I'm sorry, but I don't post here any more. I left the University of Michigan Medical School in 2010 to join a spinoff startup, my startup got acquired, and I've been super-busy working for the new company ever since.
“Large collections of electronic patient records have long provided abundant, but under-explored information on the real-world use of medicines. But when used properly these records can provide longitudinal observational data which is perfect for data mining,” Duan said. “Although such records are maintained for patient administration, they could provide a broad range of clinical information for data analysis. A growing interest has been drug safety.”
In this paper, the researchers proposed two novel algorithms—a likelihood ratio model and a Bayesian network model—for adverse drug effect discovery. Although the performance of these two algorithms is comparable to the state-of-the-art algorithm, Bayesian confidence propagation neural network, by combining three works, the researchers say one can get better, more diverse results.
I saw this a few weeks ago, and while I haven't had the time to delve deep into the details of this particular advance, it did at least give me more reason for hope with respect to the big picture of which it is a part.
It brought to mind the controversy over Vioxx starting a dozen or so years ago, documented in a 2004 article in the Cleveland Clinic Journal of Medicine. Vioxx, released in 1999, was a godsend to patients suffering from rheumatoid arthritic pain, but a longitudinal study published in 2000 unexpectedly showed a higher incidence of myocardial infarctions among Vioxx users compared with the former standard-of-care drug, naproxen. Merck, the patent holder, responded that the difference was due to a "protective effect" it attributed to naproxen rather than a causative adverse effect of Vioxx.
One of the sources of empirical evidence that eventually discredited Merck's defense of Vioxx's safety was a pioneering data mining epidemiological study conducted by Graham et al. using the live electronic medical records of 1.4 million Kaiser Permanente of California patients. Their findings were presented first in a poster in 2004 and then in the Lancet in 2005. Two or three other contemporaneous epidemiological studies of smaller non-overlapping populations showed similar results. A rigorous 18-month prospective study of the efficacy of Vioxx's generic form in relieving colon polyps showed an "unanticipated" significant increase in heart attacks among study participants.
Merck's withdrawal of Vioxx was an early victory for Big Data, though it did not win the battle alone. What the controversy did do was demonstrate the power of data mining in live electronic medical records. Graham and his colleagues were able to retrospectively construct what was effectively a clinical trial based on over 2 million patient-years of data. The fact that EMR records are not as rigorously accurate as clinical trial data capture was rendered moot by the huge volume of data analyzed.
Today, the value of Big Data in epidemiology is unquestioned, and the current focus is on developing better analytics and in parallel addressing concerns about patient privacy. The HITECH Act and Obamacare are increasing the rate of electronic biomedical data capture, and improving the utility of such data by requiring the adoption of standardized data structures and controlled vocabularies.
We are witnessing the dawning of an era, and hopefully the start of the transformation of our broken healthcare system into a learning organization.
I believe if we reduce the time between intention and action, it causes a major change in what you can do, period. When you actually get it down to two seconds, it’s a different way of thinking, and that’s powerful. And so I believe, and this is what a lot of people believe in academia right now, that these on-body devices are really the next revolution in computing.
I am convinced that wearable devices, in particular heads-up devices of which Google Glass is an example, will be playing a major role in medical practice in the not-too-distant future. The above quote from Thad Starner describes the leverage point such devices will exploit: the gap that now exists between deciding to make use of a device and being able to carry out the intended action.
Right now it takes me between 15 and 30 seconds to get my iPhone out and do something useful with it. Even in its current primitive form, Google Glass can do at least some of the most common tasks for which I get out my iPhone in under five seconds, such as taking a snapshot or doing a Web search.
Closing the gap between intention and action will open up potential computing modalities that do not currently exist, entirely novel use case scenarios that are difficult even to envision before a critical mass of early adopter experience is achieved.
The Technology Review interview from which I extracted the quote raises some of the potential issues wearable tech needs to address, but the value proposition driving adoption will soon be truly compelling.
I'm adding some drill-down links below.
Practices tended to use few formal mechanisms, such as formal care teams and designated care or case managers, but there was considerable evidence of use of informal team-based care and care coordination nonetheless. It appears that many of these practices achieved the spirit, if not the letter, of the law in terms of key dimensions of PCMH.
One bit of good news about the Patient Centered Medical Home (PCMH) model: here is a study showing that in spite of considerable challenges to PCMH implementation, the transformations it embodies can be and are being implemented even in small primary care practices serving disadvantaged populations.
Portuguese and Spanish researchers in the field of social robotics are working on the use of robots to interact with children who are hospitalized for the treatment of cancer, thereby providing emotional support.
The researchers are keen to take robots out of the laboratory and place them in a real environment. Until now, most of the research on social robotics has taken place in very controlled environments. As Professor Salichs from UC3M points out, 'The introduction of a group of autonomous social robots into surroundings with these characteristics is something new, and we hope that the project will help us to advance in the development of robots that are able to relate to people in complex situations and scenarios.'
Another cause for guarded optimism about health care robotics? My hope is that it will augment the efforts of often overworked staff and allow them to better prioritize the focus of their precious attention and energy. In addition to their potential social value, robots could act as in situ surveillance devices to watch for nascent or emergent health crises. My fear is that they will be used as justification for cutting costs through staff reductions, as self-checkout lanes have done in supermarkets.