Co‐Designing Solutions to Improve Communication About Serious Illness During Hospitalisation
Bibliographic record
Abstract
BACKGROUND: Serious illness conversations (SICs) that explore patient priorities are becoming increasingly important to high-quality care for those with life-limiting conditions admitted to general internal medicine wards. While patient-cited barriers to SIC include a lack of understanding of complex medical terminology, expected illness course and life-sustaining interventions, providers cite poor documentation and lack of training and support. For seriously ill patients to receive care that aligns with their values and goals, interventions that address these barriers need to be designed in consultation with patients and their providers and tailored to the context of their medical setting and broader health system. METHODS: We report on the last phase of an overarching quality improvement project aimed at increasing documentation of SIC and improving patient/caregiver and provider experiences during SIC through co-design. This phase was conducted on the general internal medicine wards at an academic teaching hospital network in Canada. Twenty-five providers spanning various disciplines and departments participated in three co-design workshops with 13 patients and caregivers between September and December 2023. Facilitated by experts in human-centred design, the workshops sought to build on existing evidence and experience with known interventions to co-produce solutions to SIC challenges. Patient, caregiver and provider priorities were established along with a set of design principles, guiding participants through solution ideation and refinement. RESULTS: The collaboratively developed design principles (clear, compassionate, informed and reciprocal) guided the co-production of solutions to improve in-hospital SICs. Workshop attendees designed solutions that built on revising existing conversation guides, harnessing electronic medical record infrastructure to create collaborative advanced care planning notes, and supporting the role of a facilitator that could offer more structured and routine SIC during hospitalisation. CONCLUSIONS: Increasing knowledge of and access to SIC resources, such as patient conversation guides, advance care planning notes and establishment of Goals of Care Facilitators, were found in co-design to be key solutions to address long-standing barriers to engaging in high-quality SIC and contribute to improving patient and provider experience and outcomes. PATIENT OR PUBLIC CONTRIBUTION: Patients and caregivers with lived experience were directly involved in the design sessions outlined in our study, setting priorities and developing solutions alongside healthcare providers and research 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 | 0.048 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| 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".