There's a wealth of information in electronic medical records.

Understanding them more deeply will accelerate precision predictive medicine.

Electronic medical records (EMRs) capture everything done with a patient, from a heart transplant to getting an aspirin. They’re a core resource for health care providers, insurers, pharmaceutical companies, and more. In spite of all of the time and money spent creating EMRs, they've never been used to full potential. EMRs could be used so much more effectively for precise, actionable outcome predictions for patients and populations. But you can't do that just by aggregating them. You can't do that just by running analytics on them.

Instead, you need a whole new way to compute on them. Backdrop Health has done that, creating a breakthrough solution that gets the maximum value from all electronic medical records. Backdrop’s novel application of machine learning makes it easier for pharmaceutical companies to bring new therapies to market more quickly and economically, for health care providers to preemptively identify health issues and improve patient outcomes, and for insurers to optimize patient health at lower cost.

Comorbidity discovery is essential in medical outcome predictions.

Finding and quantifying comorbidities is a field ripe for disruption.

A comorbidity is the existence of two or more medical conditions, such as diabetes and obesity, in a patient. Comorbidities have been used for decades, precisely quantified using proven statistical techniques. But they've usually been discovered only between pairs of diagnosed medical conditions. Most of the available clinical data has never been used completely, if at all.

Classic comorbidities only show a small part of a big picture. It's a field ripe for the novel application of big data and AI techniques. Backdrop Health was launched in 2020 to deliver this breakthrough. Backdrop greatly extends the concept of comorbidity to a full set of comorbid factors that include all pairs of co-occurring clinical variables: diagnosed conditions, procedures, medications, and lab tests.

Backdrop's CoDE: Comorbidity Discovery Engine

CoDE is the software at the heart of our solution. CoDE analyzes any body of electronic medical records to discover and quantify how every factor -- diagnosed conditions, lab tests, procedures, medications, any genetic testing data, and more -- relates to every other factor. Equally important, CoDE also computes the temporal connections between factors, allowing additional insight into disease progression.

The output from CoDE is the backdrop for that body of medical records: a comprehensive probabilistic knowledge graph that makes visible every clinical variable's relationships to every other. While traditional comorbidities reveal some useful facts, a backdrop reveals everything, for every factor of interest. And, a backdrop is free of any PHI (Personal Health Information), rendering moot any privacy concerns.

Traditional approaches require controlling for confounding variables.

Backdrop's approach eliminates the need to stratify, or subset the data.

Previous best practices for comorbidity discovery have relied on stratification. That's done to ensure uniformity of risk in the face of confounding variables such as age or ethnicity. But this results in diminished statistical power. Even a large starting data set can shrink dramatically when adjusted for many confounding variables. It's common, for example, for a drug company to license medical records for a trial and end up with just a handful of patients after stratification.

Backdrop's solution does not rely on stratification. CoDE calculates and uses each patient's individual probability for every condition, based on his or her full medical history. The full history and demographics of every patient in the sample is brought to bear on each outcome analysis. Backdrop amplifies the effective size of medical data sets by orders of magnitude. This is particularly valuable when dealing with rare diseases and with demographic factors under-represented in the data.

Backdrop's HOPE: Health Outcomes Prediction Engine

Knowing all of the comorbid factors in a medical data set is not enough for accurate and useful outcome predictions. These factors do not exist as isolated pairs, but combine to create complex webs of influence on any given outcome. Useful results require the ability to calculate the joint contribution of multiple factors which in turn depend on one another.

HOPE, the other key software module in our solution, does just that when you query your backdrop about any outcome of interest. Backdrop includes a powerful means to understand the impact of all demographic factors on outcomes. The contribution of every clinical and demographic factor to every prediction is clear, so all conclusions are fully explainable.

A Commanding Lead in Predictive Power

Backdrop's team developed its approach and its software over more than four years, with the benefit of full access to a very large set of electronic medical records. This includes almost 4 million patients and over 72 million visits over 10 years.

Teams trying to do this today have to get access to enough medical data to analyze. That data is often sanitized and redacted, to avoid exposing personal health information. Backdrop learned what to do, on a huge set of complete data.

Backdrop Lets You Maximize the Value of Your Electronic Medical Records

Customers use Backdrop's solution on the medical records that they have created or licensed. Customers create their own backdrops against which to understand patients, outcomes, and disease progression. Our business model does not involve the licensing of medical records or any other patient data.

Backdrop's core software -- CoDE and HOPE -- run securely and reliably in the cloud. They're implemented in a way that eliminates the risk of revealing any personal health information while creating or using a backdrop, while allowing full computations on your data.

A breakthrough in the precision with which we can predict clinical outcomes and disease trajectories for individual patients and populations will enhance the lives of millions.

Backdrop will deliver huge cost savings in three big markets that rely daily on electronic medical records: pharmaceutical companies, health care providers, and insurers, worldwide.

Sample Applications

Pharmaceutical Companies

Clinical trials are expensive, risky, and take years. It's expensive to license medical records, and they're often hard to harness for use. Backdrop's software can materially accelerate clinical trials by enabling improved precision in the inclusion and exclusion criteria, by simplifying the creation and management of synthetic control arms for real-world evidence trials, and more.

Health Care Providers

All health care systems have a deep interest in optimizing care and improving patient outcomes. Backdrop makes it easier for providers to preemptively identify and proactively assess likely health issues and improve care. Backdrop can also greatly reduce the 'diagnostic odyssey' of patients, especially for rare diseases, and get them the treatments they need sooner.

Insurers

All payers want to understand which diagnostics, therapies, and procedures work and how well. They care about quality of care and equality of care. They are interested in earlier interventions that enhance health and save money. Backdrop can help insurers in each of these areas, reducing costs while helping their customers stay healthier.

The creators of this solution at the University of Utah form the core of the Backdrop technical team.

The executive team is:

Mark Yandell, Ph.D.

Co-Founder and
Chief Science Officer

Jerry Rudisin

Co-Founder and
CEO

Martin Reese, Ph.D.

Co-Founder and
Outside Director

Backdrop Health

There's a wealth of information in electronic medical records.

Understanding them more deeply will accelerate precision predictive medicine.

How Can You See The Big Picture?

Comorbidity discovery is essential in medical outcome predictions.

Finding and quantifying comorbidities is a field ripe for disruption.

Backdrop Provides Unprecedented Insight

Backdrop's CoDE: Comorbidity Discovery Engine

Amplify the Power of Any Medical Data Set

Traditional approaches require controlling for confounding variables.

Backdrop's approach eliminates the need to stratify, or subset the data.

Get Fully-Explainable Outcome Predictions

Backdrop's HOPE: Health Outcomes Prediction Engine

Deep Innovation from The University of Utah

A Commanding Lead in Predictive Power

Create Your Own Backdrop on Your Data

Backdrop Lets You Maximize the Value of Your Electronic Medical Records