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PatientAttribution_com_2019.R
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PatientAttribution_com_2019.R
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########## Patient Attribution ##########
#### packages #####
library(data.table)
library(lubridate)
library(tidyverse)
library(zoo)
source('E:/CT_APCD/Sai/2019spring/data-organization/R/utils.R')
dir_code <- input_dir('E:/CT_APCD/Sai/lds2021/lds2021code/Attribution_2018_2019/')
source(file.path(dir_code, 'myDoc.R'))
source(file.path(dir_code, 'myFun.R'))
source('E:/CT_APCD/Sai/2019spring/data-organization/R/functions.R')
dir_out <- output_dir('E:/CT_APCD/Sai/lds2021/lds2021file/Attribution_result/InProcess_2019/')
## READ MC and NPPES
nppes0 <- fread("E:/CT_APCD/Sai/lds2021/lds2021file/nppes/nppes_lds_com_2019_short.csv",
colClasses = "character",
header = TRUE,
encoding = "UTF-8")
col.select <- c('medical_claim_service_line_id',
'internal_member_id',
'first_service_dt',
'procedure_code',
'diagnosis_code',
'icd_procedure_code',
'medical_claim_header_id',
'rendering_provider_id',
# 'billing_provider_id',
'submitter_id',
"product_code",
NULL)
## Outpatient value set ### coomentted by Sai 05/25/2019
MC_2019_OutP <- get_medical_vs(
vs.list = 'Outpatient',
dir.raw = "E:/CT_APCD/Sai/lds2021/lds2021file/value_sets/medical_claim/",
year.list = 2019,
select = col.select
)
MC_2018_OutP <- get_medical_vs(
vs.list = 'Outpatient',
dir.raw = "E:/CT_APCD/Sai/lds2021/lds2021file/value_sets/medical_claim/",
year.list = 2018,
select = col.select
)
## modified provider file
elig.patient <- fread("E:/CT_APCD/Beth/data6/Eligibility/By_Calendar_Year/MEDICAL_ELIGIBILITY_ALLRECORDS14_19_6b.csv",
select = c("INTERNAL_MEMBER_ID",
"GENDER_CODE",
"birth_dt"),
colClasses = "character") %>% unique()
elig.patient[, `:=`(birth_year = year(as.yearmon(birth_dt, "%Y%m")))]
elig.patient[, age:= 2019 - as.integer(birth_year)]
elig.patient <- elig.patient[age < 65]
names(elig.patient) <- tolower(names(elig.patient))
elig.patient <- elig.patient[, .(internal_member_id, gender_code, age)] %>%
unique(use.key = FALSE)
provider <- fread("E:/CT_APCD/Sai/lds2021/lds2021file/nppes/provider_small.csv",
colClasses = "character")
## identify patients whose ages more than 64
# patient_age0064 <- elig.patient[birth_dt >= 2019 - 64]$INTERNAL_MEMBER_ID # by Sai
## Get NPI list
realNPI_pcp <- unique(nppes0[spec0 == "PCP"]$NPI)
realNPI_obgyn <- unique(nppes0[spec0 == "OBGYN"]$NPI)
## remove Medicare AD claims
mcpt <- rbindlist(lapply(paste0("E:/CT_APCD/Sai/lds2021/lds2021file/medicare_medicare_ad_patients/medicare_medicare_ad_patients_", 2018:2019, ".csv"),
function(x){
fread(x, colClasses = "character")
})) %>% unique()
MC_2018_OutP <- MC_2018_OutP[!internal_member_id %in% mcpt$internal_member_id]
MC_2019_OutP <- MC_2019_OutP[!internal_member_id %in% mcpt$internal_member_id]
## Add NPI to raw dataset
MC_2019_OutP <- provider[MC_2019_OutP, on = c(provider_id = 'rendering_provider_id')]
MC_2018_OutP <- provider[MC_2018_OutP, on = c(provider_id = 'rendering_provider_id')]
## Add birth_dt to raw dataset
MC_2019_OutP <- elig.patient[MC_2019_OutP, on = c("internal_member_id"),
nomatch = 0]
MC_2018_OutP <- elig.patient[MC_2018_OutP, on = c("internal_member_id"),
nomatch = 0]
## remove facility provider
# MC_2019_OutP <- MC_2019_OutP[!provider_facility, on = 'PROVIDER_ID']
# MC_2018_OutP <- MC_2018_OutP[!provider_facility, on = 'PROVIDER_ID']
## MC to MC_EM & MC_NotEM datasets
MC_2019_OutP_EM <- MC_2019_OutP[procedure_code %in% CPTList]
MC_2019_OutP_NonEM <- MC_2019_OutP[!procedure_code %in% CPTList]
MC_2018_OutP_EM <- MC_2018_OutP[procedure_code %in% CPTList]
MC_2018_OutP_NonEM <- MC_2018_OutP[!procedure_code %in% CPTList]
## to MC_year_EM_class datasets
MC_2019_OutP_EM_pcp <- MC_2019_OutP_EM[NPI %in% realNPI_pcp]
MC_2019_OutP_EM_obgyn <- MC_2019_OutP_EM[NPI %in% realNPI_obgyn]
MC_2018_OutP_EM_pcp <- MC_2018_OutP_EM[NPI %in% realNPI_pcp]
MC_2018_OutP_EM_obgyn <- MC_2018_OutP_EM[NPI %in% realNPI_obgyn]
MC_2019_OutP_EM_other <- MC_2019_OutP_EM[!NPI %in% c(realNPI_pcp, realNPI_obgyn)]
## Patient_Attribution matrix
Patient_classification <- data.table(internal_member_id =
unique(MC_2019_OutP_EM$internal_member_id))
