I’ve got 99 problems and all of them have to do with opioids…

my eyes are nearly about to close. my mind is trying way too hard to make sense of the data in front of me. whenever i think I’m close to experiencing a light bulb moment, my entire train of thought ends up crashing.

that’s a pretty accurate summation of how this week’s been going and unfortunately, i think this is only the beginning of my problems. there’s just TOO much information, i fear i may be losing sight of my end goal.

to be a bit more specific and to give you a clearer understanding of the obstacles i’m facing right now, I’m gonna flash back to those original 4 opioid medications (fentanyl, oxycodone, methionine morphine) i was looking into into in the beginning of the my research. after comparing the data with the rest of the uc’s using the UCRex tool, I’ve discovered a total of 17 common opioid medications being given to aid against chronic pain. so now, my data set has grown from 4 to 17 which has only complicated the analysis for me. each new medication is given in different dosages and is specific to every patient, which won’t allow me to form a standard generalization that i can use in my proposed clinical guideline. i would have to list out specifications for each medication used which would only complicated the clinical guideline more than it already it.

*not sure if this would benefit y’all in any way but just in case you wanted to know the additional 13 medications that have been added to my research, here it is: codeine, naloxone, acetaminophen (Percocet, Roxicet), methadone, meperidine, hydromorphone, hydrocodone, etc… (i would list the others but it’s mostly a change in the medication brand, so…)

the point is, there’s too much data that I’ve collected at this point that it’s becoming so difficult for me to analyze. i originally thought i would compare the data from the original 4 opioid medications in the UCSF database with the other UC’s, but the medications being given in the other UC’s are different, which creates more data for me (which i assumed was a good thing), but after pouring through the names of different medications and dosages, i’m standing at the crossroads with no where to go.

I’m thinking i should backtrack a little. maybe starting by seeing the similarities between the medications used between UCSF and the other UC centers would be a great place to begin. i need to figure out if these different medications are isomers or if they’re just completely different medications. i want to figure out which medications are being overused, thereby leading to opioid abuse in these patients.

so in essence, my data set has broadened, and so have my goals. hopefully i can sift through all of this from the beginning and reach a more specified and quantifiable analysis.

also, i know showing you guys the actual data I’ve collected would be beneficial, but due to hipaa rules i cannot, but what would you think of me just posting a picture of the tools, without any data, and showing you how it allows me to retrieve all this data? comment down below and let me know what you think.

‘Til we meet again,

Avani

 

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