Investigators propose a multi-phase research, development and dissemination program to mitigate alarm fatigue and advance bedside clinical informatics through integration of human factors engineering and an open medical device-interface approach for hardware-level signals acquisition, multi-parametric algorithmic analyses and effective delivery of intelligent alerts to clinical providers. The emphasis on meaningful use in healthcare has led to unprecedented expenditures to bring medicine online.1 Yet the absence of a commensurate surge in true interoperability, open architectures and lay- developer access into medical devices and healthcare information technology is likely contributing to reported failures of these initiatives to improve patient care.2-8 Within this context, it is truly telling that one of the high- profile issues in healthcare, i.e., alarm fatigue, features bedside patient monitor systems as an exemplar of the problematic siloed design paradigm commonly found in medical technologies. As alarm fatigue affects the safety of all patients exposed to healthcare devices and systems that alarm, the study team proposes a non- proprietary approach to improve bedside monitor telemetry systems.9 Specifically, the Push Electronic Relay of Smart Alarms for End User Situational Awareness (PERSEUS) program is an integrative research and development proposal to study experimental alarm fatigue mitigation measures. As a precisely-defined and clinically based proof-of-concept initiative, the program is defined by its origins in the sharp end of healthcare delivery, explicit scope and deliverables, and a primary focus on real-world utility and patient safety impact. Investigators propose to combine the results of preliminary work (previous human factors engineering site intervention; non-commercial hardware and software prototypes) with continued HFE analyses and biomedical engineering efforts to experimentally augment existing monitor systems. The intervention will be designed to 1.) Directly access patient physiologic datastreams from monitors, 2.) Process acquired signals with multi- parametric algorithms to generate intelligent alerts, and 3.) Effectively communicate the derived high-impact information with targeted push messaging to specified clinical providers. In effect, the program's experimental intervention will work to supersede [threshold-alarm-broadcast] mechanisms with a [thresholds-processing- information-targeting-delivery] approach. Structured use-testing of the experimentally-modified patient monitor telemetry system and comparative assessment relative to base system will be conducted in a busy, high-acuity Emergency Department; proven arrhythmia-on-telemetry simulations and longitudinal tracer patient observations for outcome metrics that signify translational benefit (e.g., serious cardiopulmonary events and therapeutic interventions in real patients) will be used. The hardware and software developed to implement the experimental intervention will be compiled into a program deliverable as an open research toolbox for extramural investigations on legacy and new medical devices, with test dissemination at a sister site ED.
Significant problems exist in the medical devices and systems tasked with alerting healthcare providers to the active problems and potential issues that arise during the bedside delivery of patient care. One result is the complex phenomenon of alarm fatigue, a widespread patient safety dilemma that stems from a variety of challenging causal elements (e.g., poor real-world accuracy of single-threshold alarm algorithms, suboptimal hardware and software usability, manufacturer-imposed device limitations). Investigators propose to apply human factors engineering methods to formally study alarm fatigue and contributory factors then develop and test an open, innovative and experimental biomedical engineering / informatics intervention that will attempt to mitigate alarm fatigue through real-tim multi-parametric analyses that generate smarter, 'human-engineered' alerts for targeted 'need-to-know' delivery to clinical personnel.
| Gadhoumi, Kais; Do, Duc; Badilini, Fabio et al. (2018) Wavelet leader multifractal analysis of heart rate variability in atrial fibrillation. J Electrocardiol 51:S83-S87 |
