Berry, Sameer

INTRODUCTION: Chronic gastrointestinal (GI) conditions, including irritable bowel syndrome and other disorders of gut-brain interaction, are prevalent and costly, and fragmented care contributes to suboptimal outcomes. Virtual and multidisciplinary care models may improve outcomes. Objectives: We aimed to (i) characterize patients receiving virtual GI care, (ii) report patient engagement with and patient-reported outcomes after virtual multidisciplinary care, and (iii) report healthcare utilization and costs from a payer perspective among patients vs propensity score-matched controls identified from claims data. METHODS: We collected descriptive data on patient demographics, clinic provider visits, symptoms, and patient-reported outcomes from 3/2021 to 9/2022. To evaluate healthcare utilization and costs, we conducted difference-in-difference analyses using claims data compared with a propensity score-matched control group. RESULTS: Among 234 patients (71% women, mean age 45.4 ± 13.2 years), 51% received a new GI diagnosis, predominately disorders of gut-brain interaction (63%). Engagement criteria were met by 80% (n = 187), who completed a mean of 10 visits and median time-to-first appointment of 6 days. Dietitians (93%), psychologists (76%), and health coaches (64%) were frequently used. Engaged patients exhibited significant improvements from preclinic to postclinic in irritable bowel syndrome severity (226.7–145.6), symptom control (20.2%–86.6%), quality-of-life dysphoria (27.5–32.8), missed workdays (1.0–0.7), and satisfaction (37.4%–96.3%) (all P < 0.05). Compared with matched controls, a significantly smaller percent of patients had imaging (15% vs 44%; P < 0.0001) and GI-related emergency department visits (4% vs 10%; P = 0.0028), which contributed to reductions in GI-related costs ($443 per member per month; P = 0.047) and all-cause costs ($676 per member per month; P = 0.043), equating to annualized savings of $5,316 and $8,112, respectively. DISCUSSION: Virtual multidisciplinary GI care was associated with high engagement, improved clinical outcomes, and reduced healthcare utilization and costs.

Abstract Background and Aims Lack of clinical validation and inter-observer variability are two limitations of endoscopic assessment and scoring of disease severity in patients with ulcerative colitis [UC]. We developed a deep learning [DL] model to improve, accelerate and automate UC detection, and predict the Mayo Endoscopic Subscore [MES] and the Ulcerative Colitis Endoscopic Index of Severity [UCEIS]. Methods A total of 134 prospective videos [1550 030 frames] were collected and those with poor quality were excluded. The frames were labelled by experts based on MES and UCEIS scores. The scored frames were used to create a preprocessing pipeline and train multiple convolutional neural networks [CNNs] with proprietary algorithms in order to filter, detect and assess all frames. These frames served as the input for the DL model, with the output being continuous scores for MES and UCEIS [and its components]. A graphical user interface was developed to support both labelling video sections and displaying the predicted disease severity assessment by the artificial intelligence from endoscopic recordings. Results Mean absolute error [MAE] and mean bias were used to evaluate the distance of the continuous model’s predictions from ground truth, and its possible tendency to over/under-predict were excellent for MES and UCEIS. The quadratic weighted kappa used to compare the inter-rater agreement between experts’ labels and the model’s predictions showed strong agreement [0.87, 0.88 at frame-level, 0.88, 0.90 at section-level and 0.90, 0.78 at video-level, for MES and UCEIS, respectively]. Conclusions We present the first fully automated tool that improves the accuracy of the MES and UCEIS, reduces the time between video collection and review, and improves subsequent quality assurance and scoring.