A multisite comparison using electronic health records and natural language processing to identify the association between suicidality and hospital readmission amongst patients with eating disorders

Abstract

ObjectivesTo describe and compare the association between suicidality and subsequent readmission for patients hospitalized for eating disorder treatment, within 2 years of discharge, at two large academic medical centers in two different countries.MethodsOver an 8‐year study window from January 2009 to March 2017, we identified all inpatient eating disorder admissions at Weill Cornell Medicine, New York, USA (WCM) and South London and Maudsley Foundation NHS Trust, London, UK (SLaM). To establish each patient's—suicidality profile, we applied two natural language processing (NLP) algorithms, independently developed at the two institutions, and detected suicidality in clinical notes documented in the first week of admission. We calculated the odds ratios (OR) for any subsequent readmission within 2 years postdischarge and determined whether this was to another eating disorder unit, other psychiatric unit, a general medical hospital admission or emergency room attendance.ResultsWe identified 1126 and 420 eating disorder inpatient admissions at WCM and SLaM, respectively. In the WCM cohort, evidence of above average suicidality during the first week of admission was significantly associated with an increased risk of noneating disorder‐related psychiatric readmission (OR 3.48 95% CI = 2.03–5.99, p‐value < .001), but a similar pattern was not observed in the SLaM cohort (OR 1.34, 95% CI = 0.75–2.37, p = .32), there was no significant increase in risk of admission. In both cohorts, personality disorder increased the risk of any psychiatric readmission within 2 years.DiscussionPatterns of increased risk of psychiatric readmission from above average suicidality detected via NLP during inpatient eating disorder admissions differed in our two patient cohorts. However, comorbid diagnoses such as personality disorder increased the risk of any psychiatric readmission across both cohorts.Public SignificanceSuicidality amongst is eating disorders is an extremely common presentation and it is important we further our understanding of identifying those most at risk. This research also provides a novel study design, comparing two NLP algorithms on electronic health record data based in the United States and United Kingdom on eating disorder inpatients. Studies researching both UK and US mental health patients are sparse therefore this study provides novel data.

Examining the role of artificial intelligence to advance knowledge and address barriers to research in eating disorders

“…Within DL applications, alternative AI approaches such as large language models (LLMs) incorporate natural language processing (NLP) to train computers to analyze and understand language and communication. Examples of NLP applications that have application to EDs include the extraction of information from electronic health records (EHRs), analysis of internet activity and social media posts, as well as chatbot development (Beilharz et al, 2021;Benítez-Andrades et al, 2022;Cliffe et al, 2023). A recent systematic review suggested that NLP is in a period of robust development in the medical field, with proportionately high representation from MH fields (Wang et al, 2020).…”

“…Examples of studies that have used AI to bolster ED knowledge by extracting information from large data sets have also become increasingly prevalent in the last 2 years. As an example, an international multi-site study that used NLP to explore the association between suicidality and hospital readmission to better predict rates of readmission of patients with EDs found that co-morbid conditions including the presence of a personality disorder were the most predictive risk factor for readmission (Cliffe et al, 2023).…”