Search

Objective: Study identification refers to formalizing an effective search over biomedical databases for retrieving all eligible evidence for a systematic review. Manual construction of queries, where a user submit a search query for which a biomedical search system such as PubMed would identify the most relevant documents, has been recognized as a very costly step in conducting systematic reviews. The objective of this paper is to present an automatic query generation approach to reduce the time and labor cost of manual biomedical study identification.Materials and Methods: The evaluation benchmark is the widely adopted CLEF 2018 Technology Assisted Reviews (TAR) collection, with 72 systematic reviews on Diagnosis Test Accuracy. We use and fine-tune pre-trained language models for generating high-level key-phrases and their dense embeddings. We constructed and published a dataset consists of almost one million PubMed articles' abstracts and their keywords for fine-tuning pre-trained language models. We also use concepts that are represented in the Unified Medical Language System, UMLS, for query expansion and embedding generation. We exploit and test different clustering methods, namely Agglomerative clustering, Affinity Propagation, and K-Means, over the generated embeddings to form query clauses.Results: Our proposed methods outperform existing state-of-the-art automatic query generation models across Precision (0.0821 compared with 0.005), Recall (0.9676 compared with 0.878), and F-measures (0.2898 compared with 0.0356 in F3 measure). In addition, some of the proposed methods can even outperform the performance of the manually crafted queries in some specific measures.Conclusion: The proposed model in this paper can be utilized to form an effective initial search query that can be further refined and updated by human reviewers for achieving the desired performance. For future work, we would like to explore the application of the presented query formalization methods in existing study identification methodologies and techniques, especially those that iteratively train machine learning models based on the domain experts' feedback on the relevancy of the retrieved studies. [ABSTRACT FROM AUTHOR]

Introduction: Health informatics and digital health, two rapidly growing disciplines, are becoming increasingly important to the sustainability of health service provision, highlighted especially through the COVID-19 pandemic. To maximise the benefits of the adoption and growth of health informatics and digital health, health service managers play a critical role in leading and managing the implementation and transformation of the system, both strategically and operationally, whilst still needing to manage 'business as usual'.Objectives: The objectives of the paper are to present and discuss the findings from a scoping review identifying: 1) competencies required for health service managers leading the implementation and transformation of informatics and digital technology in the health sector; and 2) factors that are critical to building the management workforce capacity in the era of health informatics and digital health.Methods: A scoping review of the literature was conducted in 2020 focussing on identifying empirical articles published in the English language since the year 2000 using a number of keywords such as 'health informatics', 'digital health', 'electronic health', 'competencies', 'capability', 'proficiency', 'qualification', 'certification', 'health manager', 'health executive' and 'health administrator'. The literature search was guided by a PRISMA approach searching within eight databases: Scopus, ProQuest, Web of Science, ACM Digital Library, CINAHL, PubMed, Google Scholar and ProQuest Dissertations.Results: After duplicates were removed, 941 publications were included for title screening as the result of an initial review. Title screening selected 185 articles to be included for abstract screening by two reviewers confirming 19 papers relevant to the focus of the current paper which were included in data extraction and content analysis. The analysis identified the additional competency of 'information and data management' be included as a core competency for health service managers. The analysis also confirmed additional elements for the following four core management competencies that are important to health service managers working in the digital health context, including: 1) leadership; 2) operational and resource management; 3) personal, interpersonal and professional qualities, and 4) understanding the industry and environment. Factors that are critical to developing the system and organization capacity in the use of health informatics and digital health technology, and leading and managing the adoption in the healthcare organizations were identified in three categories: 1) policy/system; 2) organizational structure and processes; and 3) people factors.Conclusions: This paper has taken an important step in confirming the competency requirements for health services managers that are relevant to leading and managing in the health informatics and digital health space, consequently indicating the directions for developing a competent workforce in meeting the existing and emerging healthcare delivery challenges, both now and in the future. [ABSTRACT FROM AUTHOR]

Objective: To identify outcomes of using health information technologies to support direct resident care in residential aged care homes, for residents, staff and services.Methods: In May 2022, a systematic search used CINAHL, Cochrane CRCT, MEDLINE, Proquest, PsychINFO and Scopus databases to locate papers published after 1990. Thematic analysis was used to synthesise extracted data. Results are reported using the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) statement.Results: Of 3721 references imported for screening, 1017 duplicates were removed and 2609 excluded, leaving 95 papers for data extraction. The included articles were conducted in diverse residential care homes, and involved over 12,000 nurse, care assistant or resident participants. Thematic analysis identified a range of health information technologies were used for direct care in residential care settings, and outcomes focussed on acceptability, efficiency and success of implementation. Less frequent were outcomes focussed on residents and families, and the safety and quality-of-care delivery.Discussion: Staff outcomes, focussed on the satisfaction of staff and usability of the system, dominate in research examining health information technology used for direct care in residential aged care homes. Outcomes examining the use of health information technology in delivering improvements in resident health, well-being, quality and safety was limited. There is a need to increase using quality and safety of resident care as outcome measures. [ABSTRACT FROM AUTHOR]

Introduction: Diabetes is a chronic metabolic disease characterized by high levels of blood glucose, which can lead over time to severe impairment to the heart, blood vessels, eyes, kidneys, nerves and premature death. Diabetes is prone to complications such as kidney failure, vision loss and nerve damage. The total assessed cost of diagnosed diabetes is growing rapidly; hence, harnessing telehealth for diabetes management may be cost-effective. A few previous publications have pointed to the effectiveness of telehealth but more numerous articles indicate that the results are inconsistent and economic models are lacking. This narrative review surveys the recent literature on the implementation of telehealth for diabetes management that incorporates cost-effectiveness analyses.Materials and Methods: This paper follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [25].Results: The vast majority of articles dealing with managing Type 2 diabetes have primarily used the telephone for telehealth monitoring (followed by teleophalmology and telemonitoring). Most publications report that the telehealth solution was cost effective. The leading cost-effectiveness method was the Markov model; however, only a small number of papers extend the Markov model to critical sensitivity analyses of their outcomes. The main goal of telehealth in general is diabetes management or monitoring, followed by ophthalmology, depression management, weight loss and other goals.Conclusion: This work summarizes the literature on recent trends in telehealth options, and analyzes successes and failures in relation to both effectiveness and costs, which may be valuable to both scholars and practitioners. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to describe the iterative design, development, and evaluation of a novel mHealth learning environment for parents of children with epilepsy. The mHealth learning environment is a component of a broader behavioral intervention called eACT (Epilepsy Adherence in Children and Technology), which seeks to improve antiepileptic drug adherence in children with epilepsy. The eACT mHealth learning environment integrates theoretical principles from the learning sciences with user-centered design methods and advanced learning technologies to provide an efficient and appealing learning experience that is specifically tailored to adult learners.Methods: The eACT mHealth learning environment was developed by a multidisciplinary team of experts across three major phases including: 1) preparation, 2) iterative design, and 3) iterative development. The preparation phase included developing user personas and conducting focus groups, which identified user needs and use cases for the product. The iterative learning experience design process made use of rapid prototypes in which the product design was improved between versions, resulting in a final design proof. Usability and user experience methods were deployed to evaluate the system during the iterative development phase.Results: Results from three phases of iterative design and development are presented through user personas, usability data, and qualitative analysis. Findings suggest the eACT mHealth learning environment is highly usable and relevant to the unique needs of adult caregivers of children with epilepsy. Findings also suggest that user experience was largely positive.Conclusions: This paper reveals key insights about how formative learning experience design processes can lead to highly relevant and usable interventions for adult learners. [ABSTRACT FROM AUTHOR]

