Data mining-based clinical profiles of substance use-related emergency department utilizers.

OBJECTIVE: Substance-use is a prevalent presentation to the emergency department (ED); however, the clinical characterization of patients who are treated and discharged without admission for further treatment is under-investigated. The study aims to define and characterize the clinical profiles of this patient population. METHODS: Patients' presentations were examined by clinical data mining (chart review) of ED records of substance use-related events of individuals discharged without admission for further treatment. Records (N = 199) from three major hospitals in New York City from March and June 2017 were randomly sampled with primary diagnosis of alcohol, opioid-related and other psychoactive substance-use presentations. Qualitative thematic coding of clinical presentation with inter-rater reliability was performed. Quantitative distinctive validity tested independence through Pearson's chi-squared and analysis of variance using Fisher's F-test. RESULTS: Six distinct clinical profiles were identified, including, High Utilizers (chronically intoxicated with comorbid health conditions) (36.7%), Single Episode (20.1%), Service Request (14.1%), Altered Mental Status (13.6%), Overdose (9.0%), and Withdrawal (7.5%). The profiles differed (p < 0.05) in age, housing status, payor, mode of arrival, referral source, index visit time, prescribed treatment, triage acuity level, psychiatric history, and medical history. Differences (p < 0.05) between groups across clinical profiles in age and pain level at triage were observed. CONCLUSIONS: The identified clinical profiles represent the broad spectrum and complex nature of substance use-related ED utilization, highlighting critical factors of psychosocial and mental-health comorbidities. These findings provide a preliminary foundation to support person-centered interventions to decrease substance use-related ED utilization and to increase engagement/linkage of patients to addiction treatment.

Building evidence for the use of descriptive subtypes in youth with avoidant restrictive food intake disorder.

OBJECTIVE: To examine characteristics of patients with Avoidant/Restrictive Food Intake Disorder (ARFID) in an effort to identify and describe subtypes of the disorder. METHOD: A retrospective chart review was completed for patients aged 8-17 years assessed over a 17-year period. RESULTS: Seventy-seven patients were included in the study, the majority of whom were female (n = 56, 73%). The average age of patients was 13.7 years (SD = 2.4 years). Three specific subtypes of ARFID (aligning with example presentations outlined in the DSM-5) were identified: (a) those with weight loss and/or medical compromise as a consequence of apparent lack of interest in eating (n = 30, 39%); (b) restriction arising as a result of sensory sensitivity (n = 14, 18%); and (c) restriction based upon food avoidance and/or fear of aversive consequences of eating (n = 33, 43%). Clinical characteristics of patients varied depending on the assigned subtype. DISCUSSION: Our findings highlight the need for further research into the relative merit of subtype-assignment in patients with ARFID and whether such practice would aid in the recommended treatment. Further research is required to understand whether these categories are generalizable and applicable to other samples such as young children or adults with ARFID, and how treatment options might differ according to subtype.

A "Mental Health PPE" model of proactive mental health support for frontline health care workers during the COVID-19 pandemic.

We summarize in this article the development, roll out, and preliminary outcomes of a large-scale proactive mental health support model for frontline healthcare workers during the early stages of the COVID-19 pandemic, specifically during New York City's initial case surge in March through June of 2020. This paper summarizes the program design and output for two types of dedicated teams of behavioral health clinicians: 1) Mental Health Liaisons, who provided preventative support to COVID-19 hospital units and Emergency Departments, and 2) Mental Health Crisis Response Teams, who staffed 24/7 crisis response lines to support and mitigate staff crises as needed. In addition to the specifics of this model, we discuss the strategies, rewards, and difficulties of rapidly staging and evaluating such a model in the context of an ongoing disaster situation. We also offer recommendations for how this multi-dimensional model may be replicated in other settings.