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OBJECTIVE: Recent studies suggest many patients with non-specific low back pain presenting to public hospital EDs receive low-value care. The primary aim was to describe management of patients presenting with low back pain to the ED of a private hospital in Melbourne, Australia, and received a final ED diagnosis of non-specific low back pain. We also determined predictors of hospital admission. METHODS: Retrospective review of patients who presented with low back pain and received a final ED diagnosis of non-specific low back pain to Cabrini Malvern ED in 2015. Demographics, lumbar spinal imaging, pathology tests and medications were extracted from hospital records. Multivariate logistic regression was used to determine independent predictors of hospital admission. RESULTS: Four hundred and fifty presentations were included (60% female); 238 (52.9%) were admitted to hospital. One hundred and seventy-seven (39.3%) patients received lumbar spine imaging. Two hundred and eighty (62.2%) patients had pathology tests and 391 (86.9%) received medications, which included opioids (n = 298, 66.2%), paracetamol (n = 219, 48.7%), NSAIDs (n = 161, 35.8%), benzodiazepines (n = 118, 26.2%) and pregabalin (n = 26, 5.8%). Predictors of hospital admission included older age (odds ratio [OR] 1.03, 95% confidence interval [CI] 1.02-1.05), arrival by ambulance (OR 2.03, 95% CI 1.06-3.90) and receipt of pathology tests (OR 3.32, 95% CI 2.01-5.49) or computed tomography scans (OR 1.86, 95% CI 1.12-3.11). CONCLUSION: We observed high rates of imaging, pathology tests and hospital admissions compared with previous public hospital studies, while medication use was similar. Implementation of strategies to optimise evidence-based ED care is needed to reduce low-value care and improve patient outcomes.

OBJECTIVE: Patients, families and community members would like emergency department wait time visibility. This would improve patient journeys through emergency medicine. The study objective was to derive, internally and externally validate machine learning models to predict emergency patient wait times that are applicable to a wide variety of emergency departments. METHODS: Twelve emergency departments provided 3 years of retrospective administrative data from Australia (2017-2019). Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine learning models were developed to predict wait times at each site and were internally and externally validated. Model performance was tested on COVID-19 period data (January to June 2020). RESULTS: There were 1 930 609 patient episodes analysed and median site wait times varied from 24 to 54 min. Individual site model prediction median absolute errors varied from±22.6 min (95% CI 22.4 to 22.9) to ±44.0 min (95% CI 43.4 to 44.4). Global model prediction median absolute errors varied from ±33.9 min (95% CI 33.4 to 34.0) to ±43.8 min (95% CI 43.7 to 43.9). Random forest and linear regression models performed the best, rolling average models underestimated wait times. Important variables were triage category, last-k patient average wait time and arrival time. Wait time prediction models are not transferable across hospitals. Models performed well during the COVID-19 lockdown period. CONCLUSIONS: Electronic emergency demographic and flow information can be used to approximate emergency patient wait times. A general model is less accurate if applied without site-specific factors.

BACKGROUND: The emergency department has been a major focus for the implementation of Australia's national electronic health record, known as My Health Record. However, the association between use of My Health Record in the emergency department setting and patient care is largely unknown. The aim of this study was to explore the perspectives of emergency department clinicians regarding My Health Record use frequency, the benefits of My Health Record use (with a focus on patient care) and the barriers to use. METHODS: All 393 nursing, pharmacy, physician and allied health staff employed within the emergency department at a tertiary metropolitan public hospital in Melbourne were invited to participate in a web-based survey, between 1 May 2021 and 1 December 2021, during the height of the Delta and Omicron Covid-19 outbreaks in Victoria, Australia. RESULTS: Overall, the survey response rate was 18% (70/393). Approximately half of the sample indicated My Health Record use in the emergency department (n = 39, 56%, confidence interval [CI] 43-68%). The results showed that users typically only engaged with My Health Record less than once per shift (n = 15, 39%, CI 23-55%). Just over half (n = 19/39, 54%, CI 32-65%) of all participants who use My Health Record agreed they could remember a time when My Health Record had been critical to the care of a patient. Overall, clinicians indicated the biggest barrier preventing their use of My Health Record is that they forget to utilise the system. CONCLUSION: The results suggest that My Health Record has not been adopted as routine practice in the emergency department, by the majority of participants. Close to half of self-identified users of My Health Record do not associate use as being critical to patient care. Instead, My Health Record may only be used in scenarios that clinicians perceive will yield the greatest benefit-which clinicians in this paper suggest is patients with chronic and complex conditions. Further research tha

Objective: Patients, families and community members would like emergency department wait time visibility. This would improve patient journeys through emergency medicine. The study objective was to derive, internally and externally validate machine learning models to predict emergency patient wait times that are applicable to a wide variety of emergency departments. Methods: Twelve emergency departments provided 3 years of retrospective administrative data from Australia (2017–2019). Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine learning models were developed to predict wait times at each site and were internally and externally validated. Model performance was tested on COVID-19 period data (January to June 2020). Results: There were 1 930 609 patient episodes analysed and median site wait times varied from 24 to 54 min. Individual site model prediction median absolute errors varied from±22.6 min (95% CI 22.4 to 22.9) to ±44.0 min (95% CI 43.4 to 44.4). Global model prediction median absolute errors varied from ±33.9 min (95% CI 33.4 to 34.0) to ±43.8 min (95% CI 43.7 to 43.9). Random forest and linear regression models performed the best, rolling average models underestimated wait times. Important variables were triage category, last-k patient average wait time and arrival time. Wait time prediction models are not transferable across hospitals. Models performed well during the COVID-19 lockdown period. Conclusions: Electronic emergency demographic and flow information can be used to approximate emergency patient wait times. A general model is less accurate if applied without site-specific factors.

