In a recent survey, 92 percent of healthcare consumers believed that improving the patient experience should be a top strategic priority.
And in fact, the Affordable Care Act includes several new provisions for measuring and reporting the patient experience of care, along with improved outcomes and better-targeted healthcare delivery.
Rapid advances in medical technology hold the promise to meet these demands, but with this increased clinical complexity comes increased risk of incorrect diagnoses or unnecessary or ineffective treatment —factors that negatively impact the very experience that technology is supposed to enhance in the first place.
The primary reason for this failure is the current disjointed flow of information within our current healthcare system. We need to identify the informational gaps and synchronize all sources of patient data to create a complete picture of a patient’s needs. And we need to map this picture to the entire healthcare delivery spectrum.
Digital twin technology is quickly emerging as a way to address this challenge, providing a comprehensive approach to characterize each unique patient within the system to trigger the right care processes to prevent and treat health conditions.
A digital twin can improve both care delivery and the patient experience through effective and efficient patient-centric care
IBM defines a digital twin as “the virtual representation of a physical object or system across its life-cycle." A digital twin has represented a virtual model of a process or product in key industries. But in healthcare, it represents an individual patient. This pairing of the virtual and physical worlds allows analysis of real-time data and monitoring of responses to treatments, as well as tracking the results of behavior and lifestyle modification, to head off problems before they even occur.
As an example, a wearable sensor could track a patient’s blood pressure and map it to medication compliance. That information, as well as data on the patient’s diet, lifestyle choices and genetic composition together comprise a digital twin, an operational scenario that can be developed to provide a plan for medication and lifestyle modifications to help the patient realize the healthiest possible condition.
Expanding use cases for digital twins
The following examples offer insight into how digital twins are being deployed to improve the performance of healthcare in other unique ways:
Researchers at Oklahoma State University’s Computational Biofluidics and Biomechanics Laboratory (CBBL) worked with ANSYS to develop a series of digital twins that modeled the particles of aerosol-delivered chemotherapeutic drugs as they move through a human upper airway. Previous data showed that only 20 percent of an inhaled drug reached its tumor within the lungs, with the rest potentially damaging healthy tissues. By varying parameters such as the diameters of the particles, inhalation flow rates and the initial position of the medication within the aerosol, researchers eventually optimized the medical aerosol to have a delivery efficiency over 90 percent.
The Blue Brain Project (BBP) has a goal of building comprehensive digital models of the human brain. According to Hewlett Packard Enterprise, this can provide the basis for a potentially unlimited range of simulation, including a range of computing profiles to support models of the brain’s different levels and performance of these systems.
Sim&Cure, working with ANSYS, has developed a patient-based digital twin for treating brain aneurysms. The twin is improving the results of endovascular repair, where a catheter-guided implant is used to strengthen damaged arteries and reduce the pressure on the aneurysm caused by irregular blood flow. By creating patient-specific 10-20 seconds simulations, a surgeon can better understand the predicted results of an implant’s placement. While 10 percent of endovascular treatments typically necessitate follow-up procedures, at the time of this research, not a single procedure using the digital twins has needed an additional intervention.
The challenges of creating digital twins
With something as complex as creating a digital twin, the organization, movement and storage of data is where everything can go right or wrong. Foundationally sound algorithms need to accept vast, multidimensional, unstructured data, potentially moving at a breakneck pace. And then they need to translate this data into clear, actionable outcomes.
To visualize what's at stake, consider another project by Siemens Healthineers to develop a digital twin of the human heart. This twin can enable a surgeon to see the impact on a patient's condition and the placement of electrodes in the heart before the surgery even begins. Thousands of dollars can be saved in unnecessary costs of care or additional surgeries by ensuring the best possible outcome for the first surgical event. But with so many variables at play in simulating a human heart, a few errors in data processing could result in a catastrophic failure costing the life of a patient.
The digital twin, however, is not just about managing and analyzing data. It’s a transformative approach to creating a complex computing architecture to connect different parts of a whole system, whether an individual patient and the systems of his or her body, or the systems of care within the healthcare ecosystem. The goal is to mine actionable insights that enable better decision making and ultimately improved patient outcomes.
Digital twin technology is enabling providers and health systems to get closer to the patient than ever before, providing an increasingly more holistic and realistic portrait of each patient. While its use is still in the early stages, its potential for enabling a new level of patient experience and positive outcomes is far-reaching, bringing digital transformation in healthcare to new heights.
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Dr. Sawad Thotathil, Clinician, AVP, Healthcare for Persistent Systems, helps health systems achieve their digital transformation goals.