Objective: Chronic obstructive pulmonary disease (COPD) is a disease that causes airflow limitation to the lungs and has a high morbidity around the world. The objective of this study was to evaluate how artificial intelligence (AI) is being applied for the management of the disease, analyzing the objectives that are raised, the algorithms that are used and what results they offer.Methods: We conducted a scoping review following the Arksey and O'Malley (2005) and Levac et al. (2010) guidelines. Two reviewers independently searched, analyzed and extracted data from papers of five databases: Web of Science, PubMed, Scopus, Cinahl and Cochrane. To be included, the studies had to apply some AI techniques for the management of at least one stage of the COPD clinical process. In the event of any discrepancy between both reviewers, the criterion of a third reviewer prevailed.Results: 380 papers were identified through database searches. After applying the exclusion criteria, 67 papers were included in the study. The studies were of a different nature and pursued a wide range of objectives, highlighting mainly those focused on the identification, classification and prevention of the disease. Neural nets, support vector machines and decision trees were the AI algorithms most commonly used. The mean and median values of all the performance metrics evaluated were between 80% and 90%.Conclusions: The results obtained show a growing interest in the development of medical applications that manage the different phases of the COPD clinical process, especially predictive models. According to the performance shown, these models could be a useful complementary tool in the decision-making by health specialists, although more high-quality ML studies are needed to endorse the findings of this study. [ABSTRACT FROM AUTHOR]

Introduction: Administrative hospital databases represent an important tool for hospital financing in many national health systems and are also an important data source for clinical, epidemiological and health services research. Therefore, the data quality of such databases is of utmost importance. This paper aims to present a systematic review of root causes of data quality problems affecting administrative hospital data, creating a catalogue of potential issues for data quality analysts to explore.Methods: The MEDLINE and Scopus databases were searched using inclusion criteria based on two following concept blocks: (1) administrative hospital databases and (2) data quality. Studies' titles and abstracts were screened by two reviewers independently. Three researchers independently selected the screened studies based on their full texts and then extracted the potential root causes inferred from them. These were subsequently classified according to the Ishikawa model based on 6 categories: "Personnel", "Material", "Method", "Machine", "Mission" and "Management".Results: The result of our investigation and the contribution of this paper is a classification of the potential (105) root causes found through a systematic review of the 77 relevant studies we have identified and analyzed. The result was represented by an Ishikawa diagram. Most of the root causes (25.7%) were associated with the category "Personnel" - people's knowledge, preferences, education and culture, mostly related to clinical coders and health care providers activities. The quality of hospital documentation, within category "Material", and aspects related to financial incentives or disincentives, within category "Mission", were also frequently cited in the literature as relevant root causes for data quality issues.Conclusions: The resultant catalogue of root causes, systematized using the Ishikawa framework, provides a compilation of potential root causes of data quality issues to be considered prior to reusing these data and that can point to actions aimed at improving data quality. [ABSTRACT FROM AUTHOR]

Importance: Reducing inappropriate blood tests has been highlighted by Choosing Wisely as a key area of focus. Computer physician order entry is one modifiable contributor, but little is known about how computer ordering compares to paper methods when it comes to low-value laboratory testing.Objective: To determine which method of order entry is associated with a greater amount of appropriate lab testing. Furthermore, to identify ordering patterns for more targeted interventions in future.Design: We conducted a retrospective observational cohort study of inpatients discharged at two hospitals (one site uses paper order sets, while the other uses electronic order sets).Setting: General internal medicine wards at two Canadian teaching hospitals.Participants: At site 1 (electronic orders), all general internal medicine discharges from May 2015 and February 2016. At site 2 (paper orders), all general internal medicine discharges from April 15, 2015 to May 26, 2015.Main Outcome(s) and Measure(s): Main outcome was the percentage of inpatient discharges at each site with orders for daily laboratory tests for three days on admission. Secondary measures include proportion of tests with appropriate indications and rates of discontinuation of daily laboratory tests.Results: We reviewed 395 discharges with a mean patient age of 69.5 ± 18.9 years and mean length of stay of 12.1 days. Daily laboratory tests were more common with paper orders (site 2) compared to electronic order sets (site 1) for complete blood count (CBC) (90.8% vs. 68.5%, p < 0.001), electrolytes (93.8% vs 71.5%, p < 0.001), and creatinine (93.8% vs 70.0%, p < 0.001) testing. However, paper orders for daily laboratory tests were more often appropriate, both in CBC (76.3% vs. 38.9%, p < 0.001) and electrolyte/creatinine (80.3% vs 44.2%, p < 0.001) testing. Discontinuation of daily labs occurred more often with paper orders (35.4% vs. 6.7%, p < 0.001).Conclusions and Relevance: Compared to written orders, daily laboratory testing using electronic ordering was associated with higher rates of inappropriate indications and lower rates of discontinuation. Our results support interventions aimed at ensuring electronic order sets incorporate appropriate indications and a mechanism for discontinuation of daily lab orders. Further studies aimed at understanding how the process of completing paper or electronic orders influence appropriateness of daily laboratory orders are needed to further minimize inappropriate testing. [ABSTRACT FROM AUTHOR]

Introduction: The COVID-19 pandemic resulted in the rapid expansion of e-health services in Poland. The main aim of the study was to assess the determinants of user satisfaction and the readiness-to-use e-health applications in Polish society.Patients and Methods: The paper presents the results of the analysis of the data obtained through a computer-assisted web interviewing survey in a representative sample of 1002 adult Internet users in Poland. The survey was based on a questionnaire consisting of 55 items. The determinants of user satisfaction and the readiness-to-use of e-health solutions were assessed with univariable and hierarchical logistic regression models.Results: E-health services had been used by 60.6% of respondents from the beginning of the epidemic state in Poland to June 2020, when the survey was performed. The hierarchical model showed that user satisfaction with e-health interactions was significantly associated with HL (Health Literacy) and eHL (e-Health Literacy), COVID-19-related conspiracy beliefs score, and using a televisit because of acute symptoms not suggesting COVID-19. Readiness-to-use e-health in the future showed a significant relationship with place of residence, marital status, eHL, the self-assessment of knowledge about preventing COVID-19, the use of televisits for renewing prescriptions, and the level of satisfaction with e-health services.Conclusions: Satisfaction with e-health services depends mainly on the HL, eHL, and conspiracy beliefs of patients. Readiness-to-use e-health in the future is associated with the level of eHL and sociodemographic characteristics, but previous experience with e-health services seems to be the main predictor. [ABSTRACT FROM AUTHOR]