Objective: Recent studies suggest many patients with non-specific low back pain presenting to public hospital EDs receive low-value care. The primary aim was to describe management of patients presenting with low back pain to the ED of a private hospital in Melbourne, Australia, and received a final ED diagnosis of non-specific low back pain. We also determined predictors of hospital admission. Method(s): Retrospective review of patients who presented with low back pain and received a final ED diagnosis of non-specific low back pain to Cabrini Malvern ED in 2015. Demographics, lumbar spinal imaging, pathology tests and medications were extracted from hospital records. Multivariate logistic regression was used to determine independent predictors of hospital admission. Result(s): Four hundred and fifty presentations were included (60% female); 238 (52.9%) were admitted to hospital. One hundred and seventy-seven (39.3%) patients received lumbar spine imaging. Two hundred and eighty (62.2%) patients had pathology tests and 391 (86.9%) received medications, which included opioids (n = 298, 66.2%), paracetamol (n = 219, 48.7%), NSAIDs (n = 161, 35.8%), benzodiazepines (n = 118, 26.2%) and pregabalin (n = 26, 5.8%). Predictors of hospital admission included older age (odds ratio [OR] 1.03, 95% confidence interval [CI] 1.02-1.05), arrival by ambulance (OR 2.03, 95% CI 1.06-3.90) and receipt of pathology tests (OR 3.32, 95% CI 2.01-5.49) or computed tomography scans (OR 1.86, 95% CI 1.12-3.11). Conclusion(s): We observed high rates of imaging, pathology tests and hospital admissions compared with previous public hospital studies, while medication use was similar. Implementation of strategies to optimise evidence-based ED care is needed to reduce low-value care and improve patient outcomes.Copyright © 2021 Australasian College for Emergency Medicine

OBJECTIVE: Patients, families and community members would like emergency department wait time visibility. This would improve patient journeys through emergency medicine. The study objective was to derive, internally and externally validate machine learning models to predict emergency patient wait times that are applicable to a wide variety of emergency departments. METHOD(S): Twelve emergency departments provided 3years of retrospective administrative data from Australia (2017-2019). Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine learning models were developed to predict wait times at each site and were internally and externally validated. Model performance was tested on COVID-19 period data (January to June 2020). RESULT(S): There were 1930609 patient episodes analysed and median site wait times varied from 24 to 54min. Individual site model prediction median absolute errors varied from+/-22.6min (95%CI 22.4 to 22.9) to +/-44.0min (95%CI 43.4 to 44.4). Global model prediction median absolute errors varied from +/-33.9min (95%CI 33.4 to 34.0) to +/-43.8min (95%CI 43.7 to 43.9). Random forest and linear regression models performed the best, rolling average models underestimated wait times. Important variables were triage category, last-k patient average wait time and arrival time. Wait time prediction models are not transferable across hospitals. Models performed well during the COVID-19 lockdown period. CONCLUSION(S): Electronic emergency demographic and flow information can be used to approximate emergency patient wait times. A general model is less accurate if applied without site-specific factors.Copyright © Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.

Study objective: To derive and internally and externally validate machine-learning models to predict emergency ambulance patient door-to-off-stretcher wait times that are applicable to a wide variety of emergency departments. Method(s): Nine emergency departments provided 3 years (2017 to 2019) of retrospective administrative data from Australia. Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine-learning models were developed to predict wait times at each site and were internally and externally validated. Result(s): There were 421,894 episodes analyzed, and median site off-load times varied from 13 (interquartile range [IQR], 9 to 20) to 29 (IQR, 16 to 48) minutes. The global site prediction model median absolute errors were 11.7 minutes (95% confidence interval [CI], 11.7 to 11.8) using linear regression and 12.8 minutes (95% CI, 12.7 to 12.9) using elastic net. The individual site model prediction median absolute errors varied from the most accurate at 6.3 minutes (95% CI, 6.2 to 6.4) to the least accurate at 16.1 minutes (95% CI, 15.8 to 16.3). The model technique performance was the same for linear regression, random forests, elastic net, and rolling average. The important variables were the last k-patient average waits, triage category, and patient age. The global model performed at the lower end of the accuracy range compared with models for the individual sites but was within tolerable limits. Conclusion(s): Electronic emergency demographic and flow information can be used to estimate emergency ambulance patient off-stretcher times. Models can be built with reasonable accuracy for multiple hospitals using a small number of point-of-care variables.Copyright © 2021 American College of Emergency Physicians

