National Plan and Provider Enumeration System (NPPES)

The registry of every medical practitioner actively operating in the United States healthcare industry.

• A single large table with one row per enumerated health care provider.

• A census of individuals and organizations that bill for medical services in the United States.

• Updated weekly with new providers.

• Maintained by the United States Centers for Medicare & Medicaid Services (CMS)

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Recommended Reading

Two Methodology Documents:

NPI: What You Need To Know

Wikipedia Entry

One Haiku:

# how many doctors
# ranked sergeant, last name pepper
# practice in the states?

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Download, Import, Preparation

Download and import the national file:

library(readr)

tf <- tempfile()

npi_datapage <-
    readLines( "http://download.cms.gov/nppes/NPI_Files.html" )

latest_files <- grep( 'NPPES_Data_Dissemination_' , npi_datapage , value = TRUE )

latest_files <- latest_files[ !grepl( 'Weekly Update' , latest_files ) ]

this_url <-
    paste0(
        "http://download.cms.gov/nppes/",
        gsub( "(.*)(NPPES_Data_Dissemination_.*\\.zip)(.*)$", "\\2", latest_files )
    )

download.file( this_url , tf , mode = 'wb' )

npi_files <- unzip( tf , exdir = tempdir() )

npi_filepath <-
    grep(
        "npidata_pfile_20050523-([0-9]+)\\.csv" ,
        npi_files ,
        value = TRUE
    )

column_names <-
    names( 
        read.csv( 
            npi_filepath , 
            nrow = 1 )[ FALSE , , ] 
    )

column_names <- gsub( "\\." , "_" , tolower( column_names ) )

column_types <-
    ifelse( 
        grepl( "code" , column_names ) & 
        !grepl( "country|state|gender|taxonomy|postal" , column_names ) , 
        'n' , 'c' 
    )

columns_to_import <-
    c( "entity_type_code" , "provider_gender_code" , "provider_enumeration_date" ,
    "is_sole_proprietor" , "provider_business_practice_location_address_state_name" )

stopifnot( all( columns_to_import %in% column_names ) )

# readr::read_csv() columns must match their order in the csv file
columns_to_import <-
    columns_to_import[ order( match( columns_to_import , column_names ) ) ]

nppes_tbl <-
    readr::read_csv( 
        npi_filepath , 
        col_names = columns_to_import , 
        col_types = 
            paste0( 
                ifelse( column_names %in% columns_to_import , column_types , '_' ) , 
                collapse = "" 
            ) ,
        skip = 1
    ) 

nppes_df <- 
    data.frame( nppes_tbl )

Save Locally  

Save the object at any point:

# nppes_fn <- file.path( path.expand( "~" ) , "NPPES" , "this_file.rds" )
# saveRDS( nppes_df , file = nppes_fn , compress = FALSE )

Load the same object:

# nppes_df <- readRDS( nppes_fn )

Variable Recoding

Add new columns to the data set:

nppes_df <- 
    transform( 
        nppes_df , 
        
        individual = as.numeric( entity_type_code ) ,
        
        provider_enumeration_year =
            as.numeric( substr( provider_enumeration_date , 7 , 10 ) ) ,
        
        state_name = provider_business_practice_location_address_state_name
        
    )

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Analysis Examples with base R  

Unweighted Counts

Count the unweighted number of records in the table, overall and by groups:

nrow( nppes_df )

table( nppes_df[ , "provider_gender_code" ] , useNA = "always" )

Descriptive Statistics

Calculate the mean (average) of a linear variable, overall and by groups:

mean( nppes_df[ , "provider_enumeration_year" ] , na.rm = TRUE )

tapply(
    nppes_df[ , "provider_enumeration_year" ] ,
    nppes_df[ , "provider_gender_code" ] ,
    mean ,
    na.rm = TRUE 
)

Calculate the distribution of a categorical variable, overall and by groups:

prop.table( table( nppes_df[ , "is_sole_proprietor" ] ) )

prop.table(
    table( nppes_df[ , c( "is_sole_proprietor" , "provider_gender_code" ) ] ) ,
    margin = 2
)

