README.md
In mhairi/medicalclaims: New Hampshire Health Insurance Claims
medicalclaims
A data package with a sample of 100,000 anonymised medical claims New
Hampshire’s Comprehensive Health Information System
(https://nhchis.com/).
Installation
You can install though GitHub with:
# install.packages("devtools")
devtools::install_github("mhairi/medicalclaims")
Example
Once you’ve loaded the package, the data is in an object called
claims. The data frame has 100,000 rows and 57 variables.
library(medicalclaims)
head(claims)
#> coverage_class form_year claim_id_key cs_claim_id_key services_key
#> 1 MED 2016 140080841 140080841 284130245
#> 2 MED 2016 140080841 140080841 284130249
#> 3 MED 2016 194338559 194338559 238447503
#> 4 MED 2016 23570691 23570691 159659802
#> 5 MED 2016 23570691 23570691 159659805
#> 6 MED 2016 23570691 23570691 159659809
#> sv_line form_type sv_stat dis_stat pos age sex member_county
#> 1 2 H P NA 81 64 M 11
#> 2 1 H P NA 81 64 M 11
#> 3 10 H P NA 22 62 M 11
#> 4 20 H P NA 20 20 F 11
#> 5 10 H P NA 20 20 F 11
#> 6 30 H P NA 20 20 F 11
#> member_state product_type lob insurance_type proc_code cdt_mod1
#> 1 NH HMO COMMERCIAL HM 36415 N/
#> 2 NH HMO COMMERCIAL HM 85610 N/
#> 3 NH HMO COMMERCIAL HM 93010 N/
#> 4 NH HMO COMMERCIAL HM 87081 N/
#> 5 NH HMO COMMERCIAL HM 87880 N/
#> 6 NH HMO COMMERCIAL HM 99213 25
#> cdt_mod2 rev_code ub_bill_type adm_src adm_type client_los
#> 1 N/ NA NA NA NA NA
#> 2 N/ NA NA NA NA NA
#> 3 N/ NA NA NA NA NA
#> 4 N/ NA NA NA NA NA
#> 5 N/ NA NA NA NA NA
#> 6 N/ NA NA NA NA NA
#> icd_10_or_higher icd_proc_01_pri icd_diag_01_primary icd_diag_admit
#> 1 1 NA D6852 <NA>
#> 2 1 NA D6852 <NA>
#> 3 1 NA G5602 <NA>
#> 4 1 NA J029 <NA>
#> 5 1 NA J029 <NA>
#> 6 1 NA J029 <NA>
#> icd_diag_02 serv_prov_cw_key bill_prov_cw_key qty amt_billed amt_paid
#> 1 I776 3613074 1477238 1 25.00 0.00
#> 2 I776 3613074 1477238 1 35.00 0.00
#> 3 <NA> 659123 330257 1 21.00 20.08
#> 4 J069 919693 919693 1 9.54 9.03
#> 5 J069 919693 919693 1 18.94 15.60
#> 6 J069 919693 919693 1 165.00 87.54
#> amt_deduct amt_coins amt_copay amt_prepaid inpatient_flag
#> 1 2.28 0 0 0 N
#> 2 4.29 0 0 0 N
#> 3 0.00 0 0 0 N
#> 4 0.00 0 0 0 N
#> 5 0.00 0 0 0 N
#> 6 0.00 0 0 0 N
#> mr_line_case_key cases utils ndc claim_status_orig ecode_orig
#> 1 638 NA 1 NA 1 <NA>
#> 2 638 NA 1 NA 1 <NA>
#> 3 613 NA 1 NA 1 <NA>
#> 4 636 NA 1 NA 1 <NA>
#> 5 636 NA 1 NA 1 <NA>
#> 6 586 NA 1 NA 1 <NA>
#> claim_adjustment_logic imputed_service_key cash total
#> 1 CAL2 2016_1560554 2.28 2.28
#> 2 CAL2 2016_1560554 4.29 4.29
#> 3 CAL2 2016_15605580 0.00 20.08
#> 4 CAL2 2016_15605601 0.00 9.03
#> 5 CAL2 2016_15605601 0.00 15.60
#> 6 CAL2 2016_15605601 0.00 87.54
#> cpt_desc
#> 1 Needle insertion for collection of blood sample
#> 2 Blood test, clotting time
#> 3 EKG using at least 12 leads with interpretation and report
#> 4 Screening test for pathogenic organisms
#> 5 Strep test (Streptococcus, group A)
#> 6 Office or other outpatient visit, typically 15 mins
#> mr_line_new_desc mr_line_setting
#> 1 Pathology/Lab - Office Independent Lab
#> 2 Pathology/Lab - Office Independent Lab