Objective: EDs have the potential ability to predict patient wait times and to display this to patients and other stakeholders. Little is known about whether consumers and stakeholders would want this information and how wait time predictions might be used. The aim of the present study was to gain perspectives from consumers and health services personnel regarding the concept of emergency wait time visibility. Method(s): We conducted a qualitative interview and focus group study in 2019. Participants included emergency medicine patients, families, paramedics, well community members, and hospital/paramedic administrators from multiple EDs and organisations in Victoria, Australia. Transcripts were coded and themes presented. Result(s): One focus group and 103 semi-structured interviews were conducted in 2019 including 32 patients, 22 carers/advocates and 21 paramedics in the ED; 20 health service administrators (paramedic and hospital) and 15 community members. Consumers and paramedics face physical and psychological difficulties when wait times are not visible. Consumers believe about a 2-h wait is tolerable, beyond this most begin to consider alternative strategies for seeking care. Consumers want to see triage to doctor times; paramedics want door-to-off stretcher times (for all possible transport destinations); with 47 of 50 consumers and 30 of 31 paramedics potentially using this information. About 28 of 50 consumers would use times to inform facility or provider choice, another 19 of 50 want information once in the waiting room. During prolonged waits, 51 of 52 consumers would continue to seek care. Conclusion(s): Consumers and paramedics want wait time information visibility. They would use the information in a variety of ways, both pre-hospital and while waiting for care.Copyright © 2020 Australasian College for Emergency Medicine

Objective: Recent studies suggest many patients with non-specific low back pain presenting to public hospital EDs receive low-value care. The primary aim was to describe management of patients presenting with low back pain to the ED of a private hospital in Melbourne, Australia, and received a final ED diagnosis of non-specific low back pain. We also determined predictors of hospital admission. Method(s): Retrospective review of patients who presented with low back pain and received a final ED diagnosis of non-specific low back pain to Cabrini Malvern ED in 2015. Demographics, lumbar spinal imaging, pathology tests and medications were extracted from hospital records. Multivariate logistic regression was used to determine independent predictors of hospital admission. Result(s): Four hundred and fifty presentations were included (60% female); 238 (52.9%) were admitted to hospital. One hundred and seventy-seven (39.3%) patients received lumbar spine imaging. Two hundred and eighty (62.2%) patients had pathology tests and 391 (86.9%) received medications, which included opioids (n = 298, 66.2%), paracetamol (n = 219, 48.7%), NSAIDs (n = 161, 35.8%), benzodiazepines (n = 118, 26.2%) and pregabalin (n = 26, 5.8%). Predictors of hospital admission included older age (odds ratio [OR] 1.03, 95% confidence interval [CI] 1.02-1.05), arrival by ambulance (OR 2.03, 95% CI 1.06-3.90) and receipt of pathology tests (OR 3.32, 95% CI 2.01-5.49) or computed tomography scans (OR 1.86, 95% CI 1.12-3.11). Conclusion(s): We observed high rates of imaging, pathology tests and hospital admissions compared with previous public hospital studies, while medication use was similar. Implementation of strategies to optimise evidence-based ED care is needed to reduce low-value care and improve patient outcomes.Copyright © 2021 Australasian College for Emergency Medicine

OBJECTIVE: Patients, families and community members would like emergency department wait time visibility. This would improve patient journeys through emergency medicine. The study objective was to derive, internally and externally validate machine learning models to predict emergency patient wait times that are applicable to a wide variety of emergency departments. METHOD(S): Twelve emergency departments provided 3years of retrospective administrative data from Australia (2017-2019). Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine learning models were developed to predict wait times at each site and were internally and externally validated. Model performance was tested on COVID-19 period data (January to June 2020). RESULT(S): There were 1930609 patient episodes analysed and median site wait times varied from 24 to 54min. Individual site model prediction median absolute errors varied from+/-22.6min (95%CI 22.4 to 22.9) to +/-44.0min (95%CI 43.4 to 44.4). Global model prediction median absolute errors varied from +/-33.9min (95%CI 33.4 to 34.0) to +/-43.8min (95%CI 43.7 to 43.9). Random forest and linear regression models performed the best, rolling average models underestimated wait times. Important variables were triage category, last-k patient average wait time and arrival time. Wait time prediction models are not transferable across hospitals. Models performed well during the COVID-19 lockdown period. CONCLUSION(S): Electronic emergency demographic and flow information can be used to approximate emergency patient wait times. A general model is less accurate if applied without site-specific factors.Copyright © Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.

