The effect of a dermoscopy training programme on diagnostic accuracy and management decisions regarding pigmented skin lesions: a comparison between dermal therapists and general practitioners.

Background: Dermoscopy is known to increase the diagnostic accuracy of pigmented skin lesions (PSLs) when used by trained professionals. The effect of dermoscopy training on the diagnostic ability of dermal therapists (DTs) has not been studied so far.

Objectives: This study aimed to investigate whether DTs, in comparison with general practitioners (GPs), benefited from a training programme including dermoscopy, in both their ability to differentiate between different forms of PSL and to assign the correct therapeutic strategy.

A Business Analytics Framework for Primary Care.

In this paper, we present a Business Analytics (BA) framework, which addresses the challenge of analysing primary care outcomes for both patients and clinicians from multiple data sources in an accurate manner. A review of the process monitoring literature has been conducted in the context of healthcare management and decision making and its findings have informed the formulation of a BA conceptual framework for process monitoring and decision support in primary care. Furthermore, a real case study is conducted to demonstrate the application of the BA framework to implement a BA dashboard tool within one of the largest primary care providers in England.

Assessing Patient Engagement in Health Care: Proposal for a Modeling and Simulation Framework for Behavioral Analysis.

PRR1-10.2196/30092.

Fast decliner phenotype of chronic obstructive pulmonary disease (COPD): applying machine learning for predicting lung function loss.

Background: Chronic obstructive pulmonary disease (COPD) is a heterogeneous group of lung conditions challenging to diagnose and treat. Identification of phenotypes of patients with lung function loss may allow early intervention and improve disease management. We characterised patients with the 'fast decliner' phenotype, determined its reproducibility and predicted lung function decline after COPD diagnosis.

COVID-19 diagnosis from chest x-rays: developing a simple, fast, and accurate neural network.

Purpose: Chest x-rays are a fast and inexpensive test that may potentially diagnose COVID-19, the disease caused by the novel coronavirus. However, chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonias. Recent research using deep learning may help overcome this issue as convolutional neural networks (CNNs) have demonstrated high accuracy of COVID-19 diagnosis at an early stage.

Usability and Acceptability of a Mobile App for Behavior Change and to Improve Immunization Coverage among Children in Pakistan: A Mixed-Methods Study.

Background: Pakistan's immunization uptake rates are still significantly lower than anticipated despite several initiatives. Lack of awareness, forgetting about vaccination schedule, and vaccine misconception/misinformation are a few of the major drivers that mitigate the rates of immunization. The current COVID-19 pandemic emphasizes the importance of immunization.

The cardiovascular phenotype of Chronic Obstructive Pulmonary Disease (COPD): Applying machine learning to the prediction of cardiovascular comorbidities.

Background: Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous group of lung conditions that are challenging to diagnose and treat. As the presence of comorbidities often exacerbates this scenario, the characterization of patients with COPD and cardiovascular comorbidities may allow early intervention and improve disease management and care.

Methods: We analysed a 4-year observational cohort of 6883 UK patients who were ultimately diagnosed with COPD and at least one cardiovascular comorbidity.

An Artificial Intelligence-Based, Personalized Smartphone App to Improve Childhood Immunization Coverage and Timelines Among Children in Pakistan: Protocol for a Randomized Controlled Trial.

Background: The immunization uptake rates in Pakistan are much lower than desired. Major reasons include lack of awareness, parental forgetfulness regarding schedules, and misinformation regarding vaccines. In light of the COVID-19 pandemic and distancing measures, routine childhood immunization (RCI) coverage has been adversely affected, as caregivers avoid tertiary care hospitals or primary health centers.

Current Challenges of Digital Health Interventions in Pakistan: Mixed Methods Analysis.

Background: Digital health is well-positioned in low and middle-income countries (LMICs) to revolutionize health care due, in part, to increasing mobile phone access and internet connectivity. This paper evaluates the underlying factors that can potentially facilitate or hinder the progress of digital health in Pakistan.

Objective: The objective of this study is to identify the current digital health projects and studies being carried out in Pakistan, as well as the key stakeholders involved in these initiatives.

COPD phenotypes and machine learning cluster analysis: A systematic review and future research agenda.

Chronic Obstructive Pulmonary Disease (COPD) is a highly heterogeneous condition projected to become the third leading cause of death worldwide by 2030. To better characterize this condition, clinicians have classified patients sharing certain symptomatic characteristics, such as symptom intensity and history of exacerbations, into distinct phenotypes. In recent years, the growing use of machine learning algorithms, and cluster analysis in particular, has promised to advance this classification through the integration of additional patient characteristics, including comorbidities, biomarkers, and genomic information.

