Emergency Department Management of Out-of-Hospital Laryngeal Tubes

From the Annals of Emergency Medicine:

Study objective

Laryngeal tubes are commonly used by emergency medical services (EMS) personnel for out-of-hospital advanced airway management. The emergency department (ED) management of EMS-placed laryngeal tubes is unknown. We seek to describe ED airway management techniques, success, and complications of patients receiving EMS laryngeal tubes.

Methods

Using a keyword text search of ED notes, we identified patients who arrived at our ED with a laryngeal tube from 2010 through 2017. We performed structured chart and video reviews for all eligible patients. In our ED, emergency physicians perform all airway management, and there is no protocol dictating airway management for patients arriving with a laryngeal tube. Using descriptive methods, we report the techniques, success, and complications of ED airway management.

Results

We analyzed data on 647 patients receiving out-of-hospital laryngeal tubes, including 472 (73%) with cardiac arrest from medical causes, 75 (21%) with cardiac arrest from trauma, and 100 (15%) with other conditions. For 580 patients (89%), emergency physicians exchanged the laryngeal tube for a definitive airway in the ED. Of the 67 patients not intubated in the ED, 66 died in the ED without further airway management. Of the 580 patients intubated in the ED, orotracheal intubation was the first method attempted for 578 (>99%) and was successful on the first attempt for 515 of 578 (89%). Macintosh video laryngoscopy (88% of initial attempts) and a bougie (68% of initial attempts) were commonly used adjuncts. For 345 of 578 patients (60%), the laryngeal tube was removed before intubation attempts. For 112 of 578 patients (19%), the first intubation attempt occurred with the deflated laryngeal tube left in place. Three patients (<1%) required a surgical airway.

Conclusion

In this cohort, emergency physicians successfully exchanged an out-of-hospital laryngeal tube for an endotracheal tube, using commonly available airway management techniques. ED clinicians should be familiar with techniques for exchanging out-of-hospital extraglottic airways for an endotracheal tube.

Why crying over a terminal patient made me a better doctor

From the Post:

It is believed that depersonalization prevents contamination of the patient-doctor relationship by emotions and ensures a commitment to objectivity. This also preserves the long-standing archetype of the infallible, unflappable and sagacious physician.

Given this context, crying is viewed as an extreme emotional behavior that evinces instability and an unsuitability to grapple with thorny matters such as disease and death. As physician Paul Rousseau noted in a 2003 article in the American Journal of Hospice and Palliative Medicine, “Crying was equated with inadequacy, personal and emotional weakness, incompetence, and unprofessional behavior.” And though there is an understanding that it may inevitably happen, crying is expected to take place alone in the isolated margins of parking lots, call rooms and stairwells.

Despite the condemnations, studies show that there are plenty of tears in medicine.

Google Translate in ER for Spanish, Chinese discharge orders better than nothing, UCSF researchers say

From Becker’s:

Although Google Translate was less than 100 percent accurate in translating English emergency discharge instructions for patients who speak Spanish and Chinese, researchers are cautiously supporting its use, a study published in JAMA Internal Medicine found.

The researchers, from the University of California San Francisco, analyzed 100 sets of emergency discharge instructions translated by Google’s new machine learning algorithm, released in 2017. They found the algorithm was 92 percent accurate for Spanish and 81 percent accurate for Chinese.

Only a small percentage of the inaccurate translations — 2 percent in Spanish and 8 percent in Chinese — had the potential to cause clinically significant harm, the study found.

The translation errors were mostly grammatical or typographical in the original written English instructions, and patients who could read English would have been able to understand correctly, the study concluded.

Cedars-Sinai Taps Alexa for Smart Hospital Room Pilot

From Cedars-Sinai:

A pilot program underway in more than 100 patient rooms at Cedars-Sinai is allowing patients to use an Alexa-powered platform known as Aiva to interact hands-free with nurses and control their entertainment. Aiva is the world’s first patient-centered voice assistant platform for hospitals.

