The goal of medicareAnalytics is to simplify the data processing and modeling for medicare research projects. This project is based on the medicare analytic file data structures, and used a lot variable names specifically only for the medicare analytic file.
Since most of the medicare projects are using the same cohort filters and models, this package can help the analysts and reseach fellows to conduct analyses.
You can install the development version of medicareAnalytics from GitHub with:
# install.packages("devtools")
devtools::install_github("UMCSTaR/medicareAnalytics")library(medicareAnalytics)
library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
#> ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
#> ✓ tibble 3.1.2 ✓ dplyr 1.0.6
#> ✓ tidyr 1.1.3 ✓ stringr 1.4.0
#> ✓ readr 1.4.0 ✓ forcats 0.5.1
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> x dplyr::filter() masks stats::filter()
#> x dplyr::lag() masks stats::lag()
## basic example code
# load medicare dt
medicare = data.table::fread("/Volumes/George_Surgeon_Projects/standardized_medicare_data_using_R/analysis_ready_data/ecs_primary_surgeon_medicare2018.csv", nrows = 5000)
multiple_procedures_table(medicare, project_sepcific_procedure_tax = "e_proc_grp_lbl")
#> # A tibble: 817 x 2
#> proc_combo n_hosp_admission
#> <chr> <int>
#> 1 Ventral hernia repair (open), Ventral Hernia Repair W/Mesh 27
#> 2 Angiogram, abdomen/pelvis/lower extremity artery, Angiograp… 25
#> 3 Partial colectomy with anastomosis (open), Splenic flexure … 12
#> 4 Transluminal balloon angioplasty, venous (percutaneous), An… 11
#> 5 Angiogram, abdomen/pelvis/lower extremity artery, Angiograp… 10
#> 6 Angiogram, ateriovenous shunt for dialysis, Transluminal ba… 9
#> 7 Angiogram, abdomen/pelvis/lower extremity artery, Translumi… 8
#> 8 Incarcerated/strangulated Ventral hernia repair (open), Ven… 8
#> 9 Infrarenal abdominal aortic aneurysm/dissection repair (end… 8
#> 10 Angiogram, abdomen/pelvis/lower extremity artery, Angiograp… 7
#> # … with 807 more rows# load medicare dt
medicare = data.table::fread("/Volumes/George_Surgeon_Projects/standardized_medicare_data_using_R/analysis_ready_data/ecs_primary_surgeon_medicare2018.csv", nrows = 5000)
remove_nonUS_trained(medicare) %>%
select(-contains("id"), -contains("dt")) %>% # remove sensitive data
head()
#> val_yr_practice val_day_practice flg_assistant_surgeon flg_multi_surgeon
#> 1: 15 5186 0 1
#> 2: 7 2311 0 0
#> 3: 7 2311 0 0
#> 4: 7 2311 0 0
#> 5: 18 6269 0 0
#> 6: 5 1529 0 0
#> flg_two_surgeon flg_surgical_team us_medschool fellowship_abs flg_male
#> 1: 0 0 USMG FALSE 0
#> 2: 0 0 USMG FALSE 0
#> 3: 0 0 USMG FALSE 0
#> 4: 0 0 USMG FALSE 0
#> 5: 0 0 USMG TRUE 0
#> 6: 0 0 USMG TRUE 0
#> e_race_wbho age_at_admit e_ses_5grp AHRQ_score e_proc_grp
#> 1: 1 74 NA 10 153
#> 2: 1 80 NA 20 689
#> 3: 1 80 NA 20 167
#> 4: 1 80 NA 20 34
#> 5: 1 79 NA 8 553
#> 6: 1 70 5 2 507
#> e_proc_grp_lbl
#> 1: Partial colectomy with anastomosis (laparoscopic)
#> 2: Splenic flexure takedown (in addition to primary procedure)
#> 3: Partial colectomy with cecostomy or colostomy (open)
#> 4: Omental resection
#> 5: Creation of arteriovenous fistula
#> 6: Aorta/iliac artery/femoral artery thrombectomy/endarterectomy
#> facility_clm_yr flg_multi_cpt cpt_cd cpt_mod e_admit_type
#> 1: 2007 0 44204 3_Elective
#> 2: 2007 1 44139 1_Emergency
#> 3: 2007 1 44144 1_Emergency
#> 4: 2007 1 49255 1_Emergency
#> 5: 2007 0 36830 79, 3_Elective
#> 6: 2007 1 35355 GC, 51 3_Elective
#> flg_cmp_po_severe_not_poa flg_cmp_po_severe flg_death_30d flg_readmit_30d
#> 1: N/A (no var) 0 0 0
#> 2: N/A (no var) 1 1 0
#> 3: N/A (no var) 0 1 0
#> 4: N/A (no var) 1 1 0
#> 5: N/A (no var) 0 0 1
#> 6: N/A (no var) 0 0 0
#> flg_cmp_po_any flg_cmp_po_any_not_poa val_los flg_util_reop
#> 1: 0 N/A (no var) 5 0
#> 2: 1 N/A (no var) 18 0
#> 3: 1 N/A (no var) 18 0
#> 4: 1 N/A (no var) 18 0