#> 3 Cardiovascular PHY Cardiovascular
#> 4 Pathology/Lab - Office PHY Pathology/Lab - Office - General
#> 5 Pathology/Lab - Office PHY Pathology/Lab - Office - General
#> 6 Urgent Care Visits Urgent Care Visits: Physician
#> mr_line_abbrdesc mr_line_hcg new_diag_desc
#> 1 PHY 3. Professional Prothrombin gene mutation
#> 2 PHY 3. Professional Prothrombin gene mutation
#> 3 PHY 3. Professional Carpal tunnel syndrome, left upper limb
#> 4 PHY 3. Professional Acute pharyngitis
#> 5 PHY 3. Professional Acute pharyngitis
#> 6 PHY 3. Professional Acute pharyngitis
#> total_by_n
#> 1 2.28
#> 2 4.29
#> 3 20.08
#> 4 9.03
#> 5 15.60
#> 6 87.54
Here is how you find the procedures with the highest average cost, only
counting procedures that have appeared at least 10 times in the data.
library(tidyverse)
claims %>%
group_by(cpt_desc) %>%
summarise(
avg_cost = mean(total_by_n),
n = n()
) %>%
filter(n > 10) %>%
arrange(desc(avg_cost)) %>%
top_n(10, avg_cost)
#> # A tibble: 10 x 3
#> cpt_desc avg_cost n
#> <chr> <dbl> <int>
#> 1 OBGYN pre and postpartum care and vaginal delivery 3784. 20
#> 2 Sleep monitoring of patient (6 years or older) in sleep … 2484. 20
#> 3 Nuclear medicine study of vessels of heart 2199. 14
#> 4 Biopsy of breast accessed through the skin with stereota… 2037. 11
#> 5 MRI scan of abdomen before and after contrast 1803. 11
#> 6 Radiation therapy delivery 1580. 43
#> 7 Diagnostic exam of large bowel using an endoscope 1562. 45
#> 8 MRI scan of brain before and after contrast 1561. 63
#> 9 Removal of one knee cartilage using an endoscope 1537. 19
#> 10 CT scan of abdomen and pelvis with contrast 1411. 129
If you want to look at how expensive different diagnoses are, then you
first need to summarise over imputed_service_key and
icd_diag_01_primary. This gives us the total spending for each patient
and each diagnosis.
by_individual <-
claims %>%
group_by(new_diag_desc, imputed_service_key) %>%
summarise(spending = sum(total)) %>%
ungroup
Then we can summarise to find the most expensive diagnoses.
by_individual %>%
group_by(new_diag_desc) %>%
summarise(
avg_cost = mean(spending),
n = n()
) %>%
filter(n > 10) %>%
arrange(desc(avg_cost)) %>%
top_n(10, avg_cost)
#> # A tibble: 10 x 3
#> new_diag_desc avg_cost n
#> <chr> <dbl> <int>
#> 1 Antineoplastic Chemotherapy Session 4041. 36
#> 2 Chronic inflammatory demyelinating polyneuritis 3848. 14
#> 3 Malignant neoplasm of ovrlp sites of left female breast 3017. 16
#> 4 Hydronephrosis with renal and ureteral calculous obstruc… 2811. 12
#> 5 Crohn's disease, without complications 2536. 29
#> 6 Malignant neoplasm of unspecified site of left female br… 2439. 35
#> 7 Malignant neoplasm of brain 2158. 11
#> 8 Ulcerative colitis, without complications 1919. 25
#> 9 Encounter for full-term uncomplicated delivery 1905. 40
#> 10 Encounter for antineoplastic radiation therapy 1707. 89
mhairi/medicalclaims documentation built on May 20, 2020, 10:15 a.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
medicalclaims
A data package with a sample of 100,000 anonymised medical claims New Hampshire’s Comprehensive Health Information System (https://nhchis.com/).
