SAEM Clinical Images Series: When in Doubt, Swab It Out

eczema

A 26-year-old female with a history of atopic dermatitis presents with one week of rash that began on her lower lip but spread over her face, eyelids, and neck, plus one day of fevers and headache. She was seen at an urgent care and referred to the emergency department for evaluation and management of “impetigo.” On presentation, she endorsed nausea, headache, and mild neck pain.

Vitals: Temp 100.7° F; BP 134/85; HR 121; SpO2: 100%

General: Uncomfortable appearing.

Cardiovascular: Tachycardia

Neurological: AOx3. CN II-XII grossly intact. Moves all extremities equally and spontaneously.

Skin: Upper cutaneous lip – eroded plaque with yellow crust. Bilateral jaw line, cheek, neck, eyelids – eroded papules. Lichenified patches in antecubital fossa bilaterally.

CBC: WBC 10.7; PLT 244

Lactate: 1.31

CSF: Colorless, clear; WBC 1; RBC<1; Protein 23; Glucose 55, Gram Stain Negative

Given the patient’s history of atopic dermatitis and evidence of crusted over papules/pustules on exam, this patient’s presentation was most consistent with diagnosis of eczema herpeticum (EH).

Swabs of the upper lip lesion were positive for HSV1 DNA, which confirmed the diagnosis. Early identification and treatment of EH is critical to preventing dangerous complications including ocular involvement, viremia, meningoencephalitis, hepatitis, or secondary infection with S. aureus resulting in bacteremia. Patients with EH involving the face, periocular areas, or systemic symptoms should be admitted for intravenous antiviral therapy and supportive care.

Take-Home Points

  • The diagnosis of EH is primarily based on history and physical, but the presence of HSV in skin lesions can be confirmed by PCR.
  • Delayed treatment is associated with increased risk of complications and prolonged hospitalization.

1.Wollenberg A, Wetzel S, Burgdorf WH, Haas J. Viral infections in atopic dermatitis: pathogenic aspects and clinical management. J Allergy Clin Immunol. 2003 Oct;112(4):667-74. PMID: 14564342.

2.Aronson PL, Yan AC, Mittal MK, Mohamad Z, Shah SS. Delayed acyclovir and outcomes of children hospitalized with eczema herpeticum. Pediatrics. 2011 Dec;128(6):1161-7. PMID: 22084327.

By |2025-10-27T08:24:25-07:00Sep 29, 2025|Dermatology, SAEM Clinical Images|

ACMT Toxicology Visual Pearl: Danger in the Shallows

In addition to local pain and dermal injury, stings from this marine animal can result in what systemic symptoms?

  1. Acute liver injury
  2. Hypotension, arrhythmia, and cardiac arrest
  3. Nausea, vomiting, and respiratory arrest
  4. Numbness, tingling, and muscle paralysis

[Image courtesy of Guido Gautsch, Wikimedia Commons]

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The Discharge Severity Index: Early Research on ED Readmission Risk Assessment

discharge severity index DSI

From Triage to Discharge: As an emergency medicine clinician, you’ve likely become comfortable using the Emergency Severity Index (ESI), a critical tool helping triage patients entering the ED. But what happens when these patients leave your care? How can we anticipate who might need extra support to avoid readmission?

Let’s discuss why ED discharge risk stratification matters, the landscape of existing tools, and introduce a new effort called the Discharge Severity Index (DSI), in the context of this evolving conversation.

History of Emergency Severity Index (ESI)

As emergency medicine clinicians, we’ve all become comfortable with using the ESI. It’s simple, intuitive, and has revolutionized triage since its introduction in the late 1990s. ESI stratifies our incoming patients quickly and reliably based on anticipated resource needs and hospitalization risks, making it easy to decide who gets seen first. Over the years, ESI has gone through multiple iterations to better reflect evolving clinical priorities, workflows, and patient populations [1–4]. It became a living tool that is as dynamic and adaptive as emergency care itself.

However, as powerful as ESI is, it addresses only half the equation: what happens when patients arrive. But what about when they leave?