Study objective: To derive and internally and externally validate machine-learning models to predict emergency ambulance patient door-to-off-stretcher wait times that are applicable to a wide variety of emergency departments. Method(s): Nine emergency departments provided 3 years (2017 to 2019) of retrospective administrative data from Australia. Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine-learning models were developed to predict wait times at each site and were internally and externally validated. Result(s): There were 421,894 episodes analyzed, and median site off-load times varied from 13 (interquartile range [IQR], 9 to 20) to 29 (IQR, 16 to 48) minutes. The global site prediction model median absolute errors were 11.7 minutes (95% confidence interval [CI], 11.7 to 11.8) using linear regression and 12.8 minutes (95% CI, 12.7 to 12.9) using elastic net. The individual site model prediction median absolute errors varied from the most accurate at 6.3 minutes (95% CI, 6.2 to 6.4) to the least accurate at 16.1 minutes (95% CI, 15.8 to 16.3). The model technique performance was the same for linear regression, random forests, elastic net, and rolling average. The important variables were the last k-patient average waits, triage category, and patient age. The global model performed at the lower end of the accuracy range compared with models for the individual sites but was within tolerable limits. Conclusion(s): Electronic emergency demographic and flow information can be used to estimate emergency ambulance patient off-stretcher times. Models can be built with reasonable accuracy for multiple hospitals using a small number of point-of-care variables.Copyright © 2021 American College of Emergency Physicians

Objective: EDs have the potential ability to predict patient wait times and to display this to patients and other stakeholders. Little is known about whether consumers and stakeholders would want this information and how wait time predictions might be used. The aim of the present study was to gain perspectives from consumers and health services personnel regarding the concept of emergency wait time visibility. Method(s): We conducted a qualitative interview and focus group study in 2019. Participants included emergency medicine patients, families, paramedics, well community members, and hospital/paramedic administrators from multiple EDs and organisations in Victoria, Australia. Transcripts were coded and themes presented. Result(s): One focus group and 103 semi-structured interviews were conducted in 2019 including 32 patients, 22 carers/advocates and 21 paramedics in the ED; 20 health service administrators (paramedic and hospital) and 15 community members. Consumers and paramedics face physical and psychological difficulties when wait times are not visible. Consumers believe about a 2-h wait is tolerable, beyond this most begin to consider alternative strategies for seeking care. Consumers want to see triage to doctor times; paramedics want door-to-off stretcher times (for all possible transport destinations); with 47 of 50 consumers and 30 of 31 paramedics potentially using this information. About 28 of 50 consumers would use times to inform facility or provider choice, another 19 of 50 want information once in the waiting room. During prolonged waits, 51 of 52 consumers would continue to seek care. Conclusion(s): Consumers and paramedics want wait time information visibility. They would use the information in a variety of ways, both pre-hospital and while waiting for care.Copyright © 2020 Australasian College for Emergency Medicine

Objective The COVID-19 pandemic in Australia coincided with an early trend of reduced visits to the emergency department (ED), but to determine which patients presented less requires closer evaluation. Identifying which patient groups are presenting less frequently will provide a better understanding of health care utilisation behaviours during a pandemic and inform healthcare providers of the potential challenges in managing these groups. Methods This single-centre retrospective study examined trends in presentations in 2020 to a private, mixed paediatric and adult ED in an inner city suburb within the state of Victoria that treats both COVID-19 and non-COVID-19 patients. The 2019 dataset was used as a reference baseline for comparison. All analyses were performed using baseline characteristics and triage data. Results The total number of visits to the ED dropped from 24 775 in 2019 to 22 754 in 2020, representing an overall reduction of 8%. Significant reductions in daily presentations and admissions from the ED were observed in the months immediately following the peak of the two COVID-19 waves in the state of Victoria. Visits by those in the 0- to 17-year age group, triage categories 4 and 5 and musculoskeletal presentations were also reduced for most of 2020. Gastrointestinal/abdominal and urological/renal presentations were reduced immediately after the first COVID-19 wave, whereas infectious diseases visits were reduced during and after the second COVID-19 wave. Conclusions These findings add to the growing body of evidence regarding emergency care underutilisation during the COVID-19 pandemic. Reduced private ED presentations were observed overall and in paediatric patients, lower acuity triage categories, musculoskeletal, abdominal/gastrointestinal and urological/renal presentations during the first wave, whereas infectious disease cases were reduced during the second wave. What is known about the topic? During the first and second waves of COVID-19 in Vict

STUDY OBJECTIVE: To derive and internally and externally validate machine-learning models to predict emergency ambulance patient door-to-off-stretcher wait times that are applicable to a wide variety of emergency departments. METHODS: Nine emergency departments provided 3 years (2017 to 2019) of retrospective administrative data from Australia. Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine-learning models were developed to predict wait times at each site and were internally and externally validated. RESULTS: There were 421,894 episodes analyzed, and median site off-load times varied from 13 (interquartile range [IQR], 9 to 20) to 29 (IQR, 16 to 48) minutes. The global site prediction model median absolute errors were 11.7 minutes (95% confidence interval [CI], 11.7 to 11.8) using linear regression and 12.8 minutes (95% CI, 12.7 to 12.9) using elastic net. The individual site model prediction median absolute errors varied from the most accurate at 6.3 minutes (95% CI, 6.2 to 6.4) to the least accurate at 16.1 minutes (95% CI, 15.8 to 16.3). The model technique performance was the same for linear regression, random forests, elastic net, and rolling average. The important variables were the last k-patient average waits, triage category, and patient age. The global model performed at the lower end of the accuracy range compared with models for the individual sites but was within tolerable limits. CONCLUSION: Electronic emergency demographic and flow information can be used to estimate emergency ambulance patient off-stretcher times. Models can be built with reasonable accuracy for multiple hospitals using a small number of point-of-care variables.

