Towards an integrated and interoperable platform for telehealth and telecare

Copyright @ 2012 International Journal of Integrated Care (IJIC). This work is licensed under a (http://creativecommons.org/licenses/by/3.0) Creative Commons Attribution 3.0 Unported License.We present experience of implementing and evaluating a platform purpose designed to integrate interoperable telehealth and telecare. We chose the IEEE 11073 standards for all devices and used ZigBee wireless to support many devices concurrently and exploit its mesh networking to extend range around the entire house. We designed the home gateway to be unobtrusive; in project Hydra we used the smart meter and in other projects (Reaction, inCasa) we have developed a purpose designed plugtop ZigBee to GPRS gateway. All use common protocols and are interoperable. Technically the projects have been a success, and we have already implemented a wide range of devices on the common platform (BP, weight, SpO2, glucose, PIR, medication monitor, bed/chair sensor). Immediate feedback from participants has confirmed our goal of simplicity and convenience of use (and thus encourage adherence); and it is interesting that in discussion they then focus on the data rather than the technology. Our current goal is to exploit the potential for combination of physiological and environmental data to determine if change of habits can be detected and how this correlates with change in health. We are using this functionality to manage the frail elderly within project inCasa and we propose to present preliminary findings

Monitoring habits and physiological data in the frail elderly

There is a need for new models of care enabled by technology to support long-term and independent living of the elderly. Integrating telecare and telehealth technologies can be used to provide innovative support in an unobtrusive way. inCASA is a European Commission funded project that uses an integrated platform to monitor both health and habits data in the frail elderly and demonstrate the concept of integrated health and social services through pilot trials. This paper presents the joint analysis of habits and clinical data from the UK pilot. The aim of the analysis is to determine the correlation between change in habits behaviour, change in physiological data and deterioration in the condition of the patient

Early experiences of the use of remote patient monitoring for the long term management of chronic disease

We describe our experiences of using remote patient monitoring to support the long term management and clinical intervention in patients with chronic disease, such as CHF. Within the project we developed new algorithms to determine from vital signs collected on a daily basis, those patients requiring clinical investigation for their condition. Our aim was for patients to achieve and sustain clinically recommended values for parameters. In our study, the telemonitoring prompted clinical intervention in 37 % of patients. Our approach proved particularly effective for the newly diagnosed, and for those with long term issues of management. 1

Whole population management of patients with diabetes

An increasingly aging population is presenting greater prevalence of people with diabetes, co-morbidities and the complications. Moreover, poor management of diabetes increases risk of complications. There is need to monitor these patients more closely to ensure optimum management. However current management is based on simple clinic based blood pressure and HbA1c readings, which prove insensitive to detect problems of lifestyle and habits. We therefore developed a platform that could be deployed to all diabetes patients to take daily blood pressure and blood glucose measurements that were sent automatically to the clinician. Data was reviewed after a two week monitoring period. Those that were deemed well controlled were asked to return the devices, which were cycled to the next patient. Others were asked to make an appointment with the clinician for review. 37% of patients required intervention. When stratified for risk using parameters from the EPR we found the greatest change in HbA1c in the low risk group, with the high risk group having little change. The greatest problem was denial in the recently diagnosed and lapse in others, resulting in poor adherence to medication and lifestyle

Dynamic Threshold Analysis of Daily Oxygen Saturation for Improved Management of COPD Patients

© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.This study presents a novel dynamic threshold algorithm that is applied to daily self-measured SpO2 data for management of COPD patients in remote patient monitoring to improve accuracy of detection of exacerbation. Conventional approaches based on a fixed threshold applied to a single SpO2 reading to detect deterioration in patient condition are known to have poor accuracy and result in high false alarm rates. This study develops and evaluates use of a dynamic threshold algorithm to reduce false alarm rates. Daily data from four COPD patients with a record of clinical interventions during the period were selected for analysis. We model the SpO2 time series data as a combination of a trend and a stochastic component (residual). We estimate the long-term trend using a locally weighed least squares (low-pass) filter over a long-term processing window. Results show that the time evolution of the long-term trend indicated exacerbation with improved accuracy compared to a fixed threshold in our study population. Deterioration in the condition of a patient also resulted in an increase in the standard deviation of the residual (σres), from 2% or less when the patient is in a healthy condition to 4% or more when condition deteriorates. Statistical analysis of the residuals showed they had a normal distribution when the condition of the patient was stable but had a long tail on the lower side during deterioration

