Food hygiene knowledge and behaviour among domestic food handlers during COVID 19 pandemic in Bangladesh.

The COVID-19 pandemic reshaped every aspect of life, including food safety. Understanding food safety behaviour at home is necessary for developing effective strategies to mitigate foodborne disease during and after this pandemic. This study administered a cross-sectional survey among 503 domestic food handlers to examine the food handlers' food safety concerns during the pandemic and pandemic-related knowledge and hygiene behaviour (PRKHB) in Bangladesh. The results found that only 35.8% of respondents in this study were more concerned about food safety because of the COVID-19 pandemic. Although the results found a good PRKHB among 95.8% of urban food handlers, overall, 62% showed a poor level of PRKHB. Only 38.8% reported washing their hands after returning home or preparing meals every time. The regression model found that food safety concerns positively related to the PRKHB, and participants who lived in rural areas had a negative association with the PRKHB. The study also explored sociodemographic variations and significant differences observed between urban and rural areas. Meticulous educational campaigns and targeted messages to the food handlers on food safety risks, food handling practices and hand hygiene are necessary to minimise the foodborne disease burden in this region.

Influence of food safety concerns on safe food purchasing at rural and urban consumers in Bangladesh.

Food contamination and food adulteration are widely known public health issues in Bangladesh. This research explores consumers' perceptions of food hazards and safe food purchasing strategies. The study uses data from a survey of 450 primary household food purchasers in an urban supermarket and two traditional bazaars in urban and regional areas. This study explored the differences between consumers in urban and regional areas. The urban supermarket consumers showed more significant concerns about food hazards and safe food purchasing strategies than consumers at the urban and regional bazaars. Urban consumers indicated a higher food safety perception, whereas regional consumers were concerned about food safety deterioration. This study finds that urban supermarket consumers considered biological food hazards more during food purchasing than the regional bazaar. Most of the consumers in this study were concerned about chemical food hazards such as formalin and pesticide residues, and these consumers considered food purchasing factors such as tastes and expiry dates more while purchasing food. On the other hand, consumers concerned about biological hazards emphasised food purchasing place and product origin. Urban and regional bazaar consumers considered prices more than urban supermarket consumers. The study also explores sociodemographic variations, such as women having more concern about chemical food hazards than men and people with less formal education levels showing less concern about food hazards. Therefore, dissemination of safe food knowledge, adequate infrastructure, monitoring, and policymaking are some immediate necessary steps to improve food safety situations and safe food accessibility in Bangladesh.

Application of Deep Learning Models for Automated Identification of Parkinson's Disease: A Review (2011-2021).

Parkinson's disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally.

Uptake of telehealth services funded by Medicare in Australia.

OBJECTIVE: The aim of this study is to identify the extent to which the Medicare item numbers and incentives, introduced in July 2011, have been effective in stimulating telehealth activity in Australia. METHODS: A retrospective descriptive study utilising data on the uptake of telehealth item numbers and associated in-person services, from July 2011 to April 2014, were obtained from Medicare Australia. The main outcome measures were number of telehealth services over time, plus uptake proportionate to in-person services, by jurisdiction, by speciality, and by patient gender. RESULTS: Specialist consultations delivered by video communication and rebated by Medicare rose to 6000 per month, which is 0.24% of the total number of specialist consultations. The highest proportional uptake was in geriatrics and psychiatry. In 52% per cent of video consultations the patient was supported by an on-site healthcare provider, most commonly a general practitioner. There were substantial jurisdictional differences. A significantly lower percentage of female patients were rebated for item 99, which is primarily used by surgeons. CONCLUSIONS: Medicare rebates and incentives, which are generous by world standards, have resulted in specialist video consultations being provided to underserved areas, although gaps still remain that need new models of care to be developed. WHAT IS KNOWN ABOUT THE TOPIC?: Video consultations have been rebated by Medicare since July 2011 as a means of increasing access to specialist care in rural areas, aged care facilities and Aboriginal health services. WHAT DOES THIS PAPER ADD?: The uptake of this telehealth initiative has grown over time, but still remains low. For half the video consultations the patient was supported by an on-site healthcare provider, most commonly a general practitioner. Geriatrics and psychiatry are the specialties with the highest proportional uptake. WHAT ARE THE IMPLICATIONS FOR PRACTITIONERS?: New models of care with a greater focus on consultation-liaison with primary care providers need to be developed to realise the potential of this initiative and to fill continuing gaps in services.

