Identification of target population in the implementation of navigator-delivered home ePRO for patients with cancer receiving treatment.
Bibliographic record
Abstract
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.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".