A recent article in Nature highlighted the immense potential and barriers to adoption of digital twins in medicine:

 

“…the biggest promise and challenge in applying computational modeling at the level of individual patients: given that biological heterogeneity leads to a wide range of responses to illness and treatments, can computational models, together with the right kinds of data, help the medical team intervene with more effective and better-timed interventions, tailored to an individual patient and resulting in better outcomes?”

 

While the biological complexity has slowed the adoption of Medical Digital Twins (MDT), this limitation does not exist in industry, which has allowed applications to flourish, such as in the design and manufacturing of jet engines. By creating virtual replicas of physical assets, digital twins allow for real-time monitoring, predictive maintenance, and the simulation of interventions without the risk or cost associated with manipulating the assets themselves.  Why build and test 100 jet engines when you can test a high-fidelity replica in-silico?  And why make changes to your radiology operations without knowing if it will move the needle on your KPI’s?

 

There is an immediate, practical application of digital twins with the promise to provide better imaging care to more patients.  At Quantivly, we’re building on these industrial foundations to pioneer an ambitious application of digital twins, not for individual patient care, but for the holistic operational system of hospital radiology departments.

 

Quantivly's Operational Digital Twin: A System-Wide Approach

Quantivly’s approach to this challenge is innovative and ambitious. We are developing an operational digital twin for radiology departments to succeed in our mission to provide better imaging care to more patients.  Instead of modeling complex biological mechanisms, our digital twin models the complexity of radiology operations: performance of physical assets (MRI and CT scanners), human workflows (radiologic technologists, radiologists, sedation teams), exam quality and safety, department financial performance, and much more.  

Given the diverse stakeholders in healthcare, it is critical that the model allows interrogation, simulation, and forecasting of the various metrics according to the goal of each stakeholder: e.g. increase access to imaging (higher volume), increase revenue and return on capital investment, improve image quality and safety.

Radiology sits at the nexus of patient care for the majority of all serious conditions, making it the perfect proving ground for this technology. The department’s complexity, both in terms of the data it generates and its operational challenges, makes it ripe for optimization through digital twinning.

 

Under the Hood: The Technology Enabling Quantivly's Digital Twin

At the heart of Quantivly’s digital twin is a novel ontology purpose-built for radiology operations that includes new foundational concept. Simple examples include the concepts of exam and acquisition durations, vital for reconstructing a complete picture of operational performance and analyzing key performance indicators like exam delays and slot utilization.

Harmonizing data from diverse sources, including HL7 and DICOM, is a crucial step in this process. This harmonization not only facilitates the integration of data from multiple sources (horizontal scaling) but also enables the addition of new descriptors (vertical scaling) such as repeat images and image quality assessments. This continuous calibration of our digital twin with harmonized data allows for a dynamic, accurate representation of radiology operations.

 

Beyond Retrospective Analysis: Predictive Analytics and Forecasting

Drawing inspiration from the asset–twin system abstraction, Quantivly’s digital twin and the real-world radiology department interact through a continuous exchange of data and control inputs. This interaction is not limited to retrospective analysis. By leveraging predictive analytics and forecasting, our digital twin can recommend interventions that promise to improve efficiency and outcomes significantly.

For instance, by analyzing trends and patterns in operational data, Quantivly’s digital twin might suggest modifications to an imaging protocol or workflow adjustments that could help deliver better care to more patients, more efficiently.

 

Advanced Modeling for Operational Excellence

At the core of Quantivly’s innovation is not just the accumulation and harmonization of data but the intelligent application of this data through advanced modeling. A standout example of this is our scheduling model, a sophisticated system designed to optimize the complex logistics inherent in radiology operations.

 

Transforming Scheduling with Smart Recommendations

The scheduling of radiology exams involves a myriad of variables, from equipment availability and patient needs to staff schedules and emergency priorities. Traditionally, this puzzle has been navigated through manual planning—a process both time-consuming and prone to inefficiencies. Quantivly’s approach revolutionizes this through the Smart Recommendation Engine (SRE), which utilizes our scheduling model to provide actionable insights and recommendations for future scheduling enhancements.

By analyzing patterns, identifying bottlenecks, and forecasting demands, the SRE offers strategic suggestions aimed at maximizing departmental efficiency and patient throughput. Initially, implementing these recommendations may require manual updates in the Radiology Information System (RIS), providing a controlled transition towards more optimal scheduling practices.

 

Towards Automation in Scheduling

Quantivly envisions a future where scheduling in radiology departments can be largely automated, leveraging the power of our models to solve the scheduling puzzle more effectively than humanly possible. The complexity of scheduling, akin to a constantly evolving puzzle, is precisely the kind of challenge that computers excel at—processing vast amounts of data to find the most efficient solutions in real-time.

This transition to automation will not only streamline operations but also free up valuable human resources to focus on areas where personal judgment and interaction are irreplaceable, further enhancing the quality of patient care.

 

The Role of Quantivly's Smart Recommendation Engine

The SRE is at the heart of our strategy to move from manual to automated processes. By continuously learning from operational data, it becomes increasingly adept at identifying opportunities for improvement. Whether suggesting minor adjustments to the schedule or proposing significant operational overhauls, the SRE is an indispensable tool in the quest for operational excellence.

As we refine and expand our models, we anticipate the SRE evolving into an even more powerful asset, capable of making real-time recommendations and, eventually, implementing changes autonomously within predefined parameters. This vision represents a significant leap towards fully optimized, data-driven operations in radiology departments.

 

The Future of Radiology Operations

The implications of deploying digital twins in radiology are profound. By enhancing operational efficiency, we can reduce patient wait times, streamline the patient experience, and alleviate staff stress. This operational optimization translates directly into improved patient care, embodying the win-win-win scenario that digital twins promise.

The development of sophisticated models like our scheduling model, powered by Quantivly’s unified data layer, marks the beginning of a new era in radiology operations. With the Smart Recommendation Engine leading the charge towards automation, we’re not just predicting the future of healthcare operations; we’re actively creating it.

Quantivly is at the forefront of this technological revolution in healthcare, demonstrating the vast potential of operational digital twins to transform not just radiology departments but the broader landscape of healthcare delivery.

 

Join us!

Book a demo at the top of the page or visit us in person at SIIM 2024 in Startup Kiosk #9. Join us in exploring the future of healthcare operations with Quantivly’s digital twin technology. Discover how we’re leveraging this innovative approach to transform radiology departments and, ultimately, patient care. Together, we can reimagine the possibilities of healthcare delivery.