Research wastage
The term “research wastage” was coined for research that does not result in health care benefits for patients. In their seminal series, Chalmers et al estimated that of the $240 billion invested in North American health care research during 2010, 85% was misspent.
They identified a number of key contributory factors, including (1) missing or ineffective research synthesis (eg, systematic review), leading to research duplication, and (2) misalignment of researcher and end-user objectives 1).
RECODE-DCM (REsearch Objectives and Common Data Elements for Degenerative Cervical Myelopathy) aims to improve efficient use of health care resources within the field of Degenerative Cervical Myelopathy (DCM) by using a multi-stakeholder partnership to define the DCM research priorities, to develop a minimum dataset for DCM clinical studies, and confirm a Degenerative Cervical Myelopathy definition.
This requires a multi-stakeholder partnership and multiple parallel consensus development processes. It will be conducted via 4 phases, adhering to the guidance set out by the COMET (Core Outcomes in Effectiveness Trials) and JLA (James Lind Alliance) initiatives. Phase 1 will consist of preliminary work to inform online Delphi processes (Phase 2) and a consensus meeting (Phase 3). Following the findings of the consensus meeting, a synthesis of relevant measurement instruments will be compiled and assessed as per the COSMIN (Consensus-based Standards for the Selection of Health Measurement Instruments) criteria, to allow recommendations to be made on how to measure agreed data points. Phase 4 will monitor and promote the use of eventual recommendations.
RECODE-DCM sets out to establish for the first time an index term, minimum dataset, and research priorities together. The aim is to reduce waste of health care resources in the future by using patient priorities to inform the scope of future DCM research activities. The consistent use of a standard dataset in DCM clinical studies, audit, and clinical surveillance will facilitate pooled analysis of future data and, ultimately, a deeper understanding of DCM 2).