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. METHODS: In total, 24 DTs and 96 GPs attended a training programme on PSLs. Diagnostic skills as well as therapeutic strategy were assessed, prior to the training (pretest) and after the training (post-test) using clinical images alone, as well as after the addition of dermatoscopic images (integrated post-test). Bayesian hypothesis testing was used to determine statistical significance of differences between pretest, post-test and integrated post-test scores. RESULTS: Both the DTs and the GPs demonstrated benefit from the training: at the integrated post-test, the median proportion of correctly diagnosed PSLs was 73% (range 30-90) for GPs and 63% (range 27-80) for DTs. A statistically significant difference between pretest results and integrated test results was seen, with a Bayes factor > 100. At 12 percentage points higher, the GPs outperformed DTs in the accuracy of detecting PSLs. CONCLUSIONS: The study shows that a training programme focusing on PSLs while including dermoscopy positively impacts detection of PSLs by DTs and GPs. This training programme could form an integral part of the training of DTs in screening procedures, although additional research is needed.

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. Findings: The main contributions of the presented work are the development of a conceptual BA framework and a BA dashboard tool to support management and decision making in primary care. This was evaluated through a case study of the implementation of the BA dashboard tool in London's largest primary care provider. This BA tool provides real-time information to enable simpler decision-making processes and to inform business transformation in a number of areas. The resulting increased efficiency has led to significant cost savings and improved delivery of patient care.

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

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/30092.

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. METHODS: We used the COVID-19 Radiography database [36], which contains x-ray images of COVID-19, other viral pneumonia, and normal lungs. We developed a CNN in which we added a dense layer on top of a pre-trained baseline CNN (EfficientNetB0), and we trained, validated, and tested the model on 15,153 X-ray images. We used data augmentation to avoid overfitting and address class imbalance; we used fine-tuning to improve the model's performance. From the external test dataset, we calculated the model's accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. RESULTS: Our model differentiated COVID-19 from normal lungs with 95% accuracy, 90% sensitivity, and 97% specificity; it differentiated COVID-19 from other viral pneumonia and normal lungs with 93% accuracy, 94% sensitivity, and 95% specificity. CONCLUSIONS: Our parsimonious CNN shows that it is possible to differentiate COVID-19 from other viral pneumonia and normal lungs on x-ray images with high accuracy. Our method may assist clinicians with making more accurate diagnostic decisions and support chest X-rays as a valuable screening tool for the early, rapid diagnosis of COVID-19. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13755-021-00166-4.

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. METHODS: A prospective 4 years observational study that applies machine learning tools to identify COPD phenotypes among 13 260 patients from the UK Royal College of General Practitioners and Surveillance Centre database. The phenotypes were identified prior to diagnosis (training data set), and their reproducibility was assessed after COPD diagnosis (validation data set). RESULTS: Three COPD phenotypes were identified, the most common of which was the 'fast decliner'-characterised by patients of younger age with the lowest number of COPD exacerbations and better lung function-yet a fast decline in lung function with increasing number of exacerbations. The other two phenotypes were characterised by (a) patients with the highest prevalence of COPD severity and (b) patients of older age, mostly men and the highest prevalence of diabetes, cardiovascular comorbidities and hypertension. These phenotypes were reproduced in the validation data set with 80% accuracy. Gender, COPD severity and exacerbations were the most important risk factors for lung function decline in the most common phenotype. CONCLUSIONS: In this study, three COPD phenotypes were identified prior to patients being diagnosed with COPD. The reproducibility of those phenotypes in a blind data set following COPD diagnosis suggests their generalisability among different populations.

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 significant reductions in regular childhood vaccination during pandemic have increased the risk of outbreaks of vaccine-preventable diseases. Concerns among parents over possibly exposing their children to COVID-19 during child visits may have contributed to the reported declines. Innovative and cost-effective mHealth interventions must be implemented in order to address the problem of inadequate immunization rates. In addition, it is also critical to understand the end user needs in order to reflect on the highly relevant essence of the customized healthcare experience. OBJECTIVE: The aim of this study was to learn about caregivers' attitudes toward the usability and acceptability of behavior-change smartphone applications (mobile phones) for improving immunization coverage in Pakistan. METHODS: A mixed-method design was employed for this study. The study was conducted at Aga Khan University, Hospital. Parents visiting the Community Health Center for 6-week vaccination of their children were recruited. The study was conducted in two stages. Stage 1 consisted of qualitative interviews that grasped the parent's attitudes and challenges to immunization, as well as their acceptability and accessibility of the smartphone-based behavior-change application to increase vaccine uptake. Stage 1 was followed by stage 2, in which data were collected through a questionnaire designed by using data from qualitative interviews. RESULTS: The majority of participants agreed that immunization serves an important role in protecting their child from illnesses that cause morbidity and mortality. Almost all of them emphasized the importance of using a pre-appointment method at vaccination center in order to reduce the waiting time. Furthermore, participants were also interested in AI-based behavior modification applications related to immunization. They also wanted to have applications in their native language for better understanding and communication of related information. In our study, approximately 95.2 percent of participants agreed to accept SMS immunization updates, which was also reasonably high. Lastly, the majority of them identified forgetfulness as a significant contributor to regular immunization. CONCLUSION: To enhance the uptake of childhood vaccines, overall vaccination rates, and overcome barriers related to vaccination coverage, cost-effective and user-friendly mHealth AI-based smart phone applications are required to raise awareness regarding the continuation of vaccination service and the importance of timely vaccination. Parents' experiences and attitudes must be considered while designing and evaluating the efficacy of mHealth-based interventions.

