Purpose The purpose of this research is to analyze the literature published on mobile health (mHealth) in the Indian context. It also reviews the most important research works and presents various methodologies adopted by the researchers in this domain. Design/methodology/approach The SciVerse SCOPUS database was used for extracting the literature on mobile health. The study used articles published

Objective The potential ability for weather to affect SARS-CoV-2 transmission has been an area of controversial discussion during the COVID-19 pandemic. Individuals’ perceptions of the impact of weather can inform their adherence to public health guidelines; however, there is no measure of their perceptions. We quantified Twitter users’ perceptions of the effect of weather and analyzed how they evolved

Introduction and purpose Informal caregivers of persons living with dementia have significant unmet information needs that, if met, would better equip them to provide effective care. Despite the existence of health information technologies, websites, resources, and organizations dedicated to dementia caregiving, caregivers continue to report unmet information needs. Caregivers’ continued unmet information

Objective To develop an algorithm that infers patient delivery dates (PDDs) and delivery-specific details from Electronic Health Records (EHRs) with high accuracy; enabling pregnancy-level outcome studies in women’s health. Materials and Methods We obtained EHR data from 1,060,100 female patients treated at Penn Medicine hospitals or outpatient clinics between 2010–2017. We developed an algorithm called

Background The introduction of a paper-based Community Case Management (CCM) in Malawi has contributed to a reduction of child morbidity and mortality rates. In addition, the introduction of electronic Community Case Management (eCCM) (smartphones with built in CCM apps) may help to reduce the under-five mortality rates even further. Purpose It is not uncommon for Apps with a similar area of interest

Background Quality indicators (QIs) are being increasingly used in medicine to compare and improve the quality of care delivered. The feasibility of data collection is an important prerequisite for QIs. Information technology can improve efforts to measure processes and outcomes. In intensive care units (ICU), QIs can be automatically measured by exploiting data from clinical information systems (CIS)

Background For patients requiring admission to the Intensive Care Unit (ICU), transfers of care (TOC) during admission to and discharge from the ICU are particularly high-risk periods for medication errors. In the Australian setting, commonly general wards and the ICU do not share an integrated Electronic Medical ecord (EMR) and specifically an Electronic Medication Management System (EMMS) as part

Background Reliable localization and tracking of the eye region in the pediatric hospital environment is a significant challenge for clinical decision support and patient monitoring applications. Existing work in eye localization achieves high performance on adult datasets but performs poorly in the busy pediatric hospital environment, where face appearance varies because of age, position and the presence

Background and Objective Diabetic foot is one of the important complications of diabetes, which is occurred due to the destructive parameters in different anatomical sites of feet. Management and monitoring of these parameters are very important to decrease or prevent foot ulcers. We aimed to develop a smart wearable device to monitor these parameters to prevent diabetic foot. Methods Following literature

Background Patients and physicians engaging together in the electronic health record (EHR) during clinical visits may provide opportunities to both improve patient understanding and reduce medical errors. Objective To assess the potential impact of a patient EHR display intervention on patient quality and safety. We hypothesized that if patients had a dedicated display with an explicit invitation to

Purpose Various interventions have been designed to help consumers better evaluate the credibility of online health information (OHI). However, assessing information credibility remained the most widely reported challenge by online health consumers. This review aims to provide an overview of major intervention approaches for improving consumer ability to evaluate OHI credibility in order to identify

Background Considering the clinical heterogeneity of the bipolar disorder, difficulties are encountered in making the correct diagnosis. Although a number of common findings have been found in studies on the neurocognitive profile of bipolar disorder, the search for a neurocognitive endophenotype has failed. The aim of this study is to separate bipolar disorder patients from healthy controls with higher

Background Accurate differentiation and prioritization in emergency department (ED) triage is important to identify high-risk patients and to efficiently allocate of finite resources. Using data available from patients with suspected cardiovascular disease presenting at ED triage, this study aimed to train and compare the performance of four common machine learning models to assist in decision making

Purpose To develop and internally validate an illness burden index among Medicare beneficiaries before or after a cancer diagnosis. Methods Data source: SEER-CAHPS, linking Surveillance, Epidemiology, and End Results (SEER) cancer registry, Medicare enrollment and claims, and Medicare Consumer Assessment of Healthcare Providers and Systems (Medicare CAHPS) survey data providing self-reported sociodemographic

Background QTc-prolongation is an independent risk factor for developing life-threatening arrhythmias. Risk management of drug-induced QTc-prolongation is complex and digital support tools could be of assistance. Bindraban et al. and Berger et al. developed two algorithms to identify patients at risk for QTc-prolongation. Objective The main aim of this study was to compare the performances of these

