What question did this study set out to answer?

The aim is to create a framework for monitoring and managing performance drift in safety-critical medical devices.

June 22, 20260 citationsOpen Access

Cumulative Drift-Risk Budgeting: A Reliability-Monitoring Framework for Triggered Maintenance and Change Control in Safety-Critical Medical Technologies

Key Points

The aim is to create a framework for monitoring and managing performance drift in safety-critical medical devices.
Proposed Regulatory Drift Budgeting (RDB) framework for monitoring device reliability.
Defined device-specific drift metrics and a drift-to-risk transfer function.
Included calibration requirements and validation boundaries for operational assessment.
Introduced a formal model for monitoring device performance and reliability.
Presented synthetic demonstrations for various medical device types, although lacking real-world validation.

Abstract

This Zenodo record contains the public preprint and reproducibility package for: "Cumulative Drift-Risk Budgeting: A Reliability-Monitoring Framework for Triggered Maintenance and Change Control in Safety-Critical Medical Technologies" This work proposes Regulatory Drift Budgeting (RDB) as a reliability-monitoring framework for converting repeated field or performance drift into a cumulative, auditable intervention rule. The framework defines a device-specific drift metric Wₜ, a monotone drift-to-risk transfer function g (Wₜ), a cumulative budget B (t), pre-alert and trigger states, reset logic, data-validity gates, calibration requirements, and an evidence boundary for future operational validation. The contribution is methodological and validation-bounded. The preprint presents a formal model, conditional hazard-bound interpretation, implementation requirements, and transparent synthetic demonstrations for Software as a Medical Device (SaMD), in vitro diagnostic devices (IVDs), and monitored medical devices (MDs). It does not claim real-world validation, clinical effectiveness, regulatory conformity, FDA or notified-body acceptance, or superiority over existing monitoring methods. The package includes the preprint, supplementary methods, figures, synthetic controller logs, parameter tables, scenario summary tables, simulation protocol, citation metadata, checksum file, and license/copyright notice. No human participant data, patient-level data, proprietary manufacturer data, confidential device field data, real adverse-event records, or regulatory-outcome datasets are included. This Zenodo version corresponds to the public preprint/reproducibility package associated with the manuscript submitted to Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability on 20 June 2026.

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