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Enregistrement W4417019814 · doi:10.1182/blood-2025-2639

Evaluating the implementation of an electronic patient-reported outcome system supporting self-management of treatment-related impairments among patients with lymphoma

2025· article· en· W4417019814 sur OpenAlexaff
Christian Lopez, Jennifer Jones, Tony Reiman, Sarah Neil‐Sztramko, Kristin Campbell, David M. Langelier, Jonathan Greenland, Jacqueline L. Bender, Gillian Strudwick, Vishal Kukreti

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueCancer survivorship and care
Établissements canadiensMemorial University of NewfoundlandUniversity of CalgaryUniversity of British ColumbiaMcMaster University Medical CentreUniversity of TorontoHamilton Health SciencesSaint John Regional HospitalPrincess Margaret Cancer Centre
Organismes subventionnairesnon disponible
Mots-clésReferralActivities of daily livingQuality of life (healthcare)Patient-reported outcomeMEDLINERehabilitation

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,008
score de la tête « metaresearch » (Gemma)0,022
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,008
Score d'incertitude au seuil0,041

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0080,022
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,011
Tête enseignante GPT0,316
Écart entre enseignants0,305 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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