About

So much research and EHR patient data exists all around the world already.

But nobody has put all of that together into one app to use in clinical practice, and it wouldn't even be possible to digest all these mountains of information and make sense of it without the help of technology.

All this data is especially helpful for rare diseases, which by nature are rare because every tiny shred of information helps to diagnose and find appropriate treatment for these patients.

Some diseases only have 5 or 10 affected patients worldwide; how would a general physician in the Australian wilderness know that one of his patients with deteriorating strange clinical presentation actually has one of those rare diseases?

On the other hand, many patients are still undiagnosed when in fact they may have a known rare disease, or some of them suffer from the same rare disease that hasn't been officially “diagnosed” yet.

How would any physician know that there are 3 other patients around the world that have the exact same clinical presentation as the mystery patient in front of them?

This is what we are trying to solve. We are developing a platform that can connect any EHR (using deidentified data only) and any available published peer-reviewed medical research to allow proprietary algorithms to discover new diseases, diagnose patients earlier with known diseases using predictive algorithms, and find and recommend appropriate management for each patient at the point of care, regardless of the specialty and department the patient is being seen in.

The goal is to get people diagnosed, get them diagnosed earlier, and get them the appropriate care, in every single health setting anywhere in the world, no matter the zip code.

A patient should never be disadvantaged in their healthcare just because they don't have access to specialty care due to location, economic status, or whatever else the reason might be.

We aim to deliver specialty care tailored to whatever department the patient is being seen in, automatically at the point of care.

Our “OG” predictive model was originally developed for Acute Hepatic Porphyria (AHP) out of an academic research collaboration between UCLA and UCSF. As such, this model is public domain and we invite everyone to find the errors, inefficiencies and iron out the kinks!

Our article is available here, and our AHP model is available here.

We are both. Academia can be propelled forward much faster with the help of industry expertise and resources. If anyone has ever tried their hand on multicenter research projects, you know the struggle, red tape, and time-wasting that goes into that. This project necessitates as many data sources as possible in order to develop unbiased algorithms that work in any patient population in any EHR structure. We founded a legal entity called zebraMD Inc. in 2023 in order to allow us to take on funding and collaborative partnerships with other entities, academic centers, and industries to complete this project much faster than what we otherwise could in our daytime roles as academic physicians.

We are always transparent about our funding, contracts, data sources, and outputs.

The original AHP predictive model was developed with NIH research funding and industry sponsorship by Alnylam Pharmaceuticals through UCSF and UCLA. The zebraMD clinical management algorithm for diagnosed patients is being developed using industry sponsorship by Alnylam Pharmaceuticals, our existing partners since the OG project in 2020.

We also currently have an NSF and an NIH SBIR grant pending for the further development of the app (both predictive and management functions).

The data sources we use for the predictive algorithm development come from deidentified EHR data (classified as non-human subjects research, what that means is here) from UCLA, UCSF, Los Angeles County Department of Health, and Dartmouth Health (New Hampshire).

The predictive models we are developing are freely accessible to academia for research purposes, as is our virtual pooling platform that can be utilized for further global research.

The institutions that allow us to beta test the clinical management algorithm inside their EHR do so in the form of a quality improvement project or in the form of an academic research project with IRB review.

Our project has been reviewed by the local IRB at each of the participating institutions.

Because we are not currently using federal funding, we have independent IRBs for each site. Once we do have federal funding, UCSF will become the relying IRB for all current and future sites except Los Angeles County/Department of Health (LA-DHS), which will rely on the UCLA IRB.

The IRB determination differs from site to site due to the varying availability of tools that can identify electronic health records.

IRB status:

• UCLA: IRB exempt, approved
• UCSF: Full IRB review, approved
• LA/DHS: IRB exempt, approved
• Dartmouth Health: IRB review in progress

We continue to form partnerships with orphan drug pharmaceutical companies who develop therapies for rare and genetic diseases. Our project is mutually beneficial to our parties since it is in our best interest as physicians to get people diagnosed and then managed appropriately. Pharmaceutical companies would like to diagnose more patients and obtain more clinical (deidentified) data analysis reports detailing the efficacy of their existing therapies and showing potential new therapeutic targets for diseases that currently have no treatment. Ultimately, this enables both of us to further personalize and tailor therapies to each individual patient.

We are also considering a health system subscription model such as UpToDate (a medical library of sorts that physicians use and their health system employers pay for), as well as continuous grant support.

We cannot solely rely on grant support as it can take years (!!) to get grants even in the small business for-profit space and in a world where millions of rare disease patients continue to be undiagnosed and mismanaged every day, we cannot wait months or years for grant support.

If you are interested in a partnership with us and would like to help us achieve our goals with zebraMD, please contact us here.

Our secret sauce is the development of a cloud-based platform with proprietary algorithms that can connect any EHR and medical records and medical databases of published research without the need for any patient-level EHR data sharing, using virtual pooling technology.

Our point-of-care algorithms showing predictive and management recommendations have academic references for each point to allow the reader to see exactly where the information came from. All of our algorithm output is validated by physician specialists before making it to the published app.

Using this technology, we are building a generalizable predictive algorithm that can be used for any disease in any population and any EHR.