Patient_classification[,
`:=` (In_2019_OutP_EM_pcp =
as.numeric(internal_member_id %in%
unique(MC_2019_OutP_EM_pcp$internal_member_id)),
In_2019_OutP_EM_obgyn =
as.numeric(internal_member_id %in%
unique(MC_2019_OutP_EM_obgyn$internal_member_id)),
In_2018_OutP_EM_pcp =
as.numeric(internal_member_id %in%
unique(MC_2018_OutP_EM_pcp$internal_member_id)),
In_2018_OutP_EM_obgyn =
as.numeric(internal_member_id %in%
unique(MC_2018_OutP_EM_obgyn$internal_member_id)))]
## patient attribution flow chart step1
Patient_classification[In_2019_OutP_EM_pcp == 1,
Attribution := 'PCP_2019']
## patient attribution flow chart step2
Patient_classification[In_2019_OutP_EM_pcp == 0 & In_2018_OutP_EM_pcp == 1,
Attribution := 'PCP_2018']
## patient attribution flow chart step3
Patient_classification[In_2019_OutP_EM_pcp == 0 & In_2018_OutP_EM_pcp == 0 &
In_2019_OutP_EM_obgyn == 1,
Attribution := 'OBGYN_2019']
## patient attribution flow chart step4
Patient_classification[In_2019_OutP_EM_pcp == 0 & In_2018_OutP_EM_pcp == 0 &
In_2019_OutP_EM_obgyn == 0 & In_2018_OutP_EM_obgyn == 1,
Attribution := 'OBGYN_2018']
## patient attribution flow chart step5
Patient_classification[In_2019_OutP_EM_pcp == 0 & In_2018_OutP_EM_pcp == 0 &
In_2019_OutP_EM_obgyn == 0 & In_2018_OutP_EM_obgyn == 0,
Attribution := 'Unattributed']
Patient_classification[!internal_member_id %in% c(unique(MC_2019_OutP_EM$internal_member_id),
unique(MC_2018_OutP_EM$internal_member_id)),
Attribution := 'No E&M service Record']
## frequency table for 6 attribution categories.
tb.pat_class <- as.data.table(table(Patient_classification$Attribution))
tb.pat_class[, percent := N / sum(tb.pat_class$N)]
colnames(tb.pat_class) <- c('AttriClass', 'Count', 'percent')
### save tables
fwrite(Patient_classification, ## actual patient attribution table
paste0(dir_out, 'PatientClass.csv'))
fwrite(tb.pat_class, ## frequency table for attribution categories
paste0(dir_out, 'tb_PatientClass.csv'))
## assign patient_id lists for each attribution categories into 5 variable names.
patient_to_pcp_2019 <- Patient_classification[Attribution == 'PCP_2019']$internal_member_id
patient_to_pcp_2018 <- Patient_classification[Attribution == 'PCP_2018']$internal_member_id
patient_to_obgyn_2019 <- Patient_classification[Attribution == 'OBGYN_2019']$internal_member_id
patient_to_obgyn_2018 <- Patient_classification[Attribution == 'OBGYN_2018']$internal_member_id
patient_unattr <- Patient_classification[Attribution == 'Unattributed']$internal_member_id
###################### FIND NPI for each Patient #############################
#### Part 1.1
MC_pcp_2019 <- MC_2019_OutP_EM_pcp[internal_member_id %in% patient_to_pcp_2019]
MC_pcp_2018 <- MC_2018_OutP_EM_pcp[internal_member_id %in% patient_to_pcp_2018]
MC_obgyn_2019 <- MC_2019_OutP_EM_obgyn[internal_member_id %in% patient_to_obgyn_2019]
MC_obgyn_2018 <- MC_2018_OutP_EM_obgyn[internal_member_id %in% patient_to_obgyn_2018]
MC_unattr_2019 <- MC_2019_OutP_EM[internal_member_id %in% patient_unattr]
fwrite(MC_pcp_2019,
paste0(dir_out, 'MC_pcp_2019.csv'))
fwrite(MC_pcp_2018,
paste0(dir_out, 'MC_pcp_2018.csv'))
fwrite(MC_obgyn_2019,
paste0(dir_out, 'MC_obgyn_2019.csv'))
fwrite(MC_obgyn_2018,
paste0(dir_out, 'MC_obgyn_2018.csv'))
fwrite(MC_unattr_2019,
paste0(dir_out, 'MC_unattributed_2019.csv'))
##### TieBreaker SS1 ####
## SS1.1 ##
group <- MC_pcp_2019
{
cat('start at ', as.character(Sys.time()), '\n')
tmp1 <- group[,
list(Freq = length(unique(medical_claim_header_id))),
by = list(internal_member_id, NPI)]
tmp2 <- tmp1[tmp1[, .I[Freq == max(Freq)], by = internal_member_id]$V1]
out_unique <- tmp2[tmp2[, .I[length(unique(NPI))==1], by = internal_member_id]$V1]
out_tie <- tmp2[tmp2[, .I[length(unique(NPI))>1], by = internal_member_id]$V1]
cat('finish at', as.character(Sys.time()))
}
Patient_PCP_2019_UniqueVisits_EM <- out_unique[, Identify := 'Visits_EM']
Patient_PCP_2019_TieVisits_EM <- out_tie
fwrite(out_unique[, Identify := 'Visits_EM'],
paste0(dir_out, 'Patient_PCP_2019_UniqueVisits_EM.csv'))
fwrite(out_tie,
paste0(dir_out, 'Patient_PCP_2019_TieVisits_EM.csv'))
info.summary <- data.table(matrix(c('MC_PCP_2019_Visits_EM',
length(unique(group$internal_member_id)),
dim(out_unique)[1],
