Search

Objective: In this study we investigate inter-operator differences in determining systolic and diastolic pressure from auscultatory sound recordings of Korotkoff sounds. We introduce a new method to record and convert Korotkoff sounds to a high fidelity sound file which can be replayed under optimal conditions by multiple operators, for the independent determination of systolic and diastolic pressure points. Approach: We have developed a digitised data base of 643 NIBP records from 216 subjects. The Korotkoff signals of 310 good quality records were digitised and the Korotkoff sounds converted to high fidelity audio files. A randomly selected subset of 90 of these data files, were used by an expert panel to independently detect systolic and diastolic points. We then developed a semi-automated method of visualising processed Korotkoff sounds, supported by simple algorithms to detect systolic and diastolic pressure points that provided new insights on the reasons for large differences recorded by the expert panel. Main Results: Detailed analysis of the 90 randomly selected records revealed that peak root mean square (RMS) energy of the Korotkoff sounds, ranged from 3.3 to 84 mV rms, with the lower bound below the audible range of 4–6 mV rms. The diastolic phase was below the minimum auditory threshold in only 47/90 records. This indicates that for approximately 50% of all records diastole could not be determined from Phase V silence. The maximum relative error recorded for systolic pressure between the two methods, auscultatory and visual/algorithmic, was 30.8 mmHg with a mean error of 8.0 ± 5.4 mmHg. We explore the impact of signal morphology and intensity of the Korotkoff sounds, as well as noise, cardiac arrhythmia and the hearing acuity of the operator, on the accuracy of the measurement. Significance: We conclude that large intra-personal variability in Korotkoff signal morphology and amplitudes, as well as variations in the hearing acuity of the operator, make accurate NIBP measurements using sphygmomanometry difficult and should not be used as the gold standard against which automated NIBP devices are calibrated. We propose an alternative method of visualizing the energy of the Korotkoff sounds and applying simple algorithms to determine systolic and diastolic pressure points, which whilst mimicking classical sphygmomanometry eliminates the problems associated with operator hearing acuity and complex and variable Korotkoff signal morphology.

While privacy and security concerns dominate public cloud services, Homomorphic Encryption (HE) is seen as an emerging solution that can potentially assure secure processing of sensitive data by third-party cloud vendors. It relies on the fact that computations can occur on encrypted data without the need for decryption, although there are major stumbling blocks to overcome before the technology is considered mature for production cloud environments. This paper examines a proposed technology platform, known as the Homomorphic Encryption Bus (HEB), that leverages HE with data obfuscation methods over a minimal network interaction model, allowing a uniform, flexible and general approach to cloud-based privacy-preserving system integration. The platform is uniquely designed to overcome barriers limiting the mainstream application of existing Fully Homomorphic Encryption (FHE) schemes in the cloud. A client-server interaction model involving ciphertext decryption on the client end is necessary to achieve resetting of 'noisy' ciphertexts in place of a much more inefficient (server only) recryption procedure. Data perturbation techniques are used to obfuscate intermediate data decrypted on the client-side of ciphertext interactions, in a way that is unintelligible to the client. In addition to efficient noise resetting, interactions involving data perturbations also achieve plaintext (binary to integer-based and vice versa) message space swapping, and conversion of accumulated integer-based encodings to a reduced embedded binary form. There appears to be little existing literature that examines these techniques as a means of broadening HE processing capabilities and practical application over the cloud. Interaction performance is examined in terms of timing and multiplicative circuit depth costs, through a simple equation evaluation and against standard recryption.

In this study, data quality analyses were performed on raw signals from two types of home telehealth measurements; the pulse oximetry and the blood pressure. The results have confirmed that home telehealth pulse oximetry and blood pressure data quality issues do affect the reliability of a decision support system (DSS) for the particular algorithms and data sets used in this study. Both techniques (the manual outlier removal and the automated signal quality analysis) have improved the performance of the DSS. Therefore, these automated signal quality tools are considered useful and will be included in the DSS for the purpose of data quality assurance. This finding has also provided an additional method that can reduce the workload imposed when performing signal recording verification manually.

