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Small-bore drains (≤ 16 Fr) are used in many centers to manage all pleural effusions. The goal of this study was to determine the proportion of avoidable chest drains and associated complications when a strategy of routine chest drain insertion is in place.

We retrospectively reviewed consecutive pleural procedures performed in the Radiology Department of the McGill University Health Centre over one year (August 2015–July 2016). Drain insertion was the default drainage strategy. An interdisciplinary workgroup established criteria for drain insertion, namely: pneumothorax, pleural infection (confirmed/highly suspected), massive effusion (more than 2/3 of hemithorax with severe dyspnea /hypoxemia), effusions in ventilated patients and hemothorax. Drains inserted without any of these criteria were deemed potentially avoidable.

A total of 288 procedures performed in 205 patients were reviewed: 249 (86.5%) drain insertions and 39 (13.5%) thoracenteses. Out of 249 chest drains, 113 (45.4%) were placed in the absence of drain insertion criteria and were deemed potentially avoidable. Of those, 33.6% were inserted for malignant effusions (without subsequent pleurodesis) and 34.5% for transudative effusions (median drainage duration of 2 and 4 days, respectively). Major complications were seen in 21.5% of all procedures. Pneumothorax requiring intervention (2.1%), bleeding (0.7%) and organ puncture or drain misplacement (2%) only occurred with drain insertion. Narcotics were prescribed more frequently following drain insertion vs. thoracentesis (27.1% vs. 9.1%, p = 0.03).

Routine use of chest drains for pleural effusions leads to avoidable drain insertions in a large proportion of cases and causes unnecessary harms.

Increasing health-care costs are a major concern, especially in the USA. The purpose of this paper is to predict the hospital charges of a patient before being admitted. This will help a patient who is getting admitted: “electively” can plan his/her finance. Also, this can be used as a tool by payers (insurance companies) to better forecast the amount that a patient might claim.

This research method involves secondary data collected from New York state’s patient discharges of 2017. A stratified sampling technique is used to sample the data from the population, feature engineering is done on categorical variables. Different regression techniques are being used to predict the target value “total charges.”

Total cost varies linearly with the length of stay. Among all the machine learning algorithms considered, namely, random forest, stochastic gradient descent (SGD) regressor, K nearest neighbors regressor, extreme gradient boosting regressor and gradient boosting regressor, random forest regressor had the best accuracy with R² value 0.7753. “Age group” was the most important predictor among all the features.

This model can be helpful for patients who want to compare the cost at different hospitals and can plan their finances accordingly in case of “elective” admission. Insurance companies can predict how much a patient with a particular medical condition might claim by getting admitted to the hospital.

Health care can be a costly affair if not planned properly. This research gives patients and insurance companies a better prediction of the total cost that they might incur.

Traffic incidents vary considerably in their severity, and the dispatch categories assigned during emergency ambulance calls aim to identify those incidents in greatest need of a lights and sirens (L&S) response. The purpose of this study was to determine whether dispatch categories could discriminate between those traffic incidents that do/do not require an L&S response.

A retrospective cohort study of ambulance records was conducted. The predictor variable was the Traffic/Transportation dispatch categories assigned by call-takers. The outcome variable was whether each incident required an L&S response. Possible thresholds for identifying dispatch categories that require an L&S response were developed. Sensitivity and specificity were calculated for each threshold.

There were 17,099 patients in 13,325 traffic incidents dispatched as Traffic/Transportation over the study period. “Possible death at scene” ‘had the highest odds (OR 22.07, 95% CI 1.06–461.46) and “no injuries” the lowest odds (OR 0.28 95% CI 0.14–0.58) of requiring an L&S response compared to the referent group. The area under the ROC curve was 0.65, 95% CI [0.64, 0.67]. It was found that Traffic/Transportation dispatch categories allocated during emergency ambulance calls had limited ability to discriminate those incidents that do/do not require an L&S response to the scene of a crash.

This research makes a unique contribution, as it considers traffic incidents not as a single entity but rather as a number of dispatch categories which has practical implications for those emergency medical services dispatching ambulances to the scene.

The purpose of this paper is to describe the development of clinically credible skill practice and assessment guides for intraosseous (IO) needle insertion and laryngeal mask airway (LMA) insertion as two essential components of advanced life support (ALS) training.

A modified Delphi approach was used to determine expert consensus in the application of IO and LMA devices for the pre-hospital and emergency setting. Nine pre-hospital clinical specialists were recruited to participate in this Delphi study to determine consensus of clinical expert practice.

Two rounds were required to obtain a performance and assessment checklist for each skill. Both lists were then further modified to maximise their useability. However, the development of a “validated” checklist using a pre-determined process such as a Delphi approach is challenged. Rather, the implementation of these tools in a stated context, and analysis of the data they generate, is an essential aspect of validation which the Delphi approach does not address.

Participant feedback regarding the rationale for their scores was limited in this study in order to minimise participant input and maximise completion of all rounds of the study. Further, devices used in the study may no longer be first-line choices with the advent of more modern devices including semi-automatic IO devices and LMAs which do not have inflatable cuffs. The refined checklists are able to be adapted to these newer devices.

Pre-hospital education contexts which may not have access to expert assessors who are skilled in providing global judgements now have access to clinically relevant skill-specific assessment tools for IO and LMA insertion.

Worldwide, ALS accreditation and competence is demanded of countless health professionals, and to date, validated practice and assessment guides specifically developed for the emergency setting for which they are used, are not available in the published literature. This paper proposes to fill that gap, in addition to guiding clinical education researchers in strategies to develop valid assessment tools through rigorous critique.