Derakshani, Donya

INTRODUCTION: Online patient education materials are essential in improving access to and understanding of clinical information. Artificial intelligence (AI), specifically ChatGPT, offers a novel way to synthesize complex information and is increasingly being used for patient education, including in obstetrics and gynecology. This study compares the understandability and actionability of patient education materials about hysterectomy and uterine fibroids generated by ChatGPT with those published by the American College of Obstetricians & Gynecologists (ACOG). METHODS: In a randomized cross-sectional study, participants were allocated to one of four groups: ACOG–Fibroids, ChatGPT–Fibroids, ACOG–Hysterectomy, or ChatGPT–Hysterectomy. The ACOG groups reviewed the corresponding ACOG FAQ webpage, while the ChatGPT groups reviewed responses generated by ChatGPT to the same FAQs. Participants then completed demographic questions and the Patient Education Materials Assessment Tool (PEMAT). The primary outcome was the equivalence of PEMAT scores between ACOG and ChatGPT. RESULTS: Equivalence was not demonstrated between ChatGPT and ACOG for either topic. For fibroid material, ACOG was rated higher on both understandability (86.3 versus 82.6 points, P =.039) and actionability (65.4 versus 53.2 points, P =.003) on superiority testing. For hysterectomy content, no significant difference was observed. No interaction was found between material type and either education level or personal history of fibroids/hysterectomy. CONCLUSIONS/IMPLICATIONS: PEMAT scores for ACOG were superior to ChatGPT for fibroid content, but neither equivalent nor superior for hysterectomy content. While ChatGPT offers a responsive, iterative source of information, its understandability and actionability may depend on topic-specific factors, highlighting both the promise and limitations of AI for patient education.

Abstract Introduction Pelvic inflammatory disease (PID) is a common and potentially serious reproductive health condition that may require hospitalization for proper management. Accurate prediction of the length of stay (LOS) for PID hospitalizations can aid clinicians in resource allocation, patient counseling, and improving overall patient care. Prior studies have explored potential risk factors for a poor clinical course among women hospitalized with PID, but to our knowledge, no study to date has explored potential predictors of LOS in PID hospitalization. Objective This study aims to characterize the predictors for LOS in PID hospitalizations based on patient and hospital characteristics using a large national database. Methods The National Inpatient Sample (NIS) database was queried from 2019 to 2020 for all female patients 18 years and older with a primary diagnosis of acute pelvic inflammatory disease using ICD-10 codes. Data on patient demographics, clinical characteristics, comorbidities, and hospital-related variables were collected using variables as defined by NIS and summarized using descriptive statistics. Bivariate and multivariable associations between LOS and covariates were assessed using linear regression models and estimates were reported as regression coefficients. Covariates that were statistically significant in the bivariate models were included as covariates during the multivariate regression analysis. Results A total of 14,505 hospitalized female patients with a principal diagnosis code for acute PID were included. Mean LOS for PID hospitalization was 5.8 days, with a range of 0 – 88 days. The average patient age was 42.9 years (age range 18 – 90 years old), with 55.1% White, 23.4% Black, 12.5% Hispanic, and 2.3% Asian or Pacific Islander. The main primary payers included Medicaid (40.0%), private insurance (36.1%), Medicare (19.1%), and self-pay (4.7%). About 34% of patients were in the lowest quartile for median household income, 45% in the middle two quartiles, and 19% in the highest quartile. In univariable analysis, increased LOS was associated with increasing age, larger hospital bed size, urban teaching hospital status, and the presence of hypertension, diabetes, obesity, or immunodeficiency. Decreased LOS was associated with Hispanic or Native American race and having a non-Medicare primary payer. In multivariable analysis, increased LOS was associated with increasing age (+0.06 days per increasing year, p < 0.0001) and large hospital bed size (+0.6 days compared to small bed size, p = 0.03). Decreased LOS was associated with Asian or Pacific Islander race (-1.1 days compared to White race, p = 0.02), Native American race (-1.4 days compared to White race, p = 0.03), and being billed as ‘no charge’ (-2.1 days compared to Medicare, p = 0.03). Conclusions This study identifies possible predictors of LOS in PID hospitalization, providing insights that may help optimize resource allocation and better characterize factors that may impact PID management. Several factors, including age, race, expected primary payer, and bed size of hospital were found to significantly impact LOS. Additionally, this study highlights the potential role of social determinants of health in PID hospitalization and management and can help guide future research and initiatives aimed at improving population health and addressing disparities in PID. Disclosure No.