Category Archives: emergency medicine

Two most common pediatric migraine medications no more effective than placebo

Neither of the two drugs used most frequently to prevent migraines in children–amitriptyline and topiramate–is more effective than a placebo, according to results of the Childhood and Adolescent Migraine Prevention (CHAMP) trial published this week in The New England Journal of Medicine. The investigators found no significant differences in reduction in headache frequency or headache-related disability in childhood and adolescent migraine with amitriptyline, topiramate, or placebo over a period of 24 weeks.

The active drugs were associated with higher rates of adverse events. One child on topiramate attempted suicide. Three taking amitriptyline had mood changes; one told his mother he wanted to hurt himself, while another wrote suicide notes at school and was hospitalized.screen-shot-2016-10-28-at-4-01-47-pm

Migraine headaches are common in children. Up to 11 percent of 7- to 11-year-olds and 23 percent of 15-year-olds have migraines.


A Medical Degree in Paperwork

A recent study in Annals of Internal Medicine found that physicians in four office-based specialties spent the majority of their time on documentation and paperwork:

  • Overall, physicians spent 27.0% of their total time on direct clinical face time with patients and 49.2% of their time on documentation (paper and electronic).
  • While in the examination room with patients, physicians spent 52.9% of the time on direct clinical face time and 37.0% on documentation.
  • Physicians reported 1 to 2 hours of after-hours work each night, devoted mostly to electronic health record tasks.

A Forbes commentary noted there was a steady increase in the proportion of physician time taken up by paperwork. Reasons for this trend include

  • The multitude of diverse stakeholders requiring increasing amounts of documentation in the paperwork, including administration, lawyers and insurance companies. For example, for a patient with a broken arm, insurance companies require that we ask about at least ten organ systems (a “review of systems“) and document our findings completely. So at least part of that clinical face time is spent asking the patient with the broken arm about things like pain with urination and visual change.
  • The stakeholders, and not the physicians, design the forms, and so they are not designed in a way that fits a medical way of thinking about a patient encounter and are often redundant. For example, when I transfer a patient from one hospital to another, I fill out a form for the lawyers (the EMTALA form, explaining the medical necessity of the transfer) and a form for the insurers explaining the medical necessity of the transfer. These contain a lot of the same information.
  • Hospitals and clinics do not seem to be investing in clerical and administrative support for doctors, sometimes because of regulations requiring a physician complete the forms. For example, 90% of the information on the two transfer rationale forms I mentioned above both could be completed by clerical personnel, or could be copied by a non-MD from one form onto the other.

The Forbes piece notes that the trend pushing ever increasing paperwork burdens onto physicians is a recipe for low career satisfaction and burnout.

On Informing Families a Child has Died

I am sharing a moving piece by Naomi Rosenberg, an emergency room doctor at Temple University Hospital in Philadelphia, about telling a mother her child has died. It is hauntingly familiar; over the past 20 years as a pediatric emergency medicine physician, I have followed a sequence similar to the steps she describes in her piece. It is the hardest part of the job, as it should be. It never gets easier–this, too, is as it should be. I am thankful that few others among my friends and family have outlived their own child.

Should Doctors Undergo Opioid Prescribing Risk Training?

Earlier this week, an advisory panel recommended that the Food and Drug Administration require doctors who prescribe painkillers s to undergo training aimed at reducing misuse and abuse of the medications. The New York Times notes:

It is the second time since 2010 that an F.D.A. panel has recommended expanding safety measures for painkillers. But the training plans instituted about four years ago are voluntary, and data shows that under half of the doctors targeted by the effort have completed the training.


Despite the rising opioid-related death rate since the initial FDA panel’s initial recommendation in 2010, the panel strongly recommended training physicians. Given the amount of training we all underwent in preparation for US cases during the most recent Ebola epidemic, the physician training for this pandemic, far more deadly on our shores, seems both feasible and urgent.

Emergency Department Return Visits as a Quality Metric

A recent JAMA publication lead-authored by Dr. Amber Sabbatini examined the scientific soundness of emergency department (ED) return visits as a measure of the ED’s quality of care. Emergency department return visits have been considered for wider adoption as a quality metric, especially for those patients who are hospitalized during the return ED visit. The “quality” that this metric is intended to measure is the quality of ED care delivered, including the safety of the ED physician’s decision to discharge the patient.  Patients returning to the ED within 7, 14 and 30 days of the initial visit thus are thought to reflect lower quality of care, particularly if readmitted, as this reflects progression of the patient’s illness to a more severe state after they were mistakenly sent home.

The authors compared in-hospital clinical and utilization outcomes (deaths, need for intensive care unit (ICU) care, length of stay and cost) between two groups of patients: those who were admitted during their initial ED visit, and those who returned to the ED and were hospitalized. They found that

patients who experienced an ED return visit that was associated with admission shortly after ED discharge had significantly lower rates of in-hospital mortality, ICU admission, and costs, but higher lengths of stay compared with admissions among patients without a return visit to the ED.

Patients who are initially sent home from the ED and then return and are readmitted are actually less sick than those admitted to the hospital initially.  In aggregate, they are not experiencing increasing severity of illness after discharge from the initial ED visit–in fact, they are less sick than those admitted initially.  In some ED’s, this effect may reflect dilution, in that a revisit alone is reason to admit patients regardless of how sick they are medically. A tongue-in-cheek ED adage states that  if the pizza boy returns to the ED to deliver another pizza, you admit him.

Putting these findings in context of Donabedian’s structure-process-outcome framework for measuring health care quality, ED revisits are being used to measure ED quality of care.  ED quality of care is a process measure as it is a health care-related activity performed for or by a patient, but quality is unmeasured here because it is so hard to measure directly for all diagnoses together.  Current ED measures of care quality include throughput metrics–ED length of stay and time from disposition decision to admission–as well as  have condition-specific metrics such as time to fibrinolytic treatment for ED patients with acute myocardial infarction.

