Evaluating the implementation of an electronic patient-reported outcome system supporting self-management of treatment-related impairments among patients with lymphoma
Bibliographic record
Abstract
Abstract Introduction: Patients treated for lymphoma often experience a high burden of treatment-related impairments that, when left unaddressed, can lead to functional disability and reduced quality of life. Electronic patient-reported outcome (ePRO) systems are evidence-based tools that can support the management of treatment-related impairments; however, their integration into routine care remains limited. Further, most existing ePRO systems rely on clinicians to review results, and few offer automated feedback or support for patient self-management. To address this, we developed REACH, a web-based application used independently by patients to monitor physical impairments and receive automated self-management support. Methods: We report on a sub-study of a larger multi-centre and multi-disease site implementation evaluation of REACH, focused on patients with lymphoma. Adult (≥ 18 years) Hodgkin or non-Hodgkin lymphoma patients were eligible to register and use REACH from the time of diagnosis until two years after completing all treatments. REACH prompted patients to complete ePRO assessments every 3 months during treatment and every 3-6 months post-treatment. Impairments assessed included fatigue, pain, activities of daily living, falls and balance, and return to work. Following each assessment, patients received tailored resource recommendations within a personalized library based on pre-defined symptom score criteria for each impairment assessed. These were categorized into three levels: 1) self-management education (i.e., links to videos, handouts, and websites); 2) suggested community workshops and programs; and 3) a recommendation to follow-up with their oncologist for further assessment and possible referral to a rehabilitation program. Quantitative data were collected using system usage metrics and a patient experience survey. Outcomes were guided by the implementation outcomes taxonomy. Results: A total of 119 lymphoma patients registered to REACH over 21 months. Of these 99 (53% male, 88% non-Hodgkin lymphoma, median age 58 (21-83) years, 47% currently receiving treatment at registration) provided consent to participate in the research study. REACH was feasible (median assessment time was 2.5 minutes) and demonstrated moderate levels of engagement (99% of participants completed ≥ 1 assessment; 50% of all assessments completed; 66% of patients viewed ≥ 1 recommended resource in their library). Nearly all participants who completed an assessment (98%) reported symptoms scores that met the pre-defined criteria for community-based program recommendations, and 29% met criteria for a follow-up recommendation with their oncologist for further assessment. Nine lymphoma participants completed the patient experience survey. The findings suggested strong feasibility, with all participants agreeing that the REACH assessments were easy to complete and understand. Acceptability was also high, with 78% agreeing that REACH met their approval. In contrast, perceptions of usefulness and fit of resources were less strongly endorsed, with 43% agreeing REACH was useful, 57% agreeing the resources were a good match, and most remaining responses being neutral rather than negative. Conclusions: REACH is a feasible, patient-driven ePRO system that delivers self-management support directly to patients. The high proportion of participants breaching moderate and severe symptom thresholds suggests REACH may help identify unmet rehabilitation needs and contribute meaningfully to survivorship care pathways. While survey data from lymphoma participants were limited, their responses reflect overall patterns in the broader REACH sample. Initial ePRO completion rates were high, but sustained engagement remains a challenge. Future refinements to assessment content and timing, personalization of resources, and implementation supports to help patients act on self-management recommendations may improve sustained engagement. As the adoption of ePRO systems within cancer care increases, these findings underscore the importance of tailoring these systems to patient needs, preferences, and clinical contexts, and highlight the potential for embedding automated self-management supports to facilitate timely and personalized symptom management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".