PURPOSE: This study aimed to explore pharmacists' and physicians' perceptions of use, barriers to use and the healthcare outcomes associated with use of Australia's national personally controlled electronic health record-known as My Health Record-in the emergency department. METHODS: A mixed methods approach was deployed, including surveys and individual semi-structured interviews. All physicians and pharmacists who work in the emergency department at Cabrini Health (a non-for-profit healthcare provider in Victoria, Australia) were invited to participate. Due to the timing of elective blocks, physician trainees were excluded from interviews. RESULTS: A total of 40 emergency medicine clinicians responded to the survey. Over 50% (n = 22) of all respondents had used My Health Record in the emergency department at least once. A total of 18 clinicians participated in the semi-structured interviews, which led to the identification of three themes with multiple sub-themes regarding My Health Record: (1) benefits; (2) effectiveness; and; (3) barriers. CONCLUSION: Participants reported My Health Record use in the emergency department delivers efficiencies for clinicians and has a heightened utility for complex patients, consistent with previous research conducted outside of the Australian setting. Barriers to use were revealed: outdated content, a lack of trust, a low perception of value, no patient record and multiple medical record systems. The participants in this study highlighted that training and awareness raising is needed in order to improve My Health Record use in the emergency department, a need stressed by physician's. Further observational research is required to explores meaningful MHR use at scale.

OBJECTIVE: EDs have the potential ability to predict patient wait times and to display this to patients and other stakeholders. Little is known about whether consumers and stakeholders would want this information and how wait time predictions might be used. The aim of the present study was to gain perspectives from consumers and health services personnel regarding the concept of emergency wait time visibility. METHODS: We conducted a qualitative interview and focus group study in 2019. Participants included emergency medicine patients, families, paramedics, well community members, and hospital/paramedic administrators from multiple EDs and organisations in Victoria, Australia. Transcripts were coded and themes presented. RESULTS: One focus group and 103 semi-structured interviews were conducted in 2019 including 32 patients, 22 carers/advocates and 21 paramedics in the ED; 20 health service administrators (paramedic and hospital) and 15 community members. Consumers and paramedics face physical and psychological difficulties when wait times are not visible. Consumers believe about a 2-h wait is tolerable, beyond this most begin to consider alternative strategies for seeking care. Consumers want to see triage to doctor times; paramedics want door-to-off stretcher times (for all possible transport destinations); with 47 of 50 consumers and 30 of 31 paramedics potentially using this information. About 28 of 50 consumers would use times to inform facility or provider choice, another 19 of 50 want information once in the waiting room. During prolonged waits, 51 of 52 consumers would continue to seek care. CONCLUSIONS: Consumers and paramedics want wait time information visibility. They would use the information in a variety of ways, both pre-hospital and while waiting for care.

Objectives: Leverage log data to explore access to My Health Record (MHR), the national electronic health record of Australia, by clinicians in the emergency department. Materials and Methods: A retrospective analysis was conducted using secondary routinely-collected data. Log data pertaining to all patients who presented to the emergency department between 2019 and 2021 of a not-for-profit hospital (that annually observes 23,000 emergency department presentations) were included in this research. Attendance data and human resources data were linked with MHR log data. The primary outcome was a dichotomous variable that indicated whether the MHR of a patient was accessed. Logistic regression facilitated the exploration of factors (user role, day of the week, and month) associated with access. Results: My Health Record was accessed by a pharmacist, doctor, or nurse in 19.60% (n = 9,262) of all emergency department presentations. Access was dominated by pharmacists (18.31%, n = 8,656). All users demonstrated a small, yet significant, increase in access every month (odds ratio = 1.07, 95% Confidence interval: 1.06-1.07, p ≤ 0.001). Discussion: Doctors, pharmacists, and nurses are increasingly accessing MHR. Based on this research, substantially more pharmacists appear to be accessing MHR, compared to other user groups. However, only one in every five patients who present to the emergency department have their MHR accessed, thereby indicating a need to accelerate and encourage the adoption and access of MHR by clinicians.

Study objective: To derive and internally and externally validate machine-learning models to predict emergency ambulance patient door–to–off-stretcher wait times that are applicable to a wide variety of emergency departments. Methods: Nine emergency departments provided 3 years (2017 to 2019) of retrospective administrative data from Australia. Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine-learning models were developed to predict wait times at each site and were internally and externally validated. Results: There were 421,894 episodes analyzed, and median site off-load times varied from 13 (interquartile range [IQR], 9 to 20) to 29 (IQR, 16 to 48) minutes. The global site prediction model median absolute errors were 11.7 minutes (95% confidence interval [CI], 11.7 to 11.8) using linear regression and 12.8 minutes (95% CI, 12.7 to 12.9) using elastic net. The individual site model prediction median absolute errors varied from the most accurate at 6.3 minutes (95% CI, 6.2 to 6.4) to the least accurate at 16.1 minutes (95% CI, 15.8 to 16.3). The model technique performance was the same for linear regression, random forests, elastic net, and rolling average. The important variables were the last k-patient average waits, triage category, and patient age. The global model performed at the lower end of the accuracy range compared with models for the individual sites but was within tolerable limits. Conclusion: Electronic emergency demographic and flow information can be used to estimate emergency ambulance patient off-stretcher times. Models can be built with reasonable accuracy for multiple hospitals using a small number of point-of-care variables.