dim(out_tie)[1],
length(unique(out_tie$internal_member_id)),
ratio <- dim(out_unique)[1]/
(dim(out_unique)[1] +
length(unique(out_tie$internal_member_id))),
max(out_tie$Freq)), nrow = 1))
## SS1.2 ##
group <- MC_pcp_2018
{ # from MC_xx_yyyy get out_unique, out_tie
tmp1 <- group[,
list(Freq = length(unique(medical_claim_header_id))),
by = list(internal_member_id, NPI)]
tmp2 <- tmp1[tmp1[, .I[Freq == max(Freq)], by = internal_member_id]$V1]
out_unique <- tmp2[tmp2[, .I[length(unique(NPI))==1], by = internal_member_id]$V1]
out_tie <- tmp2[tmp2[, .I[length(unique(NPI))>1], by = internal_member_id]$V1]
}
Patient_PCP_2018_UniqueVisits_EM <- out_unique[, Identify := 'Visits_EM']
fwrite(out_unique[, Identify := 'Visits_EM'],
paste0(dir_out, 'Patient_PCP_2018_UniqueVisits_EM.csv'))
fwrite(out_tie,
paste0(dir_out, 'Patient_PCP_2018_TieVisits_EM.csv'))
info.summary <- rbind(info.summary,
data.table(matrix(c('MC_PCP_2018_Visits_EM',
length(unique(group$internal_member_id)),
dim(out_unique)[1],
dim(out_tie)[1],
length(unique(out_tie$internal_member_id)),
ratio <- dim(out_unique)[1]/
(dim(out_unique)[1] +
length(unique(out_tie$internal_member_id))),
max(out_tie$Freq)), nrow = 1)))
## SS1.3 ##
group <- MC_obgyn_2019
{ # from MC_xx_yyyy get out_unique, out_tie
tmp1 <- group[,
list(Freq = length(unique(medical_claim_header_id))),
by = list(internal_member_id, NPI)]
tmp2 <- tmp1[tmp1[, .I[Freq == max(Freq)], by = internal_member_id]$V1]
out_unique <- tmp2[tmp2[, .I[length(unique(NPI))==1], by = internal_member_id]$V1]
out_tie <- tmp2[tmp2[, .I[length(unique(NPI))>1], by = internal_member_id]$V1]
}
Patient_OBGYN_2019_UniqueVisits_EM <- out_unique[, Identify := 'Visits_EM']
fwrite(out_unique[, Identify := 'Visits_EM'],
paste0(dir_out, 'Patient_OBGYN_2019_UniqueVisits_EM.csv'))
fwrite(out_tie,
paste0(dir_out, 'Patient_OBGYN_2019_TieVisits_EM.csv'))
info.summary <- rbind(info.summary,
data.table(matrix(c('MC_OBGYN_2019_Visits_EM',
length(unique(group$internal_member_id)),
dim(out_unique)[1],
dim(out_tie)[1],
length(unique(out_tie$internal_member_id)),
ratio <- dim(out_unique)[1]/
(dim(out_unique)[1] +
length(unique(out_tie$internal_member_id))),
max(out_tie$Freq)), nrow = 1)))
## SS1.4 ##
group <- MC_obgyn_2018
{ # from MC_xx_yyyy get out_unique, out_tie
tmp1 <- group[,
list(Freq = length(unique(medical_claim_header_id))),
by = list(internal_member_id, NPI)]
tmp2 <- tmp1[tmp1[, .I[Freq == max(Freq)], by = internal_member_id]$V1]
out_unique <- tmp2[tmp2[, .I[length(unique(NPI))==1], by = internal_member_id]$V1]
out_tie <- tmp2[tmp2[, .I[length(unique(NPI))>1], by = internal_member_id]$V1]
}
Patient_OBGYN_2018_UniqueVisits_EM <- out_unique[, Identify := 'Visits_EM']
fwrite(out_unique[, Identify := 'Visits_EM'],
paste0(dir_out, 'Patient_OBGYN_2018_UniqueVisits_EM.csv'))
fwrite(out_tie,
paste0(dir_out, 'Patient_OBGYN_2018_TieVisits_EM.csv'))
info.summary <- rbind(info.summary,
data.table(matrix(c('MC_OBGYN_2018_Visits_EM',
length(unique(group$internal_member_id)),
dim(out_unique)[1],
dim(out_tie)[1],
length(unique(out_tie$internal_member_id)),
ratio <- dim(out_unique)[1]/
(dim(out_unique)[1] +
length(unique(out_tie$internal_member_id))),
max(out_tie$Freq)), nrow = 1)))
###### document summary ####
names(info.summary) <- c("visit",
"number_of_member",
"number_of_member_with_unique_npi",
"number_of_npi_tie",
"number_of_member_with_npi_tie",
"ratio_of_member_with_unique_npi",
"max_freq_in_npi_tie"
)
fwrite(info.summary,
paste0(dir_out, 'summarySS1.csv'))
######## SS1 complete #############
##### TieBreaker SS2 ####
info.summary <- fread(paste0(dir_out, 'summarySS1.csv'), header = TRUE,
colClasses = 'character', encoding = 'UTF-8')
## SS2.1 ##
Patient_PCP_2019_TieVisits_EM <- fread(paste0(dir_out, 'Patient_PCP_2019_TieVisits_EM.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
MC_PCP_2019_OutP_SS1 <- Patient_PCP_2019_TieVisits_EM[MC_2019_OutP,
on = c(internal_member_id = 'internal_member_id',
NPI = 'NPI')][Freq > 0]
group <- MC_PCP_2019_OutP_SS1
{
tmp1 <- group[,
list(Freq = length(unique(medical_claim_header_id))),
by = list(internal_member_id, NPI)]
tmp2 <- tmp1[tmp1[, .I[Freq == max(Freq)], by = internal_member_id]$V1]
out_unique <- tmp2[tmp2[, .I[length(unique(NPI))==1], by = internal_member_id]$V1]
