Emergency department adult fiberoptic intubations: Incidence, indications and implications for training

From Academic Emergency Medicine:

Background

To describe the frequency, indications and outcomes of flexible fiberoptic intubations (FFI) performed in the emergency department (ED).

Methods

From the National Emergency Airway Registry (NEAR), we identified all encounters during 7/1/02 through 12/31/12 with the use of FFI. We determined patient, provider and intubation characteristics, success and failure rates, and modes of intubation rescue.

Results

Among 17,910 intubations of patients >15 years old at 13 EDs, FFI was used in 204 cases (1.1%, 95% CI = 0.26%‐2.0%). FFI was the first method chosen (primary FFI) in 180 encounters (1%, 95% CI = 0.2%‐1.8%). The most common indication for FFI was airway obstruction (36.1%; 95% CI = 24.6%‐47.7%). For primary FFI, first attempt intubation success was 51.1% (95% CI = 43.6%‐58.6%), and overall intubation success with FFI was 74.3% (95% CI = 65.7%‐82.9%). FFI was used as a rescue airway strategy in 24 cases (0.1% of all encounters) and was successful in 17 of those (70.8%; 95% CI = 65.4%‐85.2%).

Conclusions

ED FFI is uncommon, and typically used as a non‐surgical alternative for airway obstruction. First attempt ED FFI is successful in half of cases, and in two‐thirds of rescue attempts. This data provides an important baseline to help better characterize the nature of FFI as a rare critical procedure in the ED, and offers an empiric basis for ongoing discussions on the optimal role of FFI in ED training and practice.

Sampling suggests nearly a third of Medicare telehealth payments are improper

From HealthcareDive:

  • CMS paid about $3.7 million on telehealth claims that did not meet Medicare requirements, according to a new report by HHS Office of Inspector General.
  • The OIG reviewed 191,118 distant-site telehealth claims from 2014 and 2015 totaling $13.8 million in payments. In a random sample of 100 claims, 31 failed to qualify for Medicare due to geographic and other restrictions.
  • Of the 31 disallowed claims, 24 were because the patient received services in a nonrural setting. For example, one patient’s originating site was a doctor’s office in Lynchburg, VA, which is within a metropolitan statistical area.

Hospitals Say Amended Timetable for Sexual Assault Training Bill is Not Realistic

From HealthNews Illinois:

The Illinois Health and Hospital Association said Tuesday that a new timetable for legislation aiming to increase the number of providers trained to treat sexual assault victims is not realistic.

The bill requires that sexual assault survivors receive specialized care by a qualified medical provider within 90 minutes of arriving at a hospital. An amendment to the proposal would push up the deadline hospitals have to meet the requirements to 2021, up from 2023.

“We have deep concerns that the staffing mandates in the amendment would make it nearly impossible for hospitals to successfully implement the required steps and activities in order to provide quality care to sexual assault survivors in the timeframe the bill requires and in every region of the state, including rural communities,” IHA said in a statement.

Misdiagnosis of Cerebral Vein Thrombosis in the Emergency Department

From Stroke:

Background and Purpose—Rates of cerebral venous thrombosis (CVT) misdiagnosis in the emergency department and outcomes associated with misdiagnosis have been underexplored.

Methods—Using administrative data, we identified adults with CVT at New York, California, and Florida hospitals from 2005 to 2013. Our primary outcome was probable misdiagnosis of CVT, defined as a treat-and-release emergency department visit for headache or seizure within 14 days before CVT. In addition, logistic regression was used to compare rates of clinical outcomes in patients with and without probable CVT misdiagnosis. We performed a confirmatory study at 2 tertiary care centers.

Results—We identified 5966 patients with CVT in whom 216 (3.6%; 95% confidence interval [CI], 1.1%–4.1%) had a probable misdiagnosis of CVT. After adjusting for demographics, risk factors for CVT, and the Elixhauser comorbidity index, probable CVT misdiagnosis was not associated with in-hospital mortality (odds ratio, 0.14; 95% CI, 0.02–1.05), intracerebral hemorrhage (odds ratio, 0.97; 95% CI, 0.57–1.65), or unfavorable discharge disposition (odds ratio, 0.90; 95% CI, 0.61–1.32); a longer length of hospital stay was seen among misdiagnosed patients with CVT (odds ratio, 1.62; 95% CI, 1.04–2.50). In our confirmatory cohort, probable CVT misdiagnosis occurred in 8 of 134 patients with CVT (6.0%; 95% CI, 2.6%–11.4%).

Conclusions—In a large, heterogeneous multistate cohort, probable misdiagnosis of CVT occurred in 1 of 30 patients but was not associated with the adverse clinical outcomes included in our study.

Nurse Practitioner Closed Claims Study: Top Risks in the Changing Delivery of Primary Care

From The Doctors Company (hat tip: Dr. Menadue):

By 2025, it’s projected that nurse practitioners will represent almost one-third of the family practice workforce.

Nurse practitioners can help improve medical practice productivity by allowing doctors to see more patients and focus on those patients requiring complex care. Increasingly, the growing need for primary care services will be met with nurse practitioners.

The Doctors Company studied nurse practitioner medical malpractice claims over a six-year period and compared them to claims against primary care physicians (family medicine and internal medicine) to identify risk management issues that may be unique to nurse practitioners.

Using deep learning to predict emergency room visits

From Phys:

At IBM Research, we are exploring new solutions for a range of health care challenges. One such challenge is emergency room (ER) overcrowding, which can lead to long wait times for treatment. Overcrowding results in part from people visiting the ER for non-emergency conditions rather than relying on primary physicians. Patients who use the ER for non-emergency situations are more likely to return to the ER multiple times (Poole et al. 2016), further contributing to overcrowding. Identifying those patients who are likely to return to the ER may enable hospitals to intervene to ensure access to necessary care outside the ER and potentially alleviate overcrowding.

My team at IBM Research-China took on this challenge. We developed a novel neural network model to predict how many times a person will visit the ER based on information from his or her electronic health records (EHRs). The model is based on a typical recurrent neural network, but unlike traditional machine learning methods, it exhibits dynamic temporal behavior based on EHR information and has a complex structure to better model the correlation between ER visits and other patient data (Figure 1). We used the model to make precise predictions of whether and how many times a person will visit the ER and found that it outperformed other common techniques. For example, precision of our model was 6.59 percent greater than a typical logistic regression model in predicting whether a person will visit the ER and >90 percent greater in predicting number of ER visits compared with linear regression model. Our model also had approximately 2 percent greater precision than the popular XGboost model in predicting number of ER visits.

By better predicting how many times a person will visit the ER, we hope that this model might enable hospitals to establish, prioritize, and target interventions to ensure that patients have access to the care they require outside an ER setting.

What Happened When a Portland Hospital Starting Treating Addiction Like Any Other Illness

From Gizmodo:

Over the past few years, a hospital in Portland, Oregon has dramatically shifted its approach to treating patients with substance use disorders, including opioid addiction. And since then, the hospital has undergone a “sea change” in the way its medical staff think about and address addiction, according to a study published Wednesday in Journal of Hospital Medicine.