Goals of care or goals of life? A qualitative study of clinicians’ and patients’ experiences of hospital discharge using Patient-Oriented Discharge Summaries (PODS)
Bibliographic record
Abstract
Abstract Background Recognizing the need for improved communication with patients at the point of hospital discharge, a group of clinicians, patients, and designers in Toronto, Canada collaborated to develop a standardized tool known as the Patient-Oriented Discharge Summary (PODS). Although quantitative results suggest PODS helps mitigate gaps in knowledge, a qualitative inquiry from the clinician and patient perspective of psychiatric hospital discharge using PODS has not been widely explored. Our aim was to explore clinicians’ and patients’ experiences with PODS. Methods We used a qualitative thematic analysis to explore clinicians’ (n = 10) and patients’ (n = 6) experiences with PODS. We used convenience sampling to identify and invite potential participants at the Center for Addiction and Mental Health in Toronto, Canada to participate in semi-structured interviews between February 2019 and September 2019. Data were analyzed using a thematic analysis approach to develop descriptive themes. Results Emerging themes from the data between clinicians and patients were both different and complementary. Clinicians described PODS using the concept of “goals of care.” They relayed their experiences with PODS as a discrete event and emphasized its role in meeting their “goals of care” for discharge planning. Patients provided more of a “goals of life” perspective on recovery. They characterized PODS as only one facet of their recovery journey and not necessarily as a discrete or memorable event. Patients focused on their outcomes post-discharge and situated their experiences with PODS through its relation to their overall recovery. Conclusions PODS was experienced differently by clinicians and patients. Clinicians experienced PODS as helpful in orienting them to the fulfillment of goals of care. Patients did not experience PODS as a particularly memorable intervention. Due to the information advantage that clinicians have about PODS, it is not surprising that clinicians and patients experienced the PODS differently. This study expanded our understanding of hospital discharge from clinicians and patients perspectives, and suggests that there are additional areas that need improvement.
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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 | 0.026 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".