Improving Patient Care via Digital Twins

improving-patient-care-via-digital-twins

One of the major challenges in the healthcare field is being able to accurately assess the overall health of a patient. Most people see a number of different healthcare providers. Even though electronic medical records (EMRs) have taken hold (EMR penetration in office-based settings in the U.S. is at about 86%, according to 2017 data from the Office of the National Coordinator for Health Information Technology), there is relatively little information sharing between practices unless records are requested. Further, the rise in wearable digital technology, such as the Fitbit and Apple Watch, is generating even more health data that rarely is transmitted to health professionals to monitor and analyze.

The disaggregated nature of health data may become a relic of the past, thanks to the research being done into the use of digital twins for healthcare monitoring. Often used in manufacturing, digital twins are exact replicas of an asset, process, or system that is created by capturing sensor and/or operational data relevant to the dynamics of how that real-world entity operates throughout its life cycle.

Capturing and Tracking Patient Data

Within the patient care field, digital twins enable doctors and other healthcare providers to capture and track patient data and then construct a digital model of the patient. They incorporate a variety of care data, including vital medical information from medical records, current medication, imaging studies, and patient-provided health data from exercise or health monitoring applications.

Digital twins used in manufacturing often capture and aggregate data collected by sensors on machines, which is then sent back to a platform via an Internet of Things (IoT) network. In contrast, much of the patient digital twin data will need to be pulled from a wide variety of medical records systems and then be augmented by any patient-worn fitness or health devices. This will require significant technical and operational coordination between medical records technology providers, caregivers, payers, and privacy advocates.

While there will be challenges involved with coordinating data aggregation, digital twins likely will provide significant initial benefits. Patients and caregivers will have a complete medical picture that likely will be updated with new data on a regular basis. Doctors and specialists will be able to take that data and create a digital model of the patient that can be used as a virtual test bed for future treatment, without endangering the patient.

Reaping the Benefits of Digital Twins

Siemens Healthineers is in the process of working on the development of digital twins of individual patients’ entire bodies. In addition to a patient’s clinical data, these twins would also be capable of integrating cellular, molecular, and genetic information from the patient. Armed with this information, physicians may decide whether a specific drug would be likely to help and at what dosage it should be given, based on the digital model.

The end goal is to identify health problems even before they become clinically detectable by using artificial intelligence algorithms to mine through and detect and highlight patient health risks. Because the digital twin would be continuously updated with new data being gathered, the algorithms will increasingly grow more accurate over time. The individual predictions and courses of actions prescribed by the digital twin could be used to prevent illness or disease.

As one example of this type of approach, France-based healthcare startup Sim&Cure has developed a patient-based digital twin for treating aneurysms. The company has secured regulatory approval for a digital twin that helps surgeons select and deploy endovascular implants that optimize aneurysm repair.

Once a patient is prepped for surgery, Sim&Cure’s software creates a 3D model of the aneurysm and the surrounding blood vessels based on 3D rotational angiography imaging. Software uses this personalized model to properly size the aneurysm site and offers the surgeon a selection of devices appropriate and available for implant. After the appropriate device is selected, the software creates an input file for analysis in ANSYS Mechanical. The surgeon can then run 10- to-20-second simulations in the twin, focusing in on the device, to help them gain a focused understanding of the interactive relationship between the implant and the aneurysm. Preliminary trials have been successful. While 10% of endovascular treatments necessitate follow-up procedures, not a single procedure using Sim&Cure’s software has required additional intervention.

Generating Revenue

Tractica projects that using digital twins in patient care and personalized monitoring applications will generate $152.6 million in global annual revenue by 2025, up from just $15.0 million in 2018 at a compound annual growth rate (CAGR) of 33.6%. This use case, along with additional use cases in healthcare, will generate more than $660 million in 2025 healthcare digital twin annual revenue, up from about $56 million in 2018. Tractica’s forthcoming report on digital twins will discuss 28 distinct use cases for digital twins across eight industry groups and five global regions. The report will be published in late May 2019.

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