paper-with-me

홈 › Papers

Revealing Patient-Reported Experiences in Healthcare from Social Media using the DAPMAV Framework

2022-10-09 · Curtis Murray, Lewis Mitchell, Jonathan Tuke, Mark Mackay

Understanding patient experience in healthcare is increasingly important and desired by medical professionals in a patient-centered care approach. Healthcare discourse on social media presents an opportunity to gain a unique perspective on patient-reported experiences, complementing traditional survey data. These social media reports often appear as first-hand accounts of patients' journeys through the healthcare system, whose details extend beyond the confines of structured surveys and at a far larger scale than focus groups. However, in contrast with the vast presence of patient-experience data on social media and the potential benefits the data offers, it attracts comparatively little research attention due to the technical proficiency required for text analysis. In this paper, we introduce the Design-Acquire-Process-Model-Analyse-Visualise (DAPMAV) framework to provide an overview of techniques and an approach to capture patient-reported experiences from social media data. We apply this framework in a case study on prostate cancer data from /r/ProstateCancer, demonstrate the framework's value in capturing specific aspects of patient concern (such as sexual dysfunction), provide an overview of the discourse, and show narrative and emotional progression through these stories. We anticipate this framework to apply to a wide variety of areas in healthcare, including capturing and differentiating experiences across minority groups, geographic boundaries, and types of illnesses.

📄 PDF Abstract BibTeX arXiv:2210.04232

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

A Sentiment Analysis of Breast Cancer Treatment Experiences and Healthcare Perceptions Across Twitter

2018-05-25 · Eric M. Clark, Ted James, Chris A. Jones, Amulya Alapati 외

Background: Social media has the capacity to afford the healthcare industry with valuable feedback from patients who reveal and express their medical decision-making process, as well as self-reported quality of life indi…

Decision MakingSentiment Analysis

Probabilistic emotion and sentiment modelling of patient-reported experiences

2024-01-09 · Curtis Murray, Lewis Mitchell, Jonathan Tuke, Mark Mackay

This study introduces a novel methodology for modelling patient emotions from online patient experience narratives. We employed metadata network topic modelling to analyse patient-reported experiences from Care Opinion, …

Information RetrievalRecommendation SystemsRetrieval

A Comparative Study on Patient Language across Therapeutic Domains for Effective Patient Voice Classification in Online Health Discussions

2024-07-23 · Giorgos Lysandrou, Roma English Owen, Vanja Popovic, Grant Le Brun 외

There exists an invisible barrier between healthcare professionals' perception of a patient's clinical experience and the reality. This barrier may be induced by the environment that hinders patients from sharing their e…

Language Modellingtext similarity

Social Media as a Sensor: Analyzing Twitter Data for Breast Cancer Medication Effects Using Natural Language Processing

2024-02-26 · Seibi Kobara, Alireza Rafiei, Masoud Nateghi, Selen Bozkurt 외

Breast cancer is a significant public health concern and is the leading cause of cancer-related deaths among women. Despite advances in breast cancer treatments, medication non-adherence remains a major problem. As elect…

Analysis of Voluntarily Reported Data Post Mesh Implantation for Detecting Public Emotion and Identifying Concern Reports

2025-09-03 · Indu Bala, Lewis Mitchell, Marianne H Gillam arxiv

Mesh implants are widely utilized in hernia repair surgeries, but postoperative complications present a significant concern. This study analyzes patient reports from the Manufacturer and User Facility Device Experience (…

Sentiment Analysis