Journal Article

Roadmap for the evolution of monitoring: developing and evaluating waveform-based variability-derived artificial intelligence-powered predictive clinical decision support software tools

Hudek, Natasha, Ramchandani, Rashi, Ramsay, Tim, Brehaut, Jamie, Barnaby, Doug, Scales, Nathan, Herry, Christophe, Seely, Andrew J. E., Newman, Kimberley, Perry, Jeffrey, Burns, Karen E. A., Jones, Daniel, Dhanani, Sonny, Fernando, Shannon

2024

Overview

Background Continuous waveform monitoring is standard-of-care for patients at risk for or with critically illness. Derived from waveforms, heart rate, respiratory rate and blood pressure variability contain useful diagnostic and prognostic information; and when combined with machine learning, can provide predictive indices relating to severity of illness and/or reduced physiologic reserve. Integration of predictive models into clinical decision support software (CDSS) tools represents a potential evolution of monitoring. Methods We perform a review and analysis of the multidisciplinary steps required to develop and rigorously evaluate predictive clinical decision support tools based on monitoring. Results Development and evaluation of waveform-based variability-derived predictive models involves a multistep, multidisciplinary approach. The stepwise processes involves data science (data collection, waveform processing, variability analysis, statistical analysis, machine learning, predictive modelling), CDSS development (iterative research prototype evolution to commercial tool), and clinical research (observational and interventional implementation studies, followed by feasibility then definitive randomized controlled trials), and poses unique challenges (including technical, analytical, psychological, regulatory and commercial). Conclusions The proposed roadmap provides guidance for the development and evaluation of novel predictive CDSS tools with potential to help transform monitoring and improve care.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V
• Subject: Artificial intelligence / Artificial Intelligence - trends / Automation / Blood pressure / Clinical decision making / Computer software industry / Critical Care Medicine / Data collection / Data science / Decision Support Systems, Clinical - instrumentation / Decision Support Systems, Clinical - standards / Decision Support Systems, Clinical - trends / Disease susceptibility / Electrocardiography / Emergency medical care / Emergency Medicine / Extubation / Fourier transforms / Health care expenditures / Healthcare industry software / Heart beat / Heart rate / Humans / Intensive / Intensive care / International economic relations / Machine learning / Machine Learning - trends / Medical research / Medicine / Medicine & Public Health / Medicine, Experimental / Middleware / Monitoring, Physiologic - instrumentation / Monitoring, Physiologic - methods / Mortality / Patients / Physiology / R&D / Research & development / Review / Sepsis / Software / Software - trends / Time series / Wavelet transforms