Objectives To evaluate the changes in productivity when scribes were used by emergency physicians in emergency departments in Australia and assess the effect of scribes on throughput. Design Randomised, multicentre clinical trial. Setting Five emergency departments in Victoria used Australian trained scribes during their respective trial periods. Sites were broadly representative of Australian emergency departments: public (urban, tertiary, regional referral, paediatric) and private, not for profit. Participants 88 physicians who were permanent, salaried employees working more than one shift a week and were either emergency consultants or senior registrars in their final year of training; 12 scribes trained at one site and rotated to each study site. Interventions Physicians worked their routine shifts and were randomly allocated a scribe for the duration of their shift. Each site required a minimum of 100 scribed and non-scribed shifts, from November 2015 to January 2018. Main outcome measures Physicians' productivity (total patients, primary patients); patient throughput (door-to-doctor time, length of stay); physicians' productivity in emergency department regions. Self reported harms of scribes were analysed, and a cost-benefit analysis was done. Results Data were collected from 589 scribed shifts (5098 patients) and 3296 non-scribed shifts (23 838 patients). Scribes increased physicians' productivity from 1.13 (95% confidence interval 1.11 to 1.17) to 1.31 (1.25 to 1.38) patients per hour per doctor, representing a 15.9% gain. Primary consultations increased from 0.83 (0.81 to 0.85) to 1.04 (0.98 to 1.11) patients per hour per doctor, representing a 25.6% gain. No change was seen in door-to-doctor time. Median length of stay reduced from 192 (interquartile range 108-311) minutes to 173 (96-208) minutes, representing a 19 minute reduction (P<0.001). The greatest gains were achieved by placing scribes with senior doctors at triage, the least by using them in sub

We describe a novel ambulance diversion programme, piloted in Victoria. This article discusses creating increased emergency capacity during surge or disasters by utilising private EDs, tested during a recent thunderstorm asthma disaster and an influenza epidemic. Public hospitals and EDs often run at or over capacity during normal operations. This leaves limited ability to manage surges in demand, resulting in suboptimal outcomes for patients, public ED staff and ambulance services. It is feasible to create surge capacity in private EDs for public ambulance patients. Other states could consider this option to help manage health disasters.

Objectives To evaluate the changes in productivity when scribes were used by emergency physicians in emergency departments in Australia and assess the effect of scribes on throughput. Design Randomised, multicentre clinical trial. Setting Five emergency departments in Victoria used Australian trained scribes during their respective trial periods. Sites were broadly representative of Australian emergency departments: public (urban, tertiary, regional referral, paediatric) and private, not for profit. Participants 88 physicians who were permanent, salaried employees working more than one shift a week and were either emergency consultants or senior registrars in their final year of training; 12 scribes trained at one site and rotated to each study site. Interventions Physicians worked their routine shifts and were randomly allocated a scribe for the duration of their shift. Each site required a minimum of 100 scribed and non-scribed shifts, from November 2015 to January 2018. Main outcome measures Physicians’ productivity (total patients, primary patients); patient throughput (door-to-doctor time, length of stay); physicians’ productivity in emergency department regions. Self reported harms of scribes were analysed, and a cost-benefit analysis was done Results Data were collected from 589 scribed shifts (5098 patients) and 3296 non-scribed shifts (23838 patients). Scribes increased physicians’ productivity from 1.13 (95% confidence interval 1.11 to 1.17) to 1.31 (1.25 to 1.38) patients per hour per doctor, representing a 15.9% gain. Primary consultations increased from 0.83 (0.81 to 0.85) to 1.04 (0.98 to 1.11) patients per hour per doctor, representing a 25.6% gain. No change was seen in door-to-doctor time. Median length of stay reduced from 192 (interquartile range 108-311) minutes to 173 (96- 208) minutes, representing a 19 minute reduction (P<0.001). The greatest gains were achieved by placing scribes with senior doctors at triage, the least by using them in sub-a

