Abstract PO-005: Barriers to implementation of virtual collection of patient-reported data in the COVID-19 era
Bibliographic record
Abstract
Abstract Introduction: A marked shift in ambulatory patient assessment was instituted by cancer centers in response to the COVID-19 crisis. At our center, non-urgent appointments were deferred, and essential assessments were conducted virtually whenever possible. Prior to COVID-19, all patients attending ambulatory clinic completed an Edmonton Symptom Assessment Scale (ESAS) form via touch pad, with assistance as needed by clinic volunteers. Our purpose here was to explore how virtual conduct of clinics impacted the collection of patient-reported outcomes and to address the unmet need for recognition and management of severe symptoms, particularly depression/anxiety. Methods: We performed a mixed methods cross-sectional study to test the feasibility of remote completion of the ESAS form by patients scheduled for appointments at a weekly surgical oncology clinic at a major Canadian cancer center. Over the course of the first 5 weeks of the study, patients were phoned after their appointment to request permission to email the ESAS form and asked to return the completed form electronically. Over the next 2 weeks, patients who attended in-person appointments were asked to complete a hard-copy ESAS form in clinic. Clinically significant distress was defined a priori as score >2 for depression and >3 for anxiety. We compared compliance with the two methods (virtual vs. hard-copy) of patient-reported data collection. Results: For the entire study cohort, median age was 64 (35-89) and 48% were female. For the virtual method of ESAS completion, 45 patients had telephone contact attempted: 30 agreed to study participation, 1 declined, and 14 could not be reached despite repeated attempts. For the hard-copy method, all 22 patients approached consented to participation. For the virtual method, 15 patients successfully completed and returned the ESAS form electronically, yielding an overall compliance rate of 33%. For the hard-copy method, the compliance rate was 95% (1 patient deferred after consenting, then did not return the form). There were no differences in patient age, gender, or tumor type between the two methods. For the patients who agreed to the virtual method but did not return a completed electronic form, the following barriers were identified: unable to open/complete PDF; technology phobia; lack of motivation; patient provided invalid email address. Of the completed forms, 28% revealed a depression score >2 and 31% an anxiety score >3; 22% reported both severe depression and anxiety. There was no difference in the degree of distress reported virtually or via hard copy. Conclusions: We have identified significant barriers to the virtual completion of ESAS forms, with a lack of predictive variables. The severe degree of psychological distress reported by over 25% of respondents during the COVID era demonstrates the need for ongoing regular collection and review of these data. Innovative solutions are urgently required to overcome barriers to virtual collection of patient-reported outcomes. Citation Format: Karineh Kazazian, Wendy Johnston, Jessica Bogach, Carol J. Swallow. Barriers to implementation of virtual collection of patient-reported data in the COVID-19 era [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2020 Jul 20-22. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(18_Suppl):Abstract nr PO-005.
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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 | 0.059 | 0.160 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".