out_tie <- tmp2[tmp2[, .I[length(unique(NPI))>1], by = internal_member_id]$V1]
}
Patient_PCP_2019_UniqueVisits_OutP <- out_unique[, Identify := 'Visits_OutP']
fwrite(out_unique[, Identify := 'Visits_OutP'],
paste0(dir_out, 'Patient_PCP_2019_UniqueVisits_OutP.csv'))
fwrite(out_tie,
paste0(dir_out, 'Patient_PCP_2019_TieVisits_OutP.csv'))
tmp <- data.table(matrix(c('MC_PCP_2019_Visits_OutP',
length(unique(group$internal_member_id)),
dim(out_unique)[1],
dim(out_tie)[1],
length(unique(out_tie$internal_member_id)),
ratio <- dim(out_unique)[1]/
(dim(out_unique)[1] +
length(unique(out_tie$internal_member_id))),
max(out_tie$Freq)), nrow = 1))
names(tmp) <- names(info.summary)
info.summary <- rbind(info.summary, tmp)
## SS2.2 ##
Patient_PCP_2018_TieVisits_EM <- fread(paste0(dir_out, 'Patient_PCP_2018_TieVisits_EM.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
MC_PCP_2018_OutP_SS1 <- Patient_PCP_2018_TieVisits_EM[MC_2018_OutP,
on = c(internal_member_id = 'internal_member_id',
NPI = 'NPI')][Freq > 0]
group <- MC_PCP_2018_OutP_SS1
{
tmp1 <- group[,
list(Freq = length(unique(medical_claim_header_id))),
by = list(internal_member_id, NPI)]
tmp2 <- tmp1[tmp1[, .I[Freq == max(Freq)], by = internal_member_id]$V1]
out_unique <- tmp2[tmp2[, .I[length(unique(NPI))==1], by = internal_member_id]$V1]
out_tie <- tmp2[tmp2[, .I[length(unique(NPI))>1], by = internal_member_id]$V1]
}
Patient_PCP_2018_UniqueVisits_OutP <- out_unique[, Identify := 'Visits_OutP']
fwrite(out_unique[, Identify := 'Visits_OutP'],
paste0(dir_out, 'Patient_PCP_2018_UniqueVisits_OutP.csv'))
fwrite(out_tie,
paste0(dir_out, 'Patient_PCP_2018_TieVisits_OutP.csv'))
tmp <- data.table(matrix(c('MC_PCP_2018_Visits_OutP',
length(unique(group$internal_member_id)),
dim(out_unique)[1],
dim(out_tie)[1],
length(unique(out_tie$internal_member_id)),
ratio <- dim(out_unique)[1]/
(dim(out_unique)[1] +
length(unique(out_tie$internal_member_id))),
max(out_tie$Freq)), nrow = 1))
names(tmp) <- names(info.summary)
info.summary <- rbind(info.summary, tmp)
## SS2.3 ##
Patient_OBGYN_2019_TieVisits_EM <- fread(paste0(dir_out, 'Patient_OBGYN_2019_TieVisits_EM.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
MC_OBGYN_2019_OutP_SS1 <- Patient_OBGYN_2019_TieVisits_EM[MC_2019_OutP,
on = c(internal_member_id = 'internal_member_id',
NPI = 'NPI')][Freq > 0]
group <- MC_OBGYN_2019_OutP_SS1
{
tmp1 <- group[,
list(Freq = length(unique(medical_claim_header_id))),
by = list(internal_member_id, NPI)]
tmp2 <- tmp1[tmp1[, .I[Freq == max(Freq)], by = internal_member_id]$V1]
out_unique <- tmp2[tmp2[, .I[length(unique(NPI))==1], by = internal_member_id]$V1]
out_tie <- tmp2[tmp2[, .I[length(unique(NPI))>1], by = internal_member_id]$V1]
}
Patient_OBGYN_2019_UniqueVisits_OutP <- out_unique[, Identify := 'Visits_OutP']
fwrite(out_unique[, Identify := 'Visits_OutP'],
paste0(dir_out, 'Patient_OBGYN_2019_UniqueVisits_OutP.csv'))
fwrite(out_tie,
paste0(dir_out, 'Patient_OBGYN_2019_TieVisits_OutP.csv'))
tmp <- data.table(matrix(c('MC_OBGYN_2019_Visits_OutP',
length(unique(group$internal_member_id)),
dim(out_unique)[1],
dim(out_tie)[1],
length(unique(out_tie$internal_member_id)),
ratio <- dim(out_unique)[1]/
(dim(out_unique)[1] +
length(unique(out_tie$internal_member_id))),
max(out_tie$Freq)), nrow = 1))
names(tmp) <- names(info.summary)
info.summary <- rbind(info.summary, tmp)
## SS2.4 ##
Patient_OBGYN_2018_TieVisits_EM <- fread(paste0(dir_out, 'Patient_OBGYN_2018_TieVisits_EM.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
MC_OBGYN_2018_OutP_SS1 <- Patient_OBGYN_2018_TieVisits_EM[MC_2018_OutP,
on = c(internal_member_id = 'internal_member_id',
NPI = 'NPI')][Freq > 0]
group <- MC_OBGYN_2018_OutP_SS1
{
tmp1 <- group[,
list(Freq = length(unique(medical_claim_header_id))),
by = list(internal_member_id, NPI)]
tmp2 <- tmp1[tmp1[, .I[Freq == max(Freq)], by = internal_member_id]$V1]
out_unique <- tmp2[tmp2[, .I[length(unique(NPI))==1], by = internal_member_id]$V1]
out_tie <- tmp2[tmp2[, .I[length(unique(NPI))>1], by = internal_member_id]$V1]
}
Patient_OBGYN_2018_UniqueVisits_OutP <- out_unique[, Identify := 'Visits_OutP']
fwrite(out_unique[, Identify := 'Visits_OutP'],
paste0(dir_out, 'Patient_OBGYN_2018_UniqueVisits_OutP.csv'))
