Jim Basilakis

A system for monitoring posture and physical activity using accelerometers

Advances in technology have made possible new and innovative methods of health care delivery. Home telecare, in which the patients health is monitored remotely at home, is one such method. This paper discusses the use of a triaxial accelerometer within a home telecare system for monitoring physiological and functional parameters for daily living. A novel system is described for objectively and continuously monitoring movement, suitable for patients with chronic diseases including congestive heart failure and chronic obstructive pulmonary disease. The key design criteria were ease of use by, and comfort for, the patient together with the provision of clinically relevant information. The patients posture, energy expenditure and movement are clinically important parameters that can be measured by accelerometry. A data processing scheme in which these parameters are extracted is described.

Design of a Decision-Support Architecture for Management of Remotely Monitored Patients

Telehealth is the provision of health services at a distance. Typically, this occurs in unsupervised or remote environments, such as a patients home. We describe one such telehealth system and the integration of extracted clinical measurement parameters with a decision-support system (DSS). An enterprise application-server framework, combined with a rules engine and statistical analysis tools, is used to analyze the acquired telehealth data, searching for trends and shifts in parameter values, as well as identifying individual measurements that exceed predetermined or adaptive thresholds. An overarching business process engine is used to manage the core DSS knowledge base and coordinate workflow outputs of the DSS. The primary role for such a DSS is to provide an effective means to reduce the data overload and to provide a means of health risk stratification to allow appropriate targeting of clinical resources to best manage the health of the patient. In this way, the system may ultimately influence changes in workflow by targeting scarce clinical resources to patients of most need. A single case study extracted from an initial pilot trial of the system, in patients with chronic obstructive pulmonary disease and chronic heart failure, will be reviewed to illustrate the potential benefit of integrating telehealth and decision support in the management of both acute and chronic disease.

ECG quality measures in telecare monitoring

We analyze the use of unsupervised ECG acquisition in the home environment. An algorithm for automatically marking ECG recordings for sections of obvious artifact is described. The algorithm was validated against a set of 150 records randomly chosen from a database of ECGs and manually annotated to identify sections of artifact. Using this algorithm 4751 single lead-I ECG recordings from 24 home-dwelling patients were examined. The ECGs were collected using a remote home monitoring system. The participant ages (N=24) ranged from 54–92 years and were suffering either chronic obstructive pulmonary disease and/or congestive heart failure. Percentages of amplifier saturation, high frequency artifact, low signal power and the maximum continuous section of useable ECG are quoted. 1344 records were found to contain no artifact, while 3506 records contained 10 seconds or more of uninterrupted ECG (including the 1344 with no artifact). The results show that in the majority of cases, the capture of ECG in an unsupervised home environment is achievable.

Electrocardiogram signal quality measures for unsupervised telehealth environments

The use of telehealth paradigms for the remote management of patients suffering from chronic conditions has become more commonplace with the advancement of Internet connectivity and enterprise software systems. To facilitate clinicians in managing large numbers of telehealth patients, and in digesting the vast array of data returned from the remote monitoring environment, decision support systems in various guises are often utilized. The success of decision support systems in interpreting patient conditions from physiological data is dependent largely on the quality of these recorded data. This paper outlines an algorithm to determine the quality of single-lead electrocardiogram (ECG) recordings obtained from telehealth patients. Three hundred short ECG recordings were manually annotated to identify movement artifact, QRS locations and signal quality (discrete quality levels) by a panel of three experts, who then reconciled the annotation as a group to resolve any discrepancies. After applying a published algorithm to remove gross movement artifact, the proposed method was then applied to estimate the remaining ECG signal quality, using a Parzen window supervised statistical classifier model. The three-class classifier model, using a number of time-domain features and evaluated using cross validation, gave an accuracy in classifying signal quality of 78.7% (κ = 0.67) when using fully automated preprocessing algorithms to remove gross motion artifact and detect QRS locations. This is a similar level of accuracy to the reported human inter-scorer agreement when generating the gold standard annotation (accuracy = 70-89.3%, κ = 0.54-0.84). These results indicate that the assessment of the quality of single-lead ECG recordings, acquired in unsupervised telehealth environments, is entirely feasible and may help to promote the acceptance and utility of future decision support systems for remotely managing chronic disease conditions.

A Decision Support Architecture for Telecare Patient Management of Chronic and Complex Disease

A major challenge facing designers of telecare systems today is providing decision support to enhance the health carers review of remotely acquired monitoring data and to support clinical decision-making for the management of chronic and complex disease in this setting. We are implementing a decision support framework to analyze clinical information generated from subjects at their place of residence (home, residential care settings) and from other clinical environments. The telecare information generated from these environments is both substantial and multi-modal (physiological, questionnaire, medication data, etc.). Using the JBoss Application Server, a rules engine is used to analyze these data. The health carer will be alerted to any deterioration in the health status of a patient by way of a Web page that will stratify a clinical data summary into high, medium and low risk groups. In this way, outputs from the decision support system can be used to assist in the efficient review and risk stratification of multiple patient records, and ultimately influence changes in work flow by targeting scarce human resources to patients of most need.