Improving primary care referral to specialist services: a protocol for a 10-year global systematic review in the Australian context.

INTRODUCTION: Barriers to accessing specialist services impart a significant burden on patient outcomes and experience as well as a cost and administrative burden on health systems due to healthcare wastage and inefficiencies. This paper outlines the planned protocol for a systematic review relating to how health systems perform with regard to patient access to specialist care, and the efficacy of interventions aimed at improving this. METHODS AND ANALYSIS: Systematic review of the literature will be carried out on publications retrieved by searching the following electronic literature databases: EBSCOhost Megafile Ultimate (Cumulative Index to Nursing and Allied Health Literature (CINAHL), Academic Search Ultimate, APA Psychological Abstracts (PsycINFO), HealthSource), PubMed (Medical Literature Analysis and Retrieval System Online (MEDLINE)), Elsevier Bibliographic Database (Scopus), Excerpta Medica Database (EMBASE), Web of Science and The Cochrane Library. Articles published over a 10-year period (2012-2022) will be analysed to determine; current accessibility and availability problems faced by primary care services when referring patients to specialist care, and the effectiveness of interventions to improve primary care access to specialist services. Grey literature publications (ie, government reports, policy statements and issues papers, conference proceedings) will not be analysed in this review. Articles not published in English, Spanish or Portuguese will not be included. Two independent reviewers will conduct the initial screening, disagreements will be resolved by a third independent reviewer, following which data extraction and selection of eligible sources will be carried out. Selected articles will be categorised on study design, setting and participants. Methodological quality and heterogeneity will subsequently be assessed using the Mixed Methods Appraisal Tool. A descriptive approach will be used to review and synthesise the findings. ETHICS AND DISSEMINATION: This study does not require ethics committee review as it solely focuses on analysing published literature. Findings will be published and disseminated through a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42022354890.

Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review.

BACKGROUND: Natural Language Processing (NLP) is widely used to extract clinical insights from Electronic Health Records (EHRs). However, the lack of annotated data, automated tools, and other challenges hinder the full utilisation of NLP for EHRs. Various Machine Learning (ML), Deep Learning (DL) and NLP techniques are studied and compared to understand the limitations and opportunities in this space comprehensively. METHODOLOGY: After screening 261 articles from 11 databases, we included 127 papers for full-text review covering seven categories of articles: (1) medical note classification, (2) clinical entity recognition, (3) text summarisation, (4) deep learning (DL) and transfer learning architecture, (5) information extraction, (6) Medical language translation and (7) other NLP applications. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULT AND DISCUSSION: EHR was the most commonly used data type among the selected articles, and the datasets were primarily unstructured. Various ML and DL methods were used, with prediction or classification being the most common application of ML or DL. The most common use cases were: the International Classification of Diseases, Ninth Revision (ICD-9) classification, clinical note analysis, and named entity recognition (NER) for clinical descriptions and research on psychiatric disorders. CONCLUSION: We find that the adopted ML models were not adequately assessed. In addition, the data imbalance problem is quite important, yet we must find techniques to address this underlining problem. Future studies should address key limitations in studies, primarily identifying Lupus Nephritis, Suicide Attempts, perinatal self-harmed and ICD-9 classification.

Automated detection of airflow obstructive diseases: A systematic review of the last decade (2013-2022).

