TY - JOUR
T1 - A systematic framework for analyzing patient-generated narrative data
T2 - Protocol for a content analysis
AU - Zolnoori, Maryam
AU - Balls-Berry, Joyce E.
AU - Brockman, Tabetha A.
AU - Patten, Christi A.
AU - Huang, Ming
AU - Yao, Lixia
N1 - Funding Information:
This publication was supported by CTSA grant number TL1 TR002380 from the National Center for Advancing Translational Science. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health.
Publisher Copyright:
© Maryam Zolnoori, Joyce E Balls-Berry, Tabetha A Brockman, Christi A Patten, Ming Huang, Lixia Yao. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 26.08.2019. 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 Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on http://www.researchprotocols.org, as well as this copyright and license information must be included.
PY - 2019/8
Y1 - 2019/8
N2 - Background: Patient narrative data in online health care forums (communities) are receiving increasing attention from the scientific community for implementing patient-centered care. Natural language processing (NLP) methods are gaining more and more attention because of the enormous data volume. However, state-of-the-art NLP still cannot meet the need of high-resolution analysis of patients’ narratives. Manual qualitative analysis still plays a pivotal role in answering complicated research questions from analyzing patient narratives. Objective: This study aimed to develop a systematic framework for qualitative analysis of patient-generated narratives in online health care forums. Methods: Our systematic framework consists of 4 phases: (1) data collection, (2) data preparation, (3) content analysis, and (4) interpretation of the results. Data collection and data preparation phases are constructed based on text mining methods for identifying appropriate online health forums for data collection, differentiating posts of patients from other stakeholders, protecting patients’ privacy, sampling, and choosing the unit of analysis. Content analysis phase is built on the framework method, which facilitates and accelerates the identification of patterns and themes by an interdisciplinary research team. In the end, the focus of interpretation of the results phase is to measure the data quality and interpret the findings regarding the dimensions and aspects of patients’ experiences and concerns in their original contexts. Results: We demonstrated the usability of the proposed systematic framework using 2 case studies: one on determining factors affecting patients’ attitudes toward antidepressants and another on identifying the disease management strategies in patient with diabetes facing financial difficulties. The framework provides a clear step-by-step process for systematic content analysis of patient narratives and produces high-quality structured results that can be used for describing patterns or regularities in patients’ experiences, generating and testing hypotheses, and identifying areas of improvement in the health care systems. Conclusions: The systematic framework is a rigorous and standardized method for qualitative analysis of patient narratives. Findings obtained through such a process indicate authentic dimensions and aspects of patient experiences and shed light on patients’ concerns, needs, preferences, and values, which are the core of patient-centered care.
AB - Background: Patient narrative data in online health care forums (communities) are receiving increasing attention from the scientific community for implementing patient-centered care. Natural language processing (NLP) methods are gaining more and more attention because of the enormous data volume. However, state-of-the-art NLP still cannot meet the need of high-resolution analysis of patients’ narratives. Manual qualitative analysis still plays a pivotal role in answering complicated research questions from analyzing patient narratives. Objective: This study aimed to develop a systematic framework for qualitative analysis of patient-generated narratives in online health care forums. Methods: Our systematic framework consists of 4 phases: (1) data collection, (2) data preparation, (3) content analysis, and (4) interpretation of the results. Data collection and data preparation phases are constructed based on text mining methods for identifying appropriate online health forums for data collection, differentiating posts of patients from other stakeholders, protecting patients’ privacy, sampling, and choosing the unit of analysis. Content analysis phase is built on the framework method, which facilitates and accelerates the identification of patterns and themes by an interdisciplinary research team. In the end, the focus of interpretation of the results phase is to measure the data quality and interpret the findings regarding the dimensions and aspects of patients’ experiences and concerns in their original contexts. Results: We demonstrated the usability of the proposed systematic framework using 2 case studies: one on determining factors affecting patients’ attitudes toward antidepressants and another on identifying the disease management strategies in patient with diabetes facing financial difficulties. The framework provides a clear step-by-step process for systematic content analysis of patient narratives and produces high-quality structured results that can be used for describing patterns or regularities in patients’ experiences, generating and testing hypotheses, and identifying areas of improvement in the health care systems. Conclusions: The systematic framework is a rigorous and standardized method for qualitative analysis of patient narratives. Findings obtained through such a process indicate authentic dimensions and aspects of patient experiences and shed light on patients’ concerns, needs, preferences, and values, which are the core of patient-centered care.
KW - Deductive approach
KW - Framework method
KW - Inductive approach
KW - Online social networking
KW - Patient-centered care
KW - Qualitative research
KW - Social media
KW - Text mining
UR - http://www.scopus.com/inward/record.url?scp=85089984265&partnerID=8YFLogxK
U2 - 10.2196/13914
DO - 10.2196/13914
M3 - Article
C2 - 31452524
AN - SCOPUS:85089984265
SN - 1929-0748
VL - 8
JO - JMIR Research Protocols
JF - JMIR Research Protocols
IS - 8
M1 - 13914
ER -