry front ends. The team also used GCP Cloud Services such as DataFlow, PubSub, and Cloud Functions. Infostretch also automated cloud monitoring using terraforms in the company’s staging and production environments. Bitbucket pipelines were used for infrastructure deployment.

Pharma Partner SDK
Pharma Partner SDK

Infostretch has also developed a partner SDK which enables the company’s Pharma partners to brand certain UI aspects of the solution and build branded applications around it for data presentation, alerts and more.