A first step towards a saner, more efficient medical research enterprise.

It is insufficiently known that when asked, patients are very willing to share their medical information with researchers but much less so with public health authorities or third party companies. It is therefore ironic,  that whereas the latter two groups do not require institutional review, researchers do. Moreover, if a study pertains to a set of patients seen at different institutions, the researcher will first have to obtain review from the institutional review boards (IRBs) of each one of the institutions. For a variety of reasons there is significant  inertia working against getting one IRB to cede review of the study to another IRB. For this reason, this draft policy from the NIH is a welcome potential accelerant of biomedical research. If it is ratified, this policy will ensure that a single IRB for these multi-institutional studies becomes the rule and not the exception. A seemingly fine point to those who have never had to organize a multi-institional research study,  but it might do more to advance medicine than hundreds of millions of dollars in additional research funding.

Biomedical science is not a game for the young?

The National Institutes of Health have helpfully posted summary information about their funding patterns. It is much more revealing of the training patterns and mentoring of young investigators at our academic centers than any specific NIH policy. It is evident that whatever your terminal degree, your age at first R01 is 42-44, a full 10 years older than the age of first R01 in 1970. PhD's only are 2 years younger on average than MD's and MD-PhD's are remarkably no older than the MD's (which was not the case in 1970). What does this say about the capability of our research workforce to be energetically innovative? Are we drawing from the right pool of investigators or is there something fundamentally wrong in the institutionalized career path leading to an R01?

Celebrity-driven education

Here's an interesting angle in promoting health literacy by two of my colleagues.

Doctors do not bill to make insurance companies smarter

Those of us who have worked with electronic healthcare data have been long aware of the limitations of billing data (aka claims data, aka administrative data) for research. They are often too coarse grained for clinical research and are inherently biased to maximize income. It is motivated by these limitations that Natural Language Processing (NLP) has become increasingly important in mining clinical records for research. What a doctor writes in her notes is much more revealing of her patient's state than what she bills for. Notwithstanding there are some significant challenges in the de-identification of textual records and in transforming these records into standardized clinical categories (e.g. SNOMED). Yet the appeal of using the clinical narrative text rather than claims data is compelling. In our work in i2b2, we have seen significant overrepresentation of diagnostic codes where a diagnostic encounter to "rule out" a disease was codified as that disease in the claims data. For example, a radiologist asked to rule out rheumatoid arthritis based on an X-ray will often classify the X-ray with a billing code corresponding to rheumatoid arthritis when perusal of the full narrative text of the radiologist's notes that there were NO findings consistent with rheumatoid arthritis.

A recent article in the Boston Globe points out additional challenges in using claims data for personal medical records. The same limitations of claims data for research appear to impinge on their utility for clinical care. My colleague John Halamka makes several useful suggestions on how to improve the use of such data, including recruiting patients themselves as collaborators in refining the categorization of their clinical records or even removing gross errors. Notwithstanding, a small number of codes are likely to be quite limiting and it may be that codifying the patient's record by using the entirety of their clinical documentation (i.e. what their care providers wrote about them) will ensure the most nuanced and most faithful representation available of what the clinician was thinking about in each clinical encounter with that patient.

Stimulomics—An informatics initiative

This comes in somewhere between useful and cheeky.

Eloquent cri de coeur

This piece by Patrick Swayze speaks for itself. I would add that the investment in additional NIH funding is lessened if the results of the funded research are not delivered efficiently to our taxpayers.