#> 5: 1 N/A (no var) 4 0
#> 6: 0 N/A (no var) 4 0
#> flg_hosp_ICU_hosp flg_hosp_urban flg_hosp_urban_cbsa facility_prvnumgrp
#> 1: 1 NA 1 330273
#> 2: 1 NA 1 160028
#> 3: 1 NA 1 160028
#> 4: 1 NA 1 160028
#> 5: 1 NA 1 280060
#> 6: 1 NA 1 330101
#> flg_hosp_teach_combined val_hosp_mcday2inptday_ratio e_hosp_beds_4grp
#> 1: 0 0.04 1
#> 2: 0 0.10 1
#> 3: 0 0.10 1
#> 4: 0 0.10 1
#> 5: 1 0.13 2
#> 6: 1 0.34 4
#> val_hosp_rn2bed_ratio val_hosp_rn2inptday_ratio surgeon_volume
#> 1: 1.84 0.01 268
#> 2: 1.52 0.01 205
#> 3: 1.52 0.01 205
#> 4: 1.52 0.01 205
#> 5: 1.99 0.01 1611
#> 6: 1.97 0.01 992
#> medicare_gs_suregon had_assist_surg
#> 1: 1 1
#> 2: 0 0
#> 3: 0 0
#> 4: 0 0
#> 5: 0 0
#> 6: 0 0medicare %>%
apply_all_filters(add_variables = "val_yr_practice") %>%
select(-contains("id"), -contains("dt")) %>% # remove sensitive data
glimpse()
#> Rows: 391
#> Columns: 41
#> $ val_yr_practice <int> 26, 19, 16, 17, 12, 5, 11, 16, 26, 15, 31…
#> $ val_day_practice <int> 9298, 6742, 5642, 5895, 4188, 1536, 3678,…
#> $ flg_assistant_surgeon <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ flg_multi_surgeon <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ flg_two_surgeon <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ flg_surgical_team <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ us_medschool <chr> "USMG", "USMG", "USMG", "USMG", "USMG", "…
#> $ fellowship_abs <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
#> $ flg_male <int> 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1,…
#> $ e_race_wbho <int> 1, 4, 4, 3, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ age_at_admit <int> 77, 76, 68, 80, 83, 82, 78, 87, 83, 83, 8…
#> $ e_ses_5grp <int> 5, 3, 3, 3, 3, 3, 5, 5, 5, 5, 5, 5, 5, 5,…
#> $ AHRQ_score <int> 2, 12, 18, 18, 2, -1, 11, 8, -1, 0, 16, -…
#> $ e_proc_grp <int> 195, 195, 223, 249, 1, 195, 173, 195, 195…
#> $ e_proc_grp_lbl <chr> "Lysis of adhesions (open)", "Lysis of ad…
#> $ facility_clm_yr <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007,…
#> $ flg_multi_cpt <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ cpt_cd <int> 44005, 44005, 43632, 19307, 47562, 44005,…
#> $ cpt_mod <chr> "", "", "", "", "", "GC, ", "", "", "", "…
#> $ e_admit_type <chr> "1_Emergency", "1_Emergency", "1_Emergenc…
#> $ flg_cmp_po_severe_not_poa <chr> "N/A (no var)", "N/A (no var)", "N/A (no …
#> $ flg_cmp_po_severe <int> 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0,…
#> $ flg_death_30d <int> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,…
#> $ flg_readmit_30d <int> 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ flg_cmp_po_any <int> 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0,…
#> $ flg_cmp_po_any_not_poa <chr> "N/A (no var)", "N/A (no var)", "N/A (no …
#> $ val_los <int> 9, 7, 11, 3, 8, 16, 15, 9, 7, 42, 12, 2, …
#> $ flg_util_reop <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ flg_hosp_ICU_hosp <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ flg_hosp_urban <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, TRUE,…
#> $ flg_hosp_urban_cbsa <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ facility_prvnumgrp <int> 330214, 330064, 330169, 330169, 330169, 3…
#> $ flg_hosp_teach_combined <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ val_hosp_mcday2inptday_ratio <dbl> 0.10, 0.46, 0.34, 0.34, 0.34, 0.34, 0.08,…
#> $ e_hosp_beds_4grp <int> 4, 1, 4, 4, 4, 4, 3, 4, 4, 4, 4, 4, 4, 4,…
#> $ val_hosp_rn2bed_ratio <dbl> 1.57, 1.14, 1.27, 1.27, 1.27, 1.97, 3.54,…
#> $ val_hosp_rn2inptday_ratio <dbl> 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01,…
#> $ surgeon_volume <int> 339, 226, 160, 104, 548, 304, 718, 160, 3…
#> $ medicare_gs_suregon <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ had_assist_surg <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ n_cpt_admission <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…