A simulation study to quantitatively assess the performance of electronic prescribing systems in English NHS Hospital Trusts.

Prescribing errors are a source of preventable harm in healthcare, which may be mitigated using Electronic Prescribing (EP) systems. Anyone who routinely prescribes medication could benefit from digitally assisted automated checks to identify whether a prescription should potentially not be allowed (e.g.

Addressing the challenges of integrating digital health technologies to measure patient-centred outcomes in clinical registries.

Longitudinal patient registries generate important evidence for advancing clinical care and the regulatory evaluation of health-care products. Most national registries rely on data collected as part of routine clinical encounters, an approach that does not capture real-world, patient-centred outcomes, such as physical activity, fatigue, ability to do daily tasks, and other indicators of quality of life. Digital health technologies that obtain such real-world data could greatly enhance patient registries but unresolved challenges have so far prevented their broad adoption.

Assessment of salivary cortisol dynamics in an infantry training exercise: a pilot study.

Introduction: Measuring cortisol during military training offers insights into physiological responses to stress. We attempted precisely timed, cortisol awakening response (CAR) and pre-sleep cortisol (PSC), and diurnal slope (peak morning minus evening cortisol), during a British Army exercise. We aimed to understand cortisol dynamics and evaluate the feasibility of CAR and PSC in this environment.

Predicting time to asystole following withdrawal of life-sustaining treatment: a systematic review.

The planned withdrawal of life-sustaining treatment is a common practice in the intensive care unit for patients where ongoing organ support is recognised to be futile. Predicting the time to asystole following withdrawal of life-sustaining treatment is crucial for setting expectations, resource utilisation and identifying patients suitable for organ donation after circulatory death. This systematic review evaluates the literature for variables associated with, and predictive models for, time to asystole in patients managed on intensive care units.

Quantifying Parkinson's disease severity using mobile wearable devices and machine learning: the ParkApp pilot study protocol.

Introduction: The clinical assessment of Parkinson's disease (PD) symptoms can present reliability issues and, with visits typically spaced apart 6 months, can hardly capture their frequent variability. Smartphones and smartwatches along with signal processing and machine learning can facilitate frequent, remote, reliable and objective assessments of PD from patients' homes.

Aim: To investigate the feasibility, compliance and user experience of passively and actively measuring symptoms from home environments using data from sensors embedded in smartphones and a wrist-wearable device.

Symptom tracking in endometriosis using digital technologies: Knowns, unknowns, and future prospects.

Endometriosis is a common chronic pain condition with no known cure and limited treatment options. Digital technologies, ranging from smartphone apps to wearable sensors, have shown potential toward facilitating chronic pain assessment and management; however, to date, many of these tools have not been specifically deployed or evaluated in patients with endometriosis-associated pain. Informed by previous studies in related chronic pain conditions, we discuss how digital technologies may be used in endometriosis to facilitate objective, continuous, and holistic symptom tracking.

Estimating medication adherence from Electronic Health Records: comparing methods for mining and processing asthma treatment prescriptions.

Background: Medication adherence is usually defined as the extent of the agreement between the medication regimen agreed to by patients with their healthcare provider and the real-world implementation. Proactive identification of those with poor adherence may be useful to identify those with poor disease control and offers the opportunity for ameliorative action. Adherence can be estimated from Electronic Health Records (EHRs) by comparing medication dispensing records to the prescribed regimen.

Association of Preterm Birth and Socioeconomic Status With Neonatal Brain Structure.

Importance: Preterm birth and socioeconomic status (SES) are associated with brain structure in childhood, but the relative contributions of each during the neonatal period are unknown.

Objective: To investigate associations of birth gestational age (GA) and SES with neonatal brain morphology by testing 3 hypotheses: GA and SES are associated with brain morphology; associations between SES and brain morphology vary with GA; and associations between SES and brain structure and morphology depend on how SES is operationalized.

Design, Setting, And Participants: This cohort study recruited participants from November 2016 to September 2021 at a single center in the United Kingdom.

