Leveraging Mobile Technology to Improve Efficiency of the Consent-to-Treatment Process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: This study reports on the implementation of an electronic consent-to-treatment system (e-Consent) in a busy radiation medicine program and compares it with the previous paper-based method of documenting patient consent. METHODS: A password-protected, electronic, e-Consent application was designed in-house and installed on iPad devices to document patient consent for radiation therapy treatments. A feasibility study, followed by a program-wide deployment of e-Consent, was executed. The effectiveness and impact of e-Consent on workflow were determined by comparing the number of problems arising from the paper-based consenting method with those from the e-Consent process. Staff satisfaction and perceived impact of e-Consent on workflow were determined by a program-wide survey of e-Consent users. RESULTS: The e-Consent completion rate was 94.2% (5,600 of 5,943 forms) 1 year after implementation, indicating successful uptake at the program level. Although the paper-based method of documenting patient consent was associated with an error rate of 7% (24 of 343 forms), e-Consent was associated with an error rate of 0.32% (18 of 5,600 forms) 1 year after deployment. Results of a 10-item e-Consent user survey indicated improvement in staff workflow and high overall satisfaction with e-Consent. CONCLUSION: e-Consent is more efficient than paper-based methods for documenting patient consent. Moreover, replacing paper-based consent methods with an electronic version facilitated an improved workflow and staff satisfaction. Efforts aimed at implementing e-Consent throughout the entire cancer program are currently underway.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it