Ontologies in Big Health Data Analytics: Application to Routine Clinical Data.

Ontologies are an important big-data analytics tool. Historically code lists were created by domain experts and mapped between different coding systems. Ontologies allow us to develop better representations of clinical concepts, data and facilitate better data extracts from routine clinical data.

Building an information security strategy for EHR: guidelines for assessing the current situation.

This paper is looking at electronic health record (EHR) systems and their information security strategy. It focuses on the first step of building an information security strategy which is analysing the current situation of an EHR system. This research is based on different research methods applied to different EHR systems.

Web-services-based resource discovery model and service deployment on HealthGrids.

HealthGrids represent the next generation of advanced healthcare IT and hold the promise to untangle complex healthcare-data problems by integrating health information systems and healthcare entities. Healthcare could benefit from a new delivery approach using HealthGrids to better meet the biomedical and health-related needs. Specialized services are needed to provide unified discovery of and ubiquitous access to available HealthGrid resources.

An evaluation framework for Health Information Systems: human, organization and technology-fit factors (HOT-fit).

Background And Purpose: The realization of Health Information Systems (HIS) requires rigorous evaluation that addresses technology, human and organization issues. Our review indicates that current evaluation methods evaluate different aspects of HIS and they can be improved upon. A new evaluation framework, human, organization and technology-fit (HOT-fit) was developed after having conducted a critical appraisal of the findings of existing HIS evaluation studies.

Wavelet filtering of the P300 component in event-related potentials.

This paper presents an application of wavelet filtering to single-trial P300 component analysis. The objective of this study is to introduce a new method for analyzing the P300 component, when performing a given cognitive task, in this case, a two-choice reaction time task. The discrete wavelet transform with Daubechies wavelet is employed to detect the presence of P300 in individual trials.

Health information systems adoption: findings from a systematic review.

Earlier evaluation studies on Health Information Systems (HIS) adoption have highlighted a large number of adoption problems that were attributed to the lack of fit between technology, human and organisation factors. Lessons can be learned from these evaluation studies by identifying the most important factors of HIS adoption. In order to study the adoption issue, a qualitative systematic review has been performed using a recently introduced framework, known as HOT-fit (Human, Organisation and Technology fit).

Investigating evaluation frameworks for health information systems.

Background And Purpose: Evaluation of health information systems (HIS) enables the assessment of the extent to which HIS are fulfilling their objectives in supporting the services of healthcare delivery. This paper presents an overview of evaluation in health informatics and information systems.

Methods: Literature review on discourses, dimensions and methods of HIS and IS evaluation.

Management of medicines information for patient safety.

Ensuring patient safety in the management of medicines requires the efficient collection, administration, analysis and distribution of huge amounts of data from different sources. This requires the development of generic methods and tools for tasks such as: (1) data mining and semantics-based inference; (2) integration of heterogeneous scientific information databases relating to drugs and diseases; (3) terminology and coding issues; (4) adverse drug events and signal detection; and (5) networking and security. Collaborative research and development will be required to develop coordinated health care information services.

Impact phase kinematics of instep kicking in soccer.

The purpose of this study was to capture the lower limb kinematics before during and after ball impact of soccer kicking by examining the influence of both sampling rate and smoothing procedures. Nine male soccer players performed maximal instep kicks and the three-dimensional leg movements were captured at 1000 Hz. Angular and linear velocities and accelerations were determined using four different processing approaches: processed using a modified version of a time-frequency filtering algorithm (WGN), smoothed by a second-order low-pass Butterworth filter at 200 Hz cut-off (BWF), re-sampled at 250 Hz without smoothing (RSR) and re-sampled at 250 Hz but filtered by the same Butterworth filter at 10 Hz cut-off (RSF).

Health information systems evaluation: a focus on clinical decision supports system.

In a review of selected literature on Health Information Systems (HIS) evaluation, a specific focus on Clinical Decision Support Systems (CDSS) is taken because of their relative popularity. This paper discusses the issues and problems of CDSS evaluation such as methods, adoption and barriers. The limited use and evaluation of CDSS are still debated.

Matched time-frequency filter for ECG signal enhancement.

A new method for ECG signal enhancement is presented, based on "matched" time-frequency filtering in the Wigner representation. Performance analysis shows that the method is particularly useful for noise removal in clinical ECG.