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The increasing demand for healthcare and the static resources available necessitate data driven improvements in healthcare at large scale. The SnoMAP tool was rapidly developed to provide an automated solution that transforms and maps clinician-entered data to provide data which is fit for both administrative and clinical purposes. Accuracy of data mapping was maintained. [ABSTRACT FROM AUTHOR]

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Background: Digital health offers unprecedented opportunities to enhance health service delivery across vast geographic regions. However, these benefits can only be realized with effective capabilities and clinical leadership of the rural healthcare workforce. Little is known about how rural healthcare workers acquire skills in digital health, how digital health education or training programs are evaluated and the barriers and enablers for high quality digital health education and training. Objective: To conduct a scoping review to identify and synthesize existing evidence on digital health education and training of the rural healthcare workforce. Inclusion criteria: Sources that reported digital health and education or training in the healthcare workforce in any healthcare setting outside metropolitan areas. Methods: We searched for published and unpublished studies written in English in the last decade to August 2023. The databases searched were PubMed, Embase, Scopus, CINAHL and Education Resources Information Centre. We also searched the grey literature (Google, Google Scholar), conducted citation searching and stakeholder engagement. The JBI Scoping Review methodology and PRISMA guidelines for scoping reviews were used. Results: Five articles met the eligibility criteria. Two case studies, one feasibility study, one micro-credential and one fellowship were described. The mode of delivery was commonly modular online learning. Only one article described an evaluation, and findings showed the train-the-trainer model was technically and pedagogically feasible and well received. A limited number of barriers and enablers for high quality education or training of the rural healthcare workforce were reported across macro (legal, regulatory, economic), meso (local health service and community) and micro (day-to-day practice) levels. Conclusions: Upskilling rural healthcare workers in digital health appears rare. Current best practice points to flexible, blended training programs that are suitably embedded with interdisciplinary and collaborative rural healthcare improvement initiatives. Future work to advance the field could define rural health informatician career pathways, address concurrent rural workforce issues, and conduct training implementation evaluations. Review registration number: Open Science Framework: https://doi.org/10.17605/OSF.IO/N2RMX. [ABSTRACT FROM AUTHOR]

Background: Global action to reduce obesity prevalence requires digital transformation of the public health sector to enable precision public health (PPH). Useable data for PPH of obesity is yet to be identified, collated and appraised and there is currently no accepted approach to creating this single source of truth. This scoping review aims to address this globally generic problem by using the State of Queensland (Australia) (population > 5 million) as a use case to determine (1) availability of primary data sources usable for PPH for obesity (2) quality of identified sources (3) general implications for public health policymakers. Methods: The Preferred Reporting Items for Systematic Review and Meta-Analyses extension for scoping reviews (PRISMA-ScR) was followed. Unique search strategies were implemented for 'designed' (e.g. surveys) and 'organic' (e.g. electronic health records) data sources. Only primary sources of data (with stratification to Queensland) with evidence-based determinants of obesity were included. Primary data source type, availability, sample size, frequency of collection and coverage of determinants of obesity were extracted and curated into an evidence map. Data source quality was qualitatively assessed. Results: We identified 38 primary sources of preventive data for obesity: 33 designed and 5 organic. Most designed sources were survey (n 20) or administrative (n 10) sources and publicly available but generally were not contemporaneous (> 2 years old) and had small sample sizes (10-100 k) relative to organic sources (> 1 M). Organic sources were identified as the electronic medical record (ieMR), wearables, environmental (Google Maps, Crime Map) and billing/claims. Data on social, biomedical and behavioural determinants of obesity typically co-occurred across sources. Environmental and commercial data was sparse and interpreted as low quality. One organic source (ieMR) was highly contemporaneous (routinely updated), had a large sample size (5 M) and represented all determinants of obesity but is not currently used for public health decision-making in Queensland. Conclusions: This review provides a (1) comprehensive data map for PPH for obesity in Queensland and (2) globally translatable framework to identify, collate and appraise primary data sources to advance PPH for obesity and other noncommunicable diseases. Significant challenges must be addressed to achieve PPH, including: using designed and organic data harmoniously, digital infrastructure for high-quality organic data, and the ethical and social implications of using consumer-centred health data to improve public health. [ABSTRACT FROM AUTHOR]