BACKGROUND AND OBJECTIVE: Obstructive airway diseases, including asthma and Chronic Obstructive Pulmonary Disease (COPD), are two of the most common chronic respiratory health problems. Both of these conditions require health professional expertise in making a diagnosis. Hence, this process is time intensive for healthcare providers and the diagnostic quality is subject to intra- and inter- operator variability. In this study we investigate the role of automated detection of obstructive airway diseases to reduce cost and improve diagnostic quality. METHODS: We investigated the existing body of evidence and applied Preferred Reporting Items for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search records in IEEE, Google scholar, and PubMed databases. We identified 65 papers that were published from 2013 to 2022 and these papers cover 67 different studies. The review process was structured according to the medical data that was used for disease detection. We identified six main categories, namely air flow, genetic, imaging, signals, and miscellaneous. For each of these categories, we report both disease detection methods and their performance. RESULTS: We found that medical imaging was used in 14 of the reviewed studies as data for automated obstructive airway disease detection. Genetics and physiological signals were used in 13 studies. Medical records and air flow were used in 9 and 7 studies, respectively. Most papers were published in 2020 and we found three times more work on Machine Learning (ML) when compared to Deep Learning (DL). Statistical analysis shows that DL techniques achieve higher Accuracy (ACC) when compared to ML. Convolutional Neural Network (CNN) is the most common DL classifier and Support Vector Machine (SVM) is the most widely used ML classifier. During our review, we discovered only two publicly available asthma and COPD datasets. Most studies used private clinical datasets, so data size and data composition are inconsistent. CONCLUSIONS: Our review results indicate that Artificial Intelligence (AI) can improve both decision quality and efficiency of health professionals during COPD and asthma diagnosis. However, we found several limitations in this review, such as a lack of dataset consistency, a limited dataset and remote monitoring was not sufficiently explored. We appeal to society to accept and trust computer aided airflow obstructive diseases diagnosis and we encourage health professionals to work closely with AI scientists to promote automated detection in clinical practice and hospital settings.

Using multi-focus group method as an effective tool for eliciting business system requirements: Verified by a case study.

This research aims to explore the multi-focus group method as an effective tool for systematically eliciting business requirements for business information system (BIS) projects. During the COVID-19 crisis, many businesses plan to transform their businesses into digital businesses. Business managers face a critical challenge: they do not know much about detailed system requirements and what they want for digital transformation requirements. Among many approaches used for understanding business requirements, the focus group method has been used to help elicit BIS needs over the past 30 years. However, most focus group studies about research practices mainly focus on a particular disciplinary field, such as social, biomedical, and health research. Limited research reported using the multi-focus group method to elicit business system requirements. There is a need to fill this research gap. A case study is conducted to verify that the multi-focus group method might effectively explore detailed system requirements to cover the Case Study business's needs from transforming the existing systems into a visual warning system. The research outcomes verify that the multi-focus group method might effectively explore the detailed system requirements to cover the business's needs. This research identifies that the multi-focus group method is especially suitable for investigating less well-studied, no previous evidence, or unstudied research topics. As a result, an innovative visual warning system was successfully deployed based on the multi-focus studies for user acceptance testing in the Case Study mine in Feb 2022. The main contribution is that this research verifies the multi-focus group method might be an effective tool for systematically eliciting business requirements. Another contribution is to develop a flowchart for adding to Systems Analysis & Design course in information system education, which may guide BIS students step by step on using the multi-focus group method to explore business system requirements in practice.

Novel nested patch-based feature extraction model for automated Parkinson's Disease symptom classification using MRI images.

OBJECTIVE: Parkinson's disease (PD) is a common neurological disorder with variable clinical manifestations and magnetic resonance imaging (MRI) findings. We propose a handcrafted image classification model that can accurately (i) classify different PD stages, (ii) detect comorbid dementia, and (iii) discriminate PD-related motor symptoms. METHODS: Selected image datasets from three PD studies were used to develop the classification model. Our proposed novel automated system was developed in four phases: (i) texture features are extracted from the non-fixed size patches. In the feature extraction phase, a pyramid histogram-oriented gradient (PHOG) image descriptor is used. (ii) In the feature selection phase, four feature selectors: neighborhood component analysis (NCA), Chi2, minimum redundancy maximum relevancy (mRMR), and ReliefF are used to generate four feature vectors. (iii) Two classifiers: k-nearest neighbor (kNN) and support vector machine (SVM) are used in the classification step. A ten-fold cross-validation technique is used to validate the results. (iv) Eight predicted vectors are generated using four selected feature vectors and two classifiers. Finally, iterative majority voting (IMV) is used to attain general classification results. Therefore, this model is named nested patch-PHOG-multiple feature selectors and multiple classifiers-IMV (NP-PHOG-MFSMCIMV). RESULTS: Our presented NP-PHOG-MFSMCIMV model achieved 99.22, 98.70, and 99.53% accuracies for the collected PD stages, PD dementia, and PD symptoms classification datasets, respectively. SIGNIFICANCE: The obtained accuracies (over 98% for all states) demonstrated the performance of developed NP-PHOG-MFSMCIMV model in automated PD state classification.

