Discordance Between Satisfaction and Health Literacy Among Spanish-Speaking Patients with Limited English-Proficiency Seeking Emergency Department Care

Introduction: The emergency department (ED) is one clinical setting where issues pertaining to health communication uniquely manifest themselves on a daily basis. This pilot study sought to understand satisfaction with care, perceptions of medical staff concern, awareness, and comprehension of medical care among Spanish-speaking patients with limited English-language proficiency (LEP). Methods: A two-phase, mixed-methods approach was employed among Spanish-speaking patients with LEP that presented to an ED in West Central Florida. The prospective phase consisted of semistructured interviews ( n = 25). The retrospective phase analyzed existing patient satisfaction data collected at the study site ( n = 4,940). Results: Content analysis revealed several linguistic barriers among this patient population including limited individual autonomy, self-blame for being unable to effectively articulate concerns, and lack of clarity in understanding follow-up care plans. Retrospective analysis suggested differences between responses from Spanish-speaking patients when compared with their English-speaking counterparts. Conclusions: Our findings suggest discordance between satisfaction and health literacy in this unique patient population. Although high satisfaction was reported, this appeared to be secondary to comprehension of follow-up care instructions.

Association of Head Injury With Late-Onset Epilepsy: Results From the Atherosclerosis Risk in Communities Cohort

Background and Objectives:Late-onset epilepsy (LOE; i.e., epilepsy starting in later adulthood) is affects a significant number of individuals. Head injury is also a risk factor for acquired epilepsy, but the degree to which prior head injury may contribute to LOE is less well understood. Our objective was to determine the association between head injury and subsequent development of LOE.Methods:Included were 8,872 participants enrolled in the Atherosclerosis Risk in Communities (ARIC) study with continuous Centers for Medicare Services (CMS) fee-for-service (FFS) coverage (55.1% women, 21.6% black). We identified head injuries through 2018 from linked Medicare FFS claims for inpatient/emergency department care, active surveillance of hospitalizations, and participant self-report. LOE cases through 2018 were identified from linked Medicare FFS claims. We used Cox proportional hazards models to evaluate associations of head injury with LOE, adjusting for demographic, cardiovascular, and lifestyle factors.Results:The adjusted hazard ratio (HR) for developing LOE after a history of head injury was 1.88 (95%CI=1.44-2.43). There was evidence for dose-response associations with greater risk for LOE with increasing number of prior head injuries (HR=1.37, 95%CI=1.01-1.88 for 1 prior head injury and HR=3.55, 95%CI=2.51-5.02 for 2+ prior head injuries, compared to no head injuries) and with more severe head injury (HR=2.53, 95%CI=1.83-3.49 for mild injury and HR=4.90, 95%CI=3.15-7.64 for moderate/severe injury, compared to no head injuries). Associations with LOE were significant for head injuries sustained at older age (age≥67 years: HR=4.01, 95%CI=2.91-5.54), but not for head injuries sustained at younger age (age<67 years: HR=0.98, 95%CI=0.68-1.41).Discussion:Head injury was associated with increased risk of developing LOE, particularly when head injuries were sustained at an older age, and there was evidence for higher risk for LOE after a greater number of prior head injuries and after more severe head injuries.Classification of Evidence:This study provides Class I evidence that an increased risk of late-onset epilepsy is associated with head injury and increases further with multiple and more severe head injuries.

Integrating social determinants of health screening and referral during routine emergency department care: evaluation of reach and implementation challenges

Background Despite the importance of social determinants in health outcomes, little is known about the best practices for screening and referral during clinical encounters. This study aimed to implement universal social needs screening and community service referrals in an academic emergency department (ED), evaluating for feasibility, reach, and stakeholder perspectives. Methods Between January 2019 and February 2020, ED registration staff screened patients for social needs using a 10-item, low-literacy, English-Spanish screener on touchscreens that generated automatic referrals to community service outreach specialists and data linkages. The RE-AIM framework, specifically the constructs of reach and adoption, guided the evaluation. Reach was estimated through a number of approaches, completed screenings, and receipt of community service referrals. Adoption was addressed qualitatively via content analysis and qualitative coding techniques from (1) meetings, clinical interactions, and semi-structured interviews with ED staff and (2) an iterative “engagement studio” with an advisory group composed of ED patients representing diverse communities. Results Overall, 4608 participants were approached, and 61% completed the screener. The most common reason for non-completion was patient refusal (43%). Forty-seven percent of patients with completed screeners communicated one or more needs, 34% of whom agreed to follow-up by resource specialists. Of the 482 participants referred, 20% were reached by outreach specialists and referred to community agencies. Only 7% of patients completed the full process from screening to community service referral; older, male, non-White, and Hispanic patients were more likely to complete the referral process. Iterative staff (n = 8) observations and interviews demonstrated that, despite instruction for universal screening, patient presentation (e.g., appearance, insurance status) drove screening decisions. The staff communicated discomfort with, and questioned the usefulness of, screening. Patients (n = 10) communicated a desire for improved understanding of their unmet needs, but had concerns about stigmatization and privacy, and communicated how receptivity of screenings and outreach are influenced by the perceived sincerity of screening staff. Conclusions Despite the limited time and technical barriers, few patients with social needs ultimately received service referrals. Perspectives of staff and patients suggest that social needs screening during clinical encounters should incorporate structure for facilitating patient-staff relatedness and competence, and address patient vulnerability by ensuring universal, private screenings with clear intent. Trial registration ClinicalTrials.gov, NCT04630041.

Machine learning for outcome predictions of patients with trauma during emergency department care

ObjectivesTo develop and evaluate a machine learning model for predicting patient with trauma mortality within the US emergency departments.MethodsThis was a retrospective prognostic study using deidentified patient visit data from years 2007 to 2014 of the National Trauma Data Bank. The predictive model intelligence building process is designed based on patient demographics, vital signs, comorbid conditions, arrival mode and hospital transfer status. The mortality prediction model was evaluated on its sensitivity, specificity, area under receiver operating curve (AUC), positive and negative predictive value, and Matthews correlation coefficient.ResultsOur final dataset consisted of 2 007 485 patient visits (36.45% female, mean age of 45), 8198 (0.4%) of which resulted in mortality. Our model achieved AUC and sensitivity-specificity gap of 0.86 (95% CI 0.85 to 0.87), 0.44 for children and 0.85 (95% CI 0.85 to 0.85), 0.44 for adults. The all ages model characteristics indicate it generalised, with an AUC and gap of 0.85 (95% CI 0.85 to 0.85), 0.45. Excluding fall injuries weakened the child model (AUC 0.85, 95% CI 0.84 to 0.86) but strengthened adult (AUC 0.87, 95% CI 0.87 to 0.87) and all ages (AUC 0.86, 95% CI 0.86 to 0.86) models.ConclusionsOur machine learning model demonstrates similar performance to contemporary machine learning models without requiring restrictive criteria or extensive medical expertise. These results suggest that machine learning models for trauma outcome prediction can generalise to patients with trauma across the USA and may be able to provide decision support to medical providers in any healthcare setting.