Installation
You can install though GitHub with:
# install.packages("devtools")
devtools::install_github("mhairi/medicalclaims")
Example
Once you’ve loaded the package, the data is in an object called
claims. The data frame has 100,000 rows and 57 variables.
library(medicalclaims)
head(claims)
#> coverage_class form_year claim_id_key cs_claim_id_key services_key
#> 1 MED 2016 140080841 140080841 284130245
#> 2 MED 2016 140080841 140080841 284130249
#> 3 MED 2016 194338559 194338559 238447503
#> 4 MED 2016 23570691 23570691 159659802
#> 5 MED 2016 23570691 23570691 159659805
#> 6 MED 2016 23570691 23570691 159659809
#> sv_line form_type sv_stat dis_stat pos age sex member_county
#> 1 2 H P NA 81 64 M 11
#> 2 1 H P NA 81 64 M 11
#> 3 10 H P NA 22 62 M 11
#> 4 20 H P NA 20 20 F 11
#> 5 10 H P NA 20 20 F 11
#> 6 30 H P NA 20 20 F 11
#> member_state product_type lob insurance_type proc_code cdt_mod1
#> 1 NH HMO COMMERCIAL HM 36415 N/
#> 2 NH HMO COMMERCIAL HM 85610 N/
#> 3 NH HMO COMMERCIAL HM 93010 N/
#> 4 NH HMO COMMERCIAL HM 87081 N/
#> 5 NH HMO COMMERCIAL HM 87880 N/
#> 6 NH HMO COMMERCIAL HM 99213 25
#> cdt_mod2 rev_code ub_bill_type adm_src adm_type client_los
#> 1 N/ NA NA NA NA NA
#> 2 N/ NA NA NA NA NA
#> 3 N/ NA NA NA NA NA
#> 4 N/ NA NA NA NA NA
#> 5 N/ NA NA NA NA NA
#> 6 N/ NA NA NA NA NA
#> icd_10_or_higher icd_proc_01_pri icd_diag_01_primary icd_diag_admit
#> 1 1 NA D6852 <NA>
#> 2 1 NA D6852 <NA>
#> 3 1 NA G5602 <NA>
#> 4 1 NA J029 <NA>
#> 5 1 NA J029 <NA>
#> 6 1 NA J029 <NA>
#> icd_diag_02 serv_prov_cw_key bill_prov_cw_key qty amt_billed amt_paid
#> 1 I776 3613074 1477238 1 25.00 0.00
#> 2 I776 3613074 1477238 1 35.00 0.00
#> 3 <NA> 659123 330257 1 21.00 20.08
#> 4 J069 919693 919693 1 9.54 9.03
#> 5 J069 919693 919693 1 18.94 15.60
#> 6 J069 919693 919693 1 165.00 87.54
#> amt_deduct amt_coins amt_copay amt_prepaid inpatient_flag
#> 1 2.28 0 0 0 N
#> 2 4.29 0 0 0 N
#> 3 0.00 0 0 0 N
#> 4 0.00 0 0 0 N
#> 5 0.00 0 0 0 N
#> 6 0.00 0 0 0 N
#> mr_line_case_key cases utils ndc claim_status_orig ecode_orig
#> 1 638 NA 1 NA 1 <NA>
#> 2 638 NA 1 NA 1 <NA>
#> 3 613 NA 1 NA 1 <NA>
#> 4 636 NA 1 NA 1 <NA>
#> 5 636 NA 1 NA 1 <NA>
#> 6 586 NA 1 NA 1 <NA>
#> claim_adjustment_logic imputed_service_key cash total
#> 1 CAL2 2016_1560554 2.28 2.28
#> 2 CAL2 2016_1560554 4.29 4.29
#> 3 CAL2 2016_15605580 0.00 20.08
#> 4 CAL2 2016_15605601 0.00 9.03
#> 5 CAL2 2016_15605601 0.00 15.60
#> 6 CAL2 2016_15605601 0.00 87.54
#> cpt_desc
#> 1 Needle insertion for collection of blood sample
#> 2 Blood test, clotting time
#> 3 EKG using at least 12 leads with interpretation and report
#> 4 Screening test for pathogenic organisms
#> 5 Strep test (Streptococcus, group A)
#> 6 Office or other outpatient visit, typically 15 mins
#> mr_line_new_desc mr_line_setting
#> 1 Pathology/Lab - Office Independent Lab
#> 2 Pathology/Lab - Office Independent Lab
#> 3 Cardiovascular PHY Cardiovascular