Objective We study the use of speech recognition and information extraction to generate drafts of Australian nursing-handover documents.Methods Speech recognition correctness and clinicians’ preferences were evaluated using 15 recorder–microphone combinations, six documents, three speakers, Dragon Medical 11, and five survey/interview participants. Information extraction correctness evaluation used 260 documents, six-class classification for each word, two annotators, and the CRF++ conditional random field toolkit.Results A noise-cancelling lapel-microphone with a digital voice recorder gave the best correctness (79%). This microphone was also the most preferred option by all but one participant. Although the participants liked the small size of this recorder, their preference was for tablets that can also be used for document proofing and sign-off, among other tasks. Accented speech was harder to recognize than native language and a male speaker was detected better than a female speaker. Information extraction was excellent in filtering out irrelevant text (85% F1) and identifying text relevant to two classes (87% and 70% F1). Similarly to the annotators’ disagreements, there was confusion between the remaining three classes, which explains the modest 62% macro-averaged F1.Discussion We present evidence for the feasibility of speech recognition and information extraction to support clinicians’ in entering text and unlock its content for computerized decision-making and surveillance in healthcare.Conclusions The benefits of this automation include storing all information; making the drafts available and accessible almost instantly to everyone with authorized access; and avoiding information loss, delays, and misinterpretations inherent to using a ward clerk or transcription services.

Aim The aim of this study was to explore the potential for one set of patient information for nursing handover and documentation. Background Communication of patient information requires two processes in nursing: a verbal summary of the patients' care and another report within the nursing notes, creating duplication. Introduction Advances in speech recognition technology have provided an opportunity to consider the practicality of one set of information at the nursing end-of-shift. Methods We used content analysis to compare transcripts from 162 digitally recorded handovers and written nursing notes for similar patients within general medical-surgical wards from two metropolitan hospitals in Sydney Australia. Findings Using the Nursing Handover Minimum Dataset analysis framework similar content [n = 2109 (handover) n = 1902 (nursing notes)] was found within the handovers and notes at the end-of-shift (7:00 am and 2:00 pm). Analysis of the overarching categories demonstrated the emphasis within the differing data sources as: patient identification (31%), care planning or interventions (25%), clinical history (13%), and clinical status (13%) for handover, vs. care planning (47%), clinical status (24%), and outcomes or goals of care (12%) for nursing notes. Discussion This study has demonstrated that similar patient information is presented at handover and within documentation. Major categories are consistent with international nursing minimum datasets in use. Conclusion We can use one set of patient information (within some limitations) for two purposes with system design, practice change and education. Experiments are currently being conducted trialling speech recognition within laboratory and clinical settings. Implications for Nursing and Health Policy One set of patient information, verbally generated at handover delivering electronic documentation within one process, will transform international nursing policy for nursing handover and documentation.

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.

As the world population ages, falls among the elderly are becoming a significant burden on healthcare. Fall prevention programs provide solutions for alleviating this burden. Such programs can be supported through monitoring of the elderly with tri-axial accelerometer sensors and mobile technology in order to detect falls and ensure individuals receive rapid care. A six-month pilot program was undertaken that involved recording tri-axial accelerometer data from mobile phones designed to be worn and used by independent community-dwelling elderly individuals. Fall data gained through this pilot program has been analysed in order to determine the quality of data recorded and the feasibility of constructing a threshold based fall detection algorithm from this data. Issues are found with the sample rate and range of the recorded data. Despite this, fall detection of acceptable quality is found to be plausible through measurement of changes in posture.

