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This paper proposes a spatiotemporal clustering algorithm and its implementation in the R package spotoroo. This work is motivated by the catastrophic bushfires in Australia throughout the summer of 2019-2020 and made possible by the availability of satellite hotspot data. The algorithm is inspired by two existing spatiotemporal clustering algorithms but makes enhancements to cluster points spatially in conjunction with their movement across consecutive time periods. It also allows for the adjustment of key parameters, if required, for different locations and satellite data sources. Bushfire data from Victoria, Australia, is used to illustrate the algorithm and its use within the package. [ABSTRACT FROM AUTHOR]

Background Delivery of acute stroke endovascular intervention can be challenging because it requires complex coordination of patient and staff across many different locations. In this proof-of-concept paper we (a) examine whether WiFi fingerprinting is a feasible machine learning (ML)-based real-time location system (RTLS) technology that can provide accurate real-time location information within a hospital setting, and (b) hypothesize its potential application in streamlining acute stroke endovascular intervention. Methods We conducted our study in a comprehensive stroke care unit in Melbourne, Australia that offers a 24hour mechanical thrombectomy service. ML algorithms including K-nearest neighbors, decision tree, random forest, support vector machine and ensemble models were trained and tested on a public WiFi dataset and the study hospital WiFi dataset. The hospital dataset was collected using the WiFi explorer software (version 3.0.2) on a MacBook Pro (AirPort Extreme, Broadcom BCM43xx1.0). Data analysis was implemented in the Python programming environment using the scikit-learn package. The primary statistical measure for algorithm performance was the accuracy of location prediction. Results ML-based WiFi fingerprinting can accurately predict the different hospital zones relevant in the acute endovascular intervention workflow such as emergency department, CT room and angiography suite. The most accurate algorithms were random forest and support vector machine, both of which were 98% accurate. The algorithms remain robust when new data points, which were distinct from the training dataset, were tested. Conclusions ML-based RTLS technology using WiFi fingerprinting has the potential to streamline delivery of acute stroke endovascular intervention by efficiently tracking patient and staff movement during stroke calls. [ABSTRACT FROM AUTHOR]

We aimed to explore managerial and project staff perceptions of the pilot implementation of an algorithm-supported care navigation model, targeting people at risk of hospital readmission. The pilot was implemented from May to November 2017 at a Victorian health service (Australia) and provided to sixty-five patients discharged from the hospital to the community. All managers and the single clinician involved participated in a semi-structured interview. Participants (n = 6) were asked about their perceptions of the service design and the enablers and barriers to implementation. Interviews were transcribed verbatim and analysed according to a framework approach, using inductive and deductive techniques. Constructed themes included the following: an algorithm alone is not enough, the health service culture, leadership, resources and the perceived patient experience. Participants felt that having an algorithm to target those considered most likely to benefit was helpful but not enough on its own without addressing other contextual factors, such as the health service's capacity to support a large-scale implementation. Deductively mapping themes to the integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework highlighted that a formal facilitation would be essential for future sustainable implementations. The systematic identification of barriers and enablers elicited critical information for broader implementations of algorithm-supported models of care. [ABSTRACT FROM AUTHOR]

Objective: To pilot the Pharmacist Community Apgar Questionnaire (PharmCAQ) and evaluate its usability and capacity to develop a greater understanding of the unique factors that impact the rural recruitment and retention of pharmacists. Design: Cross‐sectional design involving face‐to‐face, telephone or video conferencing interviews. Setting: Twelve rural communities across Tasmania and Western Victoria, Australia. Participants: Participants (n = 24) included pharmacists, a Director of Clinical Services, pharmacy practice managers and senior pharmacy assistants. Main Outcome Measures: Interviews enabled the completion of the PharmCAQ, which assigns quantitative values to 50 key factors to ascertain a community's strengths and challenges associated with recruitment and retention and their relative importance to the pharmacist workforce. Results: The cumulative PharmCAQ scores indicated the tool was sensitive enough to differentiate high‐ and low‐performing communities. Overall, the highest‐rated factors considered most vital to pharmacist recruitment and retention were the reputation of the pharmacy, the ability of the pharmacist to be independent and autonomous, the loyalty of the community to the pharmacy, the level and stability of monetary compensation and the breadth of tasks available to a pharmacist. Conclusions: This study identified the strengths and challenges of participating communities and provided an insight into the shared factors to consider in recruiting and retaining pharmacists. Further, each community has unique strengths that can further be promoted in recruitment, flagging where limited resources are best used to address site specific challenges. This is more likely to ensure the matching of the right candidate with the right community. [ABSTRACT FROM AUTHOR]

