Barriers to Clinical Trial Efficiency and Patient Access: The Heart Failure Collaboratory Industry Survey
Bibliographic record
Abstract
ABSTRACT Aims Clinical trial inefficiency and lack of patient access to novel therapies have been identified as key barriers to successful heart failure innovation. The Heart Failure Collaboratory (HFC) is a consortium developed to identify and address barriers to bringing drugs and devices to market. The HFC performed an electronic cross-sectional survey of key leaders within industry to collate and interpret a sample of viewpoints on these challenges. Methods and Results From August to September 2018, self-administered survey data were electronically collected from industry partners. Group comparisons were made via Fisher’s Exact or chi-square test. Respondents most commonly rated the United States Food and Drug Administration (44.2%), Health Canada (39.5%) and European Medicines Agency (32.6%) as efficient. Respondents rated the top 3 areas with the greatest opportunity for regulatory agencies to improve efficiency: ‘improve usefulness of agency feedback’ (48.8%); ‘improved timeliness of agency responses’ (41.9%); and ‘pre-specification of required magnitude of clinical effect’ and ‘limit excessive data requirements’ (32.6%). Respondents rated items of ‘excessive clinical site staff workload’ (55.8%), ‘overly complex case report forms’ (51.2%) and ‘data input errors’ (39.5%) as the top 3 factors influencing data quality. Respondents rated items of ‘onerous prior authorization requirements’ (51.2%), ‘availability of decision-making rationale’ (46.5%) and ‘rationality of access barriers created by payers’ (44.2%) as the top 3 impediments to improving patient access to approved therapies. Conclusion This broadly distributed survey of industry respondents identified multiple specific barriers to heart failure clinical trial efficiency and patient access, which should help direct efforts toward 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 | 0.033 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".