Medication costs make up over 10% of health care dollars and are the fastest growing source of medical expense. Many state and federal officials have called for action that would add cost transparency to the complex, opaque medication supply chain believing that this will lead to lower prices and higher value care. Though this may appeal to our intuition, evidence that medication cost transparency leads to higher value care is scant. In order to add to the evidence base, four other investigators and I embarked on a 9-month investigation designing and evaluating the effect of medication cost transparency decision support built into the electronic health record. Bottom line: it makes a difference. The results were recently published and are available on the JAMIA website: https://academic.oup.com/jamia/advance-article-abstract/doi/10.1093/jamia/ocz025/5445905.
For all the ways that technology has visibly transformed our lives as consumers over the last decade, it has seemed like just a matter of time before the excitement of big data, social, local, mobile, process automation, artificial intelligence, and blockchain (nb. use of buzzwords intentional) will make their way into helping us meet the aims of precision medicine and population health. Though I am quite convinced that health care as an industry can be one of the most rapidly changing, I think it is fair to say that the health care consumer (ie patient) experience has remained fundamentally unchanged during this period. It feels, if anything, that the gap is only getting wider. What’s taking so long?
Summary: A majority of the patient information patients and care teams use to make health care decisions is effectively “locked” in clinical notes such as those written or dictated by physicians. Natural Language Processing techniques have been maturing to extract these concepts, fill information gaps, and support health care’s clinical, operational, and financial objectives. I delivered this webinar to summarize some of Atrius Health’s work using the Linguamatics I2E platform.
It’s been a while, partly because these posts still can take a while for me to write. I wanted to experiment putting a few thoughts down more informally (read: no links) and originally intended to elaborate on one of the often-overlooked problems with applying advanced statistical methods/ML/AI/”cognitive computing” to health care. That will have to wait though, because I’m realizing that there is some important background that I would like to elaborate on first. I’m going to preface this by admitting that I am no scholar on innovation, but I do consider myself a student. My thinking begins with a few practical (and very much borrowed) theories of innovation.
With all of the talk of “big data,” it can be hard to remember that there was ever any other kind of data. If you’re not talking about big data — you know, the 4 V’s: volume, variety, velocity, and veracity — you should go back to running your little science fair experiments until you’re ready to get serious. Prevalent though this message may be, it has, at least in health care, stunted our ability to focus on and capture the hidden 5th V of big data: value.
It is hard to understate just how much of a currency data has become in medicine. Whether talking about evidence-based medicine, precision medicine, or genomics, the ability to collect and distill data into information, transform it into knowledge, and use that knowledge to drive effective action is at the heart of what modern medicine seeks to accomplish. The centrality of data to this process has created well-entrenched stakeholders, which is why it comes as no surprise that the conversation around open sharing of research data following publication has shifted into controversial territory.