From bed to bench and back again: Challenges facing deployment of intracranial pressure data analysis in clinical environments

Abstract

Introduction: Numerous complex physiological models derived from intracranial pressure (ICP) monitoring data have been developed. More recently, techniques such as machine learning are being used to develop increasingly sophisticated models to aid in clinical decision-making tasks such as diagnosis and prediction. Whilst their potential clinical impact may be significant, few models based on ICP data are routinely available at a patient's bedside. Further, the ability to refine models using ongoing patient data collection is rare. In this paper we identify and discuss the challenges faced when converting insight from ICP data analysis into deployable tools at the patient bedside.

Research question: To provide an overview of challenges facing implementation of sophisticated ICP models and analyses at the patient bedside.

Material and methods: A narrative review of the barriers facing implementation of sophisticated ICP models and analyses at the patient bedside in a neurocritical care unit combined with a descriptive case study (the CHART-ADAPT project) on the topic.

Results: Key barriers found were technical, analytical, and integrity related. Examples included: lack of interoperability of medical devices for data collection and/or model deployment; inadequate infrastructure, hindering analysis of large volumes of high frequency patient data; a lack of clinical confidence in a model; and ethical, trust, security and patient confidentiality considerations governing the secondary use of patient data.

Discussion and conclusion: To realise the benefits of ICP data analysis, the results need to be promptly delivered and meaningfully communicated. Multiple barriers to implementation remain and solutions which address real-world challenges are required.

Keywords: Big data; Intracranial pressure; Machine learning.