New Insights into Stroke from Continuous Passively Collected Temperature and Sleep Data Using Wrist-Worn Wearables.

Actigraphy may provide new insights into clinical outcomes and symptom management of patients through passive, continuous data collection. We used the GENEActiv smartwatch to passively collect actigraphy, wrist temperature, and ambient light data from 27 participants after stroke or probable brain transient ischemic attack (TIA) over 42 periods of device wear. We computed 323 features using established algorithms and proposed 25 novel features to characterize sleep and temperature.

Defining clinical subtypes of adult asthma using electronic health records: Analysis of a large UK primary care database with external validation.

Introduction: Asthma is one of the commonest chronic conditions in the world. Subtypes of asthma have been defined, typically from clinical datasets on small, well-characterised subpopulations of asthma patients. We sought to define asthma subtypes from large longitudinal primary care electronic health records (EHRs) using cluster analysis.

Derivation of asthma severity from electronic prescription records using British thoracic society treatment steps.

Background: Asthma severity is typically assessed through a retrospective assessment of the treatment required to control symptoms and to prevent exacerbations. The joint British Thoracic Society and Scottish Intercollegiate Guidelines Network (BTS/SIGN) guidelines encourage a stepwise approach to pharmacotherapy, and as such, current treatment step can be considered as a severity categorisation proxy. Briefly, the steps for adults can be summarised as: no controller therapy (Step 0), low-strength Inhaled Corticosteroids (ICS; Step 1), ICS plus Long-Acting Beta-2 Agonist (LABA; Step 2), medium-dose ICS + LABA (Step 3), and finally either an increase in strength or additional therapies (Step 4).

Estimating Medication Adherence from Electronic Health Records Using Rolling Averages of Single Refill-based Estimates.

Medication adherence is usually defined as the manner in which a patient takes their medication, in relation to the regimen agreed to with their healthcare provider. Electronic Health Records (EHRs) can be used to estimate adherence in a cost-effective and non-invasive manner across large-scale populations, although there is no universally agreed optimal approach to doing so. We sought to explore patterns of asthma ICS prescription refills in a large EHR dataset, and to evaluate the use of rolling-average based measures towards short-term adherence estimation.

An EMG-based Eating Behaviour Monitoring system with haptic feedback to promote mindful eating.

Mindless eating, or the lack of awareness of the food we are consuming, has been linked to health problems attributed to unhealthy eating behaviour, including obesity. Traditional approaches used to moderate eating behaviour often rely on inaccurate self-logging, manual observations or bulky equipment. Overall, there is a clear unmet clinical need to develop an intelligent and lightweight system which can automatically monitor eating behaviour and provide feedback.

Investigating Wrist-Based Acceleration Summary Measures across Different Sample Rates towards 24-Hour Physical Activity and Sleep Profile Assessment.

Wrist-worn wearable sensors have attracted considerable research interest because of their potential in providing continuous, longitudinal, non-invasive measurements, leading to insights into Physical Activity (PA), sleep, and circadian variability. Three key practical considerations for research-grade wearables are as follows: (a) choosing an appropriate sample rate, (b) summarizing raw three-dimensional accelerometry data for further processing (accelerometry summary measures), and (c) accurately estimating PA levels and sleep towards understanding participants' 24-hour profiles. We used the CAPTURE-24 dataset, where 148 participants concurrently wore a wrist-worn three-dimensional accelerometer and a wearable camera over approximately 24 h to obtain minute-by-minute labels: sleep; and sedentary light, moderate, and vigorous PA.

Development and validation of a decision support tool for the diagnosis of acute heart failure: systematic review, meta-analysis, and modelling study.

Objectives: To evaluate the diagnostic performance of N-terminal pro-B-type natriuretic peptide (NT-proBNP) thresholds for acute heart failure and to develop and validate a decision support tool that combines NT-proBNP concentrations with clinical characteristics.

