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.
When people get sick, they have several options for obtaining health care. These include going to the emergency room, urgent care center, or calling a doctor or nurse. However, 80% of people experiencing symptoms start with an Internet search. Unfortunately, searching on Google offers spotty results and frequently leads to undue concern. For example, one is 1000x more likely to encounter “brain tumor” in web search results for “headache” than they are to ever have the disease. Undue concern is a contributor to the 40% of emergency room visits and 70% of physician visits that are considered to be inappropriate.
A recent TechCrunch article instigated some debate as to who will win the title of “Medical Expert:” physicians or algorithms. As a medical student with a background in engineering and machine learning, my perspective has led to a somewhat conflicted opinion. I have, on the one hand, seen how powerful algorithms can be, even in the medical domain, and on the other, watched and learned from master clinicians in medical school.
Continue reading on the Symcat blog Doctors or Algorithms: Who Will Win?
Though in some ways replaced by ultrasound technology, cardiac auscultation–using a stethoscope to listen to a patient’s heart–remains an important screening modality for recognizing heart disease. Auscultation serves as a cost-effective screening tool for heart disease and is of particular importance in several clinical scenarios. Less emphasis has been placed on training US clinicians in auscultation, however, making this something of a “lost art.” This may delay a patient’s diagnosis of heart disease. Continue reading