Evidence map›Paper›PMID 25005707›Full record

ArticleDrug safety2014

Advancing the field of pharmaceutical risk minimization through application of implementation science best practices.

Meredith Y Smith, Elaine Morrato

Abstract read
In one paragraph

Article in Drug safety, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Meredith Y SmithEMD Serono Inc., One Technology Place, Rockland, MA, 02370, USA, meredith.smith@emdserono.com.
Elaine Morrato

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Regulators are increasingly mandating the use of pharmaceutical risk-minimization programs for a variety of medicinal products. To date, however, evaluations of these programs have shown mixed results and relatively little attention has been directed at diagnosing the specific factors contributing to program success or lack thereof. Given the growing use of these programs in many different patient populations, it is imperative to understand how best to design, deliver, disseminate, and assess them. In this paper, we argue that current approaches to designing, implementing, and evaluating risk-minimization programs could be improved by applying evidence- and theory-based 'best practices' from implementation science. We highlight commonly encountered challenges and gaps in the design, implementation, and evaluation of pharmaceutical risk-minimization initiatives and propose three key recommendations to address these issues: (1) risk-minimization program design should utilize models and frameworks that guide what should be done to produce successful outcomes and what questions should be addressed to evaluate program success; (2) intervention activities and tools should be theoretically grounded and evidence based; and (3) evaluation plans should incorporate a mixed-methods approach, pragmatic trial designs, and a range of outcomes. Regulators, practitioners, policy makers, and researchers are encouraged to apply these best practices in order to improve the public health impact of this important regulatory tool.

Indexed as

Practice Guidelines as TopicHumansPatient SafetyRisk Management

Identifiers

PMID25005707
PMCPMC4134476

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.