Design: Individual patient level data meta-analysis and modelling study.

Setting: Fourteen studies from 13 countries, including randomised controlled trials and prospective observational studies.

Relevance, redundancy, and complementarity trade-off (RRCT): A principled, generic, robust feature-selection tool.

We present a new heuristic feature-selection (FS) algorithm that integrates in a principled algorithmic framework the three key FS components: relevance, redundancy, and complementarity. Thus, we call it relevance, redundancy, and complementarity trade-off (RRCT). The association strength between each feature and the response and between feature pairs is quantified via an information theoretic transformation of rank correlation coefficients, and the feature complementarity is quantified using partial correlation coefficients.

Validation of the myocardial-ischaemic-injury-index machine learning algorithm to guide the diagnosis of myocardial infarction in a heterogenous population: a prespecified exploratory analysis.

Background: Diagnostic pathways for myocardial infarction rely on fixed troponin thresholds, which do not recognise that troponin varies by age, sex, and time within individuals. To overcome this limitation, we recently introduced a machine learning algorithm that predicts the likelihood of myocardial infarction. Our aim was to evaluate whether this algorithm performs well in routine clinical practice and predicts subsequent events.

Mobile devices and wearable technology for measuring patient outcomes after surgery: a systematic review.

Complications following surgery are common and frequently occur the following discharge. Mobile and wearable digital health interventions (DHI) provide an opportunity to monitor and support patients during their postoperative recovery. Lack of high-quality evidence is often cited as a barrier to DHI implementation.

Assessing Parkinson's Disease at Scale Using Telephone-Recorded Speech: Insights from the Parkinson's Voice Initiative.

Numerous studies have reported on the high accuracy of using voice tasks for the remote detection and monitoring of Parkinson's Disease (PD). Most of these studies, however, report findings on a small number of voice recordings, often collected under acoustically controlled conditions, and therefore cannot scale at large without specialized equipment. In this study, we aimed to evaluate the potential of using voice as a population-based PD screening tool in resource-constrained settings.

Language function following preterm birth: prediction using machine learning.

Background: Preterm birth can lead to impaired language development. This study aimed to predict language outcomes at 2 years corrected gestational age (CGA) for children born preterm.

Methods: We analysed data from 89 preterm neonates (median GA 29 weeks) who underwent diffusion MRI (dMRI) at term-equivalent age and language assessment at 2 years CGA using the Bayley-III.

Smartphone-recorded physical activity for estimating cardiorespiratory fitness.

While cardiorespiratory fitness is strongly associated with mortality and diverse outcomes, routine measurement is limited. We used smartphone-derived physical activity data to estimate fitness among 50 older adults. We recruited iPhone owners undergoing cardiac stress testing and collected recent iPhone physical activity data.

Sex Differences in Cardiac Troponin I and T and the Prediction of Cardiovascular Events in the General Population.

Background: Cardiac troponin concentrations differ in women and men, but how this influences risk prediction and whether a sex-specific approach is required is unclear. We evaluated whether sex influences the predictive ability of cardiac troponin I and T for cardiovascular events in the general population.

Methods: High-sensitivity cardiac troponin (hs-cTn) I and T were measured in the Generation Scotland Scottish Family Health Study of randomly selected volunteers drawn from the general population between 2006 and 2011.

A Neuromotor to Acoustical Jaw-Tongue Projection Model With Application in Parkinson's Disease Hypokinetic Dysarthria.

Aim: The present work proposes the study of the neuromotor activity of the masseter-jaw-tongue articulation during diadochokinetic exercising to establish functional statistical relationships between surface Electromyography (sEMG), 3D Accelerometry (3DAcc), and acoustic features extracted from the speech signal, with the aim of characterizing Hypokinetic Dysarthria (HD). A database of multi-trait signals of recordings from an age-matched control and PD participants are used in the experimental study.

Hypothesis: The main assumption is that information between sEMG and 3D acceleration, and acoustic features may be quantified using linear regression methods.