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.

Using standards to automate electronic health records (EHRs) and to create integrated healthcare enterprises.

President Bush's 2004 Executive Order mandated the creation within the Secretary of Health and Human Services' staff of a new Office of the National Coordinator for Healthcare Information Technology (ONCHIT) that was tasked with creating the United States National Healthcare Information Network (NHIN). The Health Insurance Portability and Accountability Act of 1996 (HIPAA) and the 2004 and a subsequent 2006 Executive Orders have finally set the stage to design, and require, the use of standardized, electronic data interchange-enabled information systems as quickly as possible.

JEDI - an executive dashboard and decision support system for lean global military medical resource and logistics management.

Each individual U.S. Air Force, Army, and Navy Surgeon General has integrated oversight of global medical supplies and resources using the Joint Medical Asset Repository (JMAR). A Business Intelligence system called the JMAR Executive Dashboard Initiative (JEDI) was developed over a three-year period to add real-time interactive data-mining tools and executive dashboards. Medical resources can now be efficiently reallocated to military, veteran, family, or civilian purposes and inventories can be maintained at lean levels with peaks managed by interactive dashboards that reduce workload and errors.

Medical decision support using the Analytic Hierarchy Process.

The Analytic Hierarchy Process for medical and hospital decision support allows the user to design a hierarchical structure and weigh the trade-offs between decision criteria and alternatives to facilitate improved clinical and management decisions.