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Digital Health Benchmarking, Clinical Confirmation and Lifecyle Validation

Active
Federal

Contract Overview

Solicitation details, issuing organization, response deadlines, documents, and interested companies for this government contract opportunity.

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The Food and Drug Administration is seeking to establish a comprehensive end-to-end capability for the management of innovative digital health medical devices. This initiative focuses on three primary pillars: premarket review, evaluation science, and postmarket oversight. The goal is to create a robust infrastructure for assessing the safety and efficacy of digital health technologies throughout their entire lifecycle. To achieve this, the program will develop essential technical assets including clinically grounded failure-mode taxonomies, benchmark design principles, and standardized evaluation protocols. Key deliverables involve the creation of reusable test sets, methods for weighting clinically consequential errors, and frameworks for analyzing subgroups and use-contexts. Additionally, the contract will establish formal processes for assessing digital health confirmation and monitoring performance following software updates.

General Info

FDA seeks end-to-end management for digital health devices' safety, efficacy, and lifecycle oversight.

NAICS

541330 - Engineering Services

Place of Performance

USA

Set-Aside

NONE

Documents

0

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Timeline

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Organization & Contact Information

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AgencyFood and Drug Administration → Food And Drug Administration
Contacts2 people available
OfficeN/A
Office AddressN/A
Contacts
Cdrh Acquisition Liaison
To Be Determined To Be DeterminedContracting Officer

Full Description

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build FDA's end-to-end capability for new and innovative digital health medical devices across (1) premarket review, (2) evaluation science, and (3) postmarket oversight. The program would develop clinically grounded failure-mode taxonomies, benchmark design principles, evaluation protocols, reusable test sets or specifications where feasible, methods for weighting clinically consequential errors, approaches for subgroup and use-context analysis, and frameworks for assessing digital health confirmation and post-update performance

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