Things have changed for me, radically. I left Harvard School of Public Health back in September, with a heavy heart in some ways because of the great opportunity that HSPH offered. Some people might think I’m crazy, jumping out of Harvard to go to a clinic at a smaller institution like UMDNJ/RWJ. But I had my reasons.
The reason was opportunity and to make a big impact in patients’ day-to-day lives. At HSPH, I was working on more esoteric problems, though many resulting in changes, possibly, to public health policy or our understanding of public health. At RMA/RWJ, I will be changing patients lives daily, and my bioinformatics work and research will end up in many babies being born. Many parents will be walking away from my bioinformatics analyses with babies in their arms…how cool is that?
I took a leap, nearly sight-unseen, into what was, for me, an exotic metaworld of informatics. It’s not really ‘medical informatics’. It’s medical bioinformatics. Medbioinformatics?
Giving it a name is actually important, in a way. If you care to read about why, jump down below.
I speak generally here — as scientists, we are usually able to work with patient data on a limited basis — for example, with genetic information tied to a few clinical series or endpoints.
However, what happens when the entire detailed patient clinical record is opened up to tie directly to the patient’s genetic and genomic high-throughput data through an IRB-guided study? (I do want to mention that there is always IRB involvements assumed in this post and appropriate levels of ethics review and oversight)
In research clinical situations, the high-throughput data can pile up fast, and improving patient health and quality of life are the direct goals. The high-throughput technologies are affordable enough, now, that clinics can set up studies across a number of endpoints and end up with huge amounts of genetics and genomics data to mine.
Previously, I’ve worked with groups where a few clinical endpoints were tested against genetics data through association studies — and I imagine an entire silo of patient records and genetic data could be approached with association studies, but adding molecular biology to the mix makes for some interesting research. The high-throughput data gains additional dimensions from the clinical data.
Medicine meets bioinformatics with a shake of the hand in plenty of medical schools right now, but it’s probably not enough. Bioinformatics is well established in medical centers — like those for cancer or genetic diseases — where informatics as of necessity already got a foothold, but there are many barriers for other areas of clinical study.
The largest barriers I have found are in language and communication, and difference in methods and ways of thinking about data. Clinicians have their own statistical language that can’t always apply to what high-throughput methods require. We need to open wider channels of communication between medical clinicians and molecular and computational biologists, for that time when we must all work together.
Yet, the time’s already here when the full patient clinical endpoint record is integrated with the full patient genomic/genetic record, and where the ethics, the informatics, and the medicine all meet — and we often must work to understand each other. We can start by defining our own new language as a fusion of the two.
Medinformatics? Biomedinformatics? Translational bioinformatics? It might not have a name that fits best, but it’s a necessary effort. And when the end result is babies, you can’t go wrong.