medicalcoder is a lightweight, base-R package for working with ICD-9 and ICD-10 diagnosis and procedure codes. It provides fast, dependency-free tools to look up, validate, and manipulate ICD codes, while also implementing widely used comorbidity algorithms such as Charlson, Elixhauser, and the Pediatric Complex Chronic Conditions (PCCC). Designed for portability and reproducibility, the package avoids external dependencies—requiring only R ≥ 3.5.0—yet offers a rich set of curated ICD code libraries from the United States’ Centers for Medicare and Medicaid Services (CMS), Centers for Disease Control (CDC), and the World Health Organization (WHO).
The package balances performance with elegance: its internal caching, efficient joins, and compact data structures make it practical for large-scale health data analyses, while its clean design makes it easy to extend or audit. Whether you need to flag comorbidities, explore ICD hierarchies, or standardize clinical coding workflows, medicalcoder provides a robust, transparent foundation for research and applied work in biomedical informatics.
The primary objectives of medicalcoder are:
-
Fully self-contained
- Minimal Dependencies
- No dependencies other than base R.
- Requires R version ≥ 3.5.0 due to a change in data serialization. R 3.5.0 was released in April 2018. The initial public release of medicalcoder was in 2025.
- Several packages are listed in the Suggests section of the
DESCRIPTIONfile. These are only needed for building vignettes, other documentation, and testing. They are not required to install the package.
- No Imports
- medicalcoder does not import any non-base namespaces. This improves ease of maintenance and usability.
- Suggested packages are needed only for development work and building vignettes. They are not required for installation or use.
- That said, there are non-trivial performance gains when passing a
data.tableto thecomorbidities()function compared to passing a basedata.frameor atibblefrom the tidyverse. (See benchmarking).
- Internal lookup tables
- All required data are included in the package. If you have the
.tar.gzsource file and R ≥ 3.5.0, that is all you need to install and use the package.
- All required data are included in the package. If you have the
- Minimal Dependencies
-
Efficient implementation of multiple comorbidity algorithms
- Implements three general algorithms, each with multiple variants. Details are provided below.
- Supports flagging of subconditions within PCCC.
- Supports longitudinal flagging of comorbidities. medicalcoder will flag comorbidities based on present-on-admission indicators for the current encounter and can look back in time for a patient to flag a comorbidity if reported in a prior encounter. See examples.
-
Tools for working with ICD codes
- Lookup tables.
- Ability to work with both full codes (ICD codes with decimal points) and compact codes (ICD codes with decimal points omitted).
Why use medicalcoder
There are several tools for working with ICD codes and comorbidity algorithms. medicalcoder provides novel features:
- Unified access to multiple comorbidity algorithms through a single function:
comorbidities(). - Support for both ICD-9 and ICD-10 diagnostic and procedure codes.
- Longitudinal patient-level comorbidity flagging using present-on-admission indicators.
- Fully self-contained package (no external dependencies).
Install
CRAN
install.packages("medicalcoder")From source
If you have the .tar.gz file for version X.Y.Z, e.g., medicalcoder_X.Y.Z.tar.gz you can install from within R via:
install.packages(
pkgs = "medicalcoder_X.Y.Z.tar.gz", # replace file name with the file you have
repos = NULL,
type = "source"
)From the command line:
R CMD INSTALL medicalcoder_X.Y.Z.tar.gzQuick Start:
Example Data
Input data for comorbidities() is expected to be in a ‘long’ format. Each row is one code with additional columns for patient and/or encounter id. There are two example data sets in the package: mdcr and mdcr_longitudinal.
data(mdcr, mdcr_longitudinal, package = "medicalcoder")The mdcr data set consists of 319 856 rows. Each row contains one ICD code (code). The column icdv denotes each code as ICD-9 or ICD-10, and the dx column denotes diagnostic (1) or procedure (0) code. This data set contains diagnostic and procedure codes for 38 262 patients.
str(mdcr)
#> 'data.frame': 319856 obs. of 4 variables:
#> $ patid: int 71412 71412 71412 71412 71412 17087 64424 64424 84361 84361 ...
#> $ icdv : int 9 9 9 9 9 10 9 9 9 9 ...
#> $ code : chr "99931" "75169" "99591" "V5865" ...
#> $ dx : int 1 1 1 1 1 1 1 0 1 1 ...
head(mdcr)
#> patid icdv code dx
#> 1 71412 9 99931 1
#> 2 71412 9 75169 1
#> 3 71412 9 99591 1
#> 4 71412 9 V5865 1
#> 5 71412 9 V427 1
#> 6 17087 10 V441 1The mdcr_longitudinal data set is distinct from the mdcr data set. The major difference is that this data set contains only diagnostic codes and there are only 3 patients. The date column denotes the date of the diagnosis and allows us to look at changes in comorbidities over time.
str(mdcr_longitudinal)
#> 'data.frame': 60 obs. of 4 variables:
#> $ patid: int 9663901 9663901 9663901 9663901 9663901 9663901 9663901 9663901 9663901 9663901 ...
#> $ date : IDate, format: "2016-03-18" "2016-03-24" ...
