Innovation Insight for Healthcare Provider Digital Twins

A digital twin is a digital representation of a real-world entity or system. The implementation of a digital twin is a model that mirrors a unique physical object, process, organization, person or other abstraction. For healthcare providers, digital twins provide an abstraction of the healthcare ecosystem’s component characteristics and behaviors. These are used in combination with other real-time health system (RTHS) capabilities to provide real-time monitoring, process simulation for efficiency improvements, population health and long-term, cross-functional statistical analyses.

Digital twins have the potential to transform and accelerate decision making, reduce clinical risk, improve operational efficiencies and lower cost of care, resulting in better competitive advantage for HDOs. However, digital twins will only be as valuable as the quality of the data utilized to create them. The digital twin of a real-world entity is a method to create relevance for descriptive data about its modeled entity. How that digital twin is built and used can lead to better-informed care pathways and organizational decisions, but it can also lead clinicians and executives down a path of frustration if they get the source data wrong. The underlying systems that gather and process data are key to the success for digital twin creation. Get those systems right and digital twins can accelerate care delivery and operational efficiencies.
 

Twins in Healthcare Delivery

The fact is that HDOs have been using digital twins for years. Although rudimentary in function, digital representations of patients, workflow processes and hospital operations have already been applied by caregivers and administrators across the HDO. For example, a physician uses a digital medical record to develop a treatment plan for a patient. The information in the medical record (a rudimentary digital twin) along with the physician’s experience, training and education combine to provide a diagnostic or treatment plan. Any gaps in information must be compensated through additional data gathering, trial-and-error treatments, intuitive leaps informed through experience or simply guessing. The CIO’s task now is to remove as many of those gaps as possible using available technology to give the physician the greatest opportunity to return their patients to wellness in the most efficient possible manner.

Today, one way to close those gaps is to create the technology-based mechanisms to collect accurate data for the various decision contexts within the HDO. These contexts are numerous and include decisioning perspectives for every functional unit within the enterprise. The more accurate the data collected on a specific topic, the higher the value of the downstream digital twin to each decision maker (see Figure 1).

Figure 1: Digital Twins Are Only as Good as Their Data Source

HDO CIOs and other leaders that base decisions on poor-quality digital twins increase organizational risk and potential patient care risk. Alternatively, high-quality digital twins will accelerate digital business and patient care effectiveness by providing decision makers the best information in the correct context, in the right moment and at the right place — hallmarks of the RTHS.
 

Benefits and Uses

Digital Twin Types in Healthcare Delivery

Current practices for digital twins take two basic forms: discrete digital twins and composite digital twins. Discrete digital twins are the type that most people think about when approaching the topic. These digital twins are one-dimensional, created from a single set or source of data. An MRI study of a lung, for example, is used to create a digital representation of a patient that can be used by trained analytics processes to detect the subtle image variations that indicate a cancerous tumor. The model of the patient’s lung is a discrete digital twin. There are numerous other examples of discrete digital twins across healthcare delivery, each example tied to data collection technologies for specific clinical diagnostic purposes. Some of these data sources include vitals monitors, imaging technologies for specific conditions, sensors for electroencephalography (EEG) and electrocardiogram (ECG). All these technologies deliver discrete data describing one (or very few) aspects of a patient’s condition.

Situational awareness is at the heart of HDO digital twins. They are the culmination of information gathered from IoT and other sources to create an informed, accurate digital model of the real-world healthcare organization. Situational awareness is the engine behind various “hospital of the future,” “digital hospital” and “smart patient room” initiatives. It is at the core of the RTHS.

Digital twins, when applied through the RTHS, positively impact these organizational areas (with associated technology examples — the technologies all use one or more types of digital twins to fulfill their capability):

Care delivery:

• Clinical communication and collaboration
• Next-generation nurse call
• Alarms and notifications
• Crisis/emergency management
• Patient engagement:
• Experiential wayfinding
• Integrated patient room