Background: With the still larger scale of electronic health records (EHRs), their implementation has become increasingly complex. In this study, we focus on one large-scale EHR - Epic.Purpose: We analyze the Epic implementations in Denmark and Finland to understand how healthcare professionals experience this large-scale EHR.Method: The study is based on documentary analysis. The analyzed documents include user surveys, assessment reports, material from project partners, and research papers.Results: The Danish and Finnish Epic implementations are still troubled five and three years, respectively, after the first go-live. In Denmark, the business case and implementation process have been sharply criticized. The correction of usability problems and unstable system integrations have been slow, the time required to perform common clinical tasks has increased, and 32% of the users remain dissatisfied or very dissatisfied with the system. In Finland, the physicians and nurses experience improved technical performance but inferior usability and reduced work support compared to the EHR they used before Epic; only 4.7% (physicians) and 7.3% (nurses) agree that patient information is easy to access, and only 9.3% (physicians) and 26.2% (nurses) agree that Epic helps improve the quality of care.Conclusion: The post-implementation experiences from the two implementations contradict pre-implementation expectations. Specifically, the consequences of using Epic have become salient only after go-live. As a result, the implementing organizations and their users have predominantly found themselves in a reactive mode of fending off problems rather than a proactive mode of realizing benefits. [ABSTRACT FROM AUTHOR]

Objectives: Long-term care combined with complex follow-up processes is among the essential needs of lung transplantation. Therefore, Telemedicine-based strategies can provide an effective approach for both patients and clinicians by applying remote patient monitoring. Hence, the main objective of this study was to investigate Telemedicine and telehealth usage in lung transplantation.Method: A systematic review was conducted in four databases using keywords. Eligible studies were all English papers that developed Telemedicine-based programs to enhance patient care in lung organ transplantation. The interventions were analyzed analysis to determine the main descriptive areas. The quality of the included articles was evaluated using Mixed Methods Appraisal Tool (MMAT) tool by two authors.Results: Of the 261 retrieved articles, 27 met our inclusion criteria. Of these, 22 studies were devoted to the post-transplantation phase. All articles were published from 2002 to 2021 and the trend of publications has increased in recent years. Most of the studies were conducted in the United States and Canada. All eligible studies can be categorized into five types of Telemedicine interventions, 15 (55.56%) articles devoted to Telemonitoring, four (14.81%) for Teleconsultation, four (14.81%) articles for Telerehabilitation, three (11.11%) articles for Telespirometery, and one (3.70%) article were done regarding Tele-education.Conclusion: This integrated review provides researchers with a new understanding of Telemedicine-based care solutions. Findings show that remote patient care in lung transplantation includes various aspects, especially self-care improvement. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence is fueling a new revolution in medicine and in the healthcare sector. Despite the growing evidence on the benefits of artificial intelligence there are several aspects that limit the measure of its impact in people's health. It is necessary to assess the current status on the application of AI towards the improvement of people's health in the domains defined by WHO's Thirteenth General Programme of Work (GPW13) and the European Programme of Work (EPW), to inform about trends, gaps, opportunities, and challenges.Objective: To perform a systematic overview of systematic reviews on the application of artificial intelligence in the people's health domains as defined in the GPW13 and provide a comprehensive and updated map on the application specialties of artificial intelligence in terms of methodologies, algorithms, data sources, outcomes, predictors, performance, and methodological quality.Methods: A systematic search in MEDLINE, EMBASE, Cochrane and IEEEXplore was conducted between January 2015 and June 2021 to collect systematic reviews using a combination of keywords related to the domains of universal health coverage, health emergencies protection, and better health and wellbeing as defined by the WHO's PGW13 and EPW. Eligibility criteria was based on methodological quality and the inclusion of practical implementation of artificial intelligence. Records were classified and labeled using ICD-11 categories into the domains of the GPW13. Descriptors related to the area of implementation, type of modeling, data entities, outcomes and implementation on care delivery were extracted using a structured form and methodological aspects of the included reviews studies was assessed using the AMSTAR checklist.Results: The search strategy resulted in the screening of 815 systematic reviews from which 203 were assessed for eligibility and 129 were included in the review. The most predominant domain for artificial intelligence applications was Universal Health Coverage (N = 98) followed by Health Emergencies (N = 16) and Better Health and Wellbeing (N = 15). Neoplasms area on Universal Health Coverage was the disease area featuring most of the applications (21.7 %, N = 28). The reviews featured analytics primarily over both public and private data sources (67.44 %, N = 87). The most used type of data was medical imaging (31.8 %, N = 41) and predictors based on regions of interest and clinical data. The most prominent subdomain of Artificial Intelligence was Machine Learning (43.4 %, N = 56), in which Support Vector Machine method was predominant (20.9 %, N = 27). Regarding the purpose, the application of Artificial Intelligence I is focused on the prediction of the diseases (36.4 %, N = 47). With respect to the validation, more than a half of the reviews (54.3 %, N = 70) did not report a validation procedure and, whenever available, the main performance indicator was the accuracy (28.7 %, N = 37). According to the methodological quality assessment, a third of the reviews (34.9 %, N = 45) implemented methods for analysis the risk of bias and the overall AMSTAR score below was 5 (4.01 ± 1.93) on all the included systematic reviews.Conclusion: Artificial intelligence is being used for disease modelling, diagnose, classification and prediction in the three domains of GPW13. However, the evidence is often limited to laboratory and the level of adoption is largely unbalanced between ICD-11 categoriesand diseases. Data availability is a determinant factor on the developmental stage of artificial intelligence applications. Most of the reviewed studies show a poor methodological quality and are at high risk of bias, which limits the reproducibility of the results and the reliability of translating these applications to real clinical scenarios. The analyzed papers show results only in laboratory and testing scenarios and not in clinical trials nor case studies, limiting the supporting evidence to transfer artificial intelligence to actual care delivery. [ABSTRACT FROM AUTHOR]

Objective: Healthcare professionals are known to use their personal devices extensively for work purposes (Bring-Your-Own-Device). However, it is also a source of major concern for healthcare organisations, given the heavy reliance of patient data privacy on clinician's usage behaviour and higher risk of data breaches. Previous research into hospital BYOD security has been scarce and fragmented. Therefore, the aim of this paper is to understand the preferences, behaviour, and knowledge of Australian hospital clinical staff with respect to BYOD security through a survey.Method: An online survey was conducted among clinicians working in Australian hospitals, asking them about their BYOD related user behaviour, knowledge of best practices, and support received from their hospitals. Descriptive statistical analysis and cross tabulation were carried out on the survey data.Results: Majority of the surveyed clinicians used BYOD (87%). Some of the good behavioural practices found included use of passcode: (91.95%), never disclosing password (67.82%); strong passwords (63.22%) and no storage of patient data (59.77%) on BYOD devices. However, several risky practices were also found, particularly when usability or clinical workflow mattered to clinicians including use of personal/social apps for clinical communication (e.g. SMS: 57.47%, WhatsApp: 39.08%); connecting BYOD devices containing patient data to public hotspots (40.23%) and use of a common data backup platform for personal and patient data (79.93%). A large proportion of surveyed clinicians were either unaware or didn't receive BYOD related security training (74.71%) or policy (39.08%), but those who did were more confident across all aspects of BYOD security asked in the survey, indicating their importance.Conclusion: By taking a wholistic and socio-technical view, this study helps us to better understand BYOD related security behaviour of hospital-based clinicians and its consequential implications. It can therefore provide important insights for both technical and clinical stakeholders of BYOD within hospitals. [ABSTRACT FROM AUTHOR]