fwrite(out_tie,
paste0(dir_out, 'Patient_OBGYN_2018_TieVisits_OutP.csv'))
tmp <- data.table(matrix(c('MC_OBGYN_2018_Visits_OutP',
length(unique(group$internal_member_id)),
dim(out_unique)[1],
dim(out_tie)[1],
length(unique(out_tie$internal_member_id)),
ratio <- dim(out_unique)[1]/
(dim(out_unique)[1] +
length(unique(out_tie$internal_member_id))),
max(out_tie$Freq)), nrow = 1))
names(tmp) <- names(info.summary)
info.summary <- rbind(info.summary, tmp)
###### document summary ####
fwrite(info.summary,
paste0(dir_out, 'summarySS2.csv'))
###### SS3 #####
info.summary <- fread(paste0(dir_out, 'summarySS2.csv'), header = TRUE,
colClasses = 'character', encoding = 'UTF-8')
## SS3.1 ##
Patient_PCP_2019_TieVisits_OutP <- fread(paste0(dir_out, 'Patient_PCP_2019_TieVisits_OutP.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
MC_PCP_2019_OutP_SS2 <- Patient_PCP_2019_TieVisits_OutP[MC_2019_OutP,
on = c(internal_member_id = 'internal_member_id',
NPI = 'NPI')][Freq > 0]
group <- MC_PCP_2019_OutP_SS2
{
tmp1 <- group[,
list(Freq = length(unique(first_service_dt))),
by = list(internal_member_id, NPI)]
tmp2 <- tmp1[tmp1[, .I[Freq == max(Freq)], by = internal_member_id]$V1]
out_unique <- tmp2[tmp2[, .I[length(unique(NPI))==1], by = internal_member_id]$V1]
out_tie <- tmp2[tmp2[, .I[length(unique(NPI))>1], by = internal_member_id]$V1]
}
Patient_PCP_2019_UniqueDays_OutP <- out_unique[, Identify := 'MostDays_OutP']
fwrite(out_unique[, Identify := 'MostDays_OutP'],
paste0(dir_out, 'Patient_PCP_2019_UniqueDays_OutP.csv'))
fwrite(out_tie,
paste0(dir_out, 'Patient_PCP_2019_TieDays_OutP.csv'))
tmp <- data.table(matrix(c('MC_PCP_2019_Days_OutP',
length(unique(group$internal_member_id)),
dim(out_unique)[1],
dim(out_tie)[1],
length(unique(out_tie$internal_member_id)),
ratio <- dim(out_unique)[1]/
(dim(out_unique)[1] +
length(unique(out_tie$internal_member_id))),
max(out_tie$Freq)), nrow = 1))
names(tmp) <- names(info.summary)
info.summary <- rbind(info.summary, tmp)
## SS3.2 ##
Patient_PCP_2018_TieVisits_OutP <- fread(paste0(dir_out, 'Patient_PCP_2018_TieVisits_OutP.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
MC_PCP_2018_OutP_SS2 <- Patient_PCP_2018_TieVisits_OutP[MC_2018_OutP,
on = c(internal_member_id = 'internal_member_id',
NPI = 'NPI')][Freq > 0]
group <- MC_PCP_2018_OutP_SS2
{
tmp1 <- group[,
list(Freq = length(unique(first_service_dt))),
by = list(internal_member_id, NPI)]
tmp2 <- tmp1[tmp1[, .I[Freq == max(Freq)], by = internal_member_id]$V1]
out_unique <- tmp2[tmp2[, .I[length(unique(NPI))==1], by = internal_member_id]$V1]
out_tie <- tmp2[tmp2[, .I[length(unique(NPI))>1], by = internal_member_id]$V1]
}
Patient_PCP_2018_UniqueDays_OutP <- out_unique[, Identify := 'MostDays_OutP']
fwrite(out_unique[, Identify := 'MostDays_OutP'],
paste0(dir_out, 'Patient_PCP_2018_UniqueDays_OutP.csv'))
fwrite(out_tie,
paste0(dir_out, 'Patient_PCP_2018_TieDays_OutP.csv'))
tmp <- data.table(matrix(c('MC_PCP_2018_Days_OutP',
length(unique(group$internal_member_id)),
dim(out_unique)[1],
dim(out_tie)[1],
length(unique(out_tie$internal_member_id)),
ratio <- dim(out_unique)[1]/
(dim(out_unique)[1] +
length(unique(out_tie$internal_member_id))),
max(out_tie$Freq)), nrow = 1))
names(tmp) <- names(info.summary)
info.summary <- rbind(info.summary, tmp)
## SS3.3 ##
Patient_OBGYN_2019_TieVisits_OutP <- fread(paste0(dir_out, 'Patient_OBGYN_2019_TieVisits_OutP.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
MC_OBGYN_2019_OutP_SS2 <- Patient_OBGYN_2019_TieVisits_OutP[MC_2019_OutP,
on = c(internal_member_id = 'internal_member_id',
NPI = 'NPI')][Freq > 0]
group <- MC_OBGYN_2019_OutP_SS2
{
tmp1 <- group[,
list(Freq = length(unique(first_service_dt))),
by = list(internal_member_id, NPI)]
tmp2 <- tmp1[tmp1[, .I[Freq == max(Freq)], by = internal_member_id]$V1]
out_unique <- tmp2[tmp2[, .I[length(unique(NPI))==1], by = internal_member_id]$V1]
out_tie <- tmp2[tmp2[, .I[length(unique(NPI))>1], by = internal_member_id]$V1]
}
Patient_OBGYN_2019_UniqueDays_OutP <- out_unique[, Identify := 'MostDays_OutP']
fwrite(out_unique[, Identify := 'MostDays_OutP'],
paste0(dir_out, 'Patient_OBGYN_2019_UniqueDays_OutP.csv'))
fwrite(out_tie,
paste0(dir_out, 'Patient_OBGYN_2019_TieDays_OutP.csv'))