Discharge: More than a Binary Decision

Currently, ED discharge is largely treated as a binary decision—admit or discharge. But think about admissions: we never treat admissions as simple “yes/no” decisions. Patients can go to observation, a floor bed, step-down units, or the ICU. Each has varying resource needs and follow-up intensities. So why don’t we apply this nuanced thinking to discharge?

ED discharges aren’t straightforward. Almost 14% of patients discharged from EDs return within 30 days, often due to issues that could be preventable with better follow-up [5]. Many face barriers like misunderstanding discharge instructions, inadequate social support, and difficulty accessing outpatient care. We have powerful new follow-up tools available (e.g., nursing callback programs, telehealth, remote patient monitoring) but we often lack a clear, systematic way of figuring out which patients truly need them.

Existing Tools and Their Limitations

Multiple scoring systems have attempted to predict post-discharge adverse outcomes. Some prominent examples include:

  • LACE Score:
    • Length of stay
    • Acuity of admission
    • Comorbidities
    • Emergency visits
  • HOSPITAL Score:
    • Hemoglobin level
    • Oncology diagnosis
    • Sodium level
    • Procedure during hospitalization
    • Index admission type
    • Admissions in previous year
    • Length of stay

Yet, many of these tools weren’t specifically designed for the ED population. Our recent scoping review highlighted significant variability, limited ED-specific validation, and complexity that can hinder practical use [6].

Introducing the Discharge Severity Index (DSI): An Early-Stage Tool

Recognizing this gap, our team developed the DSI, an initial attempt at ED-specific discharge risk stratification. The idea behind DSI is to use straightforward, quickly accessible ED data points to identify patients who might benefit from enhanced follow-up.

Our single-center retrospective study analyzed ED visits, dividing the data into the derivation (75%) and validation (25%) cohorts [7]. We attempted to stratify risk based on the DSI score and measuring their 7-day readmission rates.

Our DSI score was calculated using 5 key clinical factors (0=lowest risk, 7=highest risk):

  1. Age > 65 years = 1 point
  2. Heart rate at discharge > 100 bpm = 1 point
  3. Oxygen saturation at discharge < 96% = 1 point
  4. Length of ED stay > 3 hours = 2 points
  5. Active medications > 5 during hospital stay = 2 points

Here’s what we found:

DSI LevelScore7-day Approximate Readmission Risk
1 (highest risk)6-75%
254%
33–43%
41–21%
5 (lowest risk)0<0.5%

A patient scoring a DSI 1 might benefit from immediate follow-up with telehealth, home health visits, and/or increased outpatient support. Conversely, a DSI 4 or 5 patient might safely manage standard outpatient care with minimal risk.

How is DSI Different Existing Scoring Systems?

Unlike the LACE or HOSPITAL scores, the DSI was built specifically for the ED context. It uses data readily available at discharge, allowing rapid identification of patients who may require more intensive post-discharge follow-up. It’s meant for nursing or automated tools to assign this to the patient, without requiring more provider resources.

But, let’s be clear: the DSI is not perfect. We intentionally started simple (similar to how ESI began) to get people thinking about stratifying discharge risks. For instance:

  • Length of Stay (LOS): Right now, LOS includes waiting room times, boarding delays, and other systems-level issues, making it an imperfect measure of medical complexity.
  • Vital Signs at Discharge Only: Using only discharge vitals doesn’t account for patients who had unstable earlier vitals during their ED stay.
  • Missing Comorbidities: The current DSI doesn’t explicitly factor in comorbidities or past medical history, which we know affect patient outcomes.

Why This Matters to You

It’s important to grasp the complexity behind discharge decisions just as clearly as they understand triage. Discharge isn’t simply sending patients home; it’s anticipating what happens next and appropriately preparing patients to succeed.

Implementing structured discharge risk stratification not only supports better clinical outcomes but also helps teach clinicians to think about care beyond the ED walls. With more accurate identification of high-risk patients, residents can be better prepared to integrate innovative follow-up resources into patient care.

Where do we go from here?