Background: Computerised Physician Order Entry (CPOE) software is increasingly used across the world to improve medication safety. However, few high-quality studies have reviewed the impact of CPOE on prescribing errors and patient harm.Objective: To investigate the effect of a hybrid CPOE-paper prescribing system on prescribing errors at a secondary hospital site.Design: An interrupted time-series study was conducted by identifying prescribing errors via prospective medical chart review before and after the implementation of CPOE across three medical wards.Participants: The medication orders of all patients admitted to the medical wards during the study period were reviewed.Intervention: Implementation of a CPOE across three medical wards.Measures: A blinded expert panel risk stratified the errors according to level of severity, preventability and potential for harm. Pearson's chi square and segmented regressions were used to determine if there were differences in prescribing errors pre- and post-CPOE implementation.Key Results: A total of 10,535 medication orders were reviewed pre-CPOE and 13,841 medication orders reviewed post-CPOE. Analysis demonstrated that after implementation of CPOE there were reductions in the proportion of orders with one or more of any error (-30.1%, 95 %CI: -36.5%, -23.7%, p < 0.001). Reductions in the proportion of orders with one or more errors were seen across the error categories of dosing errors (-20.1%, 95 %CI: -25.1%, -15%, p < 0.001), procedural/administrative errors (-18.9%, 95 %CI: -22.8%, -15%, p < 0.001), and therapeutic errors (-2.6%, 95 %CI: -4.1%, -1%, p = 0.002). Post-CPOE there were reductions in the proportion of orders with at least one non-intercepted serious error (-12.6%, 95 %CI: -16.4%, -8.8%, p < 0.001).Conclusion: The introduction of CPOE was associated with reductions in prescribing errors. There is also evidence that this translated into a reduced risk of harm to patients post-CPOE implementation through the reduction in actual adverse drug events. [ABSTRACT FROM AUTHOR]

Objective: Cardio-metabolic risk assessment in the general population is of paramount importance to reduce diseases burdened by high morbility and mortality. The present paper defines a strategy for out-of-hospital cardio-metabolic risk assessment, based on data acquired from contact-less sensors.Methods: We employ Structural Equation Modeling to identify latent clinical variables of cardio-metabolic risk, related to anthropometric, glycolipidic and vascular function factors. Then, we define a set of sensor-based measurements that correlate with the clinical latent variables.Results: Our measurements identify subjects with one or more risk factors in a population of 68 healthy volunteers from the EU-funded SEMEOTICONS project with accuracy 82.4%, sensitivity 82.5%, and specificity 82.1%.Conclusions: Our preliminary results strengthen the role of self-monitoring systems for cardio-metabolic risk prevention. [ABSTRACT FROM AUTHOR]

Background: Care pathways in renal transplantation involve multi-speciality coordination and administration of clinical data across organisational boundaries. The potential for information technology (IT) to support the service through data management, communication and national registration has been reported. However, no previous national-level evaluation on the current state of digital transformation has been undertaken.Methods: We undertook structured phone interviews with transplant coordinators at all 23 transplant centres in the United Kingdom (UK). The interview covered topics including clinical workflows during transplant referral and current data management processes. Based on established frameworks, we elicited IT capabilities, readiness and infrastructure to evaluate the digital maturity at each centre. We analysed interview data thematically and synthesised results across centres to identify common challenges and inform recommendations.Results: Transplant coordinators across centres reported similar care pathways with patients being referred by to transplant centres from regional renal referral centres. Analysis revealed that a key challenge revolved around the inability to access data across organisational boundaries. This resulted in dependence on postal or electronic mail to transfer clinical data, such as blood results or medication lists. Most centres had a hospital-wide electronic medical record, which was unable to meet the specific workflow requirements of transplantation. This was primarily because implemented solutions were unable to provide a single unified view of transplant-related data. As a result, transplant coordinators reported several workarounds to manage clinical data, such as paper folders, Excel© sheets and scanned files on hospital shared drives.Conclusions: Existing front-line IT solutions and wider infrastructure did not support the requirements of care pathways in renal transplantation. Digital transformation should focus on the need to surface patient data across organisational boundaries and provide specific views of data that complement the clinical workflow. This study highlights how regional interoperability remains a major priority to support multi-centre specialist services. [ABSTRACT FROM AUTHOR]

Background and Objectives: The importance of clinical natural language processing (NLP) has increased with the adoption of electronic health records (EHRs). One of the critical tasks in clinical NLP is named entity recognition (NER). Clinical NER in the Serbian language is a severely under-researched area. The few approaches that have been proposed so far are based on rules or machine-learning models with hand-crafted features, while current state-of-the-art models have not been explored. The objective of this paper is to assess the performance of state-of-the-art NER methods on clinical narratives in the Serbian language.Materials and Methods: We designed an experimental setup for a comprehensive evaluation of state-of-the-art NER models. The gold standard corpus we used for the evaluation is comprised of discharge summaries from the Clinic for Nephrology at the University Clinical Center of Serbia. The following models were evaluated: conditional random fields (CRF), multilingual transformers (BERT Multilingual and XLM RoBERTa), and long short-term memory (LSTM) recurrent neural networks, and their ensembles. In addition, we investigated the necessity of the pretraining task of transformer based models and the use of pretrained word embeddings with LSTM model.Results: Our results show that individually CRF had the best precision, the pretrained BERT Multilingual model had the best recall values, and the LSTM model had the best F1 score. The best performance was achieved by combining the existing models in a majority voting ensemble with an F1 score of 0.892. The presented results are similar to the inter annotator agreement on our gold standard corpus and are comparable to existing state-of-the-art results for clinical NER reported in literature.Conclusion: Existing state-of-the-art models can provide viable results for clinical named entity recognition when applied to languages with the complexity of the Serbian language without major modifications. [ABSTRACT FROM AUTHOR]

