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Enregistrement W2134780987 · doi:10.1200/jco.2013.53.2960

Patient Navigation Improves the Care Experience for Patients With Newly Diagnosed Cancer

2013· letter· en· W2134780987 sur OpenAlexaboutno aff
Samantha Hendren, Kevin Fiscella

Notice bibliographique

RevueJournal of Clinical Oncology · 2013
Typeletter
Langueen
DomaineMedicine
ThématiqueGlobal Cancer Incidence and Screening
Établissements canadiensnon disponible
Organismes subventionnairesNational Cancer InstituteAgency for Healthcare Research and Quality
Mots-clésMedicineCancerBreast cancerDiseaseFamily medicineInternal medicine

Résumé

récupéré en direct d'OpenAlex

In 1990, Dr Harold P. Freeman created patient navigation (PN) at Harlem Hospital Center in New York City, to address the problem of late-stage breast cancer presentations among poor and minority patients in the Harlem community. This program offered culturally sensitive disease management and care coordination to remove barriers to timely evaluation of breast abnormalities and initiation of treatment. The term “patient navigation” was an apt descriptor for this program, which sought to provide patients with a map and a guide (the navigator) to prevent them from getting lost (to follow-up) in a fragmented and bewildering medical system. Freeman’s initial program was unequivocally successful in achieving its goals; patients with suspicious cancer screening findings were significantly more likely to complete their diagnostic evaluation and to do so in a timely fashion when paired with a navigator. Since that time, PN has been shown repeatedly to improve rates and timeliness of follow-up of cancer screening abnormalities in various populations. However, screening and screening follow-up are only the beginning in the continuum of care for patients with cancer, and the cancer treatment communities have looked with hope towards PN as a possible remedy for disparities in care for patients after a cancer diagnosis. Over time, PN programs have evolved to encompass broader goals than Freeman’s initial vision. That is, current PN programs for patients with diagnosed cancer generally have dual missions: to avoid problems with care coordination and timeliness (avoid patients getting lost to follow-up); and to optimize patient-reported outcomes such as quality of life (QOL), satisfaction, and distress (avoid patients feeling lost). However, despite nationwide enthusiasm for PN for patients with cancer, the evidence that it improves either care coordination or patient-reported outcomes has remained unproven, after three randomized trials failed to show an effect. In the article that accompanies this editorial, Wagner et al present new evidence that PN may make a difference for patients with newly diagnosed cancer. They conducted a cluster-randomized trial of a nurse-navigator program for patients with breast, colorectal, and lung cancer in an integrated healthcare system in Washington and Idaho. This program did not specifically target poor and/or minority patients, but focused on any new patient with cancer. The primary outcome measures were patient-reported outcomes identified as problematic areas for patients with cancer in prior work by the same authors: QOL, the care experience, problems and delays in care, as well as healthcare costs. PN was associated with improvements in the care experience, as well as significantly fewer perceived problems with care, especially psychosocial care, care coordination, and information. Improvements in measures of the care experience persisted at 1 year, suggesting that effects of navigation persisted well after the relationship with the navigator ended at 4 months. As in prior trials, there were no significant effects on timeliness of care or quality of life. Thus, PN seems to improve patients’ experience with cancer care after diagnosis. Information and psychosocial care are augmented, and patients’ perceptions of vulnerability to error and miscommunication are reduced. These are important issues from the patients’ perspective, and it may be that prior studies have not adequately measured them. A review of the study designs of prior trials likely explains the difference. The trial in Quebec by Skrutkowski et al measured effects of a nurse navigator on symptom distress, fatigue, QOL, and resource use, finding no significant differences. The trial in Rochester, NY, by Fiscella et al measured the effect of a lay navigator on time to completion of primary treatment, psychological distress, patient satisfaction with cancer-related care, and QOL. The trial did not show significant differences overall, but a subgroup analysis suggested that patients with limited English proficiency and who were uninsured had greater satisfaction with cancer care when navigated. In a trial by Ell et al, adherence to treatment was measured, and similarly high adherence was seen for low-income Latinas with breast or gynecological cancer randomly assigned to informational materials compared with those randomly assigned to informational materials plus telephone navigation. Thus, most prior trials did not directly measure the experience of care or patient perceptions of care problems. However, the question remains: why has PN had no effect on QOL if it improves the care experience? In measuring QOL, the surveys used in all three randomized trials of PN were versions of the Functional Assessment of Cancer Therapy (FACT) scales, which have general and disease-specific versions. As the authors point out, the FACT instruments may not have the psychometric properties to show an effect of any psychosocial, informational, and care-coordination intervention. This is because differences in QOL among patients with cancer, over the short term of most trials, are dominated by cancer stage and treatment adverse effects; these major contributors to QOL may be affected little by PN. Why then is timeliness of care not affected by PN, when the original program in Harlem successfully prevented delays and loss to JOURNAL OF CLINICAL ONCOLOGY E D I T O R I A L VOLUME 32 NUMBER 1 JANUARY 1 2014

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesIntégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,443
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

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

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 Opus0,118
Tête enseignante GPT0,463
Écart entre enseignants0,344 · 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 tête enseignante, pas un consensus.

Devis d'étudeSans objet
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

Citations21
Publié2013
Routes d'admission1
Résumé présentoui

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