Intelligent Clinical Decision Support

Abstract

Early recognition of pathologic cardiorespiratory stress and forecasting cardiorespiratory decompensation in the critically ill is difficult even in highly monitored patients in the Intensive Care Unit (ICU). Instability can be intuitively defined as the overt manifestation of the failure of the host to adequately respond to cardiorespiratory stress. The enormous volume of patient data available in ICU environments, both of high-frequency numeric and waveform data accessible from bedside monitors, plus Electronic Health Record (EHR) data, presents a platform ripe for Artificial Intelligence (AI) approaches for the detection and forecasting of instability, and data-driven intelligent clinical decision support (CDS). Building unbiased, reliable, and usable AI-based systems across health care sites is rapidly becoming a high priority, specifically as these systems relate to diagnostics, forecasting, and bedside clinical decision support. The ICU environment is particularly well-positioned to demonstrate the value of AI in saving lives. The goal is to create AI models embedded in a real-time CDS for forecasting and mitigation of critical instability in ICU patients of sufficient readiness to be deployed at the bedside. Such a system must leverage multi-source patient data, machine learning, systems engineering, and human action expertise, the latter being key to successful CDS implementation in the clinical workflow and evaluation of bias. We present one approach to create an operationally relevant AI-based forecasting CDS system.

Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association

“…AI/ML models applied to bedside monitors can detect worsening of heart failure 56 and decompensation, 57,58 in ICU and emergency department settings. These models can detect subtle physiological signatures before clinical deterioration, broadening the diagnostic and therapeutic window for early intervention.…”

Collaborative strategies for deploying AI-based physician decision support systems: challenges and deployment approaches

“…Looking into the future, generalizable CDSS tools will be implemented in healthcare settings in which they were not actually developed. The details surrounding how such generalizable tools will be developed and implemented in local workflows remain up to question 14 . Other challenges include insufficient IT infrastructure in under-resourced clinical settings where building on AI is challenging.…”