Abstract P075: Oncologists’ Perceptions of the Usability of SPHERE: an Electronic Health Record Application Based on Life’s Simple 7™
Bibliographic record
Abstract
Background: We developed SPHERE, an electronic health record (EHR) data visualization application, to automatically populate with EHR data on the American Heart Association’s cardiovascular health (CVH) behaviors and factors. SPHERE was designed to increase patient-provider communication around Life’s Simple 7™, and utilized a stoplight color scheme as part of the visualization to indicate “ideal” (green), “intermediate” (yellow), and “poor” (red) CVH. Results from the SPHERE Study showed improvements in body mass index and diabetes status among eligible patients seen in the intervention clinic as compared to the control clinic. It remains unknown whether the use of SPHERE would be acceptable to oncologists in the survivorship care setting, even though cancer survivors are at high risk for developing cardiovascular disease. Methods: We conducted one-on-one interviews, using a think-aloud protocol, with 14 oncologists (10 community practice, 4 academic) who were practicing in the NCI Community Oncology Research Program (NCORP). During the 30-minute interview period, we asked each oncologist to use SPHERE and we obtained user feedback on the SPHERE application. Results: Half of the oncologists were male, and most (78%) were medical oncologists; 2 were gynecologic oncologists, and 1 was a radiation oncologist. When asked about discussing CVH with post-treatment, good-prognosis patients, no oncologists indicated that they “never” talked to patients about CVH or initiated those discussions. Eleven oncologists responded that discussing CVH was either “somewhat important” or “very important”, and 9 oncologists indicated they were “somewhat comfortable” or “very comfortable” discussing CVH with patients. Themes identified from transcripts of the 7 hours of interviews suggest 1) strong interest in the tool, 2) high perceived importance of discussing CVH with patients who had a good prognosis, 3) high usability, and 4) a belief that the tool would be helpful and not overly burdensome in practice. Duplication of preventative health efforts with the primary care provider as well as workflow and time constraints were identified as barriers by several oncology providers. Conclusions: Overall, these preliminary data demonstrate acceptability and feasibility of implementing the SPHERE application tool within NCORP community oncology practices. These qualitative data suggest great potential for automated EHR-based applications such as SPHERE to impact the CVH of cancer patients in survivorship care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".