Dr. Erkan Hassan

Monthly Writings

Evaluations and reviews of the latest in the field.

Digital Twins in Healthcare: 4 Things to Know

SUMMARY:

  • A data driven Digital Twin represents all processes and operations related to the real world physical twin.

  • The same disease will manifest and respond differently from one patient to another, therefore the Digital Twin will be personalized from genotype to phenotype and social determinants.

  • Although shown of benefit in various industries, the Digital Twin has had limited use in healthcare.

  • Current challenges are described.

BACKGROUND

  • One of the recent technology advancements is the development of the Digital Twin (DT) which creates a virtual representation of patients, spaces or processes.

  • ·The goal is to provide a virtual digital model which can be used to develop and test care models in the virtual space.  This will allow to test quicker, cheaper and with far less potential negative outcomes than in real life.

  • Despite the high level of interest in the potential value of DTs, their current use remains low, especially in healthcare.

  • Below are the 4 things to know about DTs.

REVIEW:        

1.       DTs consist of: 1) a real space; 2) a virtual space; 3 a digital thread allowing for flow of information

  • DTs can be used to simulate the results of individual therapeutic interventions as well as disease progression for a patient.

  • DTs also extend to medical devices and hospital operational systems.

  • DT data sources include:

    • EHRs

    • Registries

    • Genomics

    • Physical markers

    • Demographics

    • Social Determinants

    • Digital components (smartphones, wearables, implantable sensors)

  • There are 3 types of DTs:

    • Product twinning – To test if the physical product will perform exactly as planned

    • Process twinning – Testing process and workflow changes

    • System or Performance Twinning – Operational data analysis to optimize interactions of components within a system.

2. DT in Medical Decision Making:

  • DTs via precision medicine test approaches to improve/maximize disease treatment and prevention based on individual patient characteristics.

  • Unlimited copies of a patient’s DT can be constructed with computational integration of thousands of possible variables.

  • Each DT can follow a different treatment path.

  • The drug or treatment path with the best results can then be selected for the real patient treatment.

  • Current DT applications being explored include:

    • Heart

    • Brain

    • Respiratory

    • Multiple sclerosis

    • Viral infections

    • Trauma management

    • Diabetes

3. DTs in Hospital Management

  • DTs replicating hospitals or treatment facilities can help in understanding and optimizing business operations processes.

  • Can address areas such as:

    • Overcrowding

    • Waiting times

    • Delays

    • Supply chain

    • Predicting Resource shortages

    • Staffing

    • Cybersecurity

  • Strategies to improve communication and patient centered care can be tested via DTs to assess outcomes before real life adoption.

 

4. DTs Challenges

  • Technical limitations of data collection and data flow of digitally twinned sensors

  • Data security and privacy of interconnected devices

  • Socio-ethical issues:

    • Artificial Intelligence

    • The Internet of Things

    • Big Data

    • Robotics

    • Who owns the extracted data?

  • Can DTs adequately predict outcomes alone be sufficient for therapy selection or preventative care processes?

 

 

CONCLUSIONS:

  • Digital Twins hold great potential

  • Numerous barriers prevent Digital Twins from current use within the healthcare system.

  • Digital Twins should be used as decision support aids, but not to replace clinical decision making.

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Erkan Hassan