Medical device active surveillance of spontaneous reports: A literature review of signal detection methods

A monitoring framework for health care processes using Generalized Additive Models and Auto-Encoders

In recent years, there has been a considerable focus on developing effective methods for monitoring health care processes. Utilizing Statistical Process Monitoring (SPM) approaches, particularly risk-adjusted control charts, has emerged as a highly promising approach for achieving robust frameworks for this aim. Considering risk-adjusted control charts, longitudinal health care process data is typically monitored by establishing a regression relationship between various risk factors (explanatory variables) and patient outcomes (response variables). While the majority of prior research has primarily employed logistic models in risk-adjusted control charts, there are more intricate health care processes that necessitate the incorporation of both parametric and nonparametric risk factors. In such scenarios, the Generalized Additive Model (GAM) proves to be a suitable choice, albeit it often introduces higher computational complexity and associated challenges. Surprisingly, there are limited instances where researchers have proposed advancements in this direction. The primary objective of this paper is to introduce an SPM framework for monitoring health care processes using a GAM over time, coupled with a novel risk-adjusted control chart driven by machine learning techniques. This control chart is implemented on a data set encompassing two stroke types: ischemic and hemorrhagic. The key focus of this study is to monitor the stability of the relationship between stroke types and predefined explanatory variables over time within this data set. Extensive simulation results, based on real data from patients with acute stroke, demonstrate the remarkable flexibility of the proposed method in terms of its detection capabilities compared to conventional approaches.

Evidence-based clinical engineering: Health information technology adverse events identification and classification with natural language processing

The primary goal of this project is to create a framework to extract Real-World Evidence to support Health Technology Assessment, Health Technology Management, Evidence-Based Maintenance, and Post Market Surveillance (as outlined in the EU Medical Device Regulation 2017/745) of medical devices using Natural Language Processing (NLP) and Artificial Intelligence. An initial literature review on Spontaneous Reporting System databases, Health Information Technologies (HIT) fault classification, and Natural Language Processing has been conducted, from which it clearly emerges that adverse events related to HIT are increasing over time. The proposed framework uses NLP techniques and Explainable Artificial Intelligence models to automatically identify HIT-related adverse event reports. The designed model employs a pre-trained version of ClinicalBERT that has been fine-tuned and tested on 3,075 adverse event reports extracted from the FDA MAUDE database and manually labelled by experts.

Evaluations of statistical methods for outlier detection when benchmarking in clinical registries: a systematic review

OBJECTIVES

Benchmarking is common in clinical registries to support the improvement of health outcomes by identifying underperforming clinician or health service providers. Despite the rise in clinical registries and interest in publicly reporting benchmarking results, appropriate methods for benchmarking and outlier detection within clinical registries are not well established, and the current application of methods is inconsistent. The aim of this review was to determine the current statistical methods of outlier detection that have been evaluated in the context of clinical registry benchmarking.

DESIGN

A systematic search for studies evaluating the performance of methods to detect outliers when benchmarking in clinical registries was conducted in five databases: EMBASE, ProQuest, Scopus, Web of Science and Google Scholar. A modified healthcare modelling evaluation tool was used to assess quality; data extracted from each study were summarised and presented in a narrative synthesis.

RESULTS

Nineteen studies evaluating a variety of statistical methods in 20 clinical registries were included. The majority of studies conducted application studies comparing outliers without statistical performance assessment (79%), while only few studies used simulations to conduct more rigorous evaluations (21%). A common comparison was between random effects and fixed effects regression, which provided mixed results. Registry population coverage, provider case volume minimum and missing data handling were all poorly reported.

CONCLUSIONS

The optimal methods for detecting outliers when benchmarking clinical registry data remains unclear, and the use of different models may provide vastly different results. Further research is needed to address the unresolved methodological considerations and evaluate methods across a range of registry conditions.

PROSPERO REGISTRATION NUMBER

CRD42022296520.

Materiovigilance in Perspective: Understanding Its Concept and Practice in the Global Healthcare System

Materiovigilance (Mv) has the same purpose and approach in ensuring patient safety as pharmacovigilance but deals with medical devices associated with adverse events (MDAEs) and their monitoring. Mv has been instrumental in recalling many defective or malfunctioning devices based on their safety data. All MDAEs, such as critical or non-critical, known, or unknown, those with inadequate or incomplete specifications, and frequent or rare events should be reported and evaluated. Mv helps to improve medical devices' design and efficiency profile and avoid device-related complications and associated failures. It alerts consumers and health professionals regarding counterfeit or substandard devices. Common events reported through Mv are device breakage and malfunction, entry- and exit-site infections, organ perforations or injuries, need for surgery and even death, and life cycle assessment of devices. Health authorities globally have developed reporting frameworks with timeframes for MDAEs, such as MedWatch in the USA, MedSafe in New Zealand, and others. Health professionals and consumers need to be made aware of the significance of Mv in ensuring the safe use of medical devices and getting familiar with the reporting procedures and action plans in case of a device-induced adverse event.