The goal of ccdm is to facilitate the use of the Core Clinical Data
Model (Core CDM) and other data sources in the Cloud Data Warehouse
(CDW).
To install ccdm, the package remotes is required, and can be
installed with install.packages("remotes").
If you are able to access GitHub.com, you can then install cddm on
RStudio from GitHub with:
remotes::install_github("RollieParrish/ccdm")
Network security settings may prevent remotes::install_github() from
accessing GitHub.com. If this is the case, ccdm can be installed by
downloading the Package
Archive and running
the following code (replacing the section marked <>, including the
arrows themselves):
remotes::install_local("<FILEPATH OF ccdm.*.tar.gz FILE>")

This package assumes that:
First, set up the connection to the CDW.
library(dplyr, warn.conflicts = FALSE)
library(ccdm)
conn <- connect_cdw()
The ccdm_tbl() function is use to create pointers to Core CDM
tables. The conn connection defined above must be passed as the first
parameter, followed by the name of a table. If a name is not specified,
then a list of available tables will be displayed.
ccdm_tbl(conn)
#> TABLE_NAME TABLE_TYPE ROW_COUNT SIZE LAST_ALTERED
#> 1 ADT_EVENTS BASE TABLE 21429490 248.77 MB 2020-11-22 07:13:29
#> 2 ED_ENCOUNTERS BASE TABLE 3583088 201.07 MB 2020-11-22 07:11:38
#> 3 FLOWSHEET BASE TABLE 14625241312 314.42 GB 2020-11-22 07:19:40
#> 4 HOSPITAL_ENCOUNTERS BASE TABLE 12612180 1.86 GB 2020-11-22 07:10:17
#> 5 ICD_DIAG BASE TABLE 42371459 360.11 MB 2020-11-22 07:11:57
#> 6 ICD_PROC BASE TABLE 3126754 40.76 MB 2020-11-22 07:12:13
#> 7 LABS BASE TABLE 31406895 1.01 GB 2020-11-22 07:24:13
#> 8 MEDICATIONS BASE TABLE 1128618051 20.39 GB 2020-11-22 07:26:46
#> 9 ORDERS BASE TABLE 362633816 12.45 GB 2020-11-22 07:15:32
#> 10 OR_CASE_LOGS BASE TABLE 3859873 329.84 MB 2020-11-22 07:17:58
#> 11 PROBLEMS BASE TABLE 7223627 96.29 MB 2020-11-22 07:27:07
#> 12 PROVIDERS BASE TABLE 36041 3.33 MB 2020-11-22 07:10:33
#> 13 TRANSFUSIONS BASE TABLE 219759 9.90 MB 2020-11-22 07:24:56
#> COMMENT
#> 1 Admission, Discharge and Transfer Events
#> 2 ED encounters
#> 3 Flowsheet data
#> 4 Inpatients, EHOP, ED, Newborn, and Ambulatory Surgery
#> 5 ICD10 Diagnosis code details
#> 6 ICD prodecure information
#> 7 Lab results
#> 8 Med administrations
#> 9 Orders
#> 10 Procedure & Surgery information
#> 11 Problem list
#> 12 Provider and group info
#> 13 RBC Transfusions
Next we need to set up a pointer to a remote tables in Core CDM.
HOSPITAL_ENCOUNTERS is the primary table and will probably used for
most analytics projects.
Note: data has been de-identified and truncated to just a few columns in the examples below
hosp_enc <- ccdm_tbl(conn, "HOSPITAL_ENCOUNTERS")
hosp_enc
| PAT_ENC_CSN_ID | AGE | SEX | FACILITY_CD | ACCOUNT_CLASS | ARRIVAL_DTS | | :---------------- | --: | :-- | :----------- | :--------------------------- | :------------------ | | 150xxx014 | 32 | F | PSHMC | Extended Hospital Outpatient | yyyy-mm-dd 11:32:00 | | 150xxx104 | 67 | F | PSHMC | Emergency | yyyy-mm-dd 03:15:00 | | 150xxx124 | 79 | M | PSHMC | Inpatient | yyyy-mm-dd 11:10:00 | | 150xxx180 | 61 | M | PSHMC | Inpatient | yyyy-mm-dd 20:51:00 | | 150xxx617 | 26 | M | PSHMC | Inpatient | yyyy-mm-dd 17:43:00 | | 150xxx893 | 52 | F | PSHMC | Emergency | yyyy-mm-dd 17:31:00 |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.