The DSI represents an early, evolving concept. We don’t expect it to be adopted widely and imminently. Rather, we hope it sparks a broader conversation similar to the early years of the ESI. ESI began as a simple triage tool and matured through iterative development, field testing, and adaptation across varied ED environments. It became more robust, nuanced, and integrated into daily clinical operations over time. We envision a similar trajectory for the DSI.

Future iterations of the DSI will undoubtedly incorporate additional clinical variables, operational data, and even social determinants of health. But before we get there, the next step is clear: we must operationalize the DSI and test it in multiple real-world settings. Its utility must be validated not just in theory or retrospective data, but in the dynamic, complex ecosystem of actual emergency departments.

We encourage EM educators and residency programs to join us in refining the conversation about ED discharge stratification.

Whether it’s integrating DSI into discharge planning discussions, piloting it during teaching rounds, or evaluating it in post-discharge follow-up workflows, there is now an opportunity to take this idea from concept to practice for the benefit of our patients.

Let’s build upon this first step, creating tools that are practical, teachable, and clinically meaningful. Together, we can ensure that the decision to discharge is just as thoughtful, nuanced, and patient-focused as the decision to admit.

References

  1. Wuerz RC, Milne LW, Eitel DR, Travers D, Gilboy N. Reliability and validity of a new five-level triage instrument. Acad Emerg Med 2000;7(3):236–42.
  2. Wuerz RC, Travers D, Gilboy N, Eitel DR, Rosenau A, Yazhari R. Implementation and refinement of the emergency severity index. Acad Emerg Med 2001;8(2):170–6.
  3. Eitel DR, Travers DA, Rosenau AM, Gilboy N, Wuerz RC. The emergency severity index triage algorithm version 2 is reliable and valid. Acad Emerg Med 2003;10(10):1070–80.
  4. Elshove-Bolk J, Mencl F, van Rijswijck BTF, Simons MP, van Vugt AB. Validation of the Emergency Severity Index (ESI) in self-referred patients in a European emergency department. Emerg Med J 2007;24(3):170–4.
  5. Characteristics of 30-Day All-Cause Hospital Readmissions, 2016-2020 [Internet]. [cited 2025 Jul 7].
  6. Jaffe TA, Wang D, Loveless B, et al. A Scoping Review of Emergency Department Discharge Risk Stratification. West J Emerg Med 2021;22(6):1218–26. PMID 34787544
  7. Kijpaisalratana N, El Ariss AB, Balk A, et al. Development and validation of the discharge severity index for post-emergency department hospital readmissions. Am J Emerg Med. 2025;94:125-132. doi:10.1016/j.ajem.2025.04.045. PMID 40288325
By |2025-08-09T13:19:36-07:00Aug 14, 2025|Administrative, Beyond the Abstract|

ALiEM AIR Series | Vascular Module (2025)

ALiEM AIR Certified seal and Vascular 2025 module shield badge

 

Welcome to the AIR Vascular Module! After carefully reviewing all relevant posts in the past 12 months from the top 50 sites of the Digital Impact Factor [1], the ALiEM AIR Team is proud to present the highest quality online content related to related to HEENT emergencies in the Emergency Department. 8 blog posts met our standard of online excellence and were approved for residency training by the AIR Series Board. More specifically, we identified 3 AIR and 5 Honorable Mentions. We recommend programs give 4 hours of III credit for this module.

 

AIR Stamp of Approval and Honorable Mentions

 

In an effort to truly emphasize the highest quality posts, we have 2 subsets of recommended resources. The AIR stamp of approval is awarded only to posts scoring above a strict scoring cut-off of ≥30 points (out of 35 total), based on our scoring instrument. The other subset is for “Honorable Mention” posts. These posts have been flagged by and agreed upon by AIR Board members as worthwhile, accurate, unbiased, and appropriately referenced despite an average score.

 

Want asynchronous Individualized Interactive Instruction (III) credit?
Take the AIR quiz at ALiEMU. Free, 1-time login required.