Background: Atrial fibrillation (AF) is one of the most prevalent cardiac arrhythmias, which challenges the healthcare systems globally.Timely detection of AF can potentially reduce the mortality and morbidity rates as well as alleviate the economic burden caused by this.Digital solutions are shown to enhance the diagnosis of cardiac abnormalities.Objectives: By the latest advancements in the field of medical informatics and tele-health monitoring, huge amount of electro-physiological signals, such as electrocardiograms (ECG), can be easily collected.One of the most common ways for physicians/cardiologists to analyse these signals is through visual inspection.However, it is not always easy and in most cases cumbersome to analyse these big amounts of ECG data.Therefore, it is of great interest to develop models that are capable of analyzing these data and help physicians making better decisions.This paper proposes and compares well-known machine learning (ML) algorithms to diagnose short episodes of AF. This also paves the way for real-time detection of AF in clinical settings.Methods: Different ML algorithms such as Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Stacking Classifier (SC), Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost) were applied to detect AF. These models were trained using extracted statistical features from ECG signals.Results: The proposed ML models were trained on a dataset with 23 ECG records of length approximately 10 h each using leave one group out cross validation (LOGO-CV) technique and achieved the best sensitivity (Se), specificity (Sp), positive predictive value (PPV), false positive rate (FPR), and F1-score of 85.67%, 81.25%, 90.85%, 18.75% and 88.18%, respectively, to classify AF from normal sinus rhythms (NSR) in short ECG segments of 20 heartbeats.Additionally, the models were examined on three unseen datasets, namely the Long Term AF dataset, MIT-BIH Arrhythmia dataset, and MIT-BIH Normal Sinus Rhythm dataset, to assess their robustness and generalization.Conclusion: The obtained results show high performance and flexibility of some of the applied ML models compared to other well-known algorithms. In general, the empirical results confirm that ensemble methods, such as AdaBoost, generalized well and perform better than other approaches. [ABSTRACT FROM AUTHOR]

Background: Recent advances in performance of natural language processing (NLP) techniques have spurred wider use and more sophisticated applications of NLP in radiology. This study systematically reviews the trends and applications of NLP in radiology within the last five years.Methods: A search of three databases of peer-reviewed journal articles and conference papers from January 1, 2016 to April 21, 2021 resulted in a total of 228 publications included in the review. Manuscripts were analyzed by several factors, including clinical application, study setting, NLP technique, and performance.Results: Of the 228 included publications, there was an overall increase in number of studies published with an increase in use of machine learning models. NLP models showed high performance: >50% of publications reported F1 > 0.91. There was variable sample size across the studies with a median of 3708 data points, most commonly radiology reports. 145 studies utilized data from a single academic center. Applications were classified as clinical (n = 87), technical (n = 66), quality improvement (n = 61), research (n = 9), and education (n = 5).Discussion: There has been a continued increase in number of studies involving NLP in radiology. Newer NLP techniques, including word embedding, deep learning, and transformers, are being applied and show improved performance. There has been growth in the interpretative and non-interpretative use of NLP techniques in radiology and has great capacity to improve patient care and delivery. Although the performance and breadth of NLP applications is impressive, there is an overall lack of high-level evidence for actual clinical application of published tools.Conclusion: NLP applications in radiology has been increasing studied and more accurate in the last 5 years. More direct clinical application and portability of the NLP pipelines is need to reach the technology's full potential. [ABSTRACT FROM AUTHOR]

Introduction: Pneumonia is the top communicable cause of death worldwide. Accurate prognostication of patient severity with Community Acquired Pneumonia (CAP) allows better patient care and hospital management. The Pneumonia Severity Index (PSI) was developed in 1997 as a tool to guide clinical practice by stratifying the severity of patients with CAP. While the PSI has been evaluated against other clinical stratification tools, it has not been evaluated against multiple classic machine learning classifiers in various metrics over large sample size.Methods: In this paper, we evaluated and compared the prediction performance of nine classic machine learning classifiers with PSI over 34,720 adult (age 18+) patient records collected from 749 hospitals from 2009 to 2018 in the United States on Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) and Average Precision (Precision-Recall AUC).Results: Machine learning classifiers, such as Random Forest, provided a statistically highly(p < 0.001) significant improvement (∼33% in PR AUC and ∼6% in ROC AUC) compared to PSI and required only 7 input values (compared to 20 parameters used in PSI).Discussion: Because of its ease of use, PSI remains a very strong clinical decision tool, but machine learning classifiers can provide better prediction accuracy performance. Comparing prediction performance across multiple metrics such as PR AUC, instead of ROC AUC alone can provide additional insight. [ABSTRACT FROM AUTHOR]

Objective: While the challenge of estimating the efficacy of therapies using observational data has received a lot of attention, little work has been done on estimating the treatment effect from interventions. In this paper, we tackle this problem by proposing an early guidance system based on a causal Bayesian network (CBN) for recommending personalized interventions. We are interested in the elderly fall prevention context. The objective is to develop a practical tool to help doctors estimate the effects of each intervention (or compound interventions) on a given patient and then choose the one that best fits each patient's health situation to reduce the risk of falling.Methods: On a real-world elderly information base, we undertake an empirical investigation for the proposed approach, which is based on a 44-node CBN. Then, we describe what is possible to achieve using state-of-the-art machine learning methods, namely Support Virtual Machine (SVM), Decision Tree (DT), and Bayesian Network (BN), and how well these methods can be used in recommending personalized interventions compared to the proposed approach.Results: 1174 elderly patients from Lille University Hospital, between January 2005 and December 2018 are included. The results reveal that none of the classifiers is significantly superior to the others, even if BN delivers somewhat better results (41.6%) and DT most often slightly lower performance (31.2%). Results also show that none of these three classifiers performs comparable to the proposed system (89.7%). The interventions recommended by the system are in agreement with the expert's judgment to a satisfactory level. The reaction of the physicians to the proposed system in its first trial version was very favorable.Conclusion: The study showed the efficacy and utility of the causality-based strategy in recommending tailored interventions to prevent elderly falls compared to automated learning methods that had failed to infer a solid interventional paradigm for precision medicine. [ABSTRACT FROM AUTHOR]

Introduction: Recent developments in the field of Artificial Intelligence (AI) applied to healthcare promise to solve many of the existing global issues in advancing human health and managing global health challenges. This comprehensive review aims not only to surface the underlying ethical and legal but also social implications (ELSI) that have been overlooked in recent reviews while deserving equal attention in the development stage, and certainly ahead of implementation in healthcare. It is intended to guide various stakeholders (eg. designers, engineers, clinicians) in addressing the ELSI of AI at the design stage using the Ethics by Design (EbD) approach.Methods: The authors followed a systematised scoping methodology and searched the following databases: Pubmed, Web of science, Ovid, Scopus, IEEE Xplore, EBSCO Search (Academic Search Premier, CINAHL, PSYCINFO, APA PsycArticles, ERIC) for the ELSI of AI in healthcare through January 2021. Data were charted and synthesised, and the authors conducted a descriptive and thematic analysis of the collected data.Results: After reviewing 1108 papers, 94 were included in the final analysis. Our results show a growing interest in the academic community for ELSI in the field of AI. The main issues of concern identified in our analysis fall into four main clusters of impact: AI algorithms, physicians, patients, and healthcare in general. The most prevalent issues are patient safety, algorithmic transparency, lack of proper regulation, liability & accountability, impact on patient-physician relationship and governance of AI empowered healthcare.Conclusions: The results of our review confirm the potential of AI to significantly improve patient care, but the drawbacks to its implementation relate to complex ELSI that have yet to be addressed. Most ELSI refer to the impact on and extension of the reciprocal and fiduciary patient-physician relationship. With the integration of AIbased decision making tools, a bilateral patient-physician relationship may shift into a trilateral one. [ABSTRACT FROM AUTHOR]