Regional flood frequency analysis (RFFA) is widely used to estimate design floods in ungauged catchments. Both linear and non-linear methods are adopted in RFFA. The development of the non-linear RFFA method Adaptive Neuro-fuzzy Inference System (ANFIS) using data from 181 gauged catchments in south-eastern Australia is presented in this study. Three different types of ANFIS models, Fuzzy C-mean (FCM), Subtractive Clustering (SC), and Grid Partitioning (GP) were adopted, and the results were compared with the Quantile Regression Technique (QRT). It was found that FCM performs better (with relative error (RE) values in the range of 38–60%) than the SC (RE of 44–69%) and GP (RE of 42–78%) models. The FCM performs better for smaller to medium ARIs (2 to 20 years) (ARI of five years having the best performance), and in New South Wales, over Victoria. In many aspects, the QRT and FCM models perform very similarly. These developed RFFA models can be used in south-eastern Australia to derive more accurate flood quantiles. The developed method can easily be adapted to other parts of Australia and other countries. The results of this study will assist in updating the Australian Rainfall Runoff (national guide)-recommended RFFA technique. [ABSTRACT FROM AUTHOR]

Tram drivers operate a master controller to control the acceleration and braking of the tram. Operation should ensure passenger comfort and safety through smooth tram motion and the avoidance of jerkiness that may cause passengers to fall in the carriage. This work investigates current driver practices and strategies for tram driving in normal operations through interviews and the capacity of a new haptic master controller to support drivers in achieving smooth and safe tram journeys. A haptic feedback algorithm based on viscosity was implemented on the master controller to provide drivers with feedback on the rate at which they were accelerating and braking the tram. This aspect was tested in a virtual tram within a simulated inner city virtual reality environment. Results indicate that the haptic master controller and coupled viscosity feedback algorithm did not increase smoothness of driving during the simulated experiences. Despite this, the drivers indicated a preference for the provision of further haptic information to support driving tasks and the overall journey safety and smoothness. Practitioner Summary: This research comprises two studies. The first investigates strategies currently used by drivers to operate a tram smoothly in order to elicit design requirements for a haptic tram master controller. The second study evaluates the impact of a novel haptic master controller on journey smoothness within a virtual environment. [ABSTRACT FROM AUTHOR]

Abstract: This paper provides a novel Exceptional Object Analysis for Finding Rare Environmental Events (EOAFREE). The major contribution of our EOAFREE method is that it proposes a general Improved Exceptional Object Analysis based on Noises (IEOAN) algorithm to efficiently detect and rank exceptional objects. Our IEOAN algorithm is more general than already known outlier detection algorithms to find exceptional objects that may be not on the border; and experimental study shows that our IEOAN algorithm is far more efficient than directly recursively using already known clustering algorithms that may not force every data instance to belong to a cluster to detect rare events. Another contribution is that it provides an approach to preprocess heterogeneous real world data through exploring domain knowledge, based on which it defines changes instead of the water data value itself as the input of the IEOAN algorithm to remove the geographical differences between any two sites and the temporal differences between any two years. The effectiveness of our EOAFREE method is demonstrated by a real world application – that is, to detect water pollution events from the water quality datasets of 93 sites distributed in 10 river basins in Victoria, Australia between 1975 and 2010. [Copyright &y& Elsevier]

