Clinical usefulness of remote patient monitoring using e-Health technologies in patients with inflammatory bowel diseases

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.

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Wearable sensor derived decompensation index for continuous remote monitoring of COVID-19 diagnosed patients

npj Digital Medicine ◽

10.1038/s41746-021-00527-z ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Dylan M. Richards ◽

MacKenzie J. Tweardy ◽

Steven R. Steinhubl ◽

David W. Chestek ◽

Terry L. Vanden Hoek ◽

...

Keyword(s):

Machine Learning ◽

Patient Monitoring ◽

Standard Of Care ◽

Sensor Data ◽

Study Cohort ◽

Remote Patient Monitoring ◽

Health Technologies ◽

Phase Study ◽

Remote Patient ◽

Continuous Sensor

AbstractThe COVID-19 pandemic has accelerated the adoption of innovative healthcare methods, including remote patient monitoring. In the setting of limited healthcare resources, outpatient management of individuals newly diagnosed with COVID-19 was commonly implemented, some taking advantage of various personal health technologies, but only rarely using a multi-parameter chest-patch for continuous monitoring. Here we describe the development and validation of a COVID-19 decompensation index (CDI) model based on chest patch-derived continuous sensor data to predict COVID-19 hospitalizations in outpatient-managed COVID-19 positive individuals, achieving an overall AUC of the ROC Curve of 0.84 on 308 event negative participants, and 22 event positive participants, out of an overall study cohort of 400 participants. We retrospectively compare the performance of CDI to standard of care modalities, finding that the machine learning model outperforms the standard of care modalities in terms of both numbers of events identified and with a lower false alarm rate. While only a pilot phase study, the CDI represents a promising application of machine learning within a continuous remote patient monitoring system.

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Development and Validation of an Inflammatory Bowel Diseases Monitoring Index for Use With Mobile Health Technologies

Clinical Gastroenterology and Hepatology ◽

10.1016/j.cgh.2015.10.035 ◽

2016 ◽

Vol 14 (12) ◽

pp. 1742-1750.e7 ◽

Cited By ~ 34

Author(s):

Welmoed K. Van Deen ◽

Andrea E. van der Meulen-de Jong ◽

Nimisha K. Parekh ◽

Ellen Kane ◽

Aria Zand ◽

...

Keyword(s):

Mobile Health ◽

Inflammatory Bowel Diseases ◽

Health Technologies ◽

Bowel Diseases ◽

Inflammatory Bowel ◽

Monitoring Index ◽

Development And Validation

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An Exploration Into the Use of a Chatbot for Patients With Inflammatory Bowel Diseases: Retrospective Cohort Study (Preprint)

10.2196/preprints.15589 ◽

2019 ◽

Author(s):

Aria Zand ◽

Arjun Sharma ◽

Zack Stokes ◽

Courtney Reynolds ◽

Alberto Montilla ◽

...

Keyword(s):

Los Angeles ◽

Language Processing ◽

Inflammatory Bowel Diseases ◽

Tertiary Referral Center ◽

Health Technologies ◽

Management Platform ◽

Bowel Diseases ◽

Inflammatory Bowel ◽

Laboratory Investigations ◽

The University

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 (<i>UCLA eIBD</i>) 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.

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Research-Based Product Innovation to Address Critical Unmet Needs of Patients with Inflammatory Bowel Diseases

Inflammatory Bowel Diseases ◽

10.1093/ibd/izab230 ◽

2021 ◽

Vol 27 (Supplement_2) ◽

pp. S1-S16

Author(s):

Gerard Honig ◽

Paul B Larkin ◽

Caren Heller ◽

Andrés Hurtado-Lorenzo

Keyword(s):

Inflammatory Bowel Disease ◽

Inflammatory Bowel Diseases ◽

Bowel Disease ◽

Unmet Needs ◽

Digital Health ◽

Scientific Conference ◽

Health Technologies ◽

Bowel Diseases ◽

Investigational Therapies ◽

Inflammatory Bowel

Abstract Despite progress in recent decades, patients with inflammatory bowel diseases face many critical unmet needs, demonstrating the limitations of available treatment options. Addressing these unmet needs will require interventions targeting multiple aspects of inflammatory bowel disease pathology, including disease drivers that are not targeted by available therapies. The vast majority of late-stage investigational therapies also focus primarily on a narrow range of fundamental mechanisms. Thus, there is a pressing need to advance to clinical stage differentiated investigational therapies directly targeting a broader range of key mechanistic drivers of inflammatory bowel diseases. In addition, innovations are critically needed to enable treatments to be tailored to the specific underlying abnormal biological pathways of patients; interventions with improved safety profiles; biomarkers to develop prognostic, predictive, and monitoring tests; novel devices for nonpharmacological approaches such as minimally invasive monitoring; and digital health technologies. To address these needs, the Crohn’s & Colitis Foundation launched IBD Ventures, a venture philanthropy–funding mechanism, and IBD Innovate®, an innovative, product-focused scientific conference. This special IBD Innovate® supplement is a collection of articles reflecting the diverse and exciting research and development that is currently ongoing in the inflammatory bowel disease field to deliver innovative and differentiated products addressing critical unmet needs of patients. Here, we highlight the pipeline of new product opportunities currently advancing at the preclinical and early clinical development stages. We categorize and describe novel and differentiated potential product opportunities based on their potential to address the following critical unmet patient needs: (1) biomarkers for prognosis of disease course and prediction/monitoring of treatment response; (2) restoration of eubiosis; (3) restoration of barrier function and mucosal healing; (4) more effective and safer anti-inflammatories; (5) neuromodulatory and behavioral therapies; (6) management of disease complications; and (7) targeted drug delivery.

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