Background Since the World Health Organization (WHO) declared the COVID-19 as a Public Health Emergency of International Concern (PHEIC) on January 31, 2020, governments have been enfaced with crisis for timely responses. The efficacy of these responses directly depends on the social behaviors of the target society. People react to these actions with respect to the information they received from different

Background Researches on Lifestyle medicine (LM) have emerged in recent years to garner wide attention. Prostate cancer (PCa) could be prevented and treated by positive lifestyles, but the association between lifestyles and PCa is always personalized. Objectives In order to solve the heterogeneity and diversity of different data types related to PCa, establish a standardized lifestyle ontology, promote

Context Medical professionals and hospitals promote solutions like care pathways and Health Information Systems (HIS) to support medical conduct and improve the quality of medical care. Purpose This study proposes MedPath: a Domain Specific Language (DSL) for modeling care pathways based on the paradigms of Model-Based Engineering (MBE) that can be integrated into software solutions. Procedures We

Background Mobile health applications can help empowering caregivers and promote their wellbeing and their quality of life. Objective To analyze the technical and functional characteristics of mobile health applications designed for caregivers of individuals with chronic conditions and/or diseases. Materials and methods Systematic search of mobile health applications for smartphones (apps) based on

Background and objective Identification and Standardization of data elements used in clinical trials may control and reduce the cost and errors during the operational process, and enable seamless data exchange between the electronic data capture (EDC) systems and Electronic Health Record (EHR) systems. This study presents a methodology to comprehensively capture the clinical trial data element needs

Background The internet has become an important source of health information among people across the globe. However, the novel coronavirus outbreak has led to a broader dissemination of fake and misleading health information. While health professionals may be able to critically evaluate internet-based information, the skills at finding, appraising, and using COVID-19-related online information may

Background The prediction of overall survival in tongue cancer is important for planning of personalized care and patient counselling. Objectives This study compares the performance of a nomogram with a machine learning model to predict overall survival in tongue cancer. The nomogram and machine learning model were built using a large data set from the Surveillance, Epidemiology, and End Results (SEER)

Background The observed consultation length at specialty clinics, such as cardiology care, is represented by two underlying groups - one with zero service time due to patient no-shows, and the other characterized by positive values with high variance. This inconstancy affects the scheduler’s ability to accurately estimate consultation length, which, in turn, hinders effective utilization of the clinic’s

Objective The purpose of this study is to examine the relationship between the greater need for information generated by bundled payment reimbursement and the use of Health Information Exchange (HIE). Methods The study is based on a secondary data analysis using theAmerican Hospital Association (AHA) Healthcare IT Database and the AHA Annual Survey. A logistic regression was used to test the likelihood

Background Reducing the harms associated with acute kidney injury (AKI) requires addressing a wide range of patient safety issues, including polypharmacy and transitions of care, particularly for vulnerable patient groups. Computerised audit and feedback can transform the way healthcare organisations measure, analyse and learn from quality and safety data across different care settings, potentially

Background The current systems of reporting waiting time to patients in public emergency departments (EDs) has largely relied on rolling average or median estimators which have limited accuracy. This study proposes to use machine learning (ML) algorithms that significantly improve waiting time forecasts. Methods By implementing ML algorithms and using a large set of queueing and service flow variables

Objective To qualitatively evaluate the quality of the data collection process used by the Chinese national HIV/AIDS data repository (CRIMS), using a four-dimensional (4D) framework. The process is vital for the acquisition of high-quality data for ending the HIV/AIDS epidemic in China. Methods The study was carried out in China from September 2014 to April 2015. Stratified convenient sampling was

Purpose : To evaluate the application of machine learning methods, specifically Deep Neural Networks (DNN) models for intensive care (ICU) mortality prediction. The aim was to predict mortality within 96 hours after admission to mirror the clinical situation of patient evaluation after an ICU trial, which consists of 24-48 hours of ICU treatment and then “re-triage”. The input variables were deliberately

Objective With the advancement of mobile technologies, patients can access medical and patient educational information anytime and anywhere. Computer-aided patient education has been advocated as a key means of interventions for improving patient knowledge and compliance (i.e., adherence). However, evidence of the efficacy of computer-aided patient education remains relatively limited. For example

Introduction Secure patient portals have improved patient access to information, including provider notes. Although there is evidence suggesting that electronic note sharing improves communication and care quality, some studies have reported provider concerns regarding note sharing. Material and methods This mixed-methods single site study utilized survey questions from a previously published landmark