A comparative analysis of the principal component analysis and entropy weight methods to establish the indexing measurement.

BACKGROUND: As the world's largest coal producer, China was accounted for about 46% of global coal production. Among present coal mining risks, methane gas (called gas in this paper) explosion or ignition in an underground mine remains ever-present. Although many techniques have been used, gas accidents associated with the complex elements of underground gassy mines need more robust monitoring or warning systems to identify risks. This paper aimed to determine which single method between the PCA and Entropy methods better establishes a responsive weighted indexing measurement to improve coal mining safety. METHODS: Qualitative and quantitative mixed research methodologies were adopted for this research, including analysis of two case studies, correlation analysis, and comparative analysis. The literature reviewed the most-used multi-criteria decision making (MCDM) methods, including subjective methods and objective methods. The advantages and disadvantages of each MCDM method were briefly discussed. One more round literature review was conducted to search publications between 2017 and 2019 in CNKI. Followed two case studies, correlation analysis and comparative analysis were then conducted. Research ethics was approved by the Shanxi Coking Coal Group Research Committee. RESULTS: The literature searched a total of 25,831publications and found that the PCA method was the predominant method adopted, and the Entropy method was the second most widely adopted method. Two weighting methods were compared using two case studies. For the comparative analysis of Case Study 1, the PCA method appeared to be more responsive than the Entropy. For Case Study 2, the Entropy method is more responsive than the PCA. As a result, both methods were adopted for different cases in the case study mine and finally deployed for user acceptance testing on 5 November 2020. CONCLUSIONS: The findings and suggestions were provided as further scopes for further research. This research indicated that no single method could be adopted as the better option for establishing indexing measurement in all cases. The practical implication suggests that comparative analysis should always be conducted on each case and determine the appropriate weighting method to the relevant case. This research recommended that the PCA method was a dimension reduction technique that could be handy for identifying the critical variables or factors and effectively used in hazard, risk, and emergency assessment. The PCA method might also be well-applied for developing predicting and forecasting systems as it was sensitive to outliers. The Entropy method might be suitable for all the cases requiring the MCDM. There is also a need to conduct further research to probe the causal reasons why the PCA and Entropy methods were applied to each case and not the other way round. This research found that the Entropy method provides higher accuracy than the PCA method. This research also found that the Entropy method demonstrated to assess the weights of the higher dimension dataset was higher sensitivity than the lower dimensions. Finally, the comprehensive analysis indicates a need to explore a more responsive method for establishing a weighted indexing measurement for warning applications in hazard, risk, and emergency assessments.

An Exploratory Study of the Readiness of Public Healthcare Facilities in Developing Countries to Adopt Health Information Technology (HIT)/e-Health: the Case of Ghana.

There are myriad of factors used in assessing health information technology (HIT)/e-Health of healthcare institutions in developing countries and beyond. In this paper, we intended to identify and gain a deeper understanding of factors used in assessing HIT/e-Health readiness in developing countries through the identification of contextual attributes using Ghana as an exemplary developing country. Through in-depth interviews using aide memoire as interview guide, we explored Core readiness, Engagement readiness, Technological readiness, HIT funding readiness, Regulatory and policy readiness, Workforce readiness and Change Management readiness. We adapted the systematic thematic analysis of qualitative data guide suggested by Braun and Clarke (2013) and O'Connor and Gibson (Pimatisiwin 1: 63-90, 2003) in order to generate codes and build over-arching themes. While Organizational cultural readiness was found to be a more applicable theme/factor in place of Engagement readiness and Change management readiness, Resource readiness wasalso deemed a more appropriate theme for HIT funding readiness and Workforce readiness respectively. A total of 23 factors likely to promote HIT adoption in Ghana and 29 factors capable of impeding HIT adoption in Ghana and potentially in other developing countries were identified. For effective assessment of HIT readiness factors, there is a critical need for a deeper understanding of their applicability in differing settings. The outcome of this study offers a valuable insight into improving circumstances under which HIT/e-Health is adopted. When effectually carried out, assessment of this nature could be help side-step losses on large money, effort, time, delay and importantly, dissatisfaction among stakeholders while enabling change processes healthcare institutions and communities involved. This study also contributes to the limited literature on HIT/e-Health implementation scenarios while offering basis for theory-building.

