Leveraging Mobile Technology to Improve Efficiency of the Consent-to-Treatment Process
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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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".