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. The cohort was extracted from the UK Royal College of General Practitioners and Surveillance Centre database. The COPD phenotypes were identified prior to diagnosis and their reproducibility was assessed following COPD diagnosis. We then developed four classifiers for predicting cardiovascular comorbidities. RESULTS: Three subtypes of the COPD cardiovascular phenotype were identified prior to diagnosis. Phenotype A was characterised by a higher prevalence of severe COPD, emphysema, hypertension. Phenotype B was characterised by a larger male majority, a lower prevalence of hypertension, the highest prevalence of the other cardiovascular comorbidities, and diabetes. Finally, phenotype C was characterised by universal hypertension, a higher prevalence of mild COPD and the low prevalence of COPD exacerbations. These phenotypes were reproduced after diagnosis with 92% accuracy. The random forest model was highly accurate for predicting hypertension while ruling out less prevalent comorbidities. CONCLUSIONS: This study identified three subtypes of the COPD cardiovascular phenotype that may generalize to other populations. Among the four models tested, the random forest classifier was the most accurate at predicting cardiovascular comorbidities in COPD patients with the cardiovascular phenotype.

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. Innovative and cost-effective measures must be taken to understand and deal with the issue of low immunization rates. However, only a few smartphone-based interventions have been carried out in low- and middle-income countries (LMICs) to improve RCI. OBJECTIVE: The primary objectives of this study are to evaluate whether a personalized mobile app can improve children's on-time visits at 10 and 14 weeks of age for RCI as compared with standard care and to determine whether an artificial intelligence model can be incorporated into the app. Secondary objectives are to determine the perceptions and attitudes of caregivers regarding childhood vaccinations and to understand the factors that might influence the effect of a mobile phone-based app on vaccination improvement. METHODS: A mixed methods randomized controlled trial was designed with intervention and control arms. The study will be conducted at the Aga Khan University Hospital vaccination center. Caregivers of newborns or infants visiting the center for their children's 6-week vaccination will be recruited. The intervention arm will have access to a smartphone app with text, voice, video, and pictorial messages regarding RCI. This app will be developed based on the findings of the pretrial qualitative component of the study, in addition to no-show study findings, which will explore caregivers' perceptions about RCI and a mobile phone-based app in improving RCI coverage. RESULTS: Pretrial qualitative in-depth interviews were conducted in February 2020. Enrollment of study participants for the randomized controlled trial is in process. Study exit interviews will be conducted at the 14-week immunization visits, provided the caregivers visit the immunization facility at that time, or over the phone when the children are 18 weeks of age. CONCLUSIONS: This study will generate useful insights into the feasibility, acceptability, and usability of an Android-based smartphone app for improving RCI in Pakistan and in LMICs. TRIAL REGISTRATION: ClinicalTrials.gov NCT04449107; https://clinicaltrials.gov/ct2/show/NCT04449107. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/22996.

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. We aim to follow a mixed-methods strategy and to evaluate these projects and studies through a strengths, weaknesses, opportunities, and threats (SWOT) analysis to identify the internal and external factors that can potentially facilitate or hinder the progress of digital health in Pakistan. METHODS: This study aims to evaluate digital health projects carried out in the last 5 years in Pakistan with mixed methods. The qualitative and quantitative data obtained from field surveys were categorized according to the World Health Organization's (WHO) recommended building blocks for health systems research, and the data were analyzed using a SWOT analysis strategy. RESULTS: Of the digital health projects carried out in the last 5 years in Pakistan, 51 are studied. Of these projects, 46% (23/51) used technology for conducting research, 30% (15/51) used technology for implementation, and 12% (6/51) used technology for app development. The health domains targeted were general health (23/51, 46%), immunization (13/51, 26%), and diagnostics (5/51, 10%). Smartphones and devices were used in 55% (28/51) of the interventions, and 59% (30/51) of projects included plans for scaling up. Artificial intelligence (AI) or machine learning (ML) was used in 31% (16/51) of projects, and 74% (38/51) of interventions were being evaluated. The barriers faced by developers during the implementation phase included the populations' inability to use the technology or mobile phones in 21% (11/51) of projects, costs in 16% (8/51) of projects, and privacy concerns in 12% (6/51) of projects. CONCLUSIONS: We conclude that while digital health has a promising future in Pakistan, it is still in its infancy at the time of this study. However, due to the coronavirus disease 2019 (COVID-19) pandemic, there is an increase in demand for digital health and implementation of health outcomes following global social distancing protocols, especially in LMICs. Hence, there is a need for active involvement by public and private organizations to regulate, mobilize, and expand the digital health sector for the improvement of health care systems in countries.