Ensemble neural network approach detecting pain intensity from facial expressions.

This paper reports on research to design an ensemble deep learning framework that integrates fine-tuned, three-stream hybrid deep neural network (i.e., Ensemble Deep Learning Model, EDLM), employing Convolutional Neural Network (CNN) to extract facial image features, detect and accurately classify the pain. To develop the approach, the VGGFace is fine-tuned and integrated with Principal Component Analysis and employed to extract features in images from the Multimodal Intensity Pain database at the early phase of the model fusion. Subsequently, a late fusion, three layers hybrid CNN and recurrent neural network algorithm is developed with their outputs merged to produce image-classified features to classify pain levels. The EDLM model is then benchmarked by means of a single-stream deep learning model including several competing models based on deep learning methods. The results obtained indicate that the proposed framework is able to outperform the competing methods, applied in a multi-level pain detection database to produce a feature classification accuracy that exceeds 89 %, with a receiver operating characteristic of 93 %. To evaluate the generalization of the proposed EDLM model, the UNBC-McMaster Shoulder Pain dataset is used as a test dataset for all of the modelling experiments, which reveals the efficacy of the proposed method for pain classification from facial images. The study concludes that the proposed EDLM model can accurately classify pain and generate multi-class pain levels for potential applications in the medical informatics area, and should therefore, be explored further in expert systems for detecting and classifying the pain intensity of patients, and automatically evaluating the patients' pain level accurately.

Barriers to use of information and computer technology by Australia's nurses: a national survey.

AIMS AND OBJECTIVES: To support policy planning for health, the barriers to the use of health information and computer technology (ICT) by nurses in Australia were determined. BACKGROUND: Australia, in line with many countries, aims to achieve a better quality of care and health outcomes through effective and innovative use of health information. Nurses form the largest component of the health workforce. Successful adoption of ICT by nurses will be a requirement for success. No national study has been undertaken to determine the barriers to adoption. DESIGN: A self-administered postal survey was conducted. METHOD: A questionnaire was distributed to 10,000 members of the Australian Nursing Federation. Twenty possible barriers to the use of health ICT uptake were offered and responses were given on a five point Likert scale. RESULTS: Work demands, access to computers and lack of support were the principal barriers faced by nurses to their adoption of the technology in the workplace. Factors that were considered to present few barriers included age and lack of interest. While age was not considered by the respondents to be a barrier, their age was positively correlated with several barriers, including knowledge and confidence in the use of computers. CONCLUSIONS: Results indicate that to use the information and computer technologies being brought into health care fully, barriers that prevent the principal users from embracing those technologies must be addressed. Factors such as the age of the nurse and their level of job must be considered when developing strategies to overcome barriers. RELEVANCE TO CLINICAL PRACTICE: The findings of the present study provide essential information not only for national government and state health departments but also for local administrators and managers to enable clinical nurses to meet present and future job requirements.

Attitudes of Australian nurses to information technology in the workplace: a national survey.

This article reports on the views of Australian nurses as to their use of computers in the workplace. Data were collected by questionnaires mailed to 10,000 members of the 150,000-member Australian Nursing Federation, which represents 60% of the Australian nursing workforce. The response rate was 43.3%. Computer use was 20% by assistants in nursing, rising to 75% by enrolled nurses and to more than 95% by RNs. Principal uses for the computers by the nurses were for access to patients' records and for internal communication. Most respondents (79%) agreed that the use of computers had improved information access. Only 9.4% considered that adoption of a national electronic health record would not be useful to healthcare. Fewer than 5% stated that they have no interest in computers, and 87% considered that their age was never or rarely a barrier to their use of the technology. However, not all aspects of computer introduction to nursing were positive. The proportions of respondents who considered that the use of computers had made their work easier, reduced duplication of data entry, and reduced errors in handing patient data were only 42%, 32%, and 31%, respectively. Results demonstrate a positive attitude toward information technology by Australian nurses but identify issues that must be addressed to support continued interest and engagement.

