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Identification of target population in the implementation of navigator-delivered home ePRO for patients with cancer receiving treatment.

2022· article· en· W4298142784 on OpenAlexaff
Jayasree Krishnan, Chelsea McGowen, Sheila McElhany, Bryanna Diaz, Carrie C. McNair, Nicole E. Caston, D’Ambra Dent, Stacey A. Ingram, Keyonsis Hildreth, Jeffrey Franks, Andrés Azuero, Courtney Andrews, Chao‐Hui Huang, Doris Howell, Bryan J. Weiner, Bradford E. Jackson, Ethan Basch, Angela M. Stover, Gabrielle B. Rocque, Jennifer Young Pierce

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsPrincess Margaret Cancer Centre
FundersNational Institutes of Health
KeywordsMedicinePopulationCancerDiseaseStage (stratigraphy)Internal medicineFamily medicinePhysical therapy

Abstract

fetched live from OpenAlex

351 Background: One key challenge of practice transformation activities, such as remote symptom monitoring (RSM) using electronic patient reported outcomes (ePROs), is identification of patients starting treatment. In real-world settings, reliance on referrals is likely to miss patients. We describe the difficulties encountered in patient identification and the subsequent changes implemented in protocol to remediate this. Methods: We conducted two PDSA cycles focused on identification and engagement of patients for RSM at the Mitchel Cancer Institute (MCI). Target patient capture was > 75%. Modifications to the patient identification process were documented. Schedules of physicians participating in the RSM program were reviewed from 6/2021 – 5/2022 to identify eligible patients. Patients were considered eligible if they were starting chemotherapy, targeted therapy, or immunotherapy. Patients seeking a second opinion were excluded. Patient demographics, cancer type, cancer stage, and PROs were abstracted from electronic health records and the PRO platform (Carevive). Initial clinic roll-out was conducted in gynecologic oncology, with expansion to breast and thoracic oncology in 10/2021 and 3/2022, respectively. The proportion of eligible patients approached per month was reported.Results: In the first PDSA cycle, the eligibility criteria was defined. Although clinical trials included advanced disease, non-clinical staff screening expressed concern about determining advanced vs. early-stage disease. Thus, inclusion criteria was broadened to include all patients starting treatments. From 6/2021 –8/2021, navigators identified patients by screening patients who presented for chemo-education visits. The navigation team approached 23 patients during this period. However, this process didn’t identify all eligible patients as not all patients beginning treatment received chemo-education visits. In PDSA Cycle 2, the process for new patient contact from initial call for appointment through treatment was reviewed. The implementation team screened all patients in a physician’s schedule a week prior to the office visit as well as on the day of visit. This updated process identified all eligible patients starting either intravenous or oral chemotherapy. The recruitment process was modified to screen the physician schedules rather than chemo educator visits. From 9/2022-5/22, the proportion of eligible patients identified and approached remained high at 100%. This methodological screening process helped the navigation team identify all eligible patients in an efficient manner and they reported comfort in expanding to additional disease teams. Conclusions: Systematic screening of physician schedules can be successfully leveraged for patient identification and reduce time spent manually screening for eligible patients by non-clinical navigators. Clinical trial information: NCT04809740.

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)0.017
metaresearch head score (Gemma)0.022
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.082
GPT teacher head0.410
Teacher spread0.329 · 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
GenreEmpirical

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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Citations0
Published2022
Admission routes1
Has abstractyes

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