Patient Experience and Satisfaction with an e-Health Care Management Application for Inflammatory Bowel Diseases

Background: Rising healthcare expenditures have been partially attributed to suboptimal management of inflammatory bowel diseases (IBD). Electronic health interventions may help improve care management for IBD patients, but there is a need to better understand patient perspectives on these emerging technologies. Aims: The primary aim was to evaluate patient satisfaction and experience with the UCLA eIBD mobile application, an integrative care management platform with disease activity monitoring tools and educational modules. The secondary objective was to capture patient feedback on how to improve the mobile application. Methods: We surveyed IBD patients treated at the UCLA Center for Inflammatory Bowel Diseases. The patient experience survey assessed the patients’ overall satisfaction with the application, perception of health outcomes after participation in the program, and feedback on educational modules as well as areas for application improvement. Results: 50 patients were included. The responses indicated that the patients were greatly satisfied with the ease of patient–provider communication within the application and appointment scheduling features (68%). A majority of respondents (54%) also reported that program participation resulted in improved perception of disease control and quality of life. Lastly, a majority of participants (79%) would recommend this application to others. Conclusions: Mobile tools such as UCLA eIBD have promising implications for integration into patients’ daily lives. This patient satisfaction study suggests the feasibility of using this mobile application by patients and providers. We further showed that UCLA eIBD and its holistic approach led to improved patient experience and satisfaction, which can provide useful recommendations for future electronic health solutions.

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

Journal of Medical Internet Research ◽

10.2196/15589 ◽

2020 ◽

Vol 22 (5) ◽

pp. e15589 ◽

Cited By ~ 1

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 (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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Use of Narrative Concepts in Electronic Health Records to Validate Associations Between Genetic Factors and Response to Treatment of Inflammatory Bowel Diseases

Clinical Gastroenterology and Hepatology ◽

10.1016/j.cgh.2019.08.005 ◽

2020 ◽

Vol 18 (8) ◽

pp. 1890-1892 ◽

Cited By ~ 2

Author(s):

Ashwin N. Ananthakrishnan ◽

Andrew Cagan ◽

Tianxi Cai ◽

Vivian S. Gainer ◽

Guergana Savova ◽

...

Keyword(s):

Electronic Health Records ◽

Inflammatory Bowel Diseases ◽

Genetic Factors ◽

Response To Treatment ◽

Health Records ◽

Bowel Diseases ◽

Inflammatory Bowel ◽

Electronic Health

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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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