Dataset Information

A Systematic Framework for Analyzing Patient-Generated Narrative Data: Protocol for a Content Analysis.

ABSTRACT: 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. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID):RR1-10.2196/13914.

SUBMITTER: Zolnoori M

PROVIDER: S-EPMC6786846 | biostudies-other | 2019 Aug

REPOSITORIES: biostudies-other

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A Systematic Framework for Analyzing Patient-Generated Narrative Data: Protocol for a Content Analysis.

Zolnoori Maryam M Balls-Berry Joyce E JE Brockman Tabetha A TA Patten Christi A CA Huang Ming M Yao Lixia L

JMIR research protocols 20190826 8

<h4>Background</h4>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 resea ...[more]

PMID: 31452524

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Project description:BackgroundPatient-centered registries are essential in population-based clinical care for patient identification and monitoring of outcomes. Although registry data may be used in real time for patient care, the same data may further be used for secondary analysis to assess disease burden, evaluation of disease management and health care services, and research. The design of a registry has major implications for the ability to effectively use these clinical data in research.ObjectiveThis study aims to develop a systematic framework to address the data and methodological issues involved in analyzing data in clinically designed patient-centered registries.MethodsThe systematic framework was composed of 3 major components: visualizing the multifaceted and heterogeneous patient-centered registries using a data flow diagram, assessing and managing data quality issues, and identifying patient cohorts for addressing specific research questions.ResultsUsing a clinical registry designed as a part of a collaborative care program for adults with depression at Mayo Clinic, we were able to demonstrate the impact of the proposed framework on data integrity. By following the data cleaning and refining procedures of the framework, we were able to generate high-quality data that were available for research questions about the coordination and management of depression in a primary care setting. We describe the steps involved in converting clinically collected data into a viable research data set using registry cohorts of depressed adults to assess the impact on high-cost service use.ConclusionsThe systematic framework discussed in this study sheds light on the existing inconsistency and data quality issues in patient-centered registries. This study provided a step-by-step procedure for addressing these challenges and for generating high-quality data for both quality improvement and research that may enhance care and outcomes for patients.International registered report identifier (irrid)DERR1-10.2196/18366.

Project description:BackgroundVarious patient support programs exist to provide successful therapy options for patients. Pharmaceutical companies are increasingly recognizing the importance of actively supporting patients in their long-term treatment. In order to effectively assist patients, it is crucial to understand their current needs by taking a look at the patients' opinions.ObjectiveThis study focuses specifically on chronic myeloid leukemia (CML) and aims to determine if the current patient engagement offerings from pharmaceutical companies adequately address the needs of CML patients. To achieve this, the study uses content generated by CML patients to assess the patient engagement strategies of selected pharmaceutical companies, explore the relevance of medication, their products, and services, and analyze key concerns from the perspective of the patients.MethodsTo address the research questions, various methodologies were employed. Initially, desk research was conducted to identify relevant pharmaceutical companies and internet forums related to CML. Subsequently, content generated by patients was acquired and AI-driven techniques such as topic modeling and topic evolution analyses were used to examine this user-generated content (UGC) within the identified public forums. This involved analyzing topic models and tracking topic changes over time.ResultsThe desk research revealed that pharmaceutical companies primarily offer information about the disease and available treatment options. The UGC analysis confirmed the significant role played by the industry in supporting CML patients. Key areas of interest for patients include the disease itself, potential treatment methods and associated side effects, dosage of active substances, and the possibility of switching therapies due to treatment failure or resistance. Stem cell transplantation was also discussed.ConclusionsOverall, the pharmaceutical industry adequately addresses the needs of CML patients. However, there is room for improvement in educating patients about treatment options, drugs, and their side effects. Psychological support should not be neglected. Since CML patients frequently engage with clinical trial outcomes, there is potential for increased patient involvement in such trials. Further research in this area is recommended.

Dataset Information

A Systematic Framework for Analyzing Patient-Generated Narrative Data: Protocol for a Content Analysis.

Publications

A Systematic Framework for Analyzing Patient-Generated Narrative Data: Protocol for a Content Analysis.

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