Advancing the Ethical Oversight of Biomedical Research to Keep Pace with Rapid Advancements in Digital Technology

Interested? who to contact:

@Paul Denny-Gouldson (Unlicensed)

@Ashley George (Unlicensed)

The problem

To achieve broad adoption and positively impact health, digital medicine products require evidence to demonstrate that they are trustworthy. The quality and quantity of evidence required to deem a product fit-for-purpose are not agreed upon by industry, patient, or government stakeholders. Currently, there is no consensus on what constitutes “evidence' or what metrics should be used to distinguish high-quality evidence from low-quality evidence. Resources including the CTTI feasibility study database and the Fitabase research library have compiled evidence for digital products in an easily searchable manner. However, there are inconsistencies in study type designations in these catalogs. In order to make useful comparisons across different digital products, the field needs a standard ontology and quality metrics for categorizing evidence used to designate a tool as trustworthy and fit for purpose. Different types of studies require different approaches in their design and information in their reporting. Different quality metrics will apply to a feasibility study for a sensor prototype needs compared to a validation study for an out of the box product in a specific patient population. A framework for achieving consistency in reporting both methods and outcomes is required for the end-users of technology -- whether a pharma company, payer, or individual patient -- to make fully informed decisions on the quality of a product.

Proposal:

To develop a toolkit defining a crosswalk between the proposed evidentiary frameworks already proposed by DiMe (in press) and the study design and reporting necessary to support a tool as fit for purpose in clinical applications.

Objectives / proposed deliverables:

Deliver a demonstration project to:

1. Articulate a standard lexicon of digital study types

2. Define quality metrics for categorising evidence used to designate digital tools as fit-for-purpose in a clinical application (could limit to clinical trials to constrain scope). Any Comments Defining quality in study design and reporting will drive the field of digital medicine towards maturity by establishing consistency in the evidentiary base used to support clinical applications of digital medicine tools.