Pain Management

In this episode, National Registry Research Fellow Jacob Kamholz explores a national study on how pain is managed in the field. The research analyzed over 35,000 EMS records of adult patients with confirmed long bone fractures. By linking EMS data with hospital diagnoses, researchers were able to precisely identify the injuries treated by each Clinician.
The study utilized the CDC Social Vulnerability Index to account for factors like income and education levels within different communities. Researchers examined whether the administration of pain medication varied across different patient groups while controlling for age, injury location, and transport time. This methodology ensures a comprehensive look at how care is delivered in various environments.
The findings indicate that differences in treatment exist within the out-of-hospital setting. These variations were not explained by patient preferences or clinical contradictions. We invite you to listen to the full episode to learn how data can help the EMS community continue to provide high-quality care for every patient.

Read the full study here: https://www.annemergmed.com/article/S0196-0644(23)00267-6/pdf

Transcript

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Welcome back everyone. I'm your paramedic and policy researcher Jacob Camholz, and today I hope to start a discussion about an important topic and invite some introspection for my EMS family. That topic is pain management, and our research today is racial, ethnic, and socioeconomic disparities in out-of-hospital pain management for patients with long bone fractures. Pain management is a crucial aspect of emergency care, and often the first contact and opportunity for treatment is from EMS clinicians. We already know that EMS administered pain management helps and is associated with better pain management once in the emergency department. At the same time, though, we know that pain management in emergency departments isn't equally distributed. So, in 2022, Remlin Crowe and others set out to investigate racial and ethnic disparities in EMS pain management while considering important clinical and socioeconomic factors. Let's dig in. Before we get too far, I will point out that the research we're discussing today is open access, meaning that the full research publication is free and available online for anyone. Click the link to open the PDF and follow along with the data in the tables. As I noted before, knowledge of in-hospital pain management disparities does exist. But to answer the question for EMS, Dr. Crow and others required a couple different types of data. First, information about the runs that EMS takes and the treatments they provide to those patients was crucial. Additionally, the patient's ED diagnosis was required to confirm the injury that was treated by EMS, to confirm that actually a long bone fracture existed. And in this case, the ESO data collaborative seems to fit the bill perfectly. In 2020, the collaborative dataset contained nearly 10 million EMS activations from thousands of agencies, and more than 1 million of those EMS calls were linked to emergency department diagnoses, which is a major strength of this dataset and a requirement that was needed for this analysis. Thus, the inclusion criteria started here. Adults with an ERICD10 code indicating at least one long bone fracture, meaning their humerus, radius, ulna, femur, tibia, or fibula. The patient's race also needed to be known. Importantly, if the patient was not transported by 911, an opportunity to receive EMS pain medication, the case was excluded. This also required the exclusion of BLS transports to ensure that pain management was within the treatment scope. Finally, if the patient was altered as noted by AFPU or GCS, they were excluded, as any needed life-saving intervention should be prioritized over pain meds. Now that we know who this research is attempting to analyze, let's discuss the primary methods for doing just that. Since the goal of this work is to understand disparities in the treatment of pain, the study's primary outcome of importance was whether the patient received pain medication. Treatment options included any analgesia such as opioids, NSAIDs, ketamine, or even acetaminophen. Therefore, our independent variable in this situation, the variable being examined for possible connection to pain treatment, was the patient's race and ethnicity. Based on PCR charting options, patients were stratified or grouped into the following categories white non-Hispanic, Black non-Hispanic, Hispanic, and other. Now you have the framework of the study. Does the likelihood of receiving pain medication change depending on which group of patient is stratified into? I'm sure some of you are still wondering where the work socioeconomic aspect comes in as well, so let's take a moment to unpack that. The study's socioeconomic dimension is captured through the CDC's Social Vulnerability Index, or SVI. Specifically, that incorporates poverty level, unemployment rate, income, and educational attainment. SVI is helpful because it distills down multiple complex indicators into a single standardized score, making it easier for researchers to compare across different geographic regions. Now, in the context of this research, we often want to know whether outcomes we're studying, the pain meds, are actually influenced by our primary variable of interest, the race, or if other underlying factors are shaping them. This is where controlling for variables comes into play. To ensure that race and ethnicity are evaluated explicitly in the context of social determinants of health, SVI was included in this analysis, and other variables, including age, sex, fracture location, transport time, and pain score were all controlled for. Let's move into the results. This study included over 35,000 patients from more than 400 EMS agencies, each with confirmed long bone fractures, and that's a strong sample. And importantly, these were all ALS units where analgesic treatment was within the scope. Check out table one for demographic breakdowns if you're following along at home. Among patients with severe pain, those that reported pain scores 7 through 10, white non-Hispanic patients received analgesia about 72% of the time. How about black non-Hispanic patients? Unfortunately, that shows up as only 59% of the time. That's a 13% absolute difference despite similar pain severities. And when we adjust for those other factors mentioned before, like age, gender, insurance, fracture site, transport time, and SVI using multivariable modeling, that gap still holds strong. If you move to table three, you can see that black non-Hispanic patients had 35% lower odds of receiving analgesics in the out-of-hospital setting. Let that sink in for a second. These disparities persisted even after controlling for patient-level clinical factors and community-level socioeconomic vulnerability. So, we have a disparity, but can we explain it? To dig deeper, the authors reviewed a random sample of EMS clinician narratives from their PCRs where analgesics were not given, looking for legitimate reasons, things like contraindications, patients declining, or language barriers. And what they found is in table four, if you're following along, and it is very revealing. Black and white patients both refused medications at similar rates. Contraindications were similarly distributed, and non-pharmacological interventions like splinting were applied across all groups. In short, clinical appropriateness and patient preference did not explain the disparity. Even more troubling among those who did receive medication, black patients were less likely to experience meaningful pain reduction, defined as at least a two-point drop on their pain scale. So now what? Well, we know from existing research that pain is undertreated in EMS, and this study adds to the evidence that those disparities aren't occurring evenly. But this isn't about finger pointing, it's about systems. The EMS environment is fast-paced, it's cognitively demanding, and often ambiguous, and these conditions just amplify implicit biases. As clinicians, just like anybody else, we are vulnerable to unconscious stereotypes, especially when under stress. This study also reinforces how structural issues like agency policies, education gaps, or a lack of data can allow these inequities to persist. So what do we do about it? First, we need to really acknowledge that these disparities exist and that EMS is not immune. Second, let's improve training, but not just the same old bias modules. We need meaningful and reflective training that incorporates cross-cultural communication. Third, diversity in the workforce. Representation really does matter. Patients deserve to see themselves in their providers, in their clinicians, and clinicians benefit from colleagues who bring different lived experiences and perspectives to the team. Finally, quality improvement systems must start tracking and addressing inequities just as rigorously as we track cardiac arrest survival or scene times. If we're not measuring it, we're definitely not fixing it. To my EMS colleagues, this paper presents us all a challenge to keep moving forward with high quality and more equitable care. So until next time, whether you're hitting the books or hitting the streets, stay safe, stay curious, and keep bringing the science to the scene.