Patient-Human Digital Twin (Patient-HDT) Platform
Even tiny changes in patient vital signs and measured values can signal a developing problem. Time is of the essence when it comes to predicting these deviations, placing them in context, understanding them, and reacting to them with a therapeutic change. These tasks are especially demanding for physicians who have little experience with the patient group.
Challenges with ICU Care
No reliable technology for predicting ICU patients' critical events
No patient-specific predictions or risk alarms
Critical ICU events may have long-term effects on patients
Patient risk assessment is still dependent on the attending physician's competence and knowledge, which may vary between shifts and facilities
Many critical events may be predicted if there is real-time, comprehensive patient monitoring
Longer Mean Time to Respond (MTTR) after identifying a
critical event (assessment, data analysis, treatment option assessments) raises patient risk
High cost and limited ICU capacities
The Patient-Human Digital Twin
Digital twin-based technology
Provides automated and patient-specific predictive risk notifications
Provides treatment recommendations and simulations
Provides consistent and expandable knowledge base to close the knowledge gap across medical teams
Compensates for the increasing lack of specialized medical personnel
Expandable to cover various types of patient metrics and treatments
Open architecture that can integrate with, and learn from any patient-related data source
How is the Patient-HDT Platform Different?
Patient-specific, can be provided as a service to patients to improve risk prediction after they return to their everyday life
Continuous learning from patients’ digital interactions to enrich risk assessment and recommendations
Continuous and automatic updating with new medical information to improve risk predictions and mitigation recommendations
Insights from diverse Patient-HDT can be incorporated into a single dynamic medical knowledge base to use anywhere to enhance treatment quality
How to Start
Building data-driven healthcare solutions is a challenge. Not just because of patient data privacy limitations but also because of the extensive specialized medical expertise required to engineer this data and specify the necessary underlying assumptions to define data correlations necessary to provide reliable answers. This means depending on the-already limited-time and effort of specialists to provide such insights, even before being confident of the solution's value.
Our method uses raw data to define initial data-related assumptions and to determine the feasibility and value of the solution.
Therefore, our approach saves the time and effort needed by specialists and offers a structured method for using Patients HDT to enhance patient care and narrow the knowledge gap among medical personnel.
Start by the Solution Feasibility
Events Risk Profile Example
Modeling Patients, A Key Challenge for AI Solutions in Healthcare
Digital twins are expected to offer unprecedented insights into a variety of fields of medicine and healthcare. At some point, digital twins will fundamentally alter how physicians engage with their patients. Doctors would employ digital twins to diagnose patients' conditions more accurately, simulate treatment possibilities, and provide assistance during life-saving surgery.
On the other hand, the human body is an extraordinarily complex and dynamic system. Constructing a model of an organ or a patient remains a complex undertaking, made even more challenging due to the sensitivity around patients' health data.
However, utilizing the Patient-HDT Platform enables a relatively simple and staged introduction of digital twins into the medical sector. For example, the radar chart depicted here, which models a hypothetical medical risk event, can provide preliminary insights into the significance of several medical and lifestyle factors associated with that risk event and their likelihood of contributing to that event, all without application of elaborate machine learning techniques.
Once the model have been confirmed for its consistency, accuracy, and value in terms of risk reduction, the more elaborate implementation of the Patient-HDTs for the individual patients can commence..