tmp <- data.table(matrix(c('MC_OBGYN_2019_Days_OutP',
length(unique(group$internal_member_id)),
dim(out_unique)[1],
dim(out_tie)[1],
length(unique(out_tie$internal_member_id)),
ratio <- dim(out_unique)[1]/
(dim(out_unique)[1] +
length(unique(out_tie$internal_member_id))),
max(out_tie$Freq)), nrow = 1))
names(tmp) <- names(info.summary)
info.summary <- rbind(info.summary, tmp)
## SS3.4 ##
Patient_OBGYN_2018_TieVisits_OutP <- fread(paste0(dir_out, 'Patient_OBGYN_2018_TieVisits_OutP.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
MC_OBGYN_2018_OutP_SS2 <- Patient_OBGYN_2018_TieVisits_OutP[MC_2018_OutP,
on = c(internal_member_id = 'internal_member_id',
NPI = 'NPI')][Freq > 0]
group <- MC_OBGYN_2018_OutP_SS2
{
tmp1 <- group[,
list(Freq = length(unique(first_service_dt))),
by = list(internal_member_id, NPI)]
tmp2 <- tmp1[tmp1[, .I[Freq == max(Freq)], by = internal_member_id]$V1]
out_unique <- tmp2[tmp2[, .I[length(unique(NPI))==1], by = internal_member_id]$V1]
out_tie <- tmp2[tmp2[, .I[length(unique(NPI))>1], by = internal_member_id]$V1]
}
Patient_OBGYN_2018_UniqueDays_OutP <- out_unique[, Identify := 'MostDays_OutP']
fwrite(out_unique[, Identify := 'MostDays_OutP'],
paste0(dir_out, 'Patient_OBGYN_2018_UniqueDays_OutP.csv'))
fwrite(out_tie,
paste0(dir_out, 'Patient_OBGYN_2018_TieDays_OutP.csv'))
tmp <- data.table(matrix(c('MC_OBGYN_2018_Days_OutP',
length(unique(group$internal_member_id)),
dim(out_unique)[1],
dim(out_tie)[1],
length(unique(out_tie$internal_member_id)),
ratio <- dim(out_unique)[1]/
(dim(out_unique)[1] +
length(unique(out_tie$internal_member_id))),
max(out_tie$Freq)), nrow = 1))
names(tmp) <- names(info.summary)
info.summary <- rbind(info.summary, tmp)
###### document summary ####
fwrite(info.summary,
paste0(dir_out, 'summarySS3.csv'))
########## SS4 ##########
info.summary <- fread(paste0(dir_out, 'summarySS3.csv'), header = TRUE,
colClasses = 'character', encoding = 'UTF-8')
## SS4.1 ##
Patient_PCP_2019_TieDays_OutP <- fread(paste0(dir_out, 'Patient_PCP_2019_TieDays_OutP.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
MC_PCP_2019_OutP_SS3 <- Patient_PCP_2019_TieDays_OutP[MC_2019_OutP,
on = c(internal_member_id = 'internal_member_id',
NPI = 'NPI')][Freq > 0]
group <- MC_PCP_2019_OutP_SS3
{
tmp1 <- group[,
list(RecentDate = max(ymd(first_service_dt))),
by = list(internal_member_id, NPI)]
tmp2 <- tmp1[tmp1[, .I[RecentDate == max(RecentDate)], by = internal_member_id]$V1]
out_unique <- tmp2[tmp2[, .I[length(unique(NPI))==1], by = internal_member_id]$V1]
out_tie <- tmp2[tmp2[, .I[length(unique(NPI))>1], by = internal_member_id]$V1]
}
Patient_PCP_2019_UniqueRecentDate_OutP <- out_unique[, Identify := 'RecentDate_OutP']
fwrite(out_unique[, Identify := 'RecentDate_OutP'],
paste0(dir_out, 'Patient_PCP_2019_UniqueRecentDate_OutP.csv'))
Patient_PCP_2019_TieRecentDate_OutP <- out_tie
fwrite(out_tie,
paste0(dir_out, 'Patient_PCP_2019_TieRecentDate_OutP.csv'))
tmp <- data.table(matrix(c('MC_PCP_2019_RecentDate_OutP',
length(unique(group$internal_member_id)),
dim(out_unique)[1],
dim(out_tie)[1],
length(unique(out_tie$internal_member_id)),
ratio <- dim(out_unique)[1]
/(dim(out_unique)[1] +
length(unique(out_tie$internal_member_id))),
as.character(max(out_tie$RecentDate))), nrow = 1))
names(tmp) <- names(info.summary)
info.summary <- rbind(info.summary, tmp)
## SS4.2 ##
Patient_PCP_2018_TieDays_OutP <- fread(paste0(dir_out, 'Patient_PCP_2018_TieDays_OutP.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
MC_PCP_2018_OutP_SS3 <- Patient_PCP_2018_TieDays_OutP[MC_2018_OutP,
on = c(internal_member_id = 'internal_member_id',
NPI = 'NPI')][Freq > 0]
group <- MC_PCP_2018_OutP_SS3
{
tmp1 <- group[,
list(RecentDate = max(ymd(first_service_dt))),
by = list(internal_member_id, NPI)]
tmp2 <- tmp1[tmp1[, .I[RecentDate == max(RecentDate)], by = internal_member_id]$V1]
out_unique <- tmp2[tmp2[, .I[length(unique(NPI))==1], by = internal_member_id]$V1]
out_tie <- tmp2[tmp2[, .I[length(unique(NPI))>1], by = internal_member_id]$V1]
}
Patient_PCP_2018_UniqueRecentDate_OutP <- out_unique[, Identify := 'RecentDate_OutP']
fwrite(out_unique[, Identify := 'RecentDate_OutP'],
paste0(dir_out, 'Patient_PCP_2018_UniqueRecentDate_OutP.csv'))
Patient_PCP_2018_TieRecentDate_OutP <- out_tie