#> $ icdv : int 10 10 10 10 10 10 10 10 10 10 ...
#> $ code : chr "Z77.22" "IMO0002" "V87.7XXA" "J95.851" ...
head(mdcr_longitudinal)
#> patid date icdv code
#> 1 9663901 2016-03-18 10 Z77.22
#> 2 9663901 2016-03-24 10 IMO0002
#> 3 9663901 2016-03-24 10 V87.7XXA
#> 4 9663901 2016-03-25 10 J95.851
#> 5 9663901 2016-03-30 10 IMO0002
#> 6 9663901 2016-03-30 10 Z93.0Comorbidity Algorithms
There are three comorbidity methods, each with several variants, available in medicalcoder. All of which are accessible through the comorbidities() method by specifying the method argument.
General examples and explanations for when conditions are flagged are in the vignette
vignette(topic = "comorbidities", package = "medicalcoder")Pediatric Complex Chronic Conditions (PCCC)
- Version 2.0
- BMC Pediatrics: Feudtner et al. (2014)
- Consistent with R package pccc
- Version 2.1
- Updated code base with the same assessment algorithm as version 2.0.
- Version 3.0
- JAMA Network Open: Feinstein et al. (2024)
- Children’s Hospital Association Toolkit
- Version 3.1
- Updated code base with same assessment algorithm as version 3.0.
- All variants can flag conditions and subconditions.
# PCCC v3.1 example
library(medicalcoder)
cmrbs2 <-
comorbidities(
data = mdcr,
id.vars = "patid", # can use more than one column, e.g., site, patient, encounter
icd.codes = "code",
dx.var = "dx",
poa = 1, # consider all codes to be present on admission
method = "pccc_v2.1"
)
cmrbs3 <-
comorbidities(
data = mdcr,
id.vars = "patid",
icd.codes = "code",
dx.var = "dx",
poa = 1, # consider all codes to be present on admission
method = "pccc_v3.1"
)
str(cmrbs2, max.level = 0)
#> Classes 'medicalcoder_comorbidities' and 'data.frame': 38262 obs. of 16 variables:
#> - attr(*, "method")= chr "pccc_v2.1"
#> - attr(*, "id.vars")= chr "patid"
#> - attr(*, "flag.method")= chr "current"
str(cmrbs3, max.level = 0)
#> Classes 'medicalcoder_comorbidities' and 'data.frame': 38262 obs. of 49 variables:
#> - attr(*, "method")= chr "pccc_v3.1"
#> - attr(*, "id.vars")= chr "patid"
#> - attr(*, "flag.method")= chr "current"A summary of the flagged conditions is generated with a call to summary().
For pccc_v2.0 and pccc_v2.1 the data.frame returned by summary() reports the count (unique id.vars with the condition) and percentage.
For pccc_v3.0 and pccc_v3.1 the returned data.frame reports counts and percentages for how the condition was flagged based on diagnosis/procedure codes only, technology dependent codes only, or both. The dxpr_or_tech columns answer the question “did this patient have the condition”.
Further detail, examples, and explanations are in the vignette.
vignette(topic = "pccc", package = "medicalcoder")Charlson Comorbidities
There are four variants of Charlson comorbidities implemented in medicalcoder:
# Charlson example
cmrbs <-
comorbidities(
data = mdcr,
id.vars = "patid",
icd.codes = "code",
dx.var = "dx",
poa = 1, # assume all codes are present on admission
primarydx = 0L, # assume all codes are secondary diagnosis codes
method = "charlson_quan2005"
)A summary of the flagged conditions can be generated by calling summary(). Where the summary for the PCCC method was a data.frame the return for the Charlson comorbidities is a list of data frames summarizing the conditions, age category, and the index score.
More details and examples are provided in the vignette:
vignette(topic = "charlson", package = "medicalcoder")Elixhauser Comorbidities
-
Elixhauser et al. (1998)
method = elixhauser_elixhauser1988
-
Quan et al. (2005)
method = elixhauser_quan2005
- AHRQ (2017, 2022, 2023, 2024, 2025, ICD10)
-
For ICD-9 codes
method = elixhauser_ahrq_web
-
For ICD-10 codes
method = elixhauser_ahrq2022method = elixhauser_ahrq2023method = elixhauser_ahrq2024method = elixhauser_ahrq2025method = elixhauser_ahrq_icd10
-
For ICD-9 codes
# Elixhauser example
cmrbs <-
comorbidities(
data = mdcr,
id.vars = "patid",
icd.codes = "code",
dx.var = "dx",
poa = 1,
primarydx = 0L,
method = "elixhauser_ahrq_icd10"
)The summary for the results from method = elixhauser_ahrq_icd10 are similar to those for Charlson. A data.frame with the counts and percentages of distinct data[id.vars] with the noted condition, and a summary of the index scores.
More details and examples are provided in the vignette:
vignette(topic = "elixhauser", package = "medicalcoder")ICD
The package contains internal data sets with references for ICD-9 and ICD-10 US based diagnostic and procedure codes. These codes are supplemented with additional codes from the World Health Organization.
