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Record W2134780987 · doi:10.1200/jco.2013.53.2960

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

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

Bibliographic record

VenueJournal of Clinical Oncology · 2013
Typeletter
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsnot available
FundersNational Cancer InstituteAgency for Healthcare Research and Quality
KeywordsMedicineCancerBreast cancerDiseaseFamily medicineInternal medicine

Abstract

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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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0050.006
Open science0.0010.008
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.118
GPT teacher head0.463
Teacher spread0.344 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreCommentary

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".

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Citations21
Published2013
Admission routes1
Has abstractyes

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