fwrite(out_tie,
paste0(dir_out, 'Patient_PCP_2018_TieRecentDate_OutP.csv'))
tmp <- data.table(matrix(c('MC_PCP_2018_RecentDate_OutP',
length(unique(group$internal_member_id)),
dim(out_unique)[1],
dim(out_tie)[1],
length(unique(out_tie$internal_member_id)),
ratio <- dim(out_unique)[1]/
(dim(out_unique)[1] +
length(unique(out_tie$internal_member_id))),
as.character(max(out_tie$RecentDate))), nrow = 1))
names(tmp) <- names(info.summary)
info.summary <- rbind(info.summary, tmp)
## SS4.3 ##
Patient_OBGYN_2019_TieDays_OutP <- fread(paste0(dir_out, 'Patient_OBGYN_2019_TieDays_OutP.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
MC_OBGYN_2019_OutP_SS3 <- Patient_OBGYN_2019_TieDays_OutP[MC_2019_OutP,
on = c(internal_member_id = 'internal_member_id',
NPI = 'NPI')][Freq > 0]
group <- MC_OBGYN_2019_OutP_SS3
{
tmp1 <- group[,
list(RecentDate = max(ymd(first_service_dt))),
by = list(internal_member_id, NPI)]
tmp2 <- tmp1[tmp1[, .I[RecentDate == max(RecentDate)], by = internal_member_id]$V1]
out_unique <- tmp2[tmp2[, .I[length(unique(NPI))==1], by = internal_member_id]$V1]
out_tie <- tmp2[tmp2[, .I[length(unique(NPI))>1], by = internal_member_id]$V1]
}
Patient_OBGYN_2019_UniqueRecentDate_OutP <- out_unique[, Identify := 'RecentDate_OutP']
fwrite(out_unique[, Identify := 'RecentDate_OutP'],
paste0(dir_out, 'Patient_OBGYN_2019_UniqueRecentDate_OutP.csv'))
Patient_OBGYN_2019_TieRecentDate_OutP <- out_tie
fwrite(out_tie,
paste0(dir_out, 'Patient_OBGYN_2019_TieRecentDate_OutP.csv'))
tmp <- data.table(matrix(c('MC_OBGYN_2019_RecentDate_OutP',
length(unique(group$internal_member_id)),
dim(out_unique)[1],
dim(out_tie)[1],
length(unique(out_tie$internal_member_id)),
ratio <- dim(out_unique)[1]/
(dim(out_unique)[1] +
length(unique(out_tie$internal_member_id))),
as.character(max(out_tie$RecentDate))), nrow = 1))
names(tmp) <- names(info.summary)
info.summary <- rbind(info.summary, tmp)
## SS4.4 ##
Patient_OBGYN_2018_TieDays_OutP <- fread(paste0(dir_out, 'Patient_OBGYN_2018_TieDays_OutP.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
MC_OBGYN_2018_OutP_SS3 <- Patient_OBGYN_2018_TieDays_OutP[MC_2018_OutP,
on = c(internal_member_id = 'internal_member_id',
NPI = 'NPI')][Freq > 0]
group <- MC_OBGYN_2018_OutP_SS3
{
tmp1 <- group[,
list(RecentDate = max(ymd(first_service_dt))),
by = list(internal_member_id, NPI)]
tmp2 <- tmp1[tmp1[, .I[RecentDate == max(RecentDate)], by = internal_member_id]$V1]
out_unique <- tmp2[tmp2[, .I[length(unique(NPI))==1], by = internal_member_id]$V1]
out_tie <- tmp2[tmp2[, .I[length(unique(NPI))>1], by = internal_member_id]$V1]
}
Patient_OBGYN_2018_UniqueRecentDate_OutP <- out_unique[, Identify := 'RecentDate_OutP']
fwrite(out_unique[, Identify := 'RecentDate_OutP'],
paste0(dir_out, 'Patient_OBGYN_2018_UniqueRecentDate_OutP.csv'))
Patient_OBGYN_2018_TieRecentDate_OutP <- out_tie
fwrite(out_tie,
paste0(dir_out, 'Patient_OBGYN_2018_TieRecentDate_OutP.csv'))
tmp <- data.table(matrix(c('MC_OBGYN_2018_RecentDate_OutP',
length(unique(group$internal_member_id)),
dim(out_unique)[1],
dim(out_tie)[1],
length(unique(out_tie$internal_member_id)),
ratio <- dim(out_unique)[1]/
(dim(out_unique)[1] +
length(unique(out_tie$internal_member_id))),
as.character(max(out_tie$RecentDate))), nrow = 1))
names(tmp) <- names(info.summary)
info.summary <- rbind(info.summary, tmp)
###### document summary ####
fwrite(info.summary,
paste0(dir_out, 'summarySS4.csv'))
### finish Patient Attribution Matrix
Patient_PCP_2019_UniqueRecentDate_OutP <- fread(paste0(dir_out, 'Patient_PCP_2019_UniqueRecentDate_OutP.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
Patient_PCP_2018_UniqueRecentDate_OutP <- fread(paste0(dir_out, 'Patient_PCP_2018_UniqueRecentDate_OutP.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
Patient_OBGYN_2019_UniqueRecentDate_OutP <- fread(paste0(dir_out, 'Patient_OBGYN_2019_UniqueRecentDate_OutP.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
Patient_OBGYN_2018_UniqueRecentDate_OutP <- fread(paste0(dir_out, 'Patient_OBGYN_2018_UniqueRecentDate_OutP.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
Patient_Unique <- rbind(Patient_PCP_2019_UniqueVisits_EM, Patient_PCP_2019_UniqueVisits_OutP, Patient_PCP_2019_UniqueDays_OutP, Patient_PCP_2019_UniqueRecentDate_OutP,