#> 4 Pathology/Lab - Office PHY Pathology/Lab - Office - General
#> 5 Pathology/Lab - Office PHY Pathology/Lab - Office - General
#> 6 Urgent Care Visits Urgent Care Visits: Physician
#> mr_line_abbrdesc mr_line_hcg new_diag_desc
#> 1 PHY 3. Professional Prothrombin gene mutation
#> 2 PHY 3. Professional Prothrombin gene mutation
#> 3 PHY 3. Professional Carpal tunnel syndrome, left upper limb
#> 4 PHY 3. Professional Acute pharyngitis
#> 5 PHY 3. Professional Acute pharyngitis
#> 6 PHY 3. Professional Acute pharyngitis
#> total_by_n
#> 1 2.28
#> 2 4.29
#> 3 20.08
#> 4 9.03
#> 5 15.60
#> 6 87.54
Here is how you find the procedures with the highest average cost, only counting procedures that have appeared at least 10 times in the data.
library(tidyverse)
claims %>%
group_by(cpt_desc) %>%
summarise(
avg_cost = mean(total_by_n),
n = n()
) %>%
filter(n > 10) %>%
arrange(desc(avg_cost)) %>%
top_n(10, avg_cost)
#> # A tibble: 10 x 3
#> cpt_desc avg_cost n
#> <chr> <dbl> <int>
#> 1 OBGYN pre and postpartum care and vaginal delivery 3784. 20
#> 2 Sleep monitoring of patient (6 years or older) in sleep … 2484. 20
#> 3 Nuclear medicine study of vessels of heart 2199. 14
#> 4 Biopsy of breast accessed through the skin with stereota… 2037. 11
#> 5 MRI scan of abdomen before and after contrast 1803. 11
#> 6 Radiation therapy delivery 1580. 43
#> 7 Diagnostic exam of large bowel using an endoscope 1562. 45
#> 8 MRI scan of brain before and after contrast 1561. 63
#> 9 Removal of one knee cartilage using an endoscope 1537. 19
#> 10 CT scan of abdomen and pelvis with contrast 1411. 129
If you want to look at how expensive different diagnoses are, then you
first need to summarise over imputed_service_key and
icd_diag_01_primary. This gives us the total spending for each patient
and each diagnosis.
by_individual <-
claims %>%
group_by(new_diag_desc, imputed_service_key) %>%
summarise(spending = sum(total)) %>%
ungroup
Then we can summarise to find the most expensive diagnoses.
by_individual %>%
group_by(new_diag_desc) %>%
summarise(
avg_cost = mean(spending),
n = n()
) %>%
filter(n > 10) %>%
arrange(desc(avg_cost)) %>%
top_n(10, avg_cost)
#> # A tibble: 10 x 3
#> new_diag_desc avg_cost n
#> <chr> <dbl> <int>
#> 1 Antineoplastic Chemotherapy Session 4041. 36
#> 2 Chronic inflammatory demyelinating polyneuritis 3848. 14
#> 3 Malignant neoplasm of ovrlp sites of left female breast 3017. 16
#> 4 Hydronephrosis with renal and ureteral calculous obstruc… 2811. 12
#> 5 Crohn's disease, without complications 2536. 29
#> 6 Malignant neoplasm of unspecified site of left female br… 2439. 35
#> 7 Malignant neoplasm of brain 2158. 11
#> 8 Ulcerative colitis, without complications 1919. 25
#> 9 Encounter for full-term uncomplicated delivery 1905. 40
#> 10 Encounter for antineoplastic radiation therapy 1707. 89
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