Background An Electronic Health Record (EHR) has been introduced to four Irish maternity units, with further implementations planned. Previous studies indicate that healthcare professionals are concerned that EHRs may increase time spent on documentation and medication-related tasks. Objective To determine the impact of an EHR on task time distribution in a Neonatal Intensive Care Unit (NICU). Methods

Objective This study reviewed the competency and threshold standards for allied health professionals to identify the inclusion of digital health competencies. Materials and methods A nine-stage, sequential meta-synthesis of professional standards was undertaken. Statements relevant to digital health were extracted, categorised by discipline, and coded to the level in the standards, skills or knowledge

Propose Advancements in mobile technology, primarily through the use of applications, may support the process of monitoring adherence to oral therapies, controlling toxicities, or providing self-care guidelines to patients undergoing chemotherapy treatment. This study aims to assemble the available knowledge regarding the use of mobile applications by cancer patients undergoing chemotherapy treatment

Objectives Electronic Health Records (EHRs) contain scanned documents from a variety of sources such as identification cards, radiology reports, clinical correspondence, and many other document types. We describe the distribution of scanned documents at one health institution and describe the design and evaluation of a system to categorize documents into clinically relevant and non-clinically relevant

Purpose We introduce a system devoted to automatically produce structured data in radiotherapy to: (i) relate clinical outcomes with any variable; and (ii) optimize resources and procedures. Methods and material We have designed a detailed workflow for a patient to follow during radiotherapy treatments. Four elements of Oncology Information Systems (OISs) can be mainly interrelated in our system: (a)

Objectives To investigate trends in data errors over the 40 months after the implementation of an electronic medical record (eMR) system in an Australian regional Drug and Alcohol (D&A) Service. Methods One hundred and twenty three error reports and data on occasions of service were obtained from the D&A Service. Statistical analysis was conducted to describe types of errors, to compare distribution

Objectives We previously reported improved outcomes after implementing the electronic-AsthmaTracker (e-AT), a self-monitoring tool for children with asthma, at 11 ambulatory pediatric clinics. This study assesses e-AT adherence and impact across race/ethnicity subgroups. Study Design Secondary data analysis of a prospective cohort study of children ages 2–17 years with persistent asthma, enrolled from

Objective Hospital performance evaluation is vital in terms of managing hospitals and informing patients about hospital possibilities. Also, it plays a key role in planning essential issues such as electrical energy management and cybersecurity in hospitals. In addition to being able to make this measurement objectively with the help of various indicators, it can become very complicated with the participation

Background Promoting physical activity is one of the most critical components in strategies to improve both individual and population health. Previous studies on electronic wearable devices (EWDs) have shown modest improvements in physical activity, including daily steps and energy expenditures. Large population-based evidence on EWDs and physical activity is needed to better understand the patterns

Objective The coronavirus pandemic has highlighted the need to simplify data collection for critically-ill patients, particularly for physicians relocated to the ICU setting. Herein we present a simple, reproducible, and highly-customizable manual-entry tool to track ICU patients using new HIPAA-compliant Google Big Query technology for parsing large datasets. This innovative flow chart is useful and

Aim To summarize the findings from literature reviews with a view to identifying and exploring the key factors which impact on the success of an EHR implementation across different healthcare contexts. Introduction Despite the widely recognised benefits of electronic health records (EHRs), their full potential has not always been achieved, often as a consequence of the implementation process. As more

Objective To build a machine-learning model that predicts laboratory test results and provides a promising lab test reduction strategy, using spatial-temporal correlations. Materials and methods We developed a global prediction model to treat laboratory testing as a series of decisions by considering contextual information over time and across modalities. We validated our method using a critical care

Background Parkinson's disease (PD) is a neurodegenerative disease of the elderly, which leads to patients’ motor and non-motor disabilities and affects patients’ quality of daily life. Timely and effective detection of PD is a key step to medical intervention. Recently, computer aided methods for PD detection have gained lots of attention in artificial intelligence domain. Methods This paper proposed

Objective This study aims to develop and test a new computer-aided diagnosis (CAD) scheme of chest X-ray images to detect coronavirus (COVID-19) infected pneumonia. Method CAD scheme first applies two image preprocessing steps to remove the majority of diaphragm regions, process the original image using a histogram equalization algorithm, and a bilateral low-pass filter. Then, the original image and

Background With the recent advances in computational science, machine-learning methods have been increasingly used in medical research. Because such projects usually require both a clinician and a computational data scientist, there is a need for interdisciplinary research collaboration. However, there has been no published analysis of research collaboration networks in cardiovascular medicine using

