Emergency medicine patient wait time multivariable prediction models: a multicentre derivation and validation study.

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.

Predicting Ambulance Patient Wait Times: A Multicenter Derivation and Validation Study.

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.

Displaying emergency patient estimated wait times: A multi-centre, qualitative study of patient, community, paramedic and health administrator perspectives.

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.

Blood Pressure Estimation Using On-body Continuous Wave Radar and Photoplethysmogram in Various Posture and Exercise Conditions.

The pulse arrival time (PAT), pre-ejection period (PEP) and pulse transit time (PTT) are calculated using on-body continuous wave radar (CWR), Photoplethysmogram (PPG) and Electrocardiogram (ECG) sensors for wearable continuous systolic blood pressure (SBP) measurements. The CWR and PPG sensors are placed on the sternum and left earlobe respectively. This paper presents a signal processing method based on wavelet transform and adaptive filtering to remove noise from CWR signals. Experimental data are collected from 43 subjects in various static postures and 26 subjects doing 6 different exercise tasks. Two mathematical models are used to calculate SBPs from PTTs/PATs. For 38 subjects participating in posture tasks, the best cumulative error percentage (CEP) is 92.28% and for 21 subjects participating in exercise tasks, the best CEP is 82.61%. The results show the proposed method is promising in estimating SBP using PTT. Additionally, removing PEP from PAT leads to improving results by around 9%. The CWR sensors present a low-power, continuous and potentially wearable system with minimal body contact to monitor aortic valve mechanical activities directly. Results of this study, of wearable radar sensors, demonstrate the potential superiority of CWR-based PEP extraction for various medical monitoring applications, including BP measurement.

Acute Pectoralis Major Tears in Forward Deployed Active Duty U.S. Military Personnel: A Population at Risk?

The purpose of this study is to report on the causes of a series of acute pectoralis major tears in active duty deployed military personnel. Nine cases of acute pectoralis major tears evaluated at one expeditionary military treatment facility over a 4-month deployment cycle were analyzed. Nine male patients were diagnosed with tears: seven complete tears at the tendinous insertion, one complete tear at the musculotendinous junction, and one incomplete tear, with a mean age of 32 years (range, 23-52 years). All injuries occurred during bench press, with the dominant upper extremity involved 56% of the time. Injury occurred an average of 77 days into the tour (range, 3-198 days). Mean time from injury to surgical repair in the cohort was 18 days (range, 10-43 days). Combat-deployed active duty U.S. military personnel likely represent a high-risk population for this injury. Surgical repair is possible, but rarely advisable, in the deployed setting. (Journal of Surgical Orthopaedic Advances 28(2):150-157, 2019).

Acute pectoralis major tears in active duty US military personnel: Midterm outcomes of repairs performed in the forward-deployed setting.

PURPOSE: To report midterm outcomes of a series of acute pectoralis major tears repaired surgically in the deployed setting in active duty military personnel. METHODS: Analysis of acute pectoralis major tears treated at one expeditionary medical treatment facility during a 4-month time frame. RESULTS: Nine male patients with a mean age of 32 years (23-52) were diagnosed with tears; seven underwent operative fixation in the forward deployed setting. All injuries occurred during bench press, with the dominant upper extremity involved 56% of the time. With an average final follow-up of 4.1 years, the mean disabilities of the arm shoulder and hand score was 35.71 (range 31-41) and the mean American shoulder and elbow surgeons score was 90.71 (range 87-95). All personnel returned to their preinjury jobs with an average return to full preinjury function occurring at 7 months (range 4.5-10.5 months) in the operative group. CONCLUSIONS: Combat-deployed active duty US military personnel likely represent a high-risk population for this injury. Our study demonstrates that with at least 4 years of follow-up, surgical repair with a trough and trans-osseous repair technique was technically feasible and clinically successful in the deployed setting. Although this treatment may be considered in rare individual cases moving forward, due to the lengthy postoperative restrictions, inability to return back to duty in a timely manner, and lengthy rehabilitation, we no longer recommend this surgery be performed in theater.