Background: Despite the growing interest in health data science education, it is not embedded in undergraduate medical curricula and little is known about best teaching practices. This paper presents a highly innovative course in a UK university that introduces undergraduate medical students to data science. It also discusses a study on student perspectives on the learning and teaching of health data science.Methods: The pedagogical design elements of the Data Science in Medicine course are discussed, along with its syllabus, assessment methodology and flipped classroom delivery. The course has been offered to approximately 630 students over three years. Student perspectives were investigated through three focus groups with the participation of 19 students across different study years in medicine. An experiment was conducted regarding instructor-led vs. video-based modalities of online programming labs, with the participation of 8 students.Results: The course has led to improved data competency among medical students and to a positive change in their opinions about data science. Motivating the course and showing relevance to clinical practice was one of the biggest challenges. Statistics was perceived by focus group participants as an essential data skill. Including data science in the medical curriculum was perceived as important by Year 1 students, while opinions varied between Year 4/5 participants. Video-based online labs were preferred over instructor-led online labs, and they were found to be more useful and enjoyable, without leading to any significant difference in academic performance.Conclusions: Teaching data science to undergraduate medicine students is highly desirable and feasible. We recommend including statistics in the curriculum and practical skill development through simple and clinically-relevant data science tasks, supported through video-based online labs. Further reporting on similar courses is needed, as well as larger-scale studies on student perspectives. [ABSTRACT FROM AUTHOR]

Introduction: The prevalence and mortality of cardiovascular diseases are high worldwide. Telecardiology can be used to diagnose and treat these diseases. This paper aimed to review the effectiveness (positive and negative) of implemented telecardiology services in terms of clinical, economic, and patient-reported aspects.Methods: A comprehensive search was conducted in Medline (through PubMed), Scopus, ISI web of science, and IEEE Xplore databases from inception to April 7, 2021. the studies that examined the effectiveness of telecardiology interventions were included.Results: Fifty studies were included in this systematic review. Most investigations (22%) were conducted in the US. In 22% of studies, telecardiology intervention was used for patients with heart failure. Telecardiology has been used in most studies for tele-monitoring (n = 21, 42%) and tele-consultation (n = 17, 34%) and in 29 studies (58%), was applied for ECG transmission. The highest rate of effects reported by studies was clinical. Thirty-five studies (70%) reported the clinical effects; twenty-one studies reported the positive effects for the economic category, and fifteen studies reported the positive effect for patient-reported class. The most positive clinical effects of telecardiology were early diagnosis, early treatment, and mortality reduction. The most positive effect of the economic class was the reduction of health care costs. The most effects of the patient-reported category were improving the patient's quality of life and patient satisfaction.Conclusion: Telecardiology can help early diagnosis and treatment of cardiovascular diseases. It also has great potential in reducing health care costs and increasing quality of life and patient satisfaction. [ABSTRACT FROM AUTHOR]

Background: To accelerate healthcare/genomic medicine research and facilitate quality improvement, researchers have started cross-institutional collaborations to use artificial intelligence on clinical/genomic data. However, there are real-world risks of incorrect models being submitted to the learning process, due to either unforeseen accidents or malicious intent. This may reduce the incentives for institutions to participate in the federated modeling consortium. Existing methods to deal with this "model misconduct" issue mainly focus on modifying the learning methods, and therefore are more specifically tied with the algorithm.Basic Procedures: In this paper, we aim at solving the problem in an algorithm-agnostic way by (1) designing a simulator to generate various types of model misconduct, (2) developing a framework to detect the model misconducts, and (3) providing a generalizable approach to identify model misconducts for federated learning. We considered the following three categories: Plagiarism, Fabrication, and Falsification, and then developed a detection framework with three components: Auditing, Coefficient, and Performance detectors, with greedy parameter tuning.Main Findings: We generated 10 types of misconducts from models learned on three datasets to evaluate our detection method. Our experiments showed high recall with low added computational cost. Our proposed detection method can best identify the misconduct on specific sites from any learning iteration, whereas it is more challenging to precisely detect misconducts for a specific site and at a specific iteration.Principal Conclusions: We anticipate our study can support the enhancement of the integrity and reliability of federated machine learning on genomic/healthcare data. [ABSTRACT FROM AUTHOR]

Background and Objective: The COVID-19 pandemic has accelerated digital health applications in multifaceted disease management dimensions. This study aims (1) to identify risk issues relating to the rapid development and redeployment of COVID-19 related e-health systems, in primary care, and in the health ecosystems interacting with it and (2) to suggest evidence-based evaluation directions under emergency response.Method: After initial brainstorming of digital health risks posed in this pandemic, a scoping review method was adopted to collect evidence across databases of PubMed, CINAHL, and EMBASE. Peer-review publications, reports, news sources, and websites that credibly identified the challenges relating digital health scaled for COVID-19 were scrutinized. Additional supporting materials were obtained through snowball sampling and the authors' global digital health networks. Studies satisfying the selection criteria were charted based on their study design, primary care focus, and coverage of e-health areas of risk.Results: Fifty-eight studies were mapped for qualitative synthesis. Five identified digital health risk areas associated with the pandemic were governance, system design and coordination, information access, service provision, and user (professional and public) reception. We observed that rapid digital health responses may embed challenges in health system thinking, the long-term development of digital health ecosystems, and interoperability of health IT infrastructure, with concomitant weaknesses in existing evaluation theories.Conclusion: Through identifying digital health risks posed during the pandemic, this paper discussed potential directions for next-generation informatics evaluation development, to better prepare for the post-COVID-19 era, a new future epidemic, or other unforeseen global health emergencies. An updated evidence-based approach to health informatics is essential to gain public confidence in digital health across primary and other health sectors. [ABSTRACT FROM AUTHOR]

Background and Objective: The assessment of dependence in older adults currently requires a manual collection of data taken from questionnaires. This process is time consuming for the clinicians and intrudes the daily life of the elderly. This paper aims to semi-automate the acquisition and analysis of health data to assess and predict the dependence in older adults while executing one instrumental activity of daily living (IADL).Methods: In a mobile-health (m-health) scenario, we analyze whether the acquisition of data through wearables during the performance of IADLs, and with the help of machine learning techniques could replace the traditional questionnaires to evaluate dependence. To that end, we collected data from wearables, while older adults do the shopping activity. A trial supervisor (TS) labelled the different shopping stages (SS) in the collected data. We performed data pre-processing techniques over those SS and analyzed them with three machine learning algorithms: k-Nearest Neighbors (k-NN), Random Forest (RF) and Support Vector Machines (SVM).Results: Our results confirm that it is possible to replace the traditional questionnaires with wearable data. In particular, the best learning algorithm we tried reported an accuracy of 97% in the assessment of dependence. We tuned the hyperparameters of this algorithm and used embedded feature selection technique to get the best performance with a subset of only 10 features out of the initial 85. This model considers only features extracted from four sensors of a single wearable: accelerometer, heart rate, electrodermal activity and temperature. Although these features are not observational, our current proposal is semi-automatic, because it needs a TS labelling the SS (with a smartphone application). In the future, this labelling process could be automatic as well.Conclusions: Our method can semi-automatically assess the dependence, without disturbing daily activities of elderly people. This method can save clinicians' time in the evaluation of dependence in older adults and reduce healthcare costs. [ABSTRACT FROM AUTHOR]