Background: For many countries, especially those outside the USA without incentive payments, implementing and maintaining electronic medical records (EMR) is expensive and can be controversial given the large amounts of investment. Evaluating the value of EMR implementation is necessary to understand whether or not, such investment, especially when it comes from the public source, is an efficient allocation of healthcare resources. Nonetheless, most countries have struggled to measure the return on EMR investment due to the lack of appropriate evaluation frameworks. Methods: This paper outlines the development of an evidence-based digital health cost-benefit analysis (eHealth-CBA) framework to calculate the total economic value of the EMR implementation over time. A net positive benefit indicates such investment represents improved efficiency, and a net negative is considered a wasteful use of public resources. Results: We developed a three-stage process that takes into account the complexity of the healthcare system and its stakeholders, the investment appraisal and evaluation practice, and the existing knowledge of EMR implementation. The three stages include (1) literature review, (2) stakeholder consultation, and (3) CBA framework development. The framework maps the impacts of the EMR to the quadruple aim of healthcare and clearly creates a method for value assessment. Conclusions: The proposed framework is the first step toward developing a comprehensive evaluation framework for EMRs to inform health decision-makers about the economic value of digital investments rather than just the financial value. [ABSTRACT FROM AUTHOR]

Background Globally, emergency departments (EDs) are overcrowded and unable to meet an ever-increasing demand for care. The aim of this study is to comprehensively review and synthesise literature on potential solutions and challenges throughout the entire health system, focusing on ED patient fow. Methods An umbrella review was conducted to comprehensively summarise and synthesise the available evidence from multiple research syntheses. A comprehensive search strategy was employed in four databases alongside government or organisational websites in March 2023. Gray literature and reports were also searched. Quality was assessed using the JBI critical appraisal checklist for systematic reviews and research syntheses. We summarised and classifed fndings using qualitative synthesis, the Population-Capacity-Process (PCP) model, and the input/ throughput/output (I/T/O) model of ED patient fow and synthesised intervention outcomes based on the Quadruple Aim framework. Results The search strategy yielded 1263 articles, of which 39 were included in the umbrella review. Patient fow interventions were categorised into human factors, management-organisation interventions, and infrastructure and mapped to the relevant component of the patient journey from pre-ED to post-ED interventions. Most interventions had mixed or quadruple nonsignifcant outcomes. The majority of interventions for enhancing ED patient fow were primarily related to the ’within-ED’ phase of the patient journey. Fewer interventions were identifed for the ’post-ED’ phase (acute inpatient transfer, subacute inpatient transfer, hospital at home, discharge home, or residential care) and the ’pre-ED’ phase. The intervention outcomes were aligned with the aim (QAIM), which aims to improve patient care experience, enhance population health, optimise efciency, and enhance staf satisfaction. Conclusions This study found that there was a wide range of interventions used to address patient fow, but the efectiveness of these interventions varied, and most interventions were focused on the ED. Interventions for the remainder of the patient journey were largely neglected. The metrics reported were mainly focused on efciency measures rather than addressing all quadrants of the quadruple aim. Further research is needed to investigate and enhance the efectiveness of interventions outside the ED in improving ED patient fow. It is essential to develop interventions that relate to all three phases of patient fow: pre-ED, within-ED, and post-ED. [ABSTRACT FROM AUTHOR]

