Easier Said Than Done: Keys to Successful Implementation of the Distress Assessment and Response Tool (DART) Program
Bibliographic record
Abstract
PURPOSE: Systematic screening for distress in oncology clinics has gained increasing acceptance as a means to improve cancer care, but its implementation poses enormous challenges. We describe the development and implementation of the Distress Assessment and Response Tool (DART) program in a large urban comprehensive cancer center. METHOD: DART is an electronic screening tool used to detect physical and emotional distress and practical concerns and is linked to triaged interprofessional collaborative care pathways. The implementation of DART depended on clinician education, technological innovation, transparent communication, and an evaluation framework based on principles of change management and quality improvement. RESULTS: There have been 364,378 DART surveys completed since 2010, with a sustained screening rate of > 70% for the past 3 years. High staff satisfaction, increased perception of teamwork, greater clinical attention to the psychosocial needs of patients, patient-clinician communication, and patient satisfaction with care were demonstrated without a resultant increase in referrals to specialized psychosocial services. DART is now a standard of care for all patients attending the cancer center and a quality performance indicator for the organization. CONCLUSION: Key factors in the success of DART implementation were the adoption of a programmatic approach, strong institutional commitment, and a primary focus on clinic-based response. We have demonstrated that large-scale routine screening for distress in a cancer center is achievable and has the potential to enhance the cancer care experience for both patients and staff.
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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 | 0.035 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".