We developed and validated a scoring system to monitor disease activity in patients with CD and UC that can be used with mobile technologies. The indices identified clinical disease activity with area under the receiver operating characteristic curve values of 0.9 or higher in patients with CD or UC, and endoscopic activity in patients with UC but not CD.

Patients with IBD in clinical remission still cope with significantly more presenteeism and work limitations than controls; this translates in higher indirect costs and decreased quality of life. The majority have not made any adjustments to battle these problems.

Background The emergence of chatbots in health care is fast approaching. Data on the feasibility of chatbots for chronic disease management are scarce. Objective This study aimed to explore the feasibility of utilizing natural language processing (NLP) for the categorization of electronic dialog data of patients with inflammatory bowel diseases (IBD) for use in the development of a chatbot. Methods Electronic dialog data collected between 2013 and 2018 from a care management platform (UCLA eIBD) at a tertiary referral center for IBD at the University of California, Los Angeles, were used. Part of the data was manually reviewed, and an algorithm for categorization was created. The algorithm categorized all relevant dialogs into a set number of categories using NLP. In addition, 3 independent physicians evaluated the appropriateness of the categorization. Results A total of 16,453 lines of dialog were collected and analyzed. We categorized 8324 messages from 424 patients into seven categories. As there was an overlap in these categories, their frequencies were measured independently as symptoms (2033/6193, 32.83%), medications (2397/6193, 38.70%), appointments (1518/6193, 24.51%), laboratory investigations (2106/6193, 34.01%), finance or insurance (447/6193, 7.22%), communications (2161/6193, 34.89%), procedures (617/6193, 9.96%), and miscellaneous (624/6193, 10.08%). Furthermore, in 95.0% (285/300) of cases, there were minor or no differences in categorization between the algorithm and the three independent physicians. Conclusions With increased adaptation of electronic health technologies, chatbots could have great potential in interacting with patients, collecting data, and increasing efficiency. Our categorization showcases the feasibility of using NLP in large amounts of electronic dialog for the development of a chatbot algorithm. Chatbots could allow for the monitoring of patients beyond consultations and potentially empower and educate patients and improve clinical outcomes.

Background Caregiver burden is the emotional, physical, practical, and/or financial burden associated with taking care of a patient with a chronic condition. Limited literature on caregiver burden in Inflammatory Bowel Diseases (IBD) has accounted for some predictors, but its effect on work productivity (absenteeism and presenteeism) is unknown. Methods In a prospective study, patients and their respective caregivers were surveyed from November 2015 until July 2017. Data on demographics, work productivity, quality of life, disease activity, caregiver burden and productivity were collected. The burden on caregivers was assessed and associations between caregiver productivity and caregiver burden were analyzed. Additionally, predictors for caregiver burden were identified. Results One hundred two IBD patients and their respective caregiver were included. In total, 39% of IBD caregivers experienced burden. Caregivers with burden experienced significantly more absenteeism and presenteeism (65 and 85% respectively). Furthermore, 51% of caregivers felt that they should be doing more for their care recipient and felt they could do a better job at caregiving. Predictors of burden included race/ethnicity, history of fistulas, diagnosis of ulcerative colitis, higher caregiver education, and hours spent caregiving. Conclusion Caregivers with burden had significantly more productivity decrease compared to those without burden. Additionally, the majority of caregivers feel they should be providing more and better care for their recipients. The development of strategies to address caregiver’s distress and perceived burden when caring for IBD patients is warranted.

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Background Inflammatory Bowel Diseases with its complexity and heterogeneity could benefit from the increased application of Artificial Intelligence in clinical management. Aim To accurately predict adverse outcomes in patients with IBD using advanced computational models in a nationally representative dataset for potential use in clinical practice. Methods We built a training model cohort and validated our result in a separate cohort. We used LASSO and Ridge regressions, Support Vector Machines, Random Forests and Neural Networks to balance between complexity and interpretability and analyzed their relative performances and reported the strongest predictors to the respective models. The participants in our study were patients with IBD selected from The OptumLabs® Data Warehouse (OLDW), a longitudinal, real-world data asset with de-identified administrative claims and electronic health record (EHR) data. Results We included 72,178 and 69,165 patients in the training and validation set, respectively. In total, 4.1% of patients in the validation set were hospitalized, 2.9% needed IBD-related surgeries, 17% used long-term steroids and 13% of patients were initiated with biological therapy. Of the AI models we tested, the Random Forest and LASSO resulted in high accuracies (AUCs 0.70–0.92). Our artificial neural network performed similarly well in most of the models (AUCs 0.61–0.90). Conclusions This study demonstrates feasibility of accurately predicting adverse outcomes using complex and novel AI models on large longitudinal data sets of patients with IBD. These models could be applied for risk stratification and implementation of preemptive measures to avoid adverse outcomes in a clinical setting. Supplementary Information The online version contains supplementary material available at 10.1007/s10620-022-07506-8.

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Autologous hematopoietic stem cells are gaining ground as an effective and safe treatment for treating severe refractory Crohn's disease (CD). Autologous hematopoietic stem cell therapy (AHSCT) induces resetting of the immune system by de novo regeneration of T-cell repertoire and repopulation of epithelial cells by bone-marrow derived cells to help patients achieve clinical and endoscopic remission. Areas covered: Herein, the authors discuss the use of AHSCT in treating patients with CD. Improvements in disease activity have been seen in patients with severe autoimmune disease and patients with severe CD who underwent AHSCT for a concomitant malignant hematological disease. Clinical and endoscopic remission has been achieved in patients treated with AHSCT for CD. The only randomized trial published to date, the ASTIC Trial, did not support further use of AHSCT to treat CD. Yet, critics of this trial have deemed AHSCT as a promising treatment for severe refractory CD. Expert opinion: Even with the promising evidence presented for HSCT for refractory CD, protocols need to be refined through the collaboration of GI and hemato-oncology professionals. The goal is to incorporate safe AHSCT and restore tolerance by delivering an effective immune 'cease fire' as a treatment option for severe refractory CD.