Abstract: This paper presents an innovative method for tourist market segmentation-based on dominant movement patterns of tourists; that is, the travel sequences or patterns used by tourists most frequently. There were three steps to achieve this goal. In the first step, general log-linear models were adopted to identify the dominant movement patterns, while the second step was to discover the characteristics of the groups of tourists who travelled with these patterns. The Expectation–Maximisation algorithm was then used to partition tourist segments in terms of socio-demographic and travel behavioural variables. The third step was to select target markets based upon the earlier analysis. These methods were applied to a sample of tourists, over the period of a week, on Phillip Island, Victoria, Australia. A significant outcome of this research is that it will assist tourism organisations to identify tourism market segments and develop better tour packages and more efficient marketing strategies aligned to the characteristics of the tourists. [Copyright &y& Elsevier]

Abstract: This paper reports the intensive test of the new transport systems centre (TSC) algorithm applied to incident detection on freeways. The TSC algorithm is designed to fulfil the universality expectations of automated incident detection. The algorithm consists of two modules: data processing module and incident detection module. The data processing module is designed to handle specific features of different sites. The Bayesian network based incident detection module is used to store and manage general expert traffic knowledge, and to perform coherent reasoning to detect incidents. The TSC algorithm is tested using 100 field incident data sets obtained from Tullamarine Freeway and South Eastern Freeway in Melbourne, Australia. The performance of the algorithm demonstrates its competitiveness with the best performing neural network algorithm which was developed and tested using the same incident data sets in an early research. Most importantly, both the detection rate and false alarm rate of the TSC algorithm are not sensitive to the incident decision threshold, which greatly improves the stability of incident detection. In addition, a very consistent algorithm performance is achieved when the TSC algorithm is transferred from Southern Expressway of Adelaide to both Tullamarine Freeway and South Eastern Freeway of Melbourne. No substantial algorithm retraining is required. A significant step towards algorithm universality is possible from this research. [Copyright &y& Elsevier]

Rationale, aims, and objectives: Monash Watch (MW) aims to reduce potentially preventable hospitalisations in a cohort above a risk "threshold" identified by Health Links Chronic Care (HLCC) algorithms using personal, diagnostic, and service data. MW conducted regular patient monitoring through outbound phone calls using the Patient Journey Record System (PaJR). PaJR alerts are intended to act as a self‐reported barometer of stressors, resilience, and health perceptions with more alerts per call indicating greater risk. Aims: To describe predictors of PaJR alerts (self‐reported from outbound phone calls) and predictors of acute admissions based upon a Theoretical Model for Static and Dynamic Indicators of Acute Admissions. Methods: Participants: HLCC cohort with predicted 3+ admissions/year in MW service arm for >40 days; n = 244. Baseline measures—Clinical Frailty Index (CFI); Connor Davis Resilience (CD‐RISC): SF‐12v2 Health Survey scores Mental (MSC) and Physical (PSC) and ICECAP‐O. Dynamic measures: PaJR alerts/call in 10 869 MW records. Acute (non‐surgical) admissions from Victorian Admitted Episode database. Analysis: Logistic regression, correlations, and timeseries homogeneity metrics using XLSTAT. Findings Baseline indicators were significantly correlated except SF‐12_MCS. SF12‐MSC, SF12‐PSC and ICECAP‐O best predicted PaJR alerts/call (ROC: 0.84). CFI best predicted acute admissions (ROC: 0.66), adding CD‐RISC, SF‐12_MCS, SF‐12_PCS and ICECAP‐O with two‐way interactions improved model (ROC: 0.70). PaJR alerts were higher ≤10 days preceding acute admissions and significantly correlated with admissions. Patterns in PaJR alerts in four case studies demonstrated dynamic variations signifying risk. Overall, all baseline indicators were explanatory supporting the theoretical model. Timing of PaJR alerts and acute admissions reflecting changing stressors, resilience, and health perceptions were not predicted from baseline indicators but provided a trigger for service interventions. Conclusion: Both static and dynamic indicators representing stressors, resilience, and health perceptions have the potential to inform threshold models of admission risk in ways that could be clinically useful. [ABSTRACT FROM AUTHOR]