Background: There are many Machine Learning (ML) models which predict acute kidney injury (AKI) for hospitalised patients. While a primary goal of these models is to support clinical decision-making, the adoption of inconsistent methods of estimating baseline serum creatinine (sCr) may result in a poor understanding of these models' effectiveness in clinical practice. Until now, the performance of such models with different baselines has not been compared on a single dataset. Additionally, AKI prediction models are known to have a high rate of false positive (FP) events regardless of baseline methods. This warrants further exploration of FP events to provide insight into potential underlying reasons. Objective: The first aim of this study was to assess the variance in performance of ML models using three methods of baseline sCr on a retrospective dataset. The second aim was to conduct an error analysis to gain insight into the underlying factors contributing to FP events. Materials and methods: The Intensive Care Unit (ICU) patients of the Medical Information Mart for Intensive Care (MIMIC)-IV dataset was used with the KDIGO (Kidney Disease Improving Global Outcome) definition to identify AKI episodes. Three different methods of estimating baseline sCr were defined as (1) the minimum sCr, (2) the Modification of Diet in Renal Disease (MDRD) equation and the minimum sCr and (3) the MDRD equation and the mean of preadmission sCr. For the first aim of this study, a suite of ML models was developed for each baseline and the performance of the models was assessed. An analysis of variance was performed to assess the significant difference between eXtreme Gradient Boosting (XGB) models across all baselines. To address the second aim, Explainable AI (XAI) methods were used to analyse the XGB errors with Baseline 3. Results: Regarding the first aim, we observed variances in discriminative metrics and calibration errors of ML models when different baseline methods were adopted. Using Baseline 1 resulted in a 14% reduction in the f1 score for both Baseline 2 and Baseline 3. There was no significant difference observed in the results between Baseline 2 and Baseline 3. For the second aim, the FP cohort was analysed using the XAI methods which led to relabelling data with the mean of sCr in 180 to 0 days pre-ICU as the preferred sCr baseline method. The XGB model using this relabelled data achieved an AUC of 0.85, recall of 0.63, precision of 0.54 and f1 score of 0.58. The cohort size was 31,586 admissions, of which 5,473 (17.32%) had AKI. Conclusion: In the absence of a widely accepted method of baseline sCr, AKI prediction studies need to consider the impact of different baseline methods on the effectiveness of ML models and their potential implications in real-world implementations. The utilisation of XAI methods can be effective in providing insight into the occurrence of prediction errors. This can potentially augment the success rate of ML implementation in routine care. [ABSTRACT FROM AUTHOR]

Patients who require emergency admission to hospital require complex care that can be fragmented, occurring in the ED, across the ED-inpatient interface (EDii) and subsequently, in their destination inpatient ward. Our hospital had poor process efficiency with slow transit times for patients requiring emergency care. ED clinicians alone were able to improve the processes and length of stay for the patients discharged directly from the ED. However, improving the efficiency of care for patients requiring emergency admission to true inpatient wards required collaboration with reluctant inpatient clinicians. The inpatient teams were uninterested in improving time-based measures of care in isolation, but they were motivated by improving patient outcomes. We developed a dashboard showing process measures such as 4 h rule compliance rate coupled with clinically important outcome measures such as inpatient mortality. The EDii dashboard helped unite both ED and inpatient teams in clinical redesign to improve both efficiencies of care and patient outcomes. [ABSTRACT FROM AUTHOR]

Modeling of background radiation for the urban environment plays an important role in homeland security. However, background radiation is difficult to assess due to its spatial-temporal fluctuations caused by the variation in soil composition, building materials, and weather patterns etc. To address the challenge of background radiation modeling, we developed a mobile sensor network to continuously monitor the background radiation; we also proposed a maximum likelihood estimation algorithm to decouple and estimate the background’s spatial distribution and temporal fluctuation. Experimental results demonstrated how this background radiation monitoring system accurately recognized high background regions in the experimental area, and successfully captured temporal fluctuation trends of background radiation during rains. Our system provides an efficient solution to model the temporal fluctuation and spatial distribution of background radiation. [ABSTRACT FROM AUTHOR]