Remote Monitoring of Cardiac Implantable Electronic Devices

Róka, Attila; Schoenfeld, Mark H.

doi:10.19102/icrm.2011.020704

Cited by 5 publications

(1 citation statement)

References 46 publications

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“…2 Similarly, cardiac implantable electronic devices were only able to be evaluated during in-person office visits until remote monitoring capabilities were made available, and the option for remote monitoring is now included in almost every implanted device. 3 Overall, cardiac monitors are becoming smaller, more accurate, and more sophisticated. 4 The power of such technologies for the evaluation and validation of our treatments is no more evident than when one thinks of the sheer number of patients evaluated-the landmark pivotal studies that formed important platforms in the approval process for devices such as the implantable defibrillator enrolled only hundreds or thousands of patients, while hundreds of thousands of patients are involved trials such as the ALTITUDE study.…”

mentioning

confidence: 99%

Cardiac Monitoring: Then and Now

Doshi

2019

J Innov Cardiac Rhythm Manage

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mentioning

confidence: 99%

Cardiac Monitoring: Then and Now

Doshi

2019

J Innov Cardiac Rhythm Manage

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Cybersecurity threats to cardiac implantable devices: room for improvement

Tomaiko

Zawaneh

2021

Current Opinion in Cardiology

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Purpose of review For over a decade, vulnerabilities in the healthcare industry have been identified. Medical devices such as cardiovascular implantable electronic devices (CIEDs) are particularly concerning because of direct threats to patient safety and protected health information (PHI). Although these vulnerabilities have been identified and changes have been made, there is significant room for improvement. We identify changes and improvements to be made in the industry, by providers, and by patients. Recent findings Cybersecurity threats in cardiac implantable devices are legitimate concerns for patient safety and PHI. Changes to cybersecurity in these devices have been made, but are far from sufficient. Summary The number of CIEDs implanted worldwide are expected to increase over the next decade. As computer technology advances, cybersecurity threats will only continue to evolve and become more complex. The healthcare industry should seriously consider improvements to protect patients and providers.

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Clinician Preimplementation Perspectives of a Decision-Support Tool for the Prediction of Cardiac Arrhythmia Based on Machine Learning: Near-Live Feasibility and Qualitative Study

Matthiesen¹,

Diederichsen²,

Hansen³

et al. 2021

JMIR Hum Factors

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Background Artificial intelligence (AI), such as machine learning (ML), shows great promise for improving clinical decision-making in cardiac diseases by outperforming statistical-based models. However, few AI-based tools have been implemented in cardiology clinics because of the sociotechnical challenges during transitioning from algorithm development to real-world implementation. Objective This study explored how an ML-based tool for predicting ventricular tachycardia and ventricular fibrillation (VT/VF) could support clinical decision-making in the remote monitoring of patients with an implantable cardioverter defibrillator (ICD). Methods Seven experienced electrophysiologists participated in a near-live feasibility and qualitative study, which included walkthroughs of 5 blinded retrospective patient cases, use of the prediction tool, and questionnaires and interview questions. All sessions were video recorded, and sessions evaluating the prediction tool were transcribed verbatim. Data were analyzed through an inductive qualitative approach based on grounded theory. Results The prediction tool was found to have potential for supporting decision-making in ICD remote monitoring by providing reassurance, increasing confidence, acting as a second opinion, reducing information search time, and enabling delegation of decisions to nurses and technicians. However, the prediction tool did not lead to changes in clinical action and was found less useful in cases where the quality of data was poor or when VT/VF predictions were found to be irrelevant for evaluating the patient. Conclusions When transitioning from AI development to testing its feasibility for clinical implementation, we need to consider the following: expectations must be aligned with the intended use of AI; trust in the prediction tool is likely to emerge from real-world use; and AI accuracy is relational and dependent on available information and local workflows. Addressing the sociotechnical gap between the development and implementation of clinical decision-support tools based on ML in cardiac care is essential for succeeding with adoption. It is suggested to include clinical end-users, clinical contexts, and workflows throughout the overall iterative approach to design, development, and implementation.

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Remote Monitoring of Cardiac Implantable Electronic Devices

Cited by 5 publications

References 46 publications

Cardiac Monitoring: Then and Now

Cardiac Monitoring: Then and Now

Cybersecurity threats to cardiac implantable devices: room for improvement

Clinician Preimplementation Perspectives of a Decision-Support Tool for the Prediction of Cardiac Arrhythmia Based on Machine Learning: Near-Live Feasibility and Qualitative Study

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