Real-world implementation of serious illness care program (SICP) in cancer care.
Bibliographic record
Abstract
e24112 Background: The Serious Illness Care Program (SICP) is a system-based intervention, including a conversation guide, which facilitates improved advance care planning (ACP) conversations between clinicians and seriously ill patients. A recent randomized control trial found the program reduced symptoms of depression and anxiety amongst oncology out-patients and improved process outcomes. We implemented the SICP in our center to determine if the effects of this program could be translated into the real world. Methods: Two outpatient oncology clinics implemented the SICP, each over a 16-week period. Patients were identified based on an answer of “no” to the question “would I be surprised if this patient died within the next year?”, or any patient with a diagnosis of metastatic pancreatic cancer, or symptom scores of > 7 on more than three categories of the patient reported outcome dashboard. Physicians were trained on how to conduct the SICP conversation. One patient per week was identified and prepared to have the SICP conversation with the goal of at least 12 conversations in each 16-week period. Rates of SICP conversation documentation on our system’s “ACP and goals of care designation (GCD) Tracking Record” and GCD orders were recorded. Patient satisfaction after each conversation and physician comfort level over time were assessed. Results: 16 patients were identified (8 patients in each 16-week period). One patient was lost to follow-up. Of the remaining 15 patients who had the SICP conversation, 14 (93%) had documentation on the Tracking Record and 8 (53%) had a GCD order. This was a major improvement over baseline rates of documentation (e.g. < 1 % Tracking Record use and 16% GCD for patients with GI cancers). 14 patients completed satisfaction surveys, of which 12 (86%) felt “completely” or “quite a bit” more heard or understood. Physician comfort level increased from 3.6 to 4.8 and from 4.8 to 5 out of 5, respectively over each 16-week period. Conclusions: SICP implementation resulted in high rates of documentation of goals and preferences. Patients felt heard and understood by their healthcare team, and comfort in these conversations improved over time for physicians. The goal number of conversations was not met, but otherwise the SICP was feasible to implement in the real world. Further study is required to identify the appropriate triggers and barriers to routine SICP conversations.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".