Introduction: As SARS-CoV-2 spread around the world, Australia was no exception. Part of the Australian response was a robust primary care approach, involving changes to care models (including telehealth) and the widespread use of data to inform the changes. This paper outlines how a large primary care database responded to provide real-time data to inform policy and practice. Simply extracting the data is not sufficient. Understanding the data is. The POpulation Level Analysis and Reporting (POLAR) program is designed to use GP data for multiple objectives and is built on a pre-existing engagement framework established over a fifteen-year period. Initially developed to provide QA activities for general practices and population level data for General Practice support organisations, the POLAR platform has demonstrated the critical ability to design and deploy real-time data analytics solutions during the COVID-19 pandemic for a variety of stakeholders including state and federal government agencies.Methods: The system extracts and processes data from over 1,300 general practices daily. Data is de-identified at the point of collection and encrypted before transfer. Data cleaning for analysis uses a variety of techniques, including Natural Language Processing and coding of free text information. The curated dataset is then distilled into several analytic solutions designed to address specific areas of investigation of interest to various stakeholders. One such analytic solution was a model we created that used multiple data inputs to rank patient geographic areas by the likelihood of a COVID-19 outbreak. The model utilised pathology ordering, COVID-19 related diagnoses, indication of COVID-19 related concern (via progress notes) and also incorporated state based actual confirmed case figures.Results: Using the methods described, we were able to deliver real-time data feeds to practices, Primary Health Networks (PHN) and other agencies. In addition, we developed a COVID-19 geographic risk stratification based on local government areas (LGAs) to pro-actively inform the primary care response. Providing PHNs with a list of geographic priority hotspots allowed for better targeting and response of Personal Protective Equipment allocation and pop-up clinic placement.Conclusions: The program summarised here demonstrates the ability of a well-designed system underpinned by accurate and reliable data, to respond in real-time to a rapidly evolving public health emergency in a way which supports and enhances the health system response. [ABSTRACT FROM AUTHOR]

Objective: Feature selection (FS) is a crucial and at the same time challenging processing step that aims to reduce the dimensionality of complex classification or regression problems. Various techniques have been proposed in the literature to address this challenge with emphasis to medical applications. However, each one of the existing FS algorithms come with its own advantages and disadvantages introducing a certain level of bias.Materials and Methods: To avoid bias and alleviate the defectiveness of single feature selection results, an ensemble FS methodology is proposed in this paper that aggregates the results of several FS algorithms (filter, wrapper and embedded ones). Fuzzy logic is employed to combine multiple feature importance scores thus leading to a more robust selection of informative features. The proposed fuzzy ensemble FS methodology was applied on the problem of knee osteoarthritis (KOA) prediction with special emphasis on the progression of joint space narrowing (JSN). The proposed FS methodology was integrated into an end-to-end machine learning pipeline and a thorough experimental evaluation was conducted using data from the Osteoarthritis Initiative (OAI) database. Several classifiers were investigated for their suitability in the task of JSN prediction and the best performing model was then post-hoc analyzed by using the SHAP method.Results: The results showed that the proposed method presented a better and more stable performance in contrast to other competitive feature selection methods, leading to an average accuracy of 78.14% using XG Boost at 31 selected features. The post-hoc explainability highlighted the important features that contribute to the classification of patients with JSN progression.Conclusions: The proposed fuzzy feature selection approach improves the performance of the predictive models by selecting a small optimal subset of features compared to popular feature selection methods. [ABSTRACT FROM AUTHOR]

Background and Objective: Diabetes and hypertension are two prevalent and related chronic conditions. To inform the design and development of mobile health applications (mHealth apps) for people living with multiple chronic conditions, this paper examines features mentioned in developers' descriptions and user reviews of mHealth apps, along with users' attitudes toward associated features.Materials and Methods: Eleven top apps for diabetes and hypertension were identified from Google Play as of January 2020. Based on a stratified sampling strategy, 1,100 user reviews were selected to form the final dataset. Developers' descriptions were also collected for analysis. Using the grounded theory approach, we developed a feature-oriented coding scheme, which was used to identify three levels of features mentioned in app descriptions and user reviews: feature group (the highest level), feature type (the second level), and individual feature (the lowest level). Users' attitudes toward app features mentioned in user reviews were also analyzed.Results: Most top-rated apps for diabetes and hypertension under study were multifeatured, incorporating self-management, information sharing, and decision support features. At the feature-group level, most informative user reviews commented on features related to self-management, followed by decision support and information sharing. The four most frequently mentioned feature types were data entry, data export/import, data visualization, and assessment. Users expressed overwhelming positive attitudes toward app features across all feature categories. Based on users' assessments of existing features and requests for additional features, design implications for app development are provided.Conclusions: Despite the diversity of app features provided by mHealth apps and users' primarily positive attitudes toward existing app features, more comprehensive and personalized features are expected by app users to satisfy their health needs. Beyond identifying app features in user reviews, future research may seek more in-depth feedback from real-life patients for app development and design using methods like interviews and focus groups, to further enhance the overall quality of relevant mHealth apps to better support users. [ABSTRACT FROM AUTHOR]

Purpose: Femoral neck fracture is a frequent cause of hospitalization, and length of stay is an important marker of hospital cost and quality of care provided. As an extension of traditional statistical methods, machine learning provides the possibility of accurately predicting the length of hospital stay. The aim of this paper is to retrospectively identify predictive factors of the length of hospital stay (LOS) and predict the postoperative LOS by using machine learning algorithms.Method: Based on the admission and perioperative data of the patients, linear regression was used to analyze the predictive factors of the LOS. Multiple machine learning models were developed, and the performance of different models was compared.Result: Stepwise linear regression showed that preoperative calcium level (P = 0.017) and preoperative lymphocyte percentage (P = 0.007), in addition to intraoperative bleeding (p = 0.041), glucose and sodium chloride infusion after surgery (P = 0.019), Charlson Comorbidity Index (p = 0.007) and BMI (P = 0.031), were significant predictors of LOS. The best performing model was the principal component regression (PCR) with an optimal MAE (1.525) and a proportion of prediction error within 3 days of 90.91%.Conclusion: Excessive intravenous glucose and sodium chloride infusion after surgery, preoperative hypocalcemia, preoperative high percentages of lymphocytes, excessive intraoperative bleeding, lower BMI and higher CCI scores were related to prolonged LOS by using linear regression. Machine learning could accurately predict the postoperative LOS. This information allows hospital administrators to plan reasonable resource allocation to fulfill demand, leading to direct care quality improvement and more reasonable use of scarce resources. [ABSTRACT FROM AUTHOR]

