Patient and provider perspectives on the development and resolution of prescribing cascades: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Prescribing cascades occur when the side effect of a medication is treated with a second medication. The aim of the study was to understand how prescribing cascades develop and persist and to identify strategies for their identification, prevention and management. METHOD: This qualitative study employed semi-structured interviews to explore the existence of prescribing cascades and to gather patients', caregivers' and clinicians' perspectives about how prescribing cascades start, persist and how they might be resolved. Participants were older adults (over age 65) at an outpatient Geriatric Day Hospital (GDH) with possible prescribing cascades (identified by a GDH team member), their caregivers, and healthcare providers. Data were analyzed using an inductive content analysis approach. RESULTS: Fourteen participants were interviewed (eight patients, one family caregiver, one GDH pharmacist, three GDH physicians and one family physician) providing a total of 22 interviews about patient-specific cases. The complexity and contextually situated nature of prescribing cascades created challenges for all of those involved with their identification. Three themes impacted how prescribing cascades developed and persisted: varying awareness of medications and cascades; varying feelings of accountability for making decisions about medication-related care; and accessibility to an ideal environment and relevant information. Actions to prevent, identify or resolve cascades were suggested. CONCLUSION: Patients and healthcare providers struggled to recognize prescribing cascades and identify when they had occurred; knowledge gaps contributed to this challenge and led to inaction. Strategies that equip patients and clinicians with resources to recognize prescribing cascades and environmental and social supports that would help with their identification are needed. Current conceptualizations of cascades warrant additional refinement by considering the nuances our work raises regarding their appropriateness and directionality.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".