Background Intravenous medication administrations have a high incidence of error but there is limited evidence of associated factors or error severity. Objective To measure the frequency, type and severity of intravenous administration errors in hospitals and the associations between errors, procedural failures and nurse experience. Methods Prospective observational study of 107 nurses preparing and administering 568 intravenous medications on six wards across two teaching hospitals. Procedural failures (eg, checking patient identification) and clinical intravenous errors (eg, wrong intravenous administration rate) were identified and categorised by severity. Results Of 568 intravenous administrations, 69.7% (n=396; 95% CI 65.9 to 73.5) had at least one clinical error and 25.5% (95% CI 21.2 to 29.8) of these were serious. Four error types (wrong intravenous rate, mixture, volume, and drug incompatibility) accounted for 91.7% of errors. Wrong rate was the most frequent and accounted for 95 of 101 serious errors. Error rates and severity decreased with clinical experience. Each year of experience, up to 6 years, reduced the risk of error by 10.9% and serious error by 18.5%. Administration by bolus was associated with a 312% increased risk of error. Patient identification was only checked in 47.9% of administrations but was associated with a 56% reduction in intravenous error risk. Conclusions Intravenous administrations have a higher risk and severity of error than other medication administrations. A significant proportion of errors suggest skill and knowledge deficiencies, with errors and severity reducing as clinical experience increases. A proportion of errors are also associated with routine violations which are likely to be learnt workplace behaviours. Both areas suggest specific targets for intervention.

Background: Although both disaster management and disaster medicine have been used for decades, their efficiency and effectiveness have been far from perfect. One reason could be the lack of systematic utilization of modern technologies, such as eHealth, in their operations. To address this issue, researchers’ efforts have led to the emergence of the disaster eHealth (DEH) field. DEH’s main objective is to systematically integrate eHealth technologies for health care purposes within the disaster management cycle (DMC). Objective: This study aims to identify, map, and define the scope of DEH as a new area of research at the intersection of disaster management, emergency medicine, and eHealth. Methods: An extensive scoping review using published materials was carried out in the areas of disaster management, disaster medicine, and eHealth to identify the scope of DEH. This review procedure was iterative and conducted in multiple scientific databases in 2 rounds, one using controlled indexed terms and the other using similar uncontrolled terms. In both rounds, the publications ranged from 1990 to 2016, and all the appropriate research studies discovered were considered, regardless of their research design, methodology, and quality. Information extracted from both rounds was thematically analyzed to define the DEH scope, and the results were evaluated by the field experts through a Delphi method. Results: In both rounds of the research, searching for eHealth applications within DMC yielded 404 relevant studies that showed eHealth applications in different disaster types and disaster phases. These applications varied with respect to the eHealth technology types, functions, services, and stakeholders. The results led to the identification of the scope of DEH, including eHealth technologies and their applications, services, and future developments that are applicable to disasters as well as to related stakeholders. Reference to the elements of the DEH scope indicates what, when, and how current eHealth technologies can be used in the DMC. Conclusions: Comprehensive data gathering from multiple databases offered a grounded method to define the DEH scope. This scope comprises concepts related to DEH and the boundaries that define it. The scope identifies the eHealth technologies relevant to DEH and the functions and services that can be provided by these technologies. In addition, the scope tells us which groups can use the provided services and functions and in which disaster types or phases. DEH approaches could potentially improve the response to health care demands before, during, and after disasters. DEH takes advantage of eHealth technologies to facilitate DMC tasks and activities, enhance their efficiency and effectiveness, and enhance health care delivery and provide more quality health care services to the wider population regardless of their geographical location or even disaster types and phases.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

The elderly population is increasing globally, putting more pressure on aged care and public health systems. To address this issue and help increase the independence of older people, different digital technologies, including the Internet of things (IoT), can play an important role. Although there has been an increase in the number of studies in this field, most of them concentrate on IoT applications in elderly care from a technology perspective, with very little contribution from the clinical side. Therefore, this paper aims to investigate and identify the available IoT applications and their clinical utility for common diseases in elderly people. The results of this study could be useful for information technology professionals in developing and understanding the clinical requirements for IoT applications in healthcare for older people. Clinicians will also be informed about the clinical possibilities of using IoT devices in this area. Based on our findings, future research should focus on enhancing the clinical utility of current IoT applications in different settings and on developing new applications to support practitioners and older people.

Background: The phenomenon of patients missing booked appointments without canceling them—known as Did Not Show (DNS), Did Not Attend (DNA), or Failed To Attend (FTA)—has a detrimental effect on patients’ health and results in massive health care resource wastage. Objective: Our objective was to develop machine learning (ML) models and evaluate their performance in predicting the likelihood of DNS for hospital outpatient appointments at the MidCentral District Health Board (MDHB) in New Zealand. Methods: We sourced 5 years of MDHB outpatient records (a total of 1,080,566 outpatient visits) to build the ML prediction models. We developed 3 ML models using logistic regression, random forest, and Extreme Gradient Boosting (XGBoost). Subsequently, 10-fold cross-validation and hyperparameter tuning were deployed to minimize model bias and boost the algorithms’ prediction strength. All models were evaluated against accuracy, sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve metrics. Results: Based on 5 years of MDHB data, the best prediction classifier was XGBoost, with an area under the curve (AUC) of 0.92, sensitivity of 0.83, and specificity of 0.85. The patients’ DNS history, age, ethnicity, and appointment lead time significantly contributed to DNS prediction. An ML system trained on a large data set can produce useful levels of DNS prediction. Conclusions: This research is one of the very first published studies that use ML technologies to assist with DNS management in New Zealand. It is a proof of concept and could be used to benchmark DNS predictions for the MDHB and other district health boards. We encourage conducting additional qualitative research to investigate the root cause of DNS issues and potential solutions. Addressing DNS using better strategies potentially can result in better utilization of health care resources and improve health equity.