A systematic review of speech recognition technology in health care

BackgroundTo undertake a systematic review of existing literature relating to speech recognition technology and its application within health care.MethodsA systematic review of existing literature from 2000 was undertaken. Inclusion criteria were: all papers that referred to speech recognition (SR) in health care settings, used by health professionals (allied health, medicine, nursing, technical or support staff), with an evaluation or patient or staff outcomes. Experimental and non-experimental designs were considered.Six databases (Ebscohost including CINAHL, EMBASE, MEDLINE including the Cochrane Database of Systematic Reviews, OVID Technologies, PreMED-LINE, PsycINFO) were searched by a qualified health librarian trained in systematic review searches initially capturing 1,730 references. Fourteen studies met the inclusion criteria and were retained.ResultsThe heterogeneity of the studies made comparative analysis and synthesis of the data challenging resulting in a narrative presentation of the results. SR, although not as accurate as human transcription, does deliver reduced turnaround times for reporting and cost-effective reporting, although equivocal evidence of improved workflow processes.ConclusionsSR systems have substantial benefits and should be considered in light of the cost and selection of the SR system, training requirements, length of the transcription task, potential use of macros and templates, the presence of accented voices or experienced and in-experienced typists, and workflow patterns.

Managing chronic disease with home telecare: a system architecture and case study

An overview of the system architecture of a home telecare system (HTS) that was successfully deployed in city and rural Australia Is presented. We describe a case study from a patient with chronic obstructive pulmonary disease who was monitored in her home for a six-month period. A summary of patient and clinician responses to initial and final evaluation questionnaires is presented. There was generally a high level of acceptance of the HTS with both patients and their general practitioners responding favorably on its ease of use, effectiveness and likely impact on improving management of chronic disease.

Home telecare for chronic disease management

We review the development of an Internet-enabled home clinical workstation for the management of chronic disease, and the implementation of a clinical trial to test the functionality, usability and effectiveness of the system in both a city and a remote country setting. The Home Telecare System integrates with established primary care services to provide a new paradigm of active disease management through the daily collection of clinical data and assessment of functional health status, and the provision of feedback for patient self-management and education. A novel Medications Management module is also implemented to permit on line variation of prescribed medications.

A small-scale randomised controlled trial of home telemonitoring in patients with severe chronic obstructive pulmonary disease

Introduction This was a pilot study to examine the effects of home telemonitoring (TM) of patients with severe chronic obstructive pulmonary disease (COPD). Methods A randomised controlled 12-month trial of 42 patients with severe COPD was conducted. Home TM of oximetry, temperature, pulse, electrocardiogram, blood pressure, spirometry, and weight with telephone support and home visits was tested against a control group receiving only identical telephone support and home visits. Results The results suggest that TM had a reduction in COPD-related admissions, emergency department presentations, and hospital bed days. TM also seemed to increase the interval between COPD-related exacerbations requiring a hospital visit and prolonged the time to the first admission. The interval between hospital visits was significantly different between the study arms, while the other findings did not reach significance and only suggest a trend. There was a reduction in hospital admission costs. TM was adopted well by most patients and eventually, also by the nursing staff, though it did not seem to change patients’ psychological well-being. Discussion Ability to draw firm conclusions is limited due to the small sample size. However the trends of reducing hospital visits warrant a larger study of a similar design. When designing such a trial, one should consider the potential impact of the high quality of care already made available to this patient cohort.

A guideline-based decision support system for generating referral recommendations from routinely recorded home telehealth measurement data

The objectives of this paper are to present a guideline-based decision support system (GBDSS) design for supporting patient telehealth management of chronic disease and to test its performance in correctly making referral recommendations using routinely recorded measurement data from home telehealth recordings. The GBDSS has been developed to manage lung disease patients in a home telehealth environment. The system operates by checking the availability of home telehealth measurement data on a daily basis, interprets these data using a rule-based decision tree classification, and ultimately generates referral recommendations based on these measured data. The system has demonstrated discriminative power when applied in the analysis of retrospective telehealth data, as a surrogate for realtime referral generation. To this end a telehealth dataset comprising 16 chronic obstructive pulmonary disease (COPD) patients monitored over a 12 month period was used. It was shown that GBDSS referral recommendations could help reduce the number of cases that required a carers urgent attention by 72.1%, with 81.9% accuracy, 80.8% specificity and 90.4% sensitivity.