The use of patient-generated health data in the management of low anterior resection syndrome: a qualitative study
Bibliographic record
Abstract
Background: The cornerstone of low anterior resection syndrome (LARS) treatment is self-management, which requires patient engagement. Colorectal surgeons and nurses may use patient-generated health data (PGHD) to help guide patients in their use of self-management strategies for LARS. However, the perspectives of LARS experts on the use of PGHD remain largely unexplored. The objective of this study was to explore the perspectives and experiences of LARS experts regarding the use of PGHD in the management of LARS. Methods: We utilized purposive snowball sampling to identify international LARS experts, including surgeons, nurses, and LARS researchers with knowledge and expertise in LARS. We conducted individual semi-structured interviews with these experts between August 2022 and February 2024. We performed thematic analysis using the framework method to identify domains and associated themes. Results: Our sample included 16 LARS experts from five countries. Thematic analysis identified four domains and associated themes. The domains included: data collection practices, data review practices, perceived usefulness, and future directions. Within the data collection practices domain, we found that most experts asked LARS patients to collect some form of PGHD, including bowel diaries, patient-reported outcome measures, or both. Within the data review practices domain, we found that both surgeons and nurses reviewed PGHD. Most participants described finding it difficult to interpret the data and identified time constraints, legibility, and completeness as the most common barriers to reviewing data in clinic. In terms of perceived usefulness, data collection was felt to help clinicians understand symptoms and their impact and assist patients with self-management. The future directions domain revealed that most experts felt that a clinical tool in the form of an online app or website to support data collection and enhance data visualization would be useful. Finally, some participants saw promise in leveraging PGHD to inform the creation of automated treatment algorithms for LARS management. Conclusions: This study highlights many gaps in the processes of patient-generated LARS data collection and review. A clinical tool including various data collection templates and data visualization prototypes could help to address these gaps. Future research will focus on incorporating the patient perspective.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".