Objectives To evaluate the changes in productivity when scribes were used by emergency physicians in emergency departments in Australia and assess the effect of scribes on throughput. Design Randomised, multicentre clinical trial. Setting Five emergency departments in Victoria used Australian trained scribes during their respective trial periods. Sites were broadly representative of Australian emergency departments: public (urban, tertiary, regional referral, paediatric) and private, not for profit. Participants 88 physicians who were permanent, salaried employees working more than one shift a week and were either emergency consultants or senior registrars in their final year of training; 12 scribes trained at one site and rotated to each study site. Interventions Physicians worked their routine shifts and were randomly allocated a scribe for the duration of their shift. Each site required a minimum of 100 scribed and non-scribed shifts, from November 2015 to January 2018. Main outcome measures Physicians' productivity (total patients, primary patients); patient throughput (door-to-doctor time, length of stay); physicians' productivity in emergency department regions. Self reported harms of scribes were analysed, and a cost-benefit analysis was done. Results Data were collected from 589 scribed shifts (5098 patients) and 3296 non-scribed shifts (23 838 patients). Scribes increased physicians' productivity from 1.13 (95% confidence interval 1.11 to 1.17) to 1.31 (1.25 to 1.38) patients per hour per doctor, representing a 15.9% gain. Primary consultations increased from 0.83 (0.81 to 0.85) to 1.04 (0.98 to 1.11) patients per hour per doctor, representing a 25.6% gain. No change was seen in door-to-doctor time. Median length of stay reduced from 192 (interquartile range 108-311) minutes to 173 (96-208) minutes, representing a 19 minute reduction (P<0.001). The greatest gains were achieved by placing scribes with senior doctors at triage, the least by using them in sub

Despite advances in the field of male reproductive health, idiopathic male infertility, in which a man has altered semen characteristics without an identifiable cause and there is no female factor infertility, remains a challenging condition to diagnose and manage. Increasing evidence suggests that oxidative stress (OS) plays an independent role in the etiology of male infertility, with 30% to 80% of infertile men having elevated seminal reactive oxygen species levels. OS can negatively affect fertility via a number of pathways, including interference with capacitation and possible damage to sperm membrane and DNA, which may impair the sperm's potential to fertilize an egg and develop into a healthy embryo. Adequate evaluation of male reproductive potential should therefore include an assessment of sperm OS. We propose the term Male Oxidative Stress Infertility, or MOSI, as a novel descriptor for infertile men with abnormal semen characteristics and OS, including many patients who were previously classified as having idiopathic male infertility. Oxidation-reduction potential (ORP) can be a useful clinical biomarker for the classification of MOSI, as it takes into account the levels of both oxidants and reductants (antioxidants). Current treatment protocols for OS, including the use of antioxidants, are not evidence-based and have the potential for complications and increased healthcare-related expenditures. Utilizing an easy, reproducible, and cost-effective test to measure ORP may provide a more targeted, reliable approach for administering antioxidant therapy while minimizing the risk of antioxidant overdose. With the increasing awareness and understanding of MOSI as a distinct male infertility diagnosis, future research endeavors can facilitate the development of evidence-based treatments that target its underlying cause.

OBJECTIVE: The present study aims to determine if a scribe in an Australian ED can assist emergency physicians to work with increased productivity and to investigate when and where to allocate a scribe and to whom. METHODS: This was a prospective observational single-centre study conducted at a private ED in Melbourne. It evaluated one American scribe and five doctors over 6 months. A scribe is a trained assistant who performs non-clinical tasks usually performed by the doctor. The primary outcomes were patients/hour/doctor and billings/patient. Additional analyses included individual doctor productivity, productivity by ED region, shift time, day of the week and physician learning curves. Door-to-doctor time, time spent on ambulance bypass and door-to-discharge time were examined, also complaints or issues with the scribe. RESULTS: There was an overall increase in doctor consultations of 0.11 (95%CI 0.07-0.15) primary consultations per hour (13%). There was variation seen between individual doctors (lowest increase 0.06 [6%] to highest increase 0.12 [15%]). Billings per patients, door-to-doctor, door-to-discharge and ambulance bypass times remained the same. There was no advantage to allocating a scribe to a specific time of day, day of week or region of the ED. There was no learning period found. CONCLUSIONS: In the present study, scribe usage was associated with overall improvements in primary consultations per hour of 13% per scribed hour, and this varied depending on the physician. There is an economic argument for allocating scribes to some emergency physicians on days, evenings and weekends, not to trainees.

The rotation of a black hole splits its spectrum in two, yet only one sector is known in the highly-damped regime. We find the second, at least partly oblate sector, with quasinormal modes approaching the total reflection frequencies \omega(n>>1) ~ m\Omega - 2\pi iT(n-s), where \Omega and T are the horizon angular velocity and temperature, s is the field spin, and m is an azimuthal eigenvalue. Some physical implications are discussed., Comment: 4 pages, comments welcome

The rotation of a black hole splits its spectrum in two, yet only one sector is known in the highly-damped regime. We find the second, at least partly oblate sector, with quasinormal modes approaching the total reflection frequencies \omega(n>>1) ~ m\Omega - 2\pi iT(n-s), where \Omega and T are the horizon angular velocity and temperature, s is the field spin, and m is an azimuthal eigenvalue. Some physical implications are discussed., Comment: 4 pages, comments welcome

The rotation of a black hole splits its spectrum in two, yet only one sector is known in the highly-damped regime. We find the second, at least partly oblate sector, with quasinormal modes approaching the total reflection frequencies \omega(n>>1) ~ m\Omega - 2\pi iT(n-s), where \Omega and T are the horizon angular velocity and temperature, s is the field spin, and m is an azimuthal eigenvalue. Some physical implications are discussed., Comment: 4 pages, comments welcome