In Donabedian’s framework, outcomes measure the  health state of the patient resulting from health care. Revisits to the ED are not a health state; they are used as a proxy for the outcome of “worsened health status”.  By looking at the clinical course of those readmitted during a readmission post discharge from the ED, the authors show that ED revisits are not a good proxy for post-ED-discharge health status.Screen Shot 2016-03-10 at 7.00.37 AM

Thus, ED revisits do not have good construct validity as a proxy for ED quality of care–they do not measure what they purport to measure. One important contributor to this poor validity is that patient-level factors beyond the control of the hospital are significant risk factors for revisits. Social determinants—the circumstances in which people live and work—powerfully affect health; they are estimated to have twice the impact of the quality of an individual’s health care on that individual’s overall health.

The concerns about construct validity and the impact of social determinants of health are similar to those I’ve discussed elsewhere related to hospital readmissions, and related to healthcare performance metrics more broadly.



Robots vs. doctors?

Although I agree with the basic premise of this week’s Washington Post article “The Robot Doctor Will See You Now“–namely, that computers can augment medical care–the article misses the art-science balance so central to physician’s practice.  He states:

If you’ve ever gone to a doctor with an odd set of symptoms and realized that your doctor has no clue what they signify, you too might have wished he had access to some heavy computing power.

The large majority of the time, at least in my experience as a non-robotic emergency room doctor, we do have a “clue” about what the symptoms signify, and we know how to narrow the uncertainty down sufficiently to make a plan for the patient, even if residual uncertainties persist.

The example the Post article provides is about cancer genomics, a technology relevant only to those (1) with conditions where genomics help personalize treatment and (2) who have access to the few doctors with the time and other resources to access these tools.

However, a “computing power” with broader and easier applicability than the one in the Post article is the focus of an article I co-authored this week.  My co-authors and I focus on clinical prediction rules, using as a test case a prediction rule relevant to children in the emergency department for acute asthma exacerbations (about 700,000 each year).  Clinical prediction rules are tools to support physician decision-making, combining best evidence from research as well as data from an individual patient’s history, physical examination and tests. These tools augment rather than supplant physician’s decision-making, helping to predict a patient’s probability of a specific diagnosis or outcome.  Clinical prediction rules thus do not resemble the Post writer’s “Ropage1-474px-Rumack_Matthew_nomogram_with_treatment_(study)_line.pdf-2bot Doctor”.

The clinical prediction rule we use as our article’s example predicts the probability of hospital admission for children with asthma. It does not tell doctors what to do, but helps support their decision-making based on synthesis of multiple data points. Other prediction rules relevant to my clinical field predict

And there are others, which is great, and also is part of the challenge.  How do we make sure we are using the best, most up-to-date version? Overall, however, clinical prediction models have inherent advantages over human-only clinical decision making.

  1. They can take into account many more factors than can a human brain.
  2. They are consistent–they will always give the same result regardless of the experience of the physician or the socio-demographics of the patient
  3. When used to support physician judgment, they provide more accurate assessment of outcomes than physician judgment alone.

Even less relevant to medical robots, a likely cause of the Post writer’s “no clue” observation is that some patients’ complaints are non-medical, albeit quite real and impactful for the patient. In medicine, and especially in the emergency department, physicians see a lot of social morbidity we have few tools to address. A colleague has noted that sometimes the best treatment he has for some patients is giving them some money from his own wallet to access basic resources for their child (food, shelter, transportation).   In 2010, almost one-third of children living in households with incomes below the poverty level had visited an emergency department in the past year (30.2 percent), compared to 17.3 percent of children living in households at 200 percent or more of the poverty level.




Guns, Drugs and Cars

This week’s JAMA released a comparison of major causes of injury death and how they contribute to the gap in life expectancy between the US and other high-income countries. Here are their findings:

Men in the comparison countries had a life expectancy advantage of 2.2 years over US men (78.6 years vs 76.4 years), as did women (83.4 years vs 81.2 years). The injury causes of death accounted for 48% (1.02 years) of the life expectancy gap among men. Firearm-related injuries accounted for 21% of the gap, drug poisonings 14%, and MVT [motor vehicle traffic] crashes 13%. Among women, these causes accounted for 19% (0.42 years) of the gap, with 4% from firearm-related injuries, 9% from drug poisonings, and 6% from MVT crashes. The 3 injury causes accounted for 6% of deaths among US men and 3% among US women.

These findings are also shown in tabular format here, in the paper’s Table 1:Screen Shot 2016-02-10 at 10.12.45 AM

What is simultaneously so hopeful and so frustrating about these findings is that all three causes of the mortality gap between the US and other high-income nations is that there are clear, proven public health answers to all three causes of injury (with thanks to Injury Epidemiologist Dr. Dawn Comstock for her scholarship and public health advocacy messages for all three).

  1. GUNS: I have written previously about prevention of gun violence deaths, including preventive technologies such as smart guns and required training for gun ownership (just as is required for vehicle licensure.
  2. OPIOIDS: I have also written about some local approaches to the opioid epidemic, including efforts to educate prescribers to prescribe carefully and efforts to improve prescription drug records as well as better packaging to prevent pill bottles and marijuana edibles from falling into the wrong hands and mouths.
  3. MOTOR VEHICLES: Finally, for motor vehicle related deaths, public health solutions include enforcing our existing laws (e.g., drunk driving laws, penalties for driving with suspended licenses) and improving technology (e.g., easier-installation car seats).

Findings of the JAMA study have also been covered in the media.