Literature DB >> 30367349

Assessment of Devices, Diagnostics and Digital Technologies: A Review of NICE Medical Technologies Guidance.

Francisca Crispi1, Huseyin Naci2, Eva Barkauskaite3, Leeza Osipenko3, Elias Mossialos1.   

Abstract

BACKGROUND: The Medical Technologies Evaluation Programme (MTEP) of NICE in England aims to evaluate medical devices that are deemed to be cost-saving or cost-neutral and produce Medical Technology Guidance (MTG) to encourage their adoption.
OBJECTIVE: To review the MTGs since MTEP's inception in 2009 until February 2017.
METHODS: One researcher assessed all published MTGs and extracted data on the clinical and economic evidence supporting each technology. The NICE Committee's decision outcome for each assessment was also recorded. A qualitative analysis was performed on technologies that were not supported for adoption to identify the main drivers of the decision.
RESULTS: Thirty-one MTGs were reviewed. The committee fully supported the medical devices in 14 MTGs, 11 were partially supported and six were not supported. Of the MTGs, 58% had no RCT data available and the main source of evidence came from non-experimental studies. There was no statistically significant difference in the average number of RCTs and non-experimental studies between the fully-supported, partially-supported, and not-supported technologies. Whilst all the fully-supported MTGs demonstrated cost-saving results, only 50% of the not-supported MTGs did. The sponsor estimated a higher average cost-saving than the EAC in most of the cases (20/31). The qualitative evaluation suggests that the main drivers for negative decisions were the quantity or quality of studies, and costs incurred in the economic evaluation results.
CONCLUSIONS: The main drivers of the decision-making process are the quality and quantity of the submitted evidence supporting the technologies, as well as the economic evaluation results.

Mesh:

Year:  2019        PMID: 30367349     DOI: 10.1007/s40258-018-0438-y

Source DB:  PubMed          Journal:  Appl Health Econ Health Policy        ISSN: 1175-5652            Impact factor:   2.561


  2 in total

1.  Common Problems, Common Data Model Solutions: Evidence Generation for Health Technology Assessment.

Authors:  Seamus Kent; Edward Burn; Dalia Dawoud; Pall Jonsson; Jens Torup Østby; Nigel Hughes; Peter Rijnbeek; Jacoline C Bouvy
Journal:  Pharmacoeconomics       Date:  2020-12-18       Impact factor: 4.981

2.  When Does Da Vanci Robotic Surgical Systems Come Into Play?

Authors:  Hao-Yun Kao; Yi-Chen Yang; Yu-Han Hung; Yenchun Jim Wu
Journal:  Front Public Health       Date:  2022-01-31
  2 in total

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