Patient_PCP_2018_UniqueVisits_EM, Patient_PCP_2018_UniqueVisits_OutP, Patient_PCP_2018_UniqueDays_OutP, Patient_PCP_2018_UniqueRecentDate_OutP,
Patient_OBGYN_2019_UniqueVisits_EM, Patient_OBGYN_2019_UniqueVisits_OutP, Patient_OBGYN_2019_UniqueDays_OutP, Patient_OBGYN_2019_UniqueRecentDate_OutP,
Patient_OBGYN_2018_UniqueVisits_EM, Patient_OBGYN_2018_UniqueVisits_OutP, Patient_OBGYN_2018_UniqueDays_OutP, Patient_OBGYN_2018_UniqueRecentDate_OutP,
fill = TRUE)
Patient_classification1 <- Patient_Unique[Patient_classification, on = c(internal_member_id = 'internal_member_id')]
fwrite(Patient_classification1,
paste0(dir_out, 'PatientAttri_short.csv'))
### Ties left
Patient_Tie <- rbind(
Patient_PCP_2019_TieRecentDate_OutP,
Patient_PCP_2018_TieRecentDate_OutP,
Patient_OBGYN_2019_TieRecentDate_OutP,
Patient_OBGYN_2018_TieRecentDate_OutP,
fill = TRUE
)
fwrite(Patient_Tie,
paste0(dir_out, 'PatientTie.csv'))
#### Taxonomy/Specialty Freq Research ####
Patient_classification2 <- Patient_classification1
# Patient_classification2 <- nppes1[Patient_classification1, on = c(NPI = 'NPI')]
# fwrite(as.data.table(sort(table(Patient_classification2$Taxonomy1), decreasing = TRUE)),
# paste0(dir_out, 'PatientToTaxonomy.csv'))
# Patient_classification2[Taxonomy1 %in% taxo_FP, Specialty := 'FP']
# Patient_classification2[Taxonomy1 %in% taxo_GP, Specialty := 'GP']
# # Patient_classification2[Taxonomy1 %in% taxo_GM, Specialty := 'GM'] # by Sai
# Patient_classification2[Taxonomy1 %in% taxo_IM, Specialty := 'IM']
# Patient_classification2[Taxonomy1 %in% taxo_PedM, Specialty := 'PedM']
# Patient_classification2[Taxonomy1 %in% taxo_NP, Specialty := 'NP']
# # Patient_classification2[Taxonomy1 %in% taxo_PreM, Specialty := 'PreM'] # by Sai
# Patient_classification2[Taxonomy1 %in% taxo_CCNS, Specialty := 'CCNS']
# Patient_classification2[Taxonomy1 %in% taxo_PA, Specialty := 'PA']
# Patient_classification2[Taxonomy1 %in% taxo_OBGYN, Specialty := 'OBGYN']
# Patient_classification2[(!Taxonomy1 %in% c(taxo_PCP, taxo_OBGYN)) &
# (!is.na(Taxonomy1)), Specialty := 'Others']
setcolorder(Patient_classification2,
c('internal_member_id', 'Attribution', 'NPI',
'Identify',
# 'Specialty',
# 'Taxonomy1',
'Freq', 'RecentDate'))
fwrite(as.data.table(sort(table(Patient_classification2$Specialty), decreasing = TRUE)),
paste0(dir_out, 'PatientToSpecialty.csv'))
fwrite(Patient_classification2,
paste0(dir_out, 'PatAttri_detail.csv'))
fwrite(Patient_classification2[Attribution == 'Unattributed',
Identify := 'Unattributed'][Identify == "", Identify := 'Tie'],
paste0(dir_out, 'PatAttri_detail.csv'))
#### Research for the Unattributed Par
## taxoClass <- fread('E:/CT_APCD/Hongfei/files/taxonomy classification.csv',
## header = TRUE,
## colClasses = 'character',
## encoding = 'UTF-8')
## NPI_freq_unattr <- head(sort(table(MC_unattr_2019$NPI), decreasing = TRUE), n = 50)
## NPI_freq_unattr <- nppes0[as.data.table(NPI_freq_unattr), on = c(NPI = 'V1')][, .(NPI, Taxonomy1, N)]
## NPI_freq_unattr <- taxoClass[NPI_freq_unattr, on = c(taxonomy1 = 'Taxonomy1')][, .(NPI, N, taxonomy1, Grouping, Classification, Specialization, Definition)]
## write.csv(NPI_freq_unattr,
## 'file:///E:/CT_APCD/Hongfei/PatientAttribution/NPI_Freq_Unattr.csv')
## NPI_unattr <- data.table(NPI = MC_unattr_2019$NPI)
## taxo_freq_unattr_NPI <- head(sort(table(nppes0[NPI_unattr, on = c(NPI = 'NPI')]$Taxonomy1), decreasing = T), n = 50)
## taxo_freq_unattr_NPI <- taxoClass[as.data.table(taxo_freq_unattr_NPI), on = c(taxonomy1 = 'V1')][, .(taxonomy1, N, Grouping, Classification, Specialization, Definition)]
## write.csv(taxo_freq_unattr_NPI,
## 'file:///E:/CT_APCD/Hongfei/PatientAttribution/TaxoFreq_Unattr_NPI.csv')
## uniqueNPI_unattr <- data.table(NPI = unique(MC_unattr_2019$NPI))
## taxo_freq_unattr_uniqueNPI <- head(sort(table(nppes0[uniqueNPI_unattr, on = c(NPI = 'NPI')]$Taxonomy1), decreasing = T), n = 50)
## taxo_freq_unattr_uniqueNPI <- taxoClass[as.data.table(taxo_freq_unattr_uniqueNPI), on = c(taxonomy1 = 'V1')][, .(taxonomy1, N, Grouping, Classification, Specialization, Definition)]
## write.csv(taxo_freq_unattr_uniqueNPI,
## 'file:///E:/CT_APCD/Hongfei/PatientAttribution/TaxoFreq_Unattr_uniqueNPI.csv')