Priority-setting for hospital funding of high-cost innovative drugs and therapeutics: A qualitative institutional case study
Bibliographic record
Abstract
OBJECTIVES: Rising costs of innovative drugs and therapeutics (D&Ts) have led to resource allocation challenges for healthcare institutions. There is limited evidence to guide priority-setting for institutional funding of high-cost D&Ts. This study sought to identify and elaborate on the substantive principles and procedures that should inform institutional funding decisions for high-cost off-formulary D&Ts through a case study of a quaternary care paediatric hospital. METHODS: Semi-structured, qualitative interviews, both virtual and in-person, were conducted with institutional stakeholders (i.e. staff clinicians, senior leadership, and pharmacists) (n = 23) and two focus groups at The Hospital for Sick Children in Toronto, Canada. Participants involved in, and impacted by, high-cost off-formulary drug funding decisions were recruited through stratified, purposive sampling. Participants were approached for study involvement between July 27, 2020 and June 7, 2022. Data was analysed through reflexive thematic analysis. RESULTS: Institutional resource allocation for high-cost D&Ts was identified as ethically challenging but critical to sustainable access to novel therapies. Important substantive principles included: 1) clinical evidence of safety and efficacy, 2) economic considerations (direct costs, opportunity costs, value for money), 3) ethical principles (social justice, professional/organizational responsibility), and 4) disease-specific considerations. Multidisciplinary deliberation was identified as an essential procedural component of decision-making. Participants identified tension between innovation and the need for evidence-based decision-making; clinician and institutional responsibilities; and value for money and social justice. Participants emphasized the role of health system-level funding allocation in alleviating the financial and moral burden of decision-making by institutions. CONCLUSIONS: This study identifies values and processes to aid in the development and implementation of institutional resource allocation frameworks for high-cost innovative D&Ts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.029 |
| 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 | 0.013 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".