If you do not populate raw values in pcornet, use any other field with name you 
have in your database. Only condition must be met when using it make sure med 
is prescribed med, not home or any other med such as current meds and so on. 
using any type of meds will cause significant change in cohort size and will 
cause discrepancy between GPC and non-GPC (using pcornet  data) statistics. It 
is very important!!!!

Sent from my iPhone

> On Dec 1, 2016, at 14:53, Dan Connolly <dconno...@kumc.edu> wrote:
> In the "People with at least one ordered medications specific to Diabetes 
> Mellitus" query, I see:
> where (
> UPPER(a.RAW_RX_MED_NAME) like UPPER('%Acetohexamide%') or 
> UPPER(a.RAW_RX_MED_NAME) like UPPER('%D[i,y]melor%') or
> ...
> I don't think we populate RAW_RX_MED_NAME. We being KUMC. Not sure about 
> other GPC sites.
> ref
> https://informatics.gpcnetwork.org/trac/Project/attachment/ticket/539/NextDextractionCode_12.1.16.sql#L207
> I notice a long list of rxnorm_cuis in the word doc e.g. 1156200. Are those 
> in the .sql? Oh. yes. they are. Never mind.
> -- 
> Dan
> From: Dan Connolly
> Sent: Monday, November 07, 2016 2:43 PM
> To: Al'ona Furmanchuk; Satyender Goel
> Cc: <gpc-dev@listserv.kumc.edu>; Abel Kho
> Subject: data collection for next-D: i2b2, babel, OMOP, PCORnet CDM, ACS, 
> geocoding
> There's a lot of alphabet soup, here. In preparation for the Nov 15 call, I'd 
> like to get the discussion started in email. (Note the gpc-dev public 
> archive).
> I would prefer to work backward from a mocked up spreadsheet. My questions of 
> 19 Sep were:
> Does the desired form of the data have one row per patient?
> or per visit?
> Is patient-day a good enough definition of visit?
> what columns / observations / variables are expected for each row?
> Nominal, Ordinal, Interval or Ratio?
> codes for nominals?
> units?
> Mei's response, after talking with Bernie Black and Abel Kho said organize as 
> row-per-visit; yes, patient-day is close enough. She was reluctant to give 
> specifics on columns, but she said the followings are categories of variables 
> listed in the proposal:
> Clinical Variables in EMR:
> . Demographics: gender, race
> . Treatment: standard diabetes medications
> . Response to treatment: HbA1c levels, systolic and diastolic blood pressure, 
> HDL and LDL cholesterol, triglycerides
> . Medication adherence: pharmacy fill data or refill rates
> . Treatment adherence: weights, checks at least twice a year
> . Physician adherence: orders for HbA1c, urine microalbumin, pneumonia and 
> flu vaccine, and documented annual foot and eye exams
> . Health outcomes: renal disease, peripheral artery disease/amputation, 
> retinopathy, cardiovascular disease (coronary events and ischemic stroke)
> Supplemental Demographic Variables in Geocoded Data:
> Income, education, likelihood of employment, poverty status, owner-occupied 
> house value, health insurance coverage, etc.
> It would help if there were a shared copy of the proposal that we can all 
> refer to, by the way.
> I just put what I know in a next-D mock-up in google sheets. Feel free to 
> comment and suggest changes. It includes details such as that we would use 05 
> to represent race=White and 03 for Black, (following the PCORnet data model). 
> The first sheet has mocked up data and the 2nd sheet is a REDCap data 
> dictionary.
> If we are to collect "Treatment: standard diabetes medications" then we need 
> a similar level of detail. OMOP seems to have very mature methods for 
> handling drug exposures, but we don't have much experience with that. In a 
> recent data collection for breast cancer, we used a REDCap drop-down list of 
> relevant RXNorm codes drawn from the GPC terminology. This is where i2b2 and 
> babel come in. With a babel account, you can browse and get details on the 
> terminology as well as a rough sense of what data is available from each GPC 
> site. (It's possible to assemble and save a query that can be actually run at 
> all sites, though that's a bit labor-intensive at this point.)
> For HbA1c, there may be an issue of which LOINC code to use, but I expect we 
> can set that aside since we had to address it for the PCORnet CDM  
> LAB_RESULT_CDM table. But there may be multiple such results in a single 
> visit. In one recent study, I used the median to aggregate them. Would that 
> approach be appropriate here?
> And so on for the other clinical EMR data.
> For income, I have been working with UHD001 Median household income in the 
> past 12 months (in 2013 inflation-adjusted dollars) from ACS. The ACS has 
> 4000+ variables including 15 "median household income" variables (see 
> ticket:140#comment:17). Which of those 4000+ variables would you like to use 
> for education, employment, poverty, house value, health insurance coverage, 
> etc?
> -- 
> Dan
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