Calculate the sum of a linear variable, overall and by groups:

sum( nppes_df[ , "provider_enumeration_year" ] , na.rm = TRUE )

tapply(
    nppes_df[ , "provider_enumeration_year" ] ,
    nppes_df[ , "provider_gender_code" ] ,
    sum ,
    na.rm = TRUE 
)

Calculate the median (50th percentile) of a linear variable, overall and by groups:

quantile( nppes_df[ , "provider_enumeration_year" ] , 0.5 , na.rm = TRUE )

tapply(
    nppes_df[ , "provider_enumeration_year" ] ,
    nppes_df[ , "provider_gender_code" ] ,
    quantile ,
    0.5 ,
    na.rm = TRUE 
)

Subsetting

Limit your data.frame to California:

sub_nppes_df <- subset( nppes_df , state_name = 'CA' )

Calculate the mean (average) of this subset:

mean( sub_nppes_df[ , "provider_enumeration_year" ] , na.rm = TRUE )

Measures of Uncertainty

Calculate the variance, overall and by groups:

var( nppes_df[ , "provider_enumeration_year" ] , na.rm = TRUE )

tapply(
    nppes_df[ , "provider_enumeration_year" ] ,
    nppes_df[ , "provider_gender_code" ] ,
    var ,
    na.rm = TRUE 
)

Regression Models and Tests of Association

Perform a t-test:

t.test( provider_enumeration_year ~ individual , nppes_df )

Perform a chi-squared test of association:

this_table <- table( nppes_df[ , c( "individual" , "is_sole_proprietor" ) ] )

chisq.test( this_table )

Perform a generalized linear model:

glm_result <- 
    glm( 
        provider_enumeration_year ~ individual + is_sole_proprietor , 
        data = nppes_df
    )

summary( glm_result )

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Analysis Examples with dplyr  

The R dplyr library offers an alternative grammar of data manipulation to base R and SQL syntax. dplyr offers many verbs, such as summarize, group_by, and mutate, the convenience of pipe-able functions, and the tidyverse style of non-standard evaluation. This vignette details the available features. As a starting point for NPPES users, this code replicates previously-presented examples:

library(dplyr)
nppes_tbl <- as_tibble( nppes_df )

Calculate the mean (average) of a linear variable, overall and by groups:

nppes_tbl %>%
    summarize( mean = mean( provider_enumeration_year , na.rm = TRUE ) )

nppes_tbl %>%
    group_by( provider_gender_code ) %>%
    summarize( mean = mean( provider_enumeration_year , na.rm = TRUE ) )

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Analysis Examples with data.table  

The R data.table library provides a high-performance version of base R’s data.frame with syntax and feature enhancements for ease of use, convenience and programming speed. data.table offers concise syntax: fast to type, fast to read, fast speed, memory efficiency, a careful API lifecycle management, an active community, and a rich set of features. This vignette details the available features. As a starting point for NPPES users, this code replicates previously-presented examples:

library(data.table)
nppes_dt <- data.table( nppes_df )

Calculate the mean (average) of a linear variable, overall and by groups:

nppes_dt[ , mean( provider_enumeration_year , na.rm = TRUE ) ]

nppes_dt[ , mean( provider_enumeration_year , na.rm = TRUE ) , by = provider_gender_code ]

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Analysis Examples with duckdb  

The R duckdb library provides an embedded analytical data management system with support for the Structured Query Language (SQL). duckdb offers a simple, feature-rich, fast, and free SQL OLAP management system. This vignette details the available features. As a starting point for NPPES users, this code replicates previously-presented examples:

library(duckdb)
con <- dbConnect( duckdb::duckdb() , dbdir = 'my-db.duckdb' )
dbWriteTable( con , 'nppes' , nppes_df )

Calculate the mean (average) of a linear variable, overall and by groups:

dbGetQuery( con , 'SELECT AVG( provider_enumeration_year ) FROM nppes' )

dbGetQuery(
    con ,
    'SELECT
        provider_gender_code ,
        AVG( provider_enumeration_year )
    FROM
        nppes
    GROUP BY
        provider_gender_code'
)