Factors and Predictors of Revisit Among Psychiatric Consultation Patients in the Emergency Department: An Exploratory Study Using ChatGPT-Based Natural Language Analysis
10.4306/jknpa.2025.64.4.331
- Author:
Daol CHO
1
;
Kang KO
;
Jung Suk LEE
;
Byung Ook LEE
;
Sunyoung PARK
Author Information
1. Department of Psychiatry, Severance Hospital, Seoul, Korea
- Publication Type:ORIGINAL ARTICLE
- From:Journal of Korean Neuropsychiatric Association
2025;64(4):331-336
- CountryRepublic of Korea
- Language:English
-
Abstract:
Objectives:This study aimed to identify the factors associated with psychiatric emergency department revisits within three months through OpenAI-based natural language analysis of medical records and develop a predictive model for revisits.
Methods:A retrospective chart review was conducted by analyzing medical records of a general hospital over a 5-year period, from March 1, 2020 to March 1, 2025, using a GPT-4o mini API (application programming interface). The main reasons for psychiatric consultation were categorized into the top 10 causes, which included the following: suicidality (ideation or attempt), depressive symptoms, anxiety or panic symptoms, substance or alcohol use problem, cognitive or mental fluctuation, impulsiveness or aggression, sleep disturbance, psychiatric consultation for medical illness, acute psychosis symptoms, other or unspecified. The socioeconomic factor variables included economic difficulties, low educational attainment, family conflict or stress, interpersonal problems (work/ school), sexual assault or abuse, physical illness or chronic pain, and exposure to celebrity suicide.
Results:The revisit group had a significantly lower mean age than the control group, and the proportion of female patients was higher among those who revisited (adjusted residual [AR]=-2.00).Higher revisit rates were observed among patients with persistent mood disorder (AR=+11.18), acute stress or adjustment disorder (AR=+2.62), suicidality (AR=+6.94), sexual assault or abuse (AR=+6.80), and interpersonal problems (AR=+4.66). These were also presented as major predictive variables in the Shapley Additive exPlanations analysis.
Conclusion:The risk of revisits could be predicted using only the information from psychiatric emergency medical records. An automated prediction system based on the narrative data in the electronic medical records could be developed.
