What’s new in critical illness and injury science? Keeping up with Moore’s law – Are we ready for the intensive care units of tomorrow?

INTRODUCTION Modern intensive care units (ICUs) are fast-paced, highly complex medical ecosystems, dedicated to close monitoring and minute-by-minute care of the most acutely ill patients.[1,2] This highly stressful, unpredictable environment is filled with cognitive traps, blind spots, and competing priorities, especially for the frontline personnel caring for the critically ill or injured individuals.[1,3–7] Within this high-complexity realm, a quiet and gradual, yet very profound transformation is taking place. In a sense, we are witnessing a behind-the-scenes transition to artificial intelligence (AI)- and Internet-of-Things (IoT)-driven paradigm that will allow the intensivist to finally “make sense out of the chaos” of the previously “random and unpredictable” realm of “irregularly irregular” and “expectedly unstable” patterns.[2,8–13] The overall microcosm of the modern ICU can be defined as a dynamically evolving knowledge matrix, spanning an entire spectrum, from “known knowns” to “unknown unknowns”.[14] In theory, as our collective technological and clinical prowess (e.g., knowledge and skills/procedures) improves, so should our ability to more effectively and efficiently treat our critically ill patients.[15,16] The intended shift away from “unknown unknowns” is unlikely to occur without significant advances in pattern recognition, process/workflow optimization, and data “cognitive noise” reduction, among other factors.[16,17] Ultimately – at least in the utopian “perfect world” – the bedside physician should be able to dedicate majority of their cognitive effort toward making the best possible clinical decision, for the right patient, at the right time, with the right resources. The introduction of modern technologies discussed herein, provides a unique opportunity to turn such abstract concepts into reality. The IoT and blockchain technology (BcT) are well-positioned to become the cornerstones of secure clinical data management across diverse monitoring platforms in the intensive care setting.[18,19] In this context, BcT-based IoT can provide powerful data aggregation and verification functionality, while integrating and organizing critical data points from various ICU sensors, devices, human inputs, and other platforms.[20,21] When viewed as a primary construct, IoT stands to revolutionize how multiple dataflows in the ICU setting are processed and funneled into powerful decision-assist tools; moreover, when coupled with AI-based processing, an entire new level of sophistication may be attainable.[22–25] At this time, we will shift the focus of our discussion to AI and its potential role in enhancing the ICU paradigms of the future. AI tools bring with them the promise of “making order out of chaos” in an environment where many “unknown unknowns” permeate the daily operations of critical care teams. It can be postulated that such “unknown unknowns” are in reality likely to be “unrecognized knowns” – a situation where competing priorities and overwhelming sensory/information inputs simply preclude a calm and well-organized pattern (e.g., radiographic finding or subtle laboratory or vital sign trend) recognition.[8,9,16,26,27] However, the potential benefits of AI in the ICU go far beyond improved pattern recognition. More specifically, AI has the ability to create powerful cognitive prompts, thus formulating a more seamless decision-making environment and facilitating gentle course-correcting decision processes to include potentially missing or previously ignored critical information.[28,29] Examples of early successful implementations of AI in critical care include enhanced mechanical ventilation titration/weaning,[30] better prediction of prolonged mechanical ventilation and need for tracheostomy,[31] ultra-early recognition of sepsis,[32] as well as vastly improved clinical outcome prediction,[33] among other actual and theoretical applications. Not only does ICU care stand to be more precise, effective, and optimized by incorporating AI- and IoT-based technological advances but also it may become safer, with fewer adverse healthcare events.[34,35] In fact, certain applications of AI, IoT, or a combination of both technologies may not be readily apparent at the patient bedside but are likely to result in significant improvement in care delivery. For example, IoT-driven, AI-capable robots/drones may play an important role in the timely delivery of critical supplies, such as vasopressor infusions, antibiotics, and other pharmacological agents,[36,37] thus reducing time-to-treatment and other key correlates of positive clinical outcomes. Various iterations and combinations of “smart equipment” may also involve AI- and IoT-enabled systems, with the addition of radiofrequency identification tags to further optimize various processes that are mission-critical in the ICU setting, including but not limited to blood transfusion, equipment tracking and deployment, unique implant identification/tagging, advanced radiographic image and pathology specimen handling, and laboratory specimen processing. Last, but not least, there is tremendous potential for the use of the so-called "digital twins" - a framework of virtual replica of a physical environment or a patient that mirrors the behavior and other characteristics of the original. The use of "digital twins" could allow complex therapeutic interventions to be safely emulated using an accurate digital model, before actually applying them to real-life clinical scenarios and conditions.[38–43] In summary, we are witnessing the effects of early implementations of highly advanced technologies in the ICU setting. If applied thoughtfully and correctly, these new advances should offload a significant proportion of the current “cognitive burden” associated with the uncertainty, mixed or conflicting data inputs/signals, and multiple simultaneous distractions, thus allowing ICU teams, led by intensivist physicians, to focus more directly on incrementally better and safer bedside patient care, critical decision-making, and application of precise management interventions across “known knowns” while minimizing the number and effects of various “unknown unknowns.”[14]