The rotation of a black hole splits its spectrum in two, yet only one sector is known in the highly-damped regime. We find the second, at least partly oblate sector, with quasinormal modes approaching the total reflection frequencies \omega(n>>1) ~ m\Omega - 2\pi iT(n-s), where \Omega and T are the horizon angular velocity and temperature, s is the field spin, and m is an azimuthal eigenvalue. Some physical implications are discussed., Comment: 4 pages, comments welcome

BACKGROUND: Pain is the most common reason that patients present to an emergency department (ED) and is often inadequately managed. Evidence suggests that acupuncture is effective for pain relief, yet it is rarely practiced in the ED. The current study aims to assess the efficacy of acupuncture for providing effective analgesia to patients presenting with acute low back pain, migraine and ankle sprain at the EDs of four hospitals in Melbourne, Australia. METHOD: The study is a multi-site, randomized, assessor-blinded, controlled trial of acupuncture analgesia in patients who present to an ED with low back pain, migraine or ankle sprain. Patients will be block randomized to receive either acupuncture alone, acupuncture as an adjunct to pharmacotherapy or pharmacotherapy alone. Acupuncture will be applied according to Standards for Reporting Interventions in Clinical Trials of Acupuncture (STRICTA). Pain after one hour, measured using a visual analogue scale (VAS), is the primary outcome. Secondary outcomes measures include the following instruments; the Oswestry low back pain disability questionnaire, 24-hour Migraine Quality of Life questionnaire and Patient's Global Assessment of Ankle Injury Scale. These measures will be recorded at baseline, 1 hour after intervention, each hour until discharge and 48±12 hours of ED discharge. Data will also be collected on the safety and acceptability of acupuncture and health resource utilization. DISCUSSION: The results of this study will determine if acupuncture, alone or as an adjunct to pharmacotherapy provides effective, safe and acceptable pain relief for patients presenting to EDs with acute back pain, migraine or ankle sprain. The results will also identify the impact that acupuncture treatment may have upon health resource utilisation in the ED setting. TRIAL REGISTRATION: Australia and New Zealand Clinical Trials Register (ANZCTR): ACTRN12609000989246.

Since World War II, participatory development (PD), part of the alternative development movement, has evolved from a fringe approach to meeting basic human needs, to a globally recognized paradigm that is informing the strategic decisions regarding community and international development by governments and multi-lateral and indigenous NGOs, as well as management practices in the private sector. This is a study of 1) the evolution of this phenomenon since the 1940s in the context of historical events and dominant theories that inform most development (modernization-globalization and dependency-world-system), and 2) PD itself, including its basic features, methods, case examples, and roots in academic schools, related social movements, and theoretical and philosophical traditions. In the course of the analysis, developmental concepts related to PD are explored and defined, including: community, development, community development, capacity-building, project, decentralization, civil society, empowerment, self-reliance, and sustainable development. There are two important contributions to the field of development that this dissertation study makes. First, by explaining PD and its related concepts, an alternative-participatory development model is constructed that shows their inter-relationships within a social system. And second, a foundation is established to build PD theory by way of relating clear explanations of PD — its essential components and related concepts — to broad social theories that can reveal in greater scope consequences of PD in society.

Translation of the edition, edited by Israel Davidson, published in 1914 under title: Sefer shaʻashuʻim., "This book originally published as volume XVI of the series ... Records of civilization, sources and studies ...", Electronic access restricted; authentication may be required, American Council of Learned Societies History E-Book Project, (heb) heb06003.0001.001, http://hdl.handle.net/2027/heb.06003, Permission must be received for any subsequent distribution in print or electronically. Please contact info@hebook.org for more information.

Mose ben Meir Fischels Fischer, Scan von Monochrom-Mikroform

Mose ben Meir Fischels Fischer, Scan von Monochrom-Mikroform

Mose ben Meir Fischels Fischer, Scan von Monochrom-Mikroform

Numérisation effectuée à partir d'un document original., Numérisation effectuée à partir d'un document de substitution.

Datos de publicación tomados del último colofón y marca tip. en port., En el colofón de la segunda obra figura el año 1522, Sign.: AA8,a-n8, o-p6, A-L8, M6, N8, P-Q6, R-T8, U6, aa-cc8, dd4, Texto a dos col. con recl., L. gót. e inic. grab., y port. a dos tintas con escudo cardenalicio, Ex-libris ms. en port.: "Es de la Libreria de San Benito el Real de Valladolid". - "Fray Placido Antolinez possidet ad usum". - Ex libris ms en anteport.: "Frai Benito de la Torre", Enc. plateresca; restos de broches

Datos de publicación tomados del último colofón y marca tip. en port., En el colofón de la segunda obra figura el año 1522, Sign.: AA8,a-n8, o-p6, A-L8, M6, N8, P-Q6, R-T8, U6, aa-cc8, dd4, Texto a dos col. con recl., L. gót. e inic. grab., y port. a dos tintas con escudo cardenalicio, Ex-libris ms. en port.: "Es de la Libreria de San Benito el Real de Valladolid". - "Fray Placido Antolinez possidet ad usum". - Ex libris ms en anteport.: "Frai Benito de la Torre", Enc. plateresca; restos de broches