CC BY-NC-ND 4.0 · Appl Clin Inform 2020; 11(05): 769-784
DOI: 10.1055/s-0040-1718755
Research Article

Augmenting the Clinical Data Sources for Enigmatic Diseases: A Cross-Sectional Study of Self-Tracking Data and Clinical Documentation in Endometriosis

Ipek Ensari
1  Data Science Institute, Columbia University, New York, New York, United States
,
Adrienne Pichon
2  Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, United States
,
Sharon Lipsky-Gorman
2  Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, United States
,
Suzanne Bakken
2  Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, United States
3  Columbia School of Nursing, Columbia University, New York, New York, United States
,
Noémie Elhadad
2  Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, United States
› Author Affiliations
Funding This study received financial support from Columbia University Data Science Institute Postdoctoral Fellowship, Endometriosis Foundation of America, National Science Foundation (grant number: 1344668), and from National Institutes of Health, U.S. National Library of Medicine (grant number: R01 LM013043).
  

Abstract

Background Self-tracking through mobile health technology can augment the electronic health record (EHR) as an additional data source by providing direct patient input. This can be particularly useful in the context of enigmatic diseases and further promote patient engagement.

Objectives This study aimed to investigate the additional information that can be gained through direct patient input on poorly understood diseases, beyond what is already documented in the EHR.

Methods This was an observational study including two samples with a clinically confirmed endometriosis diagnosis. We analyzed data from 6,925 women with endometriosis using a research app for tracking endometriosis to assess prevalence of self-reported pain problems, between- and within-person variability in pain over time, endometriosis-affected tasks of daily function, and self-management strategies. We analyzed data from 4,389 patients identified through a large metropolitan hospital EHR to compare pain problems with the self-tracking app and to identify unique data elements that can be contributed via patient self-tracking.

Results Pelvic pain was the most prevalent problem in the self-tracking sample (57.3%), followed by gastrointestinal-related (55.9%) and lower back (49.2%) pain. Unique problems that were captured by self-tracking included pain in ovaries (43.7%) and uterus (37.2%). Pain experience was highly variable both across and within participants over time. Within-person variation accounted for 58% of the total variance in pain scores, and was large in magnitude, based on the ratio of within- to between-person variability (0.92) and the intraclass correlation (0.42). Work was the most affected daily function task (49%), and there was significant within- and between-person variability in self-management effectiveness. Prevalence rates in the EHR were significantly lower, with abdominal pain being the most prevalent (36.5%).

Conclusion For enigmatic diseases, patient self-tracking as an additional data source complementary to EHR can enable learning from the patient to more accurately and comprehensively evaluate patient health history and status.

Protection of Human and Animal Subjects

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. All procedures followed were in accordance with ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Informed consent was obtained from all patients for being included in the study.


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Supplementary Material



Publication History

Received: 17 May 2020

Accepted: 14 July 2020

Publication Date:
18 November 2020 (online)

© 2020. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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