SHUBHCHINTAK: An efficient remote health monitoring approach for elderly people

With the proliferation of IoT technology, it is anticipated that healthcare services, particularly for the elderly persons, will become a major thrust area of research in the coming days. Aim of this work is to design a fit-band containing multiple sensors to provide remote healthcare services for the elderly persons. An application has been designed to capture health data from the fit-band, pre-process the data and then send them to cloud for further analysis. A wireless Bluetooth enabled connection is proposed to establish communications between sensors and the application for data transmission. In the proposed application, there are three different front-end interfaces for three different users: system administrator, patient and doctor. The data collected from the patient's fit-band are sent to a cloud data storage, where the data will be analyzed to detect anomaly (e.g., heart attack, sleep apnea, etc.). A Convolution Neural Network (CNN) model is proposed for anomaly detection. For the classification of anomaly, a Long Short Term Memory (LSTM) model is proposed. In the presence of anomaly, the system immediately connects a doctor through a phone call. A prototype system termed as Shubhchintak has been developed in Android/IOS environment and tested with a number of users. The fit-band provides data tracking with an overall accuracy of 99%; the system provides a response with 3000 requests in less than 100 ms. Also, Shubhchintak provides a real-time feedback with an accuracy of 97%. Shubhchintak is also tested by patients and doctors of a nearby hospital. Shubhchintak is shown to be a simple to use, cost effective, comfortable, and efficient system compared to the existing state of the art solutions.

Development of a cloud-based application for the Fracture Liaison Service model of care

The aims of this study are to develop a cloud-based application of the Fracture Liaison Service for practitioners to coordinate the care of osteoporotic patients after suffering primary fractures and provide a performance feedback portal for practitioners to determine quality of care. The application provides continuity of care, improved patient outcomes, and reduced medical costs.

INTRODUCTION

The purpose of this study is to describe the content development and functionality of a cloud-based application to broadly deploy the Fracture Liaison Service (FLS) to coordinate post-fracture care for osteoporotic patients.

METHODS

The Bone Health Collaborative developed the FLS application in 2013 to support practitioners' access to information and management of patients and provide a feedback portal for practitioners to track their performance in providing quality care. A five-step protocol (identify, inform, initiate, investigate, and iterate) organized osteoporotic post-fracture care-related tasks and timelines for the application. A range of descriptive data about the patient, their medical condition, therapies and care, and current providers can be collected. Seven quality of care measures from the National Quality Forum, The Joint Commission, and the Centers for Medicare and Medicaid Services can be tracked through the application.

RESULTS

There are five functional areas including home, tasks, measures, improvement, and data. The home, tasks, and data pages are used to enter patient information and coordinate care using the five-step protocol. Measures and improvement pages are used to enter quality measures and provide practitioners with continuous performance feedback. The application resides within a portal, running on a multitenant, private cloud-based Avedis enterprise registry platform. All data are encrypted in transit and users access the application using a password from any common web browser.

CONCLUSION

The application could spread the FLS model of care across the US health care system, provide continuity of care, effectively manage osteoporotic patients, improve outcomes, and reduce medical costs.