Digital Twins for More Precise and Personalized Treatment.

The use of Digital Twins (DTs) or the digital replicas of physical entities has provided benefits to several industry sectors, most notably manufacturing. To date, the application of DTs in the healthcare sector has been minimal, however. But, as pressure increases for more precise and personalized treatments, it behooves us to investigate the potential for DTs in the healthcare context. As a proof-of-concept demonstration prior to working with real patients, we attempt in this paper, to explore the potential for creating and using DTs. We do this in a synthetic environment at this stage, making use of data that is all computer-generated. DTs of synthetic present patients are created making use of data of synthetic past patients. In the real world, the clinical objective for creating such DTs of real patients would be to enable enhanced real-time clinical decision support to enable more precise and personalized care. The objective of the numerical experiment reported in this paper, is to envisage the possibilities and challenges of such an approach. We attempt to better understand the strengths and weaknesses of applying DTs in the healthcare context to support more precise and personalized treatments.

Healthcare Optimization and Augmented Intelligence by Coupling Simulation & Modeling: An Ideal AI/ML Partnership for a Better Clinical Informatics.

Healthcare must deliver high quality, high value, patient-centric care while improving access and costs even as aging and active populations increase demand for services like knee arthroplasty. Machine learning and artificial intelligence (ML/AI) using past clinical data primarily replicates existing cause-to-effect actions. This is insufficient to forecast outcomes, costs, resource utilization and complications when radical process re-engineering like COVID- inspired telemedicine occurs. To predict episodes of care for innovative arthroplasty patient journeys, a sophisticated integrated knowledge network must model optimal novel care pathways. We focus on the first step of the patient journey: shared surgical decision making. Patient engagement is critical to successful outcomes, yet existing methods cannot model impact of specific decision variables like interactive clinician/caregiver/patient participation in pre- and post-operative rehabilitation, and other factors like comorbidities. We demonstrate coupling of simulation and AI/ML for augmented intelligence musculoskeletal virtual care decisions for knee arthroplasty. This novel coupled-solution integrates critical data and information with tacit clinician knowledge.

A Precision Post-Operative Wellness Monitoring Solution.

Multiple orthogonal challenges around escalating costs and providing quality care plague healthcare delivery, especially in OECD countries. This research in progress paper addresses the post-operative discharge phase of the patient journey and proffers a technology-enabled model that both supports a quality care experience post-discharge but also prudent management to minimise costly unplanned readmissions and thereby subscribe to a value-based care paradigm. The chosen context is stoma patients but the solution can be easily generalised to other contexts. Next steps include the conduct of clinical trials to establish proof of concept, validity and usability.

Ensuring Patient Safety by using Colored Petri Net Simulation in the Design of Heterogeneous, Multi-Vendor, Integrated, Life-Critical Wireless (802.x) Patient Care Device Networks.

Hospitals and manufacturers are designing and deploying the IEEE 802.x wireless technologies in medical devices to promote patient mobility and flexible facility use. There is little information, however, on the reliability or ultimate safety of connecting multiple wireless life-critical medical devices from multiple vendors using commercial 802.11a, 802.11b, 802.11g or pre-802.11n devices. It is believed that 802.11-type devices can introduce unintended life-threatening risks unless delivery of critical patient alarms to central monitoring systems and/or clinical personnel is assured by proper use of 802.11e Quality of Service (QoS) methods. Petri net tools can be used to simulate all possible states and transitions between devices and/or systems in a wireless device network, and can identify failure modes in advance. Colored Petri Net (CPN) tools are ideal, in fact, as they allow tracking and controlling each message in a network based on pre-selected criteria. This paper describes a research project using CPN to simulate and validate alarm integrity in a small multi-modality wireless patient monitoring system. A 20-monitor wireless patient monitoring network is created in two versions: one with non-prioritized 802.x CSM protocols and the second with simulated Quality of Service (QoS) capabilities similar to 802.11e (i.e., the second network allows message priority management.) In the standard 802.x network, dangerous heart arrhythmia and pulse oximetry alarms could not be reliably and rapidly communicated, but the second network's QoS priority management reduced that risk significantly.