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Clinician Perspectives and Design Implications in Using Patient-Generated Health Data to Improve Mental Health Practices: Mixed Methods Study
BACKGROUND: Patient-generated health data (PGHD) have been largely collected through mobile health (mHealth) apps and wearable devices. PGHD can be especially helpful in mental health, as patients’ illness history and symptom narratives are vital to developing diagnoses and treatment plans. However,...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442947/ https://www.ncbi.nlm.nih.gov/pubmed/32763884 http://dx.doi.org/10.2196/18123 |
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author | Wu, Danny T Y Xin, Chen Bindhu, Shwetha Xu, Catherine Sachdeva, Jyoti Brown, Jennifer L Jung, Heekyoung |
author_facet | Wu, Danny T Y Xin, Chen Bindhu, Shwetha Xu, Catherine Sachdeva, Jyoti Brown, Jennifer L Jung, Heekyoung |
author_sort | Wu, Danny T Y |
collection | PubMed |
description | BACKGROUND: Patient-generated health data (PGHD) have been largely collected through mobile health (mHealth) apps and wearable devices. PGHD can be especially helpful in mental health, as patients’ illness history and symptom narratives are vital to developing diagnoses and treatment plans. However, the extent to which clinicians use mental health–related PGHD is unknown. OBJECTIVE: A mixed methods study was conducted to understand clinicians’ perspectives on PGHD and current mental health apps. This approach uses information gathered from semistructured interviews, workflow analysis, and user-written mental health app reviews to answer the following research questions: (1) What is the current workflow of mental health practice and how are PGHD integrated into this workflow, (2) what are clinicians’ perspectives on PGHD and how do they choose mobile apps for their patients, (3) and what are the features of current mobile apps in terms of interpreting and sharing PGHD? METHODS: The study consists of semistructured interviews with 12 psychiatrists and clinical psychologists from a large academic hospital. These interviews were thematically and qualitatively analyzed for common themes and workflow elements. User-posted reviews of 56 sleep and mood tracking apps were analyzed to understand app features in comparison with the information gathered from interviews. RESULTS: The results showed that PGHD have been part of the workflow, but its integration and use are not optimized. Mental health clinicians supported the use of PGHD but had concerns regarding data reliability and accuracy. They also identified challenges in selecting suitable apps for their patients. From the app review, it was discovered that mHealth apps had limited features to support personalization and collaborative care as well as data interpretation and sharing. CONCLUSIONS: This study investigates clinicians’ perspectives on PGHD use and explored existing app features using the app review data in the mental health setting. A total of 3 design guidelines were generated: (1) improve data interpretation and sharing mechanisms, (2) consider clinical workflow and electronic health record integration, and (3) support personalized and collaborative care. More research is needed to demonstrate the best practices of PGHD use and to evaluate their effectiveness in improving patient outcomes. |
format | Online Article Text |
id | pubmed-7442947 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-74429472020-09-04 Clinician Perspectives and Design Implications in Using Patient-Generated Health Data to Improve Mental Health Practices: Mixed Methods Study Wu, Danny T Y Xin, Chen Bindhu, Shwetha Xu, Catherine Sachdeva, Jyoti Brown, Jennifer L Jung, Heekyoung JMIR Form Res Original Paper BACKGROUND: Patient-generated health data (PGHD) have been largely collected through mobile health (mHealth) apps and wearable devices. PGHD can be especially helpful in mental health, as patients’ illness history and symptom narratives are vital to developing diagnoses and treatment plans. However, the extent to which clinicians use mental health–related PGHD is unknown. OBJECTIVE: A mixed methods study was conducted to understand clinicians’ perspectives on PGHD and current mental health apps. This approach uses information gathered from semistructured interviews, workflow analysis, and user-written mental health app reviews to answer the following research questions: (1) What is the current workflow of mental health practice and how are PGHD integrated into this workflow, (2) what are clinicians’ perspectives on PGHD and how do they choose mobile apps for their patients, (3) and what are the features of current mobile apps in terms of interpreting and sharing PGHD? METHODS: The study consists of semistructured interviews with 12 psychiatrists and clinical psychologists from a large academic hospital. These interviews were thematically and qualitatively analyzed for common themes and workflow elements. User-posted reviews of 56 sleep and mood tracking apps were analyzed to understand app features in comparison with the information gathered from interviews. RESULTS: The results showed that PGHD have been part of the workflow, but its integration and use are not optimized. Mental health clinicians supported the use of PGHD but had concerns regarding data reliability and accuracy. They also identified challenges in selecting suitable apps for their patients. From the app review, it was discovered that mHealth apps had limited features to support personalization and collaborative care as well as data interpretation and sharing. CONCLUSIONS: This study investigates clinicians’ perspectives on PGHD use and explored existing app features using the app review data in the mental health setting. A total of 3 design guidelines were generated: (1) improve data interpretation and sharing mechanisms, (2) consider clinical workflow and electronic health record integration, and (3) support personalized and collaborative care. More research is needed to demonstrate the best practices of PGHD use and to evaluate their effectiveness in improving patient outcomes. JMIR Publications 2020-08-07 /pmc/articles/PMC7442947/ /pubmed/32763884 http://dx.doi.org/10.2196/18123 Text en ©Danny T Y Wu, Chen Xin, Shwetha Bindhu, Catherine Xu, Jyoti Sachdeva, Jennifer L Brown, Heekyoung Jung. Originally published in JMIR Formative Research (http://formative.jmir.org), 07.08.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on http://formative.jmir.org, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Wu, Danny T Y Xin, Chen Bindhu, Shwetha Xu, Catherine Sachdeva, Jyoti Brown, Jennifer L Jung, Heekyoung Clinician Perspectives and Design Implications in Using Patient-Generated Health Data to Improve Mental Health Practices: Mixed Methods Study |
title | Clinician Perspectives and Design Implications in Using Patient-Generated Health Data to Improve Mental Health Practices: Mixed Methods Study |
title_full | Clinician Perspectives and Design Implications in Using Patient-Generated Health Data to Improve Mental Health Practices: Mixed Methods Study |
title_fullStr | Clinician Perspectives and Design Implications in Using Patient-Generated Health Data to Improve Mental Health Practices: Mixed Methods Study |
title_full_unstemmed | Clinician Perspectives and Design Implications in Using Patient-Generated Health Data to Improve Mental Health Practices: Mixed Methods Study |
title_short | Clinician Perspectives and Design Implications in Using Patient-Generated Health Data to Improve Mental Health Practices: Mixed Methods Study |
title_sort | clinician perspectives and design implications in using patient-generated health data to improve mental health practices: mixed methods study |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442947/ https://www.ncbi.nlm.nih.gov/pubmed/32763884 http://dx.doi.org/10.2196/18123 |
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