Objective: To formally evaluate the written discharge advice for people with mild traumatic brain injury (mTBI). Methods: Eleven publications met the inclusion criteria: (1) intended for adults; (2) ≤two A4 pages; (3) published in English; (4) freely accessible; and (5) currently used (or suitable for use) in Australian hospital emergency departments or similar settings. Two independent raters evaluated the content and style of each publication against established standards. The readability of the publication, the diagnostic term(s) contained in it and a modified Patient Literature Usefulness Index (mPLUI) were also evaluated. Results: The mean content score was 19.18 ± 8.53 (maximum = 31) and the mean style score was 6.8 ± 1.34 (maximum = 8). The mean Flesch-Kincaid reading ease score was 66.42 ± 4.3. The mean mPLUI score was 65.86 ± 14.97 (maximum = 100). Higher scores on these metrics indicate more desirable properties. Over 80% of the publications used mixed diagnostic terminology. One publication scored optimally on two of the four metrics and highly on the others. Discussion: The content, style, readability and usefulness of written mTBI discharge advice was highly variable. The provision of written information to patients with mTBI is advised, but this variability in materials highlights the need for evaluation before distribution. Areas are identified to guide the improvement of written mTBI discharge advice. [ABSTRACT FROM AUTHOR]

Objective. Readmission rates are high following acute myocardial infarction (AMI), but risk stratification has proved difficult because known risk factors are only weakly predictive. In the present study, we applied hospital data to identify the risk of unplanned admission following AMI hospitalisations. Methods. The study included 1660 consecutive AMI admissions. Predictive models were derived from 1107 randomly selected records and tested on the remaining 553 records. The electronic medical record (EMR) model was compared with a seven-factor predictive score known as the HOSPITAL score and a model derived from Elixhauser comorbidities. All models were evaluated for the ability to identify patients at high risk of 30-day ischaemic heart disease readmission and those at risk of all-cause readmission within 12 months following the initial AMI hospitalisation. Results. The EMR model has higher discrimination than other models in predicting ischaemic heart disease readmissions (area under the curve (AUC) 0.78, 95% confidence interval (CI) 0.71-0.85 for 30-day readmission). The positive predictive value was significantly higher with the EMR model, which identifies cohorts that were up to threefold more likely to be readmitted. Factors associated with readmission included emergency department attendances, cardiac diagnoses and procedures, renal impairment and electrolyte disturbances. The EMR model also performed better than other models (AUC 0.72, 95% CI 0.66-0.78), and with greater positive predictive value, in identifying 12-month risk of all-cause readmission. Conclusions. Routine hospital data can help identify patients at high risk of readmission following AMI. This could lead to decreased readmission rates by identifying patients suitable for targeted clinical interventions. [ABSTRACT FROM AUTHOR]

Objective. Adverse drug events (ADEs) during hospital admissions are a widespread problem associated with adverse patient outcomes. The 'external cause' codes in the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) provide opportunities for identifying the incidence of ADEs acquired during hospital stays that may assist in targeting interventions to decrease their occurrence. The aim of the present study was to use routine administrative data to identify ADEs acquired during hospital admissions in a suburban healthcare network in Melbourne, Australia. Methods. Thirty-nine secondary diagnosis fields of hospital discharge data for a 1-year period were reviewed for 'diagnoses not present on admission' and assigned to the Classification of Hospital Acquired Diagnoses (CHADx) subclasses. Discharges with one or more ADE subclass were extracted for retrospective analysis. Results. From 57 205 hospital discharges, 7891 discharges (13.8%) had at least one CHADx, and 402 discharges (0.7%) had an ADE recorded. The highest proportion of ADEs was due to administration of analgesics (27%) and systemic antibiotics (23%). Other major contributors were anticoagulation (13%), anaesthesia (9%) and medications with cardiovascular side-effects (9%). Conclusion. Hospital data coded in ICD-10 can be used to identify ADEs that occur during hospital stays and also clinical conditions, therapeutic drug classes and treating units where these occur. Using the CHADx algorithm on administrative datasets provides a consistent and economical method for such ADE monitoring. [ABSTRACT FROM AUTHOR]