The status of training and education in information and computer technology of Australian nurses: a national survey.

AIMS AND OBJECTIVES: A study was undertaken of the current knowledge and future training requirements of nurses in information and computer technology to inform policy to meet national goals for health. BACKGROUND: The role of the modern clinical nurse is intertwined with information and computer technology and adoption of such technology forms an important component of national strategies in health. The majority of nurses are expected to use information and computer technology during their work; however, the full extent of their knowledge and experience is unclear. DESIGN: Self-administered postal survey. METHODS: A 78-item questionnaire was distributed to 10,000 Australian Nursing Federation members to identify the nurses' use of information and computer technology. Eighteen items related to nurses' training and education in information and computer technology. RESULTS: Response rate was 44%. Computers were used by 86.3% of respondents as part of their work-related activities. Between 4-17% of nurses had received training in each of 11 generic computer skills and software applications during their preregistration/pre-enrolment and between 12-30% as continuing professional education. Nurses who had received training believed that it was adequate to meet the needs of their job and was given at an appropriate time. Almost half of the respondents indicated that they required more training to better meet the information and computer technology requirements of their jobs and a quarter believed that their level of computer literacy was restricting their career development. Nurses considered that the vast majority of employers did not encourage information and computer technology training and, for those for whom training was available, workload was the major barrier to uptake. Nurses favoured introduction of a national competency standard in information and computer technology. CONCLUSIONS: For the considerable benefits of information and computer technology to be incorporated fully into the health system, employers must pay more attention to the training and education of nurses who are the largest users of that technology. RELEVANCE TO CLINICAL PRACTICE: Knowledge of the training and education needs of clinical nurses with respect to information and computer technology will provide a platform for the development of appropriate policies by government and by employers.

Health and aged care enabled by information technology.

One of the challenges facing health and welfare policymakers as well as researchers in most developed countries is the increasing demand for aging services and aged care. Low birth rates and rapid increases in the percentages of elderly people make aging and aged care one of the top-priority issues among the national agenda of many countries. The responses of governments have included initiatives to extend productive working lives and promote self-funded retirement; to promote healthy, active aging; and to encourage more care to be delivered in home and community settings. Technology will be a major enabler of these strategies. People requiring health services are increasingly being offered more care in their own homes and community settings as an alternative to hospital admission and to delay or avoid moving into institutional care. Research is providing intelligent technology to enable care in the home as well as to monitor safety, security, and quality. Innovation will provide greater independence and better access to care in their own homes for the elderly, sufferers of chronic illness, and persons with disability and reduce the incidence of hospital admissions and the length of stay when admissions do occur. Technologies will support families and professional caregivers and are expected to reduce costs. This paper reports on developments in technology to support care for the aged in home and community settings.

e-Health readiness assessment factors and measuring tools: A systematic review.

BACKGROUND: The evolving, adoption and high failure nature of health information technology (HIT)/IS/T systems requires effective readiness assessment to avert increasing failures while increasing system benefits. However, literature on HIT readiness assessment is myriad and fragmented. This review bares the contours of the available literature concluding in a set of manageable and usable recommendations for policymakers, researchers, individuals and organizations intending to assess readiness for any HIT implementation. OBJECTIVES: Identify studies, analyze readiness factors and offer recommendations. METHOD: Published articles 1995-2016 were searched using Medline/PubMed, Cinahl, Web of Science, PsychInfo, ProQuest. Studies were included if they were assessing IS/T/mHealth readiness in the context of HIT. Articles not written in English were excluded. Themes that emerged in the process of the data synthesis were thematically analysed and interpreted. RESULTS: Analyzed themes were found across 63 articles. In accordance with their prevalence of use, they included but not limited to "Technological readiness", 30 (46%); "Core/Need/Motivational readiness", 23 (37%); "Acceptance and use readiness", 19 (29%); "Organizational readiness", 20 (21%); "IT skills/Training/Learning readiness" (18%), "Engagement readiness", 16 (24%) and "Societal readiness" (14%). Despite their prevalence in use, "Technological readiness", "Motivational readiness" and "Engagement readiness" all had myriad and unreliable measuring tools. Core readiness had relatively reliable measuring tools, which repeatedly been used in various readiness assessment studies CONCLUSION: Thus, there is the need for reliable measuring tools for even the most commonly used readiness assessment factors/constructs: Core readiness, Engagement and buy-ins readiness, Technological readiness and IT Skills readiness as this could serve as an immediate step in conducting effective/reliable e-Health readiness assessment, which could lead to reduced HIT implementation failures.