Accurate non-invasive measurement of blood pressure in unsupervised environments continues to be a challenge, particularly in the presence of movement artefact, electrical noise and most importantly cardiac arrhythmia which are common in those aged over 65 suffering from a range of chronic conditions. Large intra personal variability in signal morphometry and amplitudes further complicates the development of reliable signal processing algorithms for NIBP measurement. In this paper we demonstrate the effect of this variability and propose that the traditional methods of human blood pressure determination by sphygmomanometry should no longer be considered a gold standard for the calibration of NIBP devices.

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.

Telehealth is the provision of health services at a distance. Typically, this occurs in unsupervised or remote environments, such as a patient's 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.

The need for a human activity recognition system arises when designing a "health smart home" which monitors its occupants to assess their health status. In this work, a rule-based system was constructed to classify the common activities of daily living based on a hierarchical approach, using location measurements from a commercial ultrasonic sensor system. Adaptive rule application was achieved by applying contextual information from adjacent time steps using a finite-state machine. Some common static and dynamic activities of daily living were chosen as the targets for classification. The system was shown to provide comparable performance with results which have been reported for more complex alternative systems. Results reported showed a minimum classification accuracies of 87.7% for the walking activity. The deployed adaptive rule-based system provides a robust and computationally inexpensive solution for common in-situ human activity recognition purposes.

Telehealth pilot projects and trial implementations are numerous but are often not fully evaluated, preventing construction of a sound evidence base and so limiting their adoption. We describe the need for a generic Telehealth project evaluation framework, within which evaluation is undertaken based on existing health systems performance indicators, using appropriately chosen measures. We provide two case studies explaining how this approach could be applied, in Australian and Canadian settings. It is argued that this framework type of approach to evaluation offers better potential for incorporating the learnings from resultant evaluations into business decisions by "learning organisations", through alignment with organisational performance considerations.

Background Increasingly, automated methods are being used to code free-text medication data, but evidence on the validity of these methods is limited. Aim To examine the accuracy of automated coding of previously keyed in free-text medication data compared with manual coding of original handwritten free-text responses (the 'gold standard'). Methods A random sample of 500 participants (475 with and 25 without medication data in the free-text box) enrolled in the 45 and Up Study was selected. Manual coding involved medication experts keying in free-text responses and coding using Anatomical Therapeutic Chemical (ATC) codes (i.e. chemical substance 7-digit level; chemical subgroup 5-digit; pharmacological subgroup 4-digit; therapeutic subgroup 3-digit). Using keyed-in free-text responses entered by non-experts, the automated approach coded entries using the Australian Medicines Terminology database and assigned corresponding ATC codes. Results Based on manual coding, 1377 free-text entries were recorded and, of these, 1282 medications were coded to ATCs manually. The sensitivity of automated coding compared with manual coding was 79% (n = 1014) for entries coded at the exact ATC level, and 81.6% (n = 1046), 83.0% (n = 1064) and 83.8% (n = 1074) at the 5, 4 and 3-digit ATC levels, respectively. The sensitivity of automated coding for blank responses was 100% compared with manual coding. Sensitivity of automated coding was highest for prescription medications and lowest for vitamins and supplements, compared with the manual approach. Positive predictive values for automated coding were above 95% for 34 of the 38 individual prescription medications examined. Conclusions Automated coding for free-text prescription medication data shows very high to excellent sensitivity and positive predictive values, indicating that automated methods can potentially be useful for large-scale, medication-related research.

Information and communications technology (ICT) is increasingly being used in management of chronic illness to facilitate shared services (virtual health networks and electronic health records), knowledge management (care rules and protocols, scheduling, information directories), as well as consumer-based health education and evidence-based clinical protocols. Common applications of ICT include home monitoring of vital signs for patients with chronic disease, as well as replacing home visits by nurses in person with telemedicine videophone consultations. A patient-managed Home Telecare System with integrated clinical signs monitoring, automated scheduling and medication reminders, as well as access to health education and daily logs, is presented as an example of ICT use for chronic disease self-management. A clinical case study demonstrates how early identification of adverse trends in clinical signs recorded in the home can either avoid hospital readmission or reduce the length of hospital stay.

