I just came across a very interesting website Iodine.com where you can install a Google Chrome plugin which automatically translates medical jargon into more common expressions on any website. For example, while reading an article it turns words such as epistaxis into nosebleed.
It can also give you crowdsourced data and experience about drugs and drug interactions.
Last year, the healthcare innovation world cup was won by AdhereTech that developed a drug box that changes its color when the next medication should be taken. Now here is Kaleo’s talking drug box that can provide spoken instructions to patients about how to administer an injection.
According to the Center for Disease Control and Prevention, more people die each year from drug overdoses than car accidents — and 70 percent of those deaths are caused by legally prescribed medication. Kaleo, a pharmaceutical firm, hopes it can change that. It’s creating a device called Evzio, a small, easy to use drug delivery system that can safely administer a life-saving dose of naloxone.
Many patients are afraid of needles, and the process of properly filling and using a syringe isn’t exactly user friendly. That’s why Kaleo equipped its device with not only clearly written instructions, but a voice: Evzio verbally tells users how to use it properly.
The company’s study concluded that 90% of patients could perform the task even though they have never done it before.
David Rothman has recently linked to RateADrug.com and said:
I’ve seen stupid applications of social media in healthcare, but this may take the cake as the dumbest I’ve seen in a good while.
I believe the concept that patients know better which drugs work the best is good, but you just cannot make sure those patients reviews are not coming from pharma representatives or companies. That’s why you can never trust the information on that site.
Mashable also has a review about RateADrug.com.
If I have to show a site to my patient that focuses on drug interactions and side effects, I would say Pharmasurveyor.com is the best one to use.
I’ve come across the third example on the BioCS blog. SIDER seems to be quite useful as well.
After using side effects to predict drug targets, we now created a public database of side effects with a total of 62269 side effects for 888 drugs. The database was created by doing text-mining on labels from various different public sources like the FDA. Furthermore, I developed rules to extract frequency information from the labels, this worked for about one third of the drug–side effect pairs.