“She Knows Me Best”: A Qualitative Study of Patient and Caregiver Views on the Role of the Primary Care Physician Follow-Up Post-Discharge
Bibliographic record
Abstract
Abstract BackgroundPatients with chronic obstructive pulmonary disease (COPD) and congestive heart failure (CHF) are at high-risk of readmission after hospital discharge. There is conflicting evidence however on whether timely follow-up with a primary care provider reduces that risk. The objective of this study is to understand the perspectives of patients with COPD and CHF, and their caregivers, on the role of primary care provider follow-up after hospital discharge.MethodsA qualitative study design with semi-structured interviews was conducted among patients or their family caregivers admitted with COPD or CHF who were enrolled in a multicenter mixed-methods study at three acute care hospitals in Ontario, Canada. Participants were interviewed between December 2017 to January 2019, the majority discharged from hospital at least 30 days prior to their interview. Interviews were analyzed independently by three authors using a deductive directed content analysis, with the fourth author cross-comparing themes.ResultsInterviews with 16 participants (eight patients and eight caregivers) revealed four main themes. First, participants valued visiting their primary care provider after discharge to build upon their longitudinal relationship. Second, primary care providers played a key role in coordinating care. Third, there were mixed views on the ideal time for follow-up, with many participants expressing a desire to delay follow-up to stabilize following their acute hospitalization. Fourth, the link between the post-discharge visit and preventing hospital readmissions was unclear to participants, who often self-triaged based on their symptoms when deciding on the need for emergency care.ConclusionsPatients and caregivers valued in-person follow-up with their primary care provider following discharge from hospital because of the trust established through pre-existing longitudinal relationships. Our results suggest policy makers should focus on improving rates of primary care provider attachment and systems supporting informational continuity.
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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 | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".