Background: To undertake a systematic review of existing literature relating to speech recognition technology and its application within health care. Methods: A 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. Results: The 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. Conclusions: SR 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.

A multi-disciplinary research team is undertaking a trial of speech-to-text (STT) technology for clinical handover management. Speech-to-text technologies allow for the capture of handover data from voice recordings using speech recognition software and systems. The text documents created from this system can be used together with traditional handover notes and checklists to enhance the depth and breadth of data available for clinical decision-making at the point of care and so improve patient care and reduce medical errors. This paper reports on a preliminary study of perceived usability by nurses of speech-to-text technology based on interviews at a "test day" and using a user-task-technology usability framework to explore expectations of nurses of the use of speech-to-text (STT) technology for clinical handover. The results of this study will be used to design field studies to test the use of speech-to-text (STT) technologies at the point of care in several hospital settings.

Physical activity recognition has emerged as an active area of research which has drawn increasing interest from researchers in a variety of fields. It can support many different applications such as safety surveillance, fraud detection, and clinical management. Accelerometers have emerged as the most useful and extensive tool to capture and assess human physical activities in a continuous, unobtrusive and reliable manner. The need for objective physical activity data arises strongly in health related research. With the shift to a sedentary lifestyle, where work and leisure tend to be less physically demanding, research on the health effects of low physical activity has become a necessity. The increased availability of small, inexpensive components has led to the development of mobile devices such as smartphones, providing platforms for new opportunities in healthcare applications. In this study 3 subjects performed directed activity routines wearing a smartphone with a built in tri-axial accelerometer, attached on a belt around the waist. The data was collected to classify 11 basic physical activities such as sitting, lying, standing, walking, and the transitions in between them. A hierarchical classifier approach was utilised with Artificial Neural Networks integrated in a rule-based system, to classify the activities. Based on our evaluation, recognition accuracy of over 89.6% between subjects and over 91.5% within subject was achieved. These results show that activities such as these can be recognised with a high accuracy rate; hence the approach is promising for use in future work.

Objective: The study investigated the use of serotonergic antidepressants (SSRIs: selective serotonin reuptake inhibitors; SNRIs: serotonin–norepinephrine reuptake inhibitors) and St John’s wort in a large NSW-based community sample, and sought to identify a potentially dangerous concomitant use of these medications. Methods: Cross-sectional data from 266,848 participants from the ‘45 and Up’ study were used. The questionnaire captures self-reported treatment for depression or anxiety and antidepressant medications in the last four weeks. Results: 5.8% of participants received treatment for depression or anxiety, with 4.7% taking an SSRI and 1.3% an SNRI. St John’s wort was taken by 0.3% of the participants. Use of SSRIs and SNRIs was reported more frequently by females than males (respectively, 64.1% vs 35.9%, 66.9% vs 33.1%). The gender difference was even more pronounced for St John’s wort (75.6% vs. 24.4%). Use of antidepressants decreased after the age of 65 years. One hundred and forty people reported concurrent use of an SSRI and an SNRI, and 11 people of an SSRI with St John’s wort. Conclusions: Around 7% of the study population aged 45–65 years reported the use of SSRIs or SNRIs, decreasing to 5% above 70 years of age. It is of concern that some individuals used an SSRI concurrently with St John’s wort.

Our overall aim in this research is to develop a health smart home design that can monitor activities of its occupants. Our design includes three stages: system physical layers and software, fusion of multi-layered data, and classification of activities of daily living. We review prior work, discuss design and implementation issues, and describe our validation approach.

This article presents a decision support system (DSS) that has been designed to manage patients with chronic obstructive pulmonary disease (COPD) using a home telehealth system. The DSS has been developed to assist home telehealth clinical support staff with their workload, and to provide more effective communication between multiple home telehealth users. The three-tier system architecture that consists of a data layer; a business logic layer; and a front-end layer employs a business process model and uses a rule engine for its logic and knowledge base. This paper discusses the design considerations involved in the construction of a DSS for the purpose of home telehealth, and illustrates how it may be developed using entirely open source software.

