Evidence map›Paper›PMID 31452524›Full record

ArticleJMIR research protocols2019

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

Maryam Zolnoori, Joyce E Balls-Berry, Tabetha A Brockman, Christi A Patten, Ming Huang, Lixia Yao

Open access · goldAbstract read
In one paragraph

Article in JMIR research protocols, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
1.3field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.

  1. Pooled it
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  9. Tracking public opinion about online education over COVID-19 in China.Educational technology research and development : ETR & D · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Maryam ZolnooriDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, United States.ORCID http://orcid.org/0000-0003-4484-2990
Joyce E Balls-BerryDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, United States.ORCID http://orcid.org/0000-0003-3497-1115
Tabetha A BrockmanCommunity Engagement Program, Center for Clinical and Translational Science, Mayo Clinic, Rochester, MN, United States.ORCID http://orcid.org/0000-0003-4008-4395
Christi A PattenCommunity Engagement Program, Center for Clinical and Translational Science, Mayo Clinic, Rochester, MN, United States.ORCID http://orcid.org/0000-0002-7194-8160
Ming HuangDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, United States.ORCID http://orcid.org/0000-0001-7367-3626
Lixia YaoDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, United States.ORCID http://orcid.org/0000-0002-5187-6120
Mayo Clinic · USMayo Clinic in Florida · US

Funding

NRSA Training CoreTL1TR002380 · NCATS · MAYO CLINIC ROCHESTER · PI Felicity T. B. Enders, ANTHONY John WINDEBANK · 2017 to 2026
$6.0M
NCATS NIH HHS TL1 TR002380
6 · The paper itself

Abstract

backgroundPatient 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.

objectiveThis study aimed to develop a systematic framework for qualitative analysis of patient-generated narratives in online health care forums.

methodsOur 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.

resultsWe 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.

conclusionsThe 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.

Indexed as

deductive approachframework methodinductive approachonline social networkingpatient-centered carequalitative researchsocial mediatext mining

Identifiers

PMID31452524
PMCPMC6786846
OpenAlexW2963423709

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.