Australian nurses access and attitudes to information technology--a national survey.

Competencies by nurses in information technology (IT) are essential to health care in Australia yet data suggest deficiencies in access and use. A study commissioned by the Australian Government aimed to determine the extent of access and use and the barriers to the use of IT among nurses across Australia. A survey was distributed to 10,000 members of the Australian Nursing Federation with a 43% overall response rate. Fewer than 15% of nurses did not use computers as part of their work. The greatest use was for client records, patient pathology and radiology results and professional development. IT uptake in health is supported by nurses who are, however frustrated by limitations to access and software that is not fit for purpose. A lack of confidence in using IT was noted by many nurses. Fewer than 20% had received pre-registration training in any aspect of IT and only 30% post registration. In addition to training, high work load, numbers of computers and inadequate technical support were the major barriers to computer use.

Older people, assistive technologies, and the barriers to adoption: A systematic review.

BACKGROUND: Older people generally prefer to continue living in their own homes rather than move into residential age care institutions. Assistive technologies and sensors in the home environment and/or bodily worn systems that monitor people's movement might contribute to an increased sense of safety and security at home. However, their use can raise ethical anxieties as little is known about how older persons perceive assistive and monitoring technologies. OBJECTIVES: To review the main barriers to the adoption of assistive technologies (ATs) by older adults in order to uncover issues of concern from empirical studies and to arrange these issues from the most critical to the least critical. METHOD: A 4-step systematic review was conducted using empirical studies: locating and identifying relevant articles; screening of located articles; examination of full text articles for inclusion/exclusion; and detail examination of the 44 articles included. RESULTS: Privacy is a top critical concern to older adults, registering a 34% of the total articles examined. Two other equally potent barriers to the adoption of ATs were trust and functionality/added value representing 27 and 25 per cent each respectively of the total studies examined. Also of serious concerns are cost of ATs and ease of use and suitability for daily use (23%) each respectively, perception of "no need" (20%), stigma (18%), and fear of dependence and lack of training (16%) each respectively. These underlying factors are generation/cohort effects and physical decline relating to aging, and negative attitudes toward technologies such as the so-called "gerontechnologies" specifically targeting older adults. However, more and more older adults adopt different kinds of ATs in order to fit in with the society. CONCLUSIONS: The identified underlying factors are generation/cohort effects and physical decline relating to aging, and negative attitudes toward technologies. The negative attitudes that are most frequently associated with technologies such as the so-called "gerontechnologies" specifically targeting older adults contain stigmatizing symbolism that might prevent them from adopting them.

Challenges for implementing wireless hand-held technology in health care: views from selected Queensland nurses.

Many health-care providers in Australia are exploring the use of wireless technology to improve service delivery. It appears that the solutions so far have been dictated by the hardware vendors and that the business case is yet to drive the implementations. A focus group discussion was facilitated with eight senior management staff involved in health care in Western Australia. This resulted in a set of challenges, which were used to invite opinions from nursing staff in Queensland. A total of 31 interviews were conducted. The analysis returned a set of 63 themes, which were grouped. These groupings reflected the challenges as lack of user-friendly applications, unreliable technology, substandard testing, shortage of staff, concerns for security, reliance on technology, existing problems, work schedule, training, outdated health policy, coverage of wireless links, confidentiality and lack of awareness. The interviews clearly indicated the need for training and awareness procedures. The present study provides some of the information necessary to realize an enterprise-wide implementation of wireless technology.