HIStalk Connect Interviews Joel Diamond, MD, FAAFP Chief Medical Officer Genomics and Precision Medicine at 2bPrecise

Joel Diamond, MD, is Chief Medical Officer, Genomics and Precision Medicine at 2bPrecise, a wholly-owned subsidiary of Allscripts.

image

Tell me a little bit about yourself and 2bPrecise.
 
I still practice family medicine a couple days a week in western Pennsylvania. I came at all of this stuff from a primary care standpoint. I was an early adopter of electronic micro-records in my office. Then I went on to help do a CPOE at one of the UPMC hospitals back in 2004.

My introduction to medical informatics is really from the provider’s side, not as a computer scientist. Eventually, I ended up becoming the chief medical officer of dbMotion when UPMC was doing due diligence on this startup company to do inter-operability to connect Cerner and Epic and all their other IT projects they have there.

DbMotion was sold to Allscripts in 2013. Then, some of the founders of dbMotion, including myself, got together and decided that there was a significant void in informatics, particularly at the point of care, around the area of precision medicine and genomics. We decided to start this entity, which is now called 2bPrecise.
 
The White House announced that it was devoting $55 million to the creation of the public database containing detailed health information. Will that help advance precision medicine or are they behind the curve?
 
I don’t think they’re behind the curve. They’re separate and parallel, to some extent. From a scientific standpoint, the need is huge in medicine to generate a database to be able to better understand the interplay between people’s genome and their phenotype, their genotype and their phenotype. We also have an immediate need to have people use genomic information, practice right now. Those are separate things. I would say we’re probably more focused on the latter.
 
How far are we from being able to use genomics and clinical and personal information to generate actionable outcomes at the point of care?
 
It’s today. We’re doing it today. The need is today. I can give you several examples from my own practice. Some of the technology is desperately behind, but we can deliver it very, very quickly.
 
There is somewhat of a misnomer that all of genomics and precision medicine has to do with cancer and cancer treatment. A lot of the presidential initiative — Joe Biden’s cancer moonshot program, etc. — are about that.

There’s another huge area of precision medicine and genomics in particular that is not cancer related. I’ll give you lots of examples of that and some of which are practical in everyday practice. That’s number one.
 
Number two is that people right now are ordering genetic tests like crazy on all sorts of things, ranging from high cholesterol to autoimmune diseases in the area of neonatology in the NICU, prenatal testing. Doctors are ordering lots and lots of genomic tests. The price is coming down incredibly on this. I’d add behavioral health too, by the way, which is another interesting side note.

The price has come down a lot. People are ordering these tests, not necessarily knowing which is the right test or which is the least-expensive test. It’s very much the wild, wild west out there in terms of people ordering.

When those results come back, the majority of those results are just PDFs or MS Word files. They’re documents that are getting stuck somewhere, maybe in the EMR. That’s the other problem.
 
Just like other healthcare information technology, we’ve got to have data that people can use. The ability to do that is today. It’s just a matter of how to do it, where to store it, and how to present it to the practitioner at the point of care.

The first part I was alluding to is, can there be some forms of decision support in helping people understand what tests to order, or which are the most cost-effective tests? A huge knowledge gap is that there are genetic tests for diseases that people don’t even know about today, like cholesterol or diabetes or things like that.
 
Patients are making decisions about having procedures or services based on the results of the tests. How useful are tests without interpretation?
 
People going to 23 and Me and things like that is a whole other issue. Let me tackle the consumer side first.

The 23 and Me profile, not to bash them at all, is snip information. That data is not full genomic sequencing. These are curiosities of how populations are related to each other genetically. Do they have some value? Perhaps, if they identify people that are at risk for certain diseases. Can they be more aware of that and can they go on to have further testing for that? A lot of controversy around that, but certainly some value.
 
On the provider side, there are some very specific medical conditions that have a genomic basis to them that will change the way that people practice. I mentioned behavioral health as an example. A couple of companies are looking at pharmacodynamics of psychiatric drugs and how those drugs are metabolized. Instead of going through a trial-and-error practice of picking an antidepressant medicine for a patient, the ability to be very precise in what drugs to order can save a huge amount of time and money, etc. for picking the right medication.

That’s an area that’s gaining a huge amount of popularity around psychiatrists right now. That’s an example where people are ordering these tests and utilizing them and making value of them in practice today. Again, those reports are largely coming back as just a document, without the ability to take that raw genetic information and store it and make it usable later on retrospectively to say, "Here’s another condition. Is it associated with diseases?" as science catches up.
 