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. This combination would allow researchers to more reliably identify new COPD phenotypes, as well as better characterize existing ones, with the aim of improving diagnosis and developing novel treatments. Here, we systematically review the last decade of research progress, which uses cluster analysis to identify COPD phenotypes. Collectively, we provide a systematized account of the extant evidence, describe the strengths and weaknesses of the main methods used, identify gaps in the literature, and suggest recommendations for future research.

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. It also makes the process of case identification and key outcome measures transparent. We describe a process we have operationalised in our research. We use ontologies to resolve the semantics of complex health care data. The use of the method is demonstrated through a pregnancy case identification method. Pregnancy data are recorded in different coding systems and stored in different general practice systems; and pregnancy has its own complexities in that not all pregnancies proceed to term, they have different lengths and involve multiple providers of health care.

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. In this paper we define eight elements that can be used as guidelines for how best to assess the current situation of any EHR system.

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. The different types of services available on HealthGrids are classified into two levels, the operational-level services and the management-level services. This paper takes a fresh approach to address the problems of resource discovery in HealthGrids based on Web services (WS) and WS technologies and proposes a WS-based resource discovery model.

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. A critical appraisal of selected HIS and IS evaluation frameworks is undertaken in order to identify HIS evaluation dimensions and measures. The frameworks are compared based on their inclusion of human, organizational and technological factors. RESULTS: We found that an increasing number of evaluation studies deal with two distinct trends of HIS: one considers human and organizational issues and the other is concerned with the employment of a subjectivist approach. Our review indicates that current evaluation methods complement each other in that they evaluate different aspects of HIS and they can be improved upon. CONCLUSIONS: Evaluation is complex; it is easy to measure many things but not necessarily the right ones. Nevertheless, it is possible to consider, a HIS evaluation framework with more comprehensive and specific measures that would incorporate technological, human and organizational issues to facilitate HIS evaluation.

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. HOT-fit builds on previous models of IS evaluation--in particular, the IS Success Model and the IT-Organization Fit Model. This paper introduces the new framework for HIS evaluation that incorporates comprehensive dimensions and measures of HIS and provides a technological, human and organizational fit. METHODS: Literature review on HIS and IS evaluation studies and pilot testing of developed framework. The framework was used to evaluate a Fundus Imaging System (FIS) of a primary care organization in the UK. The case study was conducted through observation, interview and document analysis. RESULTS: The main findings show that having the right user attitude and skills base together with good leadership, IT-friendly environment and good communication can have positive influence on the system adoption. CONCLUSIONS: Comprehensive, specific evaluation factors, dimensions and measures in the new framework (HOT-fit) are applicable in HIS evaluation. The use of such a framework is argued to be useful not only for comprehensive evaluation of the particular FIS system under investigation, but potentially also for any Health Information System in general.

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). The paper identifies and highlights the following critical adoption factors: technology (ease of use, system usefulness, system flexibility, time efficiency, information accessibility and relevancy); human (user training, user perception, user roles, user skills, clarity of system purpose, user involvement); organisation (leadership and support, clinical process, user involvement, internal communication, inter organisational system, as well as the fit between them. The findings can be used to guide future system development and inform relevant decision making.

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. Easy access to high-quality information will provide better health care and increased cost efficiency.

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). The WGN approach appeared to establish representative kinematics, whereas the other procedures failed to remove noisy oscillation from the baseline of signal (BWF), lost the peaks of rapid changes (RSR) or produced totally distorted movement patterns (RSF). The results indicate that the procedures used by some previous studies may have been insufficient to adequately capture the lower limb motion near ball impact. We propose a new time-frequency filtering technique as a better way to smooth data whose frequency content varies dramatically.

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. Wavelet filtering is applied to remove noise and unwanted frequency components from discrete wavelet transform (DWT) coefficients based on prior knowledge of event-related potentials (ERPs). The filtering mask is computed from the grand-average of wavelet coefficients over all participants. With this filtering, the P300 component is accurately localized in both time and scale. The findings suggest the procedure to have considerable potential for the analysis of time-series data in the behavioral neurosciences.

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. Clinical evaluations of CDSS performed in the actual clinical settings may provide better understanding of their adoption, particularly in the diagnostic function. New HIS evaluation frameworks that incorporate technological, human and organisational issues may be useful to complement existing ones.