Evaluation of the National Health Services (NHS) Direct Pilot Telehealth programme: cost-effectiveness analysis

Objective to evaluate the cost-effectiveness of a pilot telehealth programme applied to a wide population of patients with chronic obstructive pulmonary disease (COPD). Design: Vital signs data was transmitted from the home of the patient on a daily basis using a patient monitoring system for review by community nurse to assist decisions on management. Setting: Community services for patients diagnosed with COPD Participants: Two Primary Care Trusts (PCTs) enrolled 321 patients diagnosed with COPD into the telehealth programme. 227 patients having a complete baseline record of at least 88 days of continuous remote monitoring and meeting all inclusion criteria were included in the statistical analysis. Intervention: Remote monitoring Methods: Resource and cost data associated with patient events (in-patient hospitalisation, accident and emergency (A&E) and home visits) 12 months before, immediately before and during monitoring, equipment, start-up and administration were collected and compared to determine cost-effectiveness of the programme. Main outcome measures: Cost-effectiveness of programme, impact on resource usage, and patterns of change in resource usage. Results: Cost-effectiveness was determined for the two PCTs and the two periods before monitoring to provide four separate estimates. Cost-effectiveness had high variance both between the PCTs and between the comparison periods ranging from a saving of £140800 ($176,000) to an increase of £9600 ($12,000). The average saving was £1023 ($1280) per patient per year. The largest impact was on length of stay with a fall in the average length of in-patient care in PCT1 from 11.5 days in the period 12 months before monitoring to 6.5 days during monitoring, and similarly in PCT2 from 7.5 days to 5.2 days. Conclusion: There was a wide discrepancy in the results from the two PCTs. This places doubt on outcomes and may indicate also why the literature on cost-effectiveness remains inconclusive. The wide variance on savings and the uncertainty of monitoring cost does not allow a definitive conclusion on the cost effectiveness as an outcome of this study. It might well be that the average saving was £1023 ($1280) per patient per year but the variance is too great to allow this to be statistically significant. Each locality based clinical service provides a service to achieve the same clinical goal, but they do so in significantly different ways. The introduction of remote monitoring has a profound effect on team learning and clinical practice and thus distorts the cost effectiveness evaluation of the use of the technology. Cost-effectiveness studies will continue to struggle to provide a definitive answer because outcome measurements are too dependent on factors other than the technology

Designing effective visualizations of habits data to aid clinical decision making

This article has been made available through the Brunel Open Access Publishing Fund.Background: Changes in daily habits can provide important information regarding the overall health status of an individual. This research aimed to determine how meaningful information may be extracted from limited sensor data and transformed to provide clear visualization for the clinicians who must use and interact with the data and make judgments on the condition of patients. We ascertained that a number of insightful features related to habits and physical condition could be determined from usage and motion sensor data. Methods: Our approach to the design of the visualization follows User Centered Design, specifically, defining requirements, designing corresponding visualizations and finally evaluating results. This cycle was iterated three times. Results: The User Centered Design method was successfully employed to converge to a design that met the main objective of this study. The resulting visualizations of relevant features that were extracted from the sensor data were considered highly effective and intuitive to the clinicians and were considered suitable for monitoring the behavior patterns of patients. Conclusions: We observed important differences in the approach and attitude of the researchers and clinicians. Whereas the researchers would prefer to have as many features and information as possible in each visualization, the clinicians would prefer clarity and simplicity, often each visualization having only a single feature, with several visualizations per page. In addition, concepts considered intuitive to the researchers were not always to the clinicians