Take the Vascular Module →

Highlighted Quality Posts: Vascular 2025

 

SiteArticleAuthorDateLabel
EMCritPulmonary embolism diagnosis and treatment of low-risk PEDr. Josh FarkasMarch 5, 2024

AIR

EMCritAortic dissectionDr. Josh FarkasSeptember 28, 2024AIR
EMDocsAcute chest syndromeDr. Rachel BridwellJune 27, 2024AIR
EMCritApproach to chest painDr. Josh FarkasJanuary 15, 2024HM
Rebel EMDon’t forget the IO in the critically ill patientDr. Kristen WileyApril 29, 2024HM
RCEMlearningCervical artery dissectionDr. Jason LouisJanuary 22, 2024HM
CanadiEMIs IO cannulation an underutilized method of emergency vascular accessDr. Ming LiOctober 15, 2024HM
PedEM MorselsKounis syndromeDr. Christyn MagillMarch 22, 2023HM

 

(AIR = Approved Instructional Resource; HM = Honorable Mention)

 

If you have any questions or comments on the AIR series, or this AIR module, please contact us!

Reference

    1. Lin M, Phipps M, Chan TM, et al. Digital Impact Factor: A Quality Index for Educational Blogs and Podcasts in Emergency Medicine and Critical Care. Ann Emerg Med. 2023;82(1):55-65. doi:10.1016/j.annemergmed.2023.02.011, PMID 36967275

 

 

When Research Meets Social Media Expertise: Lessons from the PECARN-ALiEM Partnership

PECARN - ALiEM partnership twitter X
From Pipe Dream to Proven Strategy: How a 4-year partnership between PECARN and ALiEM created a replicable framework for evidence-based research dissemination

Sometimes the best collaborations begin with simple questions. Following Dr. Nathan Kuppermann’s grand rounds presentation in 2018, I had the opportunity to discuss an idea with him as PECARN’s Steering Committee Chair: might there be untapped potential in using social media platforms like Twitter to amplify PECARN’s research impact? Five years later, that initial conversation has grown into a reality with a systematic approach and measurable outcomes.

Social media is not just about fads and marketing. In fact, it represents the foreseeable future for information dissemination, even in scientific research, because it meets learners and providers where they already are. Rather than hoping clinicians would stumble upon publications in traditional journals, we should actively bring the research to the platforms they frequently check.

Why Organizational Social Media Requires Strategic Planning

Organizational social media for research dissemination can’t just “do social media.” This endeavor requires fundamentally different approaches than personal academic accounts. While individual faculty might share insights casually or build personal brands, research organizations need systematic frameworks that ensure consistency, maintain academic rigor, and deliver measurable impact.

The critical distinction: institutional social media isn’t about intuition or viral content—it demands rigorous planning, dedicated resources, and iterative optimization based on analytics. Just as we wouldn’t launch a research study without proper methodology and oversight, we shouldn’t approach organizational research dissemination without strategic frameworks and quality control systems.

The Partnership Model: When Research Meets Social Media Expertise

Our approach began with recognizing a fundamental truth: most research organizations lack the specialized expertise needed for effective social media presence. Rather than building these capabilities from scratch, PECARN partnered with ALiEM, leveraging our existing social media infrastructure and experience. What started as an experimental collaboration became a four-year case study, which we recently published in JMIR Formative Research [1]. We share our processes, outcomes, and lessons learned to provide a replicable framework and roadmap for other research organizations considering similar initiatives on Twitter/X (or alternative social media platforms).

The Foundation: Building Sustainable Infrastructure

Organizational Inputs:

  • Research Organization (PECARN) – content expertise and credibility
  • Social Media Experts (ALiEM) – Twitter/X platform knowledge and audience understanding
  • Funding & Leadership Support – executive champions and resource allocation
  • Technical Infrastructure – analytics tools, scheduling platforms, communication systems

The 5-Person Dream Team:

  • Content Writers (2): Physician-researchers who understand both clinical context and platform constraints
  • Peer Reviewers (2): Quality control experts ensuring academic rigor
  • Account Monitors (2): Daily engagement specialists building community
  • Analytics Manager (1): Data scientist tracking performance and optimization
  • Graphic Designer (1): Visual content specialist (added after 2 years based on data)

We created 2-person teams for key roles to ensure sustainability and backup coverage. Faculty have competing priorities, and redundancy ensures consistent output despite scheduling challenges.

pecarn ALiEM twitter X partnership research dissemination architect

What the Numbers Taught Us

The key to our success wasn’t guesswork—it was rigorous analytics tracking and iterative evidence-based improvement. Over the 4 years (2020-23), 569 tweets were published, 99 PECARN journal publications were featured, and we grew an audience of over 2,000 followers.

