Easier Said Than Done: Keys to Successful Implementation of the Distress Assessment and Response Tool (DART) Program
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: 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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 it