stagnating progress…

unfortunately, this has been a pretty drab week for me. not only wasn’t i able to sort through the copious amounts of data i’d collected last week, but i’d end up confusing myself even more. so, in the middle of the week, on wednesday to be specific, I decided to retrace my steps and just start from scratch. so I went back to the data I collected over the summer. I noticed that the total patient population/sample size that RDB (research data browser) held, was higher than what it was during the summer, and intuitively this makes sense since new patients are constantly coming into the hospitals, so the data constantly needs to be updated. so, I re-evaluated the data/cohort groups I got from the summer using the sixteen ICD9/10 codes pertaining to depression, anxiety, drug abuse, etc… and the 4 original opioid pain medications I was looking into – methadone, fentanyl, morphine, and oxycodone. the original cohort groups I got when matching each medication with each of the ICD9/10 codes ranged between 1578-2348 patients in each group.

however, with the new data I was able to get a larger sample size per cohort. now this is a double-edged sword. it’s a good thing because I now have a more updated and accurate sample size to work with, but it’s also a horrible thing as well because that means there are more patients being prescribed and given these opioid pain medications when they shouldn’t be, leading to more of the population struggling with opioid abuse. this is a new conundrum that I need to get to the bottom of which I hope to work on next week along with my planned research. in addition, this observation adds on to the severity of my research because opioid abuse needs to be prevented.

I know you’ve been waiting a long time to see this, so here is the research data browser tool that I’ve been working with for the past few weeks. I mainly used the medications and diagnoses sections because that is where I would find all the ICD9/10 codes as well as the specific medications I was looking into. the results would then pop up in cohort boxes below the race and sex tabs. the results would appear as de-identified patient users that would list out each patient’s demographics and everything else once it was translated into identified patient information. it’s a fairly user-friendly tool and it works quite quickly and yields me a IMG_9058ton of information at once, so I love using it.

Here’s until next time 🙂


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