In the pilot project, patient rooms are equipped with Amazon Echos and patients simply tell the device what they need. For example, patients can turn their TV off and on and change channels by giving verbal commands like, “Alexa, change the channel to ESPN.” A patient who needs assistance getting out of bed might say, “Alexa, tell my nurse I need to get up to use the restroom.”

The patient’s request is routed to the mobile phone of the appropriate caregiver, whether a nurse, clinical partner, manager or administrator. A pain medicine request would be routed to a registered nurse, for example, while a bathroom request would be routed to a clinical partner. If the request is not answered in a timely manner, the Aiva platform sends it up the chain of command.

Sorry, ER patients. People with elective procedures get the hospital beds first.

From the Post:

In a medical emergency, you may have a surprisingly difficult time finding a bed in a hospital. This is because elective admissions — that is, patients whose hospital stays have been scheduled in advance — take priority over emergencies.

Such a preference for elective admissions might be unexpected, as emergency patients are, by definition, emergencies. But elective patients have attributes that make them financially attractive. They arrive promptly in the morning; they are well-insured; and they undergo invasive procedures that represent a significant revenue stream for hospitals.

Emergency department triage prediction of clinical outcomes using machine learning models

From Biomedcentral:

Background

Development of emergency department (ED) triage systems that accurately differentiate and prioritize critically ill from stable patients remains challenging. We used machine learning models to predict clinical outcomes, and then compared their performance with that of a conventional approach—the Emergency Severity Index (ESI).

Methods

Using National Hospital and Ambulatory Medical Care Survey (NHAMCS) ED data, from 2007 through 2015, we identified all adult patients (aged ≥ 18 years). In the randomly sampled training set (70%), using routinely available triage data as predictors (e.g., demographics, triage vital signs, chief complaints, comorbidities), we developed four machine learning models: Lasso regression, random forest, gradient boosted decision tree, and deep neural network. As the reference model, we constructed a logistic regression model using the five-level ESI data. The clinical outcomes were critical care (admission to intensive care unit or in-hospital death) and hospitalization (direct hospital admission or transfer). In the test set (the remaining 30%), we measured the predictive performance, including area under the receiver-operating-characteristics curve (AUC) and net benefit (decision curves) for each model.

Results

Of 135,470 eligible ED visits, 2.1% had critical care outcome and 16.2% had hospitalization outcome. In the critical care outcome prediction, all four machine learning models outperformed the reference model (e.g., AUC, 0.86 [95%CI 0.85–0.87] in the deep neural network vs 0.74 [95%CI 0.72–0.75] in the reference model), with less under-triaged patients in ESI triage levels 3 to 5 (urgent to non-urgent). Likewise, in the hospitalization outcome prediction, all machine learning models outperformed the reference model (e.g., AUC, 0.82 [95%CI 0.82–0.83] in the deep neural network vs 0.69 [95%CI 0.68–0.69] in the reference model) with less over-triages in ESI triage levels 1 to 3 (immediate to urgent). In the decision curve analysis, all machine learning models consistently achieved a greater net benefit—a larger number of appropriate triages considering a trade-off with over-triages—across the range of clinical thresholds.

Conclusions

Compared to the conventional approach, the machine learning models demonstrated a superior performance to predict critical care and hospitalization outcomes. The application of modern machine learning models may enhance clinicians’ triage decision making, thereby achieving better clinical care and optimal resource utilization.

Walmart drops price of virtual visits from $40 to $4

From Becker’s:

Walmart is offering employees a 90 percent discount on telemedicine, dropping the price of a virtual visit from $40 to $4, The Denver Post reports.

The retailer reduced the cost of telemedicine services Jan. 1 to increase options for employees seeking care, a spokesperson confirmed to Becker’s Hospital Review. Walmart’s health benefits currently cover more than 1 million people enrolled it its Associates’ Medical Plan. Through this plan, virtual visits through the Doctor On Demand app are covered like a normal physician’s office visit.