Background The use of telehealth technologies to remotely monitor patients suffering chronic diseases may enable preemptive treatment of worsening health conditions before a significant deterioration in the subject's health status occurs, requiring hospital admission. Objective The objective of this study was to develop and validate a classification algorithm for the early identification of patients, with a background of chronic obstructive pulmonary disease (COPD), who appear to be at high risk of an imminent exacerbation event. The algorithm attempts to predict the patient's condition one day in advance, based on a comparison of their current physiological measurements against the distribution of their measurements over the previous month. Method The proposed algorithm, which uses a classification and regression tree (CART), has been validated using telehealth measurement data recorded from patients with moderate/severe COPD living at home. The data were collected from February 2007 to January 2008, using a telehealth home monitoring unit. Results The CART algorithm can classify home telehealth measurement data into either a ‘low risk' or ‘high risk' category with 71.8% accuracy, 80.4% specificity and 61.1% sensitivity. The algorithm was able to detect a ‘high risk' condition one day prior to patients actually being observed as having a worsening in their COPD condition, as defined by symptom and medication records. Conclusion The CART analyses have shown that features extracted from three types of physiological measurements; forced expiratory volume in 1s (FEV 1 ), arterial oxygen saturation (SPO 2 ) and weight have the most predictive power in stratifying the patients condition. This CART algorithm for early detection could trigger the initiation of timely treatment, thereby potentially reducing exacerbation severity and recovery time and improving the patient's health. This study highlights the potential usefulness of automated analysis of home telehealth data in the early detection of exacerbation events among COPD patients.

Information and communication technologies may be used to provide health care services to people living at home. The term "home telecare" has been coined for this service. The elderly and patients with chronic pulmonary conditions, heart disease and diabetes have been thought to be obvious beneficiaries. The evidence base supporting home telecare is growing; however, there is a need for studies of long-term deployment and integration with existing health system processes. We discuss the experiences gained from one such pilot conducted in the Sydney West Area Health Service, which examines the integration of home telecare within the framework of an existing respiratory ambulatory care service. Interim results demonstrate high levels of reliability and positive patient attitude towards use of home monitoring. Clinical staff acceptance levels appeared lower. Effects on health burden, such as hospital admissions and nurse workload, were not significantly altered. The study results have been essential in developing local telecare knowledge within the health care community.

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 carer's urgent attention by 72.1%, with 81.9% accuracy, 80.8% specificity and 90.4% sensitivity.

Fledgling clinical decision support systems (DSSs) are being designed on the false assumption that consistent, good-quality signals are created in the unsupervised telehealth environment, but it has in fact been shown that signal quality is often very poor. Hence, it is important to investigate the detrimental impact of failing to recognize erroneous clinical parameter values. This study combines previous work in this area, related to artifact detection in electrocardiogram (ECG) signals, and piecewise-linear trend detection in longitudinal heart rate parameter records, to investigate the impact of choosing to ignore ECG signal quality prior to trend detection in the heart rate (HR) records. Using an artifact detection algorithm to improve the HR estimates from the ECG signals, when compared to reference HR values derived from human annotated 2453 ECGs from nine patients, resulted in a decrease in the estimation bias from 2.54 BPM (beat per minute) to 0.70 BPM and a decrease in the standard error from 0.47 BPM to 0.17 BPM. The application of the same artifact detection also results in a significant improvement in trend fitting, when compared to a fitting of the reference HR values, by reducing the mean RMSE value of the error in the trend fit from 2.14 BPM to 0.78 BPM and standard error from 0.49 BPM to 0.10 BPM. As trend detection will be a component of future telehealth decision support systems, signal quality measures for unsupervised measurements are of paramount importance.