Background: Type 2 diabetes mellitus (T2DM) and hypertension (HTN), both non-communicable diseases, are leading causes of death globally, with more imbalances in lower middle-income countries. Furthermore, poor treatment and management are known to lead to intensified healthcare utilization and increased medical care costs and impose a significant societal burden, in these countries, including Indonesia. Predicting future clinical outcomes can determine the line of treatment and value of healthcare costs, while ensuring effective patient care. In this paper, we present the prediction of length of stay (LoS) and mortality among hospitalized patients at a tertiary referral hospital in Tasikmalaya, Indonesia, between 2016 and 2019. We also aimed to determine how socio-demographic characteristics, and T2DM- or HTN-related comorbidities affect inpatient LoS and mortality.Methods: We analyzed insurance claims data of 4376 patients with T2DM or HTN hospitalized in the referral hospital. We used four prediction models based on machine-learning algorithms for LoS prediction, in relation to disease severity, physician-in-charge, room type, co-morbidities, and types of procedures performed. We used five classifiers based on multilayer perceptron (MLP) to predict inpatient mortality and compared them according to training time, testing time, and Area under Receiver Operative Curve (AUROC). Classifier accuracy measures, which included positive predictive value (PPV), negative predictive value (NPV), F-Measure, and recall, were used as performance evaluation methods.Results: A Random forest best predicted inpatient LoS (R2, 0.70; root mean square error [RMSE], 1.96; mean absolute error [MAE], 0.935), and the gradient boosting regression model also performed similarly (R2, 0.69; RMSE, 1.96; MAE, 0.935). For inpatient mortality, best results were observed using MLP with back propagation (AUROC 0.899; 69.33 and 98.61 for PPV and NPV, respectively). The other classifiers, stochastic gradient descent with regression loss function, Huber, and random forest models all showed an average performance.Conclusions: Linear regression model best predicted LoS and mortality was best predicted using MLP. Patients with primary diseases such as T2DM or HTN may have comorbidities that can prolong inpatient LoS. Physicians play an important role in disseminating health related information. These predictions could assist in the development of health policies and strategies that reduce disease burden in resource-limited settings. [ABSTRACT FROM AUTHOR]

Purpose: Long-distance caregiving (LDC) technologies play a significant role in enabling distant care and facilitating living-alone older adults to keep socially connected. However, there is scarce research exploring the older adults' attitudes towards and intention to use such technologies. This paper is based on a systematic review of existing literature to explore the multifarious factors influencing independent community-living older adults' attitudes towards and intention to use LDC technologies.Methods: Articles published in English between 2006 and 2020 were reviewed by searching electronic databases of PubMed, ProQuest, EBSCOhost. The inclusion criteria were limited to quantitative, qualitative, or mixed-methods studies that involved: 1) distant caregiving; 2) older adults aged 60 years or above, who were living alone or with only their spouse in the community (even though the samples might also involve other non-older adults); 3) technologies including ICT-based devices, systems, or programs enabling data transmission were used; 4), intention to use or behavioral usage in regard to the technologies were reported or discussed.Results: In total, 41 out of 8674 articles were included. Both determinants and moderators of affecting the use of the ICT-based LDC technologies were identified with theoretical guidance. To summarize, there are personal factors involved, such as personality, concerns regarding security and privacy, health conditions, requisite knowledge, financial conditions, and influence from significant others, encompassing formal and informal caregivers; and factors related to the devices, in terms of their user-friendliness and functionality.Conclusion: This review highlights the importance of striking a good balance between functionality and privacy concerns, besides considering the direct and indirect cost to users. LDC technology education should be promoted at the societal level to facilitate older adults' better understanding of the device utilities by enhancing their technological literacy. Implications for various stakeholders to cope with the challenges of an aging population are also discussed. [ABSTRACT FROM AUTHOR]

Aim Background: Many health care services are implementing or planning to undergo digital transformation to keep pace with increasing Electronic Medical Record (EMR) functionality. The aim of this study was to objectively measure nursing care delivery before and following introduction of an EMR.Design and Methods: An extensive program of work to expand an EMR across our health service using a 'big bang' methodology was undertaken. The program incorporated digital care delivery workflows including physiological observations, clinical notes and closed loop medication management. The validated Work Observation Method by Activity Timing (WOMBAT) method was applied to undertake a direct observational time and motion study of nurses' work in a major Australian hospital immediately prior to and six months following the introduction of a full clinical EMR.Results: Time and motion results were from observing approximately one week of nursing time pre (paper) to six months post (EMR) implementation. A non-significant 6.4% increase in the proportion of time spent on direct care was observed when using the EMR with a statistically significant increase in mean time per direct care task (2.5 min vs 3.9 min, p = 0.001). The proportion of time spent on medication-related activities did not significantly change although the average time per task rose from 2.0 to 2.9 min (p = 0.008). A significant reduction in proportion of time spent in transit and indirect care tasks when using the electronic workflows was reported. No statistically significant changes to the proportions of time spent on professional communication, direct care or documentation were observed.Conclusions: Successful EMR implementation is possible without adversely affecting allocation of nursing time. Our findings from deploying a large scale EMR across all healthcare craft groups and workflows have described for nurses that an EMR enables them to spend longer with patients per direct care episode and use their time on other activities more effectively. [ABSTRACT FROM AUTHOR]

Background: Wrong medication and wrong dosage are major risks in the pharmaceutical industry, as many medication errors occur when dispensing medication. The dispensing process in its current form is limited in verifying the patient's identity before dispensing the medication. Furthermore, this process does not offer a robust method for providing accurate medication intake instructions. Therefore, we have developed a framework to accurately and securely overcome issues associated with transferring patient credentials and prescription information. The long-term goal of this research is to develop a framework to mitigate medication dispensing errors. One of the framework components is the mobile application that uses near-field communication (NFC) to transfer information. Therefore, in this paper, we designed a user study to assess the proposed NFC-based mobile application in terms of usefulness and ease of use compared with the traditional method of picking up a prescribed medication.Methods: We conducted a usability study with 21 participants to perform four tasks to simulate the process of picking up a prescribed medication using the proposed NFC application method and the traditional method of picking up medication. Then, we asked the participants to complete two post-questionnaires after using each method to evaluate the participants' experience of the process. Next, we asked the participants to complete an additional questionnaire about the usefulness of the NFC application method. Finally, we conducted semi-structured interviews with the participants to get more evidence to back up the questionnaire answers.Results: Our findings show that 91% of the participants believe using the NFC application method will improve patient safety during the medication pickup process. Nearly 97% of participants found the NFC application method easy to use. Our findings show that the participants scored lower when using the NFC application method compared with the traditional method when trying to identify the wrong medication after dispensing. In addition, 90% of the participants successfully identified the wrong medication when using the NFC application method, compared to only 38% when using the traditional method. Finally, the results show that the participants preferred using the NFC application method in terms of information availability, security, and privacy.Conclusions: The study findings show that the proposed NFC application for managing patients' prescriptions and picking up medication might improve patient safety. The results show that the participants believe the NFC application will mitigate medication dispensing errors, at least from their end. The participants believe the application will provide a fast and accurate method of verifying dispensed medication from the patient end. Moreover, the application will help the patient to track their current prescription, which also helps them remember the medication intake instructions. Finally, the study indicates that the application will provide a secure, private, and accurate method to help verify the patient's identity, thus minimizing medication errors during the medication dispensing process. [ABSTRACT FROM AUTHOR]