You can get a table of ICD codes via get_icd_codes().
str(medicalcoder::get_icd_codes())
#> 'data.frame': 227534 obs. of 9 variables:
#> $ icdv : int 9 9 9 9 9 9 9 9 9 9 ...
#> $ dx : int 0 0 0 0 0 0 1 0 1 0 ...
#> $ full_code : chr "00" "00.0" "00.01" "00.02" ...
#> $ code : chr "00" "000" "0001" "0002" ...
#> $ src : chr "cms" "cms" "cms" "cms" ...
#> $ known_start : int 2003 2003 2003 2003 2003 2003 1997 2003 1997 2003 ...
#> $ known_end : int 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
#> $ assignable_start: int NA NA 2003 2003 2003 2003 NA NA 1997 2003 ...
#> $ assignable_end : int NA NA 2015 2015 2015 2015 NA NA 2015 2015 ...The columns are:
icdv: integer value 9 or 10; for ICD-9 or ICD-10dx: integer 0 or 1; 0 = procedure code, 1 = diagnostic codefull_code: character string for the ICD code with any appropriate decimal point.code: character string for the compact ICD code, that is, the ICD code without any decimal point, e.g., the full code C00.1 has the compact code form C001.-
src: character string denoting the source of the ICD code information.-
cms: The ICD-9-CM, ICD-9-PCS, ICD-10-CM, or ICD-10-PCS codes curated by the Centers for Medicare and Medicaid Services (CMS). -
cdc: CDC mortality coding. -
who: World Health Organization.
-
-
known_start: The earliest (fiscal) year when source data for the code was available in the source code for medicalcoder. Codes from CMS are for the United States fiscal year. Codes from CDC and WHO are calendar year. The United States fiscal year starts October 1 and concludes September 30. For example, fiscal year 2013 started October 1 2012 and concluded September 30 2013.To reemphasize that the year is for the data within medicalcoder. For ICD-9-CM, the codes went into effect for fiscal year 1980. The source code only has documented source files for the codes dating back to
known_end: The latest (fiscal) year when the code was part of the ICD system and/or known within the medicalcoder lookup tables.-
Assignable codes. Some codes are header codes, e.g., ICD-10-CM three-digit code Z94 is a header code because the four-digit codes Z94.0, Z94.1, Z94.2, Z94.3, Z94.4, Z94.5, Z94.6, Z94.7, Z94.8, and Z94.9 exist. All but Z94.8 are assignable codes because no five-digit codes with the same initial four-digits exist. Z94.8 is a header code because the five-digit codes Z94.81, Z94.82, Z94.83, Z94.84, and Z94.89 exist.
-
assignable_start: Earliest (fiscal) year when the code was assignable. -
assignable_end: Latest (fiscal) year when the code was assignable.
-
subset(
x = lookup_icd_codes("^Z94", regex = TRUE, full.codes = TRUE, compact.codes = FALSE),
subset = src == "cms",
select = c("full_code", "known_start", "known_end", "assignable_start", "assignable_end")
)
#> full_code known_start known_end assignable_start assignable_end
#> 1 Z94 2014 2026 NA NA
#> 5 Z94.0 2014 2026 2014 2026
#> 9 Z94.1 2014 2026 2014 2026
#> 14 Z94.2 2014 2026 2014 2026
#> 17 Z94.3 2014 2026 2014 2026
#> 22 Z94.4 2014 2026 2014 2026
#> 25 Z94.5 2014 2026 2014 2026
#> 29 Z94.6 2014 2026 2014 2026
#> 33 Z94.7 2014 2026 2014 2026
#> 38 Z94.8 2014 2026 NA NA
#> 41 Z94.81 2014 2026 2014 2026
#> 42 Z94.82 2014 2026 2014 2026
#> 43 Z94.83 2014 2026 2014 2026
#> 44 Z94.84 2014 2026 2014 2026
#> 45 Z94.89 2014 2026 2014 2026
#> 46 Z94.9 2014 2026 2014 2026Additionally, the get_icd_codes() method can provide descriptions and the ICD hierarchy by using the with.descriptions and/or with.hierarchy arguments.
Functions lookup_icd_codes(), is_icd(), and icd_compact_to_full() are also provided for working with ICD codes.
More details and examples are in the vignette:
vignette(topic = "icd", package = "medicalcoder")Benchmarking
The major factors impacting the expected computation time for applying a comorbidity algorithm to a data set are:
- Data size: number of subjects/encounters.
- Data storage class: medicalcoder has been built such that no imports of other namespaces is required. That said, when a
data.tableis passed tocomorbidities()and thedata.tablenamespace is available, then S3 dispatch formergeis used, along with some other methods, to reduce memory use and reduce computation time. -
flag.method: “current” will take less time than the “cumulative” method.
Details on the benchmarking method, summary graphics, and tables, can be found on the medicalcoder GitHub benchmarking directory.
Testing
Along with the GitHub actions and testing on current versions of R, the testing directory in the medicalcoder GitHub repo reports the R CMD check results for all R versions from 3.5.0 to latest. Several with, and without Sugguests.