A lot of technology today is not being utilized. There’s a lot of science that is going to occur in the future that’s not going to be able to retrospectively use that data because they’ve stored as paper, not machine readable and quantifiable.
 
Some of your associates went to the White House and participated in the Precision Medicine Initiative. What can we expect to see in the future from collaborative efforts?
 
The biggest part is this Moonshot 2020 program for cancer. Those efforts are the most promising and the most interesting.

My bent, to be precise, is a little bit away from the cancer space, but not totally. Certainly one area in medicine where I think the Precision Medicine Initiative is real important is this notion that, at least in cancer, we typically tend to treat cancer today by its anatomic site.

You have breast cancer. You have ovarian cancer. You have lung cancer. In reality, the behavior of those cancers is very, very different depending on their genetic origin. In fact, even after treating those cancers, the DNA within those cancers may mutate and the need for picking a different type of treatment during the course of that, or when there is relapse, is going to have a genetic basis to it. That national Precision Medicine Initiative database is probably going to be very impactful.

What else are you working on?
 
There is a form of familial high cholesterol, familial hyperlipidemia, that has a genetic basis to it. It may be as high as one out of 200 people that have high cholesterol have this familial form, genetic form of it. Those patients typically don’t respond to the current treatments that we have, drugs like Lipitor and so forth, statins, etc.

Very recently, two new drugs have come out that are biological agents that have a significant effect at lowering cholesterol. They are very expensive drugs, but they work very well for people that have this familial form of high cholesterol. The challenge is, how do you identify which patients in your population should have genetic testing? That’s one challenge — which people are most likely to have familial hyperlipidemia and it is worth having this genetic testing?

Two, how would you notify clinicians, either at the point of care or on their own populations or organizations, which are those patients?
 
Three, get them to the right tests, the right panel of tests. Then have those results brought back into the EMR so people can properly understand the genetic basis of that disease. Then select a treatment for those patients, even if it’s an expensive one, that’s appropriate for that unique population.
 
The tools that we’ve developed are one within that ability to look at the EMR data in entirety. This comes from our dbMotion background. Nowadays, we would say our ability to understand the phenotypic data, not what we would formerly call the clinical data, but now the phenotypic data is available on patients. To be able to assess that and determine which genetic tests a person may need. Familial hyperlipidemia is an example of that. Then when we get these results, store that data in a machine-readable way that becomes useful.

The other important part, from an education standpoint is that most people think that when you order a test, it comes back positive or negative. Do I have this gene or don’t I? In reality, what comes off the machine is lots of variants and lots of mutations, some of which are clinically significant and some of which are called variants of unknown significance. It means they are a mutation, but we don’t know what they mean or what they might do to you. Probably nothing.
 
The problem is, next year or five years from now, science will recognize that one of those variants may be associated with something very, very different. We sequence it, we identify it, but today it’s just thrown away. We only report the positive results to you. It seems kind of a waste.

That’s another area. We are collecting that raw data from the machine and storing it with a data model that people can understand. The next part of that is marrying that genomic information with the phenotypic information. One of the areas of our expertise from dbMotion was to build some very strong vocabulary pieces in oncology to have a cross-clinical omics ontology from which we could make these associations between a patient’s labs or their clinical findings, their diagnoses, what meds they’re on, along with this genetic information that we’re getting off of this sequence here.
 
Lastly, which I think is the most important part, which we learned from dbMotion, is that all of this is great, but if it’s not being presented at the point of care, then it has no value. We have tools that allow, in a vendor-agnostic way, this information to be displayed when appropriate without alert fatigue — whether you’re on Cerner or Epic or Allscripts, whatever it may be — and have that information available and displayed at the point of care in an appropriate way.
 
Do you have any final thoughts?
 
The big thing here is there’s a ton of smart people doing work in the area of genetics right now. There are a lot of smart people doing work in information technology. The key is to bridge this chasm, and that’s a hard concept, between the phenotypical world and the genomic world. That’s exactly where we’d like to play right now.
 
I think it was Obama who used the analogy, which I loved. Imagine going to the eye doctor, and they say, “Put these glasses on. Come back in a week and tell me how they work for you.” You say, “My eyes are still blurry.” He says, “Well, try these.”

I love it because that’s how we practice all of medicine right now. It’s not like going to the eye doctor and they do the refraction and pick out the exact lenses that you need just for you. We need to do that in every other area of medicine today. That’s my passion.

↑ Back to top

Founding Sponsors

Platinum Sponsors