ArticleThe Journal of surgical research2024
A Qualitative Study of Electronic Patient-Reported Outcome Symptom Monitoring After Thoracic Surgery.
Article in The Journal of surgical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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.
Who cites it
5 citing papers in PubMed.
- Patient perspectives on electronic patient-reported outcome-based symptom management after lung cancer surgery: a qualitative study.Journal of patient-reported outcomes · 2026Trial
- Quality of life during electronic patient-reported outcome (ePRO) monitoring in thoracic surgery patients: a pilot randomized controlled trial.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026Trial
- Mixed-Methods User-Centered Design Evaluation of an Electronic Patient-Reported Outcomes System to Enhance Postdischarge Care for Thoracic Surgery Patients.The Journal of surgical research · 2026Article
- The Current Landscape of Remote Digital Symptom Monitoring for Patients With Lung Cancer: Scoping Review.Journal of medical Internet research · 2026Article
- Comparative effectiveness of remote perioperative telemonitoring in cancer surgery: a randomized trial.NPJ digital medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
introductionThoracic surgery is a mainstay of therapy for lung cancer and other chronic pulmonary conditions, but recovery is often complicated. Digital health systems can facilitate remote postoperative symptom management yet obstacles persist in their routine clinical adoption. This study aimed to identify patient-perceived barriers and facilitators to using an electronic patient-reported outcome (ePRO) monitoring platform specially designed to detect complications from thoracic surgery postdischarge.
methodsPatients (n = 16) who underwent thoracic surgery and participated in an ePRO parent study completed semistructured interviews, which were analyzed using thematic content analysis and iterative team-based coding. Themes were mapped onto the three domains of the Capability, Opportunity, and Motivation Model of behavior framework to inform ePRO design and implementation improvements.
resultsAnalysis demonstrated seven dominant themes, including barriers (1. postoperative patient physical and mental health, 2. lack of access to email and poor internet connectivity, 3. lack of clarity on ePRO use in routine clinical care, and 4. symptom item redundancy) as well as facilitators (5. ease of the ePRO assessment completion, 6. engagement with the surgical care team on ePRO use, and 7. increased awareness of symptom experience through ePRO use). Suggested ePRO improvements included offering alternatives to web-based completion, tailoring symptom assessments to individual patients, and the need for patient education on ePROs for perioperative care.
conclusionsAddressable barriers and facilitators to implementation of ePRO symptom monitoring in the thoracic surgical patient population postdischarge have been identified. Future work will test the impact of design improvements on implementation outcomes of feasibility and acceptability.
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Registered trials
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.