PIONEER WITH PURPOSE™
The US healthcare system is facing a diagnostic error crisis and legacy information technology systems are a major culprit. Legacy silos and interfaces impede physicians’ cognitive processes and communication. Further, these systems place healthcare professionals under increasing stress with the explosion of big data. According to published research, the amount of healthcare big data has exploded at a rate of roughly 10-fold over the last 20 years, while the number of doctors has increased only 2-fold. Diagnostic errors negatively impact the health and safety of patients, but also cost the US economy billions of dollars annually.
In recent years, many groups have attempted to use "artificial intelligence" algorithms (aka, neural networks) on single medical images to automate "black box" diagnostic decisions made by Radiologists today, coined “AI Automation.” Although it has proven powerful in closed-system training, these brittle algorithms perform poorly in the open and less predictable environments of clinical medicine. Further, trained algorithms show poor transferability from one healthcare system to another, and are not interpretable by medical experts. In a recent ACR survey, 93% of radiologists say these tools are unreliable in open-system clinical medicine. Top experts believe it will take >10 years to safely implement AI Automation into healthcare, leaving it in the "trough of disillusionment" on the Gartner Hype Cycle.
Yet, "Radiomics" research also supports evidence that patterns of big data from medical images, sometimes combined with other data such as genetics, can provide biometric predictions of patient outcomes, such as the likelihood of response to a certain drug treatment. However, these methods use outdated standard segmentations that have been shown repeatedly to lack even the most basic measure reproducibility. Radiomics and Radiogenomics have thus seen limited successful translation into clinical medicine.
We know that powerful new big data technology provides a great opportunity to solve massive problems. Failure is simply not an option.
We are losing the battle against exploding big data. Yet, we should be winning this battle. We need powerful new ways to ergonomically use this data for personalized and precision healthcare.
Cubismi is pioneering a Decision Flow-3D™ digital health platform to transform the explosion of health big data into targeted and visually-rich decision information. The system is designed for a leap for forward in ergonomic human-information interaction and will accelerate information flows to reduce cognitive effort and help physicians make better decisions, quickly and seamlessly.
Cubismi is pioneering a paradigm-shift digital health platform to provide a digital, interactive, and intelligent cloud-based 3D-time virtual map of each patient’s structured health data for joint use by medical professionals. This holistic map will allow doctors to glean novel insights, called biomarkers, to make better clinical decisions personalized for each patient. Further, by leveraging a powerful new type of analytics called DECISION INTELLIGENCE, experts will be able to connect separate decisions into synthesized decision-making across each patient’s journey.
With Decision Flow-3D™, clinical teams will be able to connect intelligence and analytics insights into a synthesized decision-making framework — ushering in an era of continually improved precision era outcomes at significantly lower costs.
Cubismi’s core cloud human-computer system innovation stems from proprietary patient precision biomap ‘digital twin’ technology and revolutionary interface designs. All of a patient’s digital data is mapped to anatomy, driving both improved interface designs and prediction analytics. Once mapped, the stitched data can be compared to similar population stitched data to deliver predictions using machine learning and explainable AI, such as - one day - far earlier detection of cancer.R
Cubismi’s core cloud human-computer system innovation stems from proprietary patient precision biomap ‘digital twin’ technology and revolutionary interface designs. All of a patient’s digital data is mapped to anatomy, driving both improved interface designs and prediction analytics. Once mapped, the stitched data can be compared to similar population stitched data to deliver predictions using machine learning and explainable AI, such as - one day - far earlier detection of cancer.R
Organizational-level dashboardwith novel insights.
Decision-making does not only impact patient care outcomes, it also impacts the business of healthcare. Healthcare leaders need to move beyond thinking about task workflows and take a broader view about fundamentally enabling a new age of physician Decision Iintelligence (DI).
Decision Intelligence (DI) is a proven paradigm shift machine learning (ML) and “AI” analytics methodology to manage today’s new reality of ever-increasing decision complexity. It leverages various methods for improving, supporting, automating, and augmenting individual decisions. When multiple individual decisions and/or algorithmic outputs are sythesized for improvement of a given outcome, it can be used to power continually-improved outcomes through additional analysis, monitoring, feedback, and improvement of decision-making frameworks. DI has been validated in domains such as mortgage approvals, Formula 1 racing, and Google Maps route optimization. Only by using DI analytics will we be able to assure AI tools are actually improving patient care outcomes.
“The physician clinical decision is the most important business
decision in all of healthcare.”
— Stephen Pollak, PhD, leading healthcare IT strategist
OUR BRAND PROMISE: PUTTING THE POWER OF DECISION INTELLIGENCE INTO THE HANDS OF PATIENTS AND HEALTHCARE PROFESSIONALS