Background Inpatient falls, many resulting in injury or death, are a serious problem in hospital settings. Existing falls risk assessment tools, such as the Morse Fall Scale, give a risk score based on a set of factors, but don’t necessarily signal which factors are most important for predicting falls. Artificial intelligence (AI) methods provide an opportunity to improve predictive performance while

Background Electronic approaches are becoming more widely used to obtain informed consent for research participation. Electronic consent (e-consent) provides an accessible and versatile approach to the consenting process, which can be enhanced with audio-visual and interactive features to improve participant engagement and comprehension of study procedures. Best practice guidance underpinned by ethical

Background Social media have emerged as a platform for experience and knowledge sharing in the medical community. The online medical community is garnering increasing research attention; however, there is a lack of understanding of what factors influence the helpfulness and engagement of experience sharing in the community. Methods Clinical documents manifest physicians’ experience and knowledge. This

Objective Vancomycin, a commonly used antimicrobial, has a narrow therapeutic index; therefore, Therapeutic Drug Monitoring (TDM) is required. Although the Electronic Medical Record (EMR) may improve patient care, without appropriate optimization, it can contribute to incorrectly drawn vancomycin levels. For medication administration, nurses utilize the Medication Administration Record (MAR) for medication

Objectives Acute kidney injury (AKI) is a sudden episode of kidney failure or damage and the risk of AKI is determined by the complex interactions of patient factors. In this study, we aimed to find out which risk factors in hospitalized patients are more likely to indicate severe AKI. Methods We constructed a retrospective cohort of adult patients from all inpatient units of a tertiary care academic

Objective To identify the types of adverse drug events (ADEs) that drug-drug interaction (DDI) alerts are trying to prevent in hospitalized patients. Methods This was a retrospective cross-sectional study conducted in a tertiary referral hospital in Australia. All DDI alerts encountered by prescribers during a 1-month period were evaluated for potential ADEs targeted for prevention. If the same DDI

Objective We aimed to identify machine learning (ML) models for type 2 diabetes (T2DM) prediction in community settings and determine their predictive performance. Method Systematic review of ML predictive modelling studies in 13 databases since 2009 was conducted. Primary outcomes included metrics of discrimination, calibration, and classification. Secondary outcomes included important variables,

Background Electronic health records (EHRs) are transforming and revolutionizing the healthcare industry. However, whereas developed countries have a high EHR penetration rate, adoption of EHRs in developing countries is lagging behind. Recently, the Korean and Russian governments have been pursuing economic cooperation in the Russian Far East. Thus, since 2009, Russia’s EHR market and healthcare system

Background The negative impact of unnecessary diagnostic tests on healthcare systems and patients has been widely recognized. Medical researchers in various countries have been devoting effort to reduce unnecessary diagnostic tests by using different types of interventions, including information and communications technology-based (ICT-based) intervention, educational intervention, audit and feedback

Objective The International Medical Informatics Association (IMIA) has provided recommendations on Education in Biomedical and Health Informatics (BMHI) as guidance on competencies relevant to education of BMHI specialists. However, it remains unclear how well these competencies have been adopted to guide emerging degree programs in low- and middle-income countries (LMICs). We evaluated comprehensiveness

Aims Healthcare organizations are increasingly using technology to assist in diabetes management based on telemedicine’s proven ability to improve glycemic regulation, decrease cost, and overcome barriers to effective healthcare. Nevertheless, it remains unclear how telemedicine intersects with primary care. We aim to measure the impact of a remote monitoring program for diabetes on primary care delivery

Background Despite digital health providing opportunities to enhance the quality, efficiency and safety of primary healthcare, the adoption of digital tools and technologies has been slow, partly because of poor digital health literacy. For primary healthcare systems to take full advantage of these technologies, a capable, digitally literate workforce is necessary. Still, the essential digital health

Objective The Coronavirus Disease 2019 (COVID-19) has currently ravaged through the world, resulting in over thirteen million confirmed cases and over five hundred thousand deaths, a complete change in daily life as we know it, worldwide lockdowns, travel restrictions, as well as heightened hygiene measures and physical distancing. Being able to analyse and predict the spread of this epidemic-causing

Background The lack of interoperable IT systems between residential aged care facilities (RACF) and general practitioners (GP) in primary care settings in Australia introduces the potential for medication discrepancies and other medication errors. The aim of the GRACEMED study is to determine the extent and potential severity of medication discrepancies between general practice and RACFs, and identify