Evidence map›Paper›PMID 37440504›Full record

ArticlePloS one2023

Development and validation of Medical Device Key Evidence Tool ('MeDKET'): An evidence-based framework to explain success in selected European and US companies.

Stefania Manetti, Emanuele Lettieri, Melody Zhifang Ni

Abstract read
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Article
  2. 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

3 authors.

Stefania ManettiDepartment of Management Engineering, Politecnico di Milano, Milano, Italy.ORCID 0000-0002-9652-8293
Emanuele LettieriDepartment of Management Engineering, Politecnico di Milano, Milano, Italy.
Melody Zhifang NiLondon In-Vitro Diagnostics (LIVD) Cooperative, Department of Surgery and Cancer, Imperial College London, London, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Innovating in Medical Device (MD) industry is challenging. This study aims to develop and validate an evidence-based framework that helps innovators of small and large enterprises (SEs and LEs) assess their readiness for successful MD development and deployment. We conducted a key-informant process (stage 1) where 25 international experts identified a list of emergent Health Technology Assessment (HTA) themes they believed were essential to company success. A sample of 22 European and US selected companies (13 SEs and 9 LEs) then reached a consensus on a list of key themes through a robust Delphi process (stage 2). Finally, we constructed (stage 3) and validated (stage 4) the checklist for SEs and LEs. The checklist for SEs and LEs included 21 and 15 items (i.e., fundamental Yes/No questions) with nine overlapping criteria for both SEs and LEs. In both groups, MD success was driven by three major item categories: (i) R&D assessment strategy; (ii) device-outcome measures; (iii) company profiling. Alongside the retrospective validation study, we collected 40 case studies on MDs (23 successes and 17 failures) across the selected enterprises. The retrospective validation provided the proportion of successful and failed case studies that met the 'MeDKET' criteria. We discovered that early HTA plays a pivotal role in MD industry success with different implications based on enterprise size. This study is the first of its kind to provide a holistic picture of the perceived role of early-stage HTA in MD industry success.

Indexed as

IndustryTechnology Assessment, BiomedicalChecklistConsensusRetrospective Studies

Identifiers

PMID37440504
PMCPMC10343042

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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 OpenQuestion graph.