Background: Large-scale multi-site experimental and clinical speech protocols require high-fidelity, easy-to-use speech recording technologies. However, little is known about the reliability and comparability of affordable, portable and commonly used technologies with traditional well-validated devices (e.g., a hard disc recorder with a high-quality microphone). Objective: To examine the comparability of speech and voice samples acquired from protocols involving high- and low-quality devices. Methods: Speech samples were acquired simultaneously from 15 healthy adults using four devices and analyzed acoustically for measures of timing and voice quality. For the purpose of making initial comparisons, methods were deemed comparable if the resultant acoustic data yielded root mean squared error values ≤10% and statistically significant Spearman's correlation coefficients. Results: The data suggest that there is significant and widespread variability in the quality and reliability between different acquisition methods for voice and speech recording. Not one method provided statistically similar data to the protocol using the benchmark device (i.e., a high-quality recorder coupled with a condenser microphone). Acoustic analysis cannot be assumed to be comparable if different recording methods are used to record speech. Conclusions: Findings have implications for researchers and clinicians hoping to make comparisons between labs or, where lower-quality devices are suggested, to offer equal fidelity. © 2015 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

Objectives. To describe a model that matches electronic patient care records within a given case to one or more patients within that case. Method. This retrospective study included data from all metropolitan Ambulance Victoria electronic patient care records (n = 445 576) for the time period 1 January 2009-31 May 2010. Data were captured via VACIS (Ambulance Victoria, Melbourne, Vic., Australia), an in-field electronic data capture system linked to an integrated data warehouse database. The case patient algorithm included 'Jaro-Winkler', 'Soundex' and 'weight matching' conditions. Results. The case patient matching algorithm has a sensitivity of 99.98%, a specificity of 99.91% and an overall accuracy of 99.98%. Conclusions. The case patient algorithm provides Ambulance Victoria with a sophisticated, efficient and highly accurate method of matching patient records within a given case. This method has applicability to other emergency services where unique identifiers are case based rather than patient based. [ABSTRACT FROM AUTHOR]

hutchinson a.f., thompson m.a., brand c.a., black j., anderson g.p. & irving l.b. (2010) Community care assessment of exacerbations of chronic obstructive pulmonary disease. Journal of Advanced Nursing 66(11), 2490-2499. Aim. The aim of this study was to develop a clinical algorithm to assess chronic obstructive pulmonary disease exacerbation severity in a community setting. Background. An important aspect of community management of exacerbations is assessing patient safety. Although researchers have investigated risk factors for rapid deterioration, there is a lack of evidence validating clinical measures of exacerbation severity. Methods. This was a prospective, community-based cohort study of patients enrolled in the Melbourne Longitudinal Chronic Obstructive Pulmonary Disease Cohort. The outreach team collected data on symptom severity at baseline and exacerbation onset using the Medical Research Council Dyspnoea Scale, St George Quality-of-Life Questionnaire and Symptom Severity Index. Results. Ninety-two patients were monitored from 2003 to 2005. There were 148 exacerbations: 121 (82%) were treated at home and 27 (17·5%) required hospitalization. An ordinal logistic regression model demonstrated that a combination of chronic obstructive pulmonary disease severity with dyspnoea and wheeze severity at exacerbation onset could differentiate severe from milder episodes [(OR 7·69, 95%CI: 3·9-11·5, P < 0·01), area under the receiver operating characteristics curve 0·75 (95%CI: 0·65-0·86)]. Conclusion. The majority of chronic obstructive pulmonary disease exacerbations can be safely managed in a community setting, but clinical assessment alone may not be sufficient to identify all patients who will develop complications such as respiratory failure. Further research is needed to validate clinical assessment and decision-making algorithms for community-management of chronic obstructive pulmonary disease exacerbations. [ABSTRACT FROM AUTHOR]