One hundred tasks an hour: an observational study of emergency department consultant activities.

OBJECTIVE: To determine work activity patterns undertaken by ED consultants. METHODS: A single observer time-motion study of consultants rostered to clinical shifts: primarily administrative (Duty) or clinical (Resuscitation). Direct observation of 130 h was undertaken using purpose developed time-stamping software. Primary outcome was task number and time spent in predetermined categories of activity. Comparisons occurred by role delineation, sex, weekday and time of day. RESULTS: For each observed hour consultants performed 101 discrete tasks. A high proportion was spent multitasking; 77 min of overlapping activity in each hour of observation. Consultants spent 42% of each hour on communication, 35% on direct clinical care and 24% on computer use; only 9% was spent on non-clinical tasks. Consultants spent little time (0.6%) accessing e-resources. Duty consultants undertook more tasks than Resuscitation consultants, 111 versus 90, and more time was spent on communication (47% vs 35%) and computer use (32% vs 15%) with less on clinical care (29% vs 43%). Female consultants undertook 119 tasks per hour compared with 93 for male consultants; more time was spent on communication (51% vs 38%) and computer use (28% vs 22%). No difference in activity occurred by time of day or weekday. CONCLUSION: ED consultants have very high hourly task rates dominated by communication and clinical activities and frequently multitask. The activity is relatively constant throughout the week but is influenced by sex and role delineation. Appreciation of activity distribution might allow informed interventions to realign the workload or divert tasks to supporting resources.

Hyperbaric oxygen therapy in the treatment of skin ulcers due to calcific uraemic arteriolopathy: experience from an Australian hyperbaric unit.

INTRODUCTION: Calcific uraemic arteriolopathy (CUA), also known as 'calciphylaxis', is a syndrome of ischaemic necrotic ulcers occurring in uraemic patients with end-stage renal failure. It is a debilitating condition with a high morbidity and mortality. Hyperbaric oxygen (HBO) has been used to treat such wounds for many years but evidence of its efficacy is limited. AIM: We aimed to study the efficacy of HBO on the healing of problem ulcers secondary to CUA. METHOD: A retrospective case review of all patients with chronic skin ulcers secondary to CUA treated at the Alfred Hospital Hyperbaric Unit from July 1997 to March 2006 (n = 20). RESULTS: HBO was beneficial in eleven (55%) patients, with six of these (30%) experiencing complete resolution of their ulcers on completion of their treatment. Advancing age was identified as a predictor of a positive outcome (P = 0.02). There was no statistical correlation between the number of HBO treatments and ulcer healing. CONCLUSIONS: HBO can benefit patients with chronic non-healing wounds secondary to CUA, but its precise role remains undefined.

How healthy are our emergency departments?

OBJECTIVE: To demonstrate a comprehensive workplace health survey is able to identify indicators that contribute to staff workplace welfare. METHODS: Analysis of a VicHealth workplace health survey distributed to seven suburban emergency departments. Respondents rated multiple workplace health indicators in terms of perceived importance and perceived performance. A satisfaction rating and performance gap for each indicator was calculated. RESULTS: There was a 64% response rate to 500 surveys. Staff rated a safe environment, professional standards, and staff morale the most important factors for workplace health. They were most satisfied with the flexibility of work arrangements (85.6%) and leadership (79.9%), and were least satisfied with the performance management of staff (68.5%) and job satisfaction and morale (67.2%). The largest gaps between perceived importance and performance were in the provision of safe well-lit parking, staff morale, and the use of reward and recognition systems. CONCLUSION: The VicHealth survey was an effective tool in identifying indicators that contribute to staff workplace health. Quantifiable findings allowed interdepartmental comparison and may be useful in focusing on improvements in organizational structure.