Tweet-Level Analytics: The Strategy Elements That Actually Work

Through multiple linear regression analysis, we identified 3 characteristics with statistically significant impact on both impressions and engagement:

  1. Polls (β = 0.278): Our most impactful discovery was that interactive polls became our strongest engagement driver. we used polls to introduce clinical scenarios related to featured research, allowing audiences to test their knowledge before revealing study findings.
  2. Graphics (β = 0.195): Professional graphics significantly boosted engagement, leading us to add a dedicated graphic designer to the team after 2 years. This wasn’t cosmetic—it was a data-driven personnel decision.
  3. URL Links (β = 0.173): Links to full articles didn’t just drive traffic; they contributed to increased Altmetric Attention Scores, providing measurable academic impact beyond social media metrics.

Surprisingly, emojis showed a negative correlation with engagement in our academic audience. We hypothesize that these emojis may have not resonated with our academic and healthcare professions audience— a reminder that strategies must be tailored to the desired audience.

research dissemination architect pecarn ALiEM twitter X

Lessons Learned for Building Research Dissemination Architecture

1. Analytics Are Non-Negotiable

Don’t guess about what works. Track impressions, engagement, click-through rates, and downstream academic metrics. What gets measured gets optimized.

2. Quality Control Maintains Credibility

Our peer review process for each tweet provided academic rigor for accuracy and quality, treating social media content with the same methodological care we apply to research publications. This approach strengthened PECARN’s digital credibility and built trustworthiness with our professional audience who expect evidence-based content even in 280 characters.

3. Team Redundancy Ensures Sustainability

Faculty have complex schedules. Build systems that work despite individual availability challenges.

4. Visual Content Isn’t Optional

Professional graphics aren’t “nice to have”—they’re proven engagement drivers in the era of information overload. They are worth the investment.

New Academic Role: Research Dissemination Architect

What began as grassroots FOAM (Free Open Access Medical education) with individual bloggers and social media educators has evolved into something more substantial: the emergence of the “Research Dissemination Architect” as a legitimate, potentially funded position within academic institutions and research organizations.

This represents a fundamental shift in how we think about knowledge translation careers. We’re no longer talking about faculty “doing social media on the side”—we’re talking about dedicated professional positions with specific expertise, measurable outcomes, and institutional recognition. Our recent publication in JMIR Formative Research documents our journey in this evolution. The ALiEM-PECARN partnership wasn’t just about Twitter success; it was about demonstrating that research dissemination can be a systematic, professional discipline worthy of institutional investment and academic recognition.

Conclusion

The PECARN-ALiEM partnership demonstrates that academic rigor and social media success aren’t mutually exclusive—they’re synergistic when approached systematically. Through this collaboration, we’ve contributed to establishing systematic approaches to research dissemination as a pathway toward accelerated knowledge translation.

Research Dissemination Architects represent an emerging career pathway that bridges traditional academic expertise with digital communication skills. As medical education continues evolving toward digital-first approaches, faculty who develop competency in evidence-based social media are positioning themselves at the forefront of this evolution. The framework we’ve developed offers one approach to professional research dissemination. As more organizations experiment with similar roles, we’ll undoubtedly see diverse models emerge, each contributing to our collective understanding of effective academic digital scholarship.

We hope our experience can inform others exploring this space. Whether you adapt our specific approach or develop entirely different methods, the opportunity to advance how research reaches its intended audiences has never been greater.

Reference

  1. Hooley GC, Magana JN, Woods JM, et al. Research Dissemination Strategies in Pediatric Emergency Care Using a Professional Twitter (X) Account: A Mixed Methods Developmental Study of a Logic Model Framework. JMIR Form Res. 2025;9:e59481. Published 2025 Jun 24. doi:10.2196/59481. PMID 40554778

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