: The increasing need to provide care outside of hospitals necessitates remote monitoring of basic vital signs of patients from places such as private homes and aged care facilities. While much exploratory research has been done on using Internet of Medical Things (IoMT) devices for remote monitoring, there is a requirement to examine the practicality associated with the mass use of affordable off-the-shelf devices in terms of usability, secure access to data, and integration into hospital-based information systems. This paper investigates various security aspects in nine vital signs sensor devices that can be purchased and used for homecare monitoring in Australia. Specifically, the security and privacy aspects of these devices and associated software, regulatory compliance, interoperability, and formats of the accessible data streams were investigated. It was found that the devices were not entirely secure, as personal health information could be accessed using appropriate tools. Only one vendor enabled encryption during data transmission and provided an API to access data. While the clinical use of these devices with integration into hospital systems for practical remote monitoring is not easily achievable, it is possible to use devices for day-to-day vital signs monitoring purposes in a home setting.

Smart homecare utilises advanced technologies to support, improve and promote remote healthcare in homes and communities through collecting and analysing health data and sharing this knowledge with carers and clinicians. With the continuous growth in the world's older population, smart homecare becomes increasingly crucial in providing in-home care for older adults, allowing the vital healthcare dollars to go further into other critical care needs. In addition, with the rise in the development and utilisation of innovative technologies in healthcare settings, it is vital to ensure that these technologies are guided and approved by the corresponding regulatory bodies such as FDA (Foods and Drug Administration) in the USA and TGA (Therapeutic Good Administration) in Australia. With this premise, this paper identifies four dimensions for researchers to consider when developing smart homecare solutions for in-home remote care: Technology, Data, People, and Operational Environment. The essential interplays amongst these four dimensions are discussed to identify the various enablers and barriers in the successful delivery of smart homecare solutions. As the primary output of this paper, it proposes a conceptual framework to achieve practical in-home care for the older population living independently with the support of technology, while addressing the challenges such as security and privacy of patient data. Secondly, a comprehensive and practical guide featuring seven phases is presented to support and direct researchers in implementing smart homecare solutions for remote care. The proposed framework and the guide aim to make smart homecare research practical and truly translational into broader practice.

Next generation technologies such as smart health-care, self-driving cars, and smart cities require new approaches to deal with the network traffic generated by the Internet of Things (IoT) devices, as well as efficient programming models to deploy machine learning techniques. Serverless edge computing is an emerging computing paradigm from the integration of two recent technologies, edge computing and serverless computing, that can possibly address these challenges. However, there is little work to explore the capability and performance of such a technology. In this paper, a comprehensive performance analysis of a serverless edge computing system using popular open-source frameworks, namely, Kubeless, OpenFaaS, Fission, and funcX is presented. The experiments considered different programming languages, workloads, and the number of concurrent users. The machine learning workloads have been used to evaluate the performance of the system under different working conditions to provide insights into the best practices. The evaluation results revealed some of the current challenges in serverless edge computing and open research opportunities in this emerging technology for machine learning applications.

We would like to draw your attention to the incorrect spelling of Dr G. Femia's name, co-author of the above named paper where it is reproduced as G. Fema. It is an unfortunate error, and the publishers would like to apologise to Dr G. Femia for any inconvenience caused. Automation of Optical Coherence Tomography (OCT) Tissued Morphology and Vessel Sizing With Artificial IntelligenceHeart, Lung and CirculationVol. 31PreviewOCT is a high-resolution intracoronary imaging modality, capable of visualising elastic laminae and differentiating plaque characteristics such as calcification, fibrous tissue and lipid. Morphological interpretation is crucial to optimising percutaneous coronary interventions (PCI) but requires extensive training. We explored the automation of tissue characteristics and interventional balloon sizing using readily available artificial intelligence (AI) tools. Full-Text PDF

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.

A number of homomorphic encryption application areas could be better enabled if there existed a general solution for combining sufficiently expressive logical and numerical circuit primitives. This paper examines accelerating binary operations on real numbers suitable for somewhat homomorphic encryption. A parallel solution based on Single Instruction Multiple Data (SIMD) can be used to efficiently perform combined addition, subtraction and comparison-based operations on packed binary operands in a single step. The result maximises computational efficiency, memory space usage and minimises multiplicative circuit depth. General application and performance of these accelerated binary primitives are demonstrated in a number of case studies, including min-max and sorting operations.

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.

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.

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.

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.

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.

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.

This paper appeared at the 8th Australasian Workshop on Health Informatics and Knowledge Management (HIKM 2015), Sydney, Australia, January 2015. Conferences in Research and Practice in Information Technology (CRPIT), Vol. 164, Anthony Maeder and Jim Warren, Ed. Reproduction for academic, not-for profit purposes permitted provided this text is included.

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 1 s (FEV1), arterial oxygen saturation (SPO2) 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. (C) 2015 Elsevier B.V. All rights reserved.

Objetivo: El objetivo de este estudio fue explorar el potencial para desarrollar un conjunto unico de informacion de los pacientes para utilizacion en el cambio de guardia y en la documentacion de enfermeria. Antecedentes: La comunicacion de la informacion del paciente requiere dos procesos de enfermeria: un resumen verbal de la atencion prestada a los pacientes y otro informe en anotaciones de enfermeria, creando una duplicacion. Introduccion: Los avances en la tecnologia de reconocimiento de voces han proporcionado una oportunidad para considerar la viabilidad de reunir un conjunto de informacion en el cambio de guardia al finalizar el turno de enfermeria. Metodos: Utilizamos el analisis de contenido para comparar las transcripciones de 162 cambios de guardia grabados digitalmente y anotaciones de enfermeria escritas para pacientes similares en las unidades ganerales medico-quirurgicas de dos hospitales metropolitanos en Sidney, Australia. Resultados: Utilizando el marco de analisis del Conjunto Minimo de Datos se encontro un contenido similar [n=2109 (cambio de guardia) n=1902 (anotaciones de enfermeria)] en los cambios de guardia y las anotaciones al final del turno (7:00 a.m. y 2:00 p.m.). El analisis de categorias integrales demostro el enfasis en las fuentes de datos como: identificacion del paciente (31%), planificacion de la atencion o intervenciones (25%), historial clinico (13%), y estado clinico (13%) en el cambio de guardia, vs. planificacion de la atencion (47%), estado clinico (24%), y resultados o metas de la atencion (12%) para las anotaciones de enfermeria. Discusion: Este estudio ha demostrado que una informacion similar del paciente se presento en el cambio de guardia y en la documentacion. Las principales categorias son compatibles con los conjuntos internacionales de datos minimos de enfermeria en uso. Conclusion: Podemos utilizar un unico conjunto de informacion del paciente (dentro de algunas limitaciones) para dos finalidades con diseno de sistemas, cambio de practica y educacion. Actualmente, se estan realizando experimentos para probar el reconocimiento de voces en laboratorios y entornos clinicos. Implicaciones para la enfermeria y la politica sanitaria: Un unico conjunto de informacion de los pacientes, generado verbalmente en el cambio de guardia permitiendo la documentacion electronica en un unico proceso, transformara la politica internacional de enfermeria para el cambio de guardia y la documentacion de enfermeria.

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.