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PatientAttribution_mc_2018.R
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PatientAttribution_mc_2018.R
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########## Patient Attribution ##########
## This version is using my Medicare and Medicare advantage list,
## and only includes current year.
#### packages #####
library(data.table)
library(lubridate)
library(tidyverse)
source('E:/CT_APCD/Sai/2019spring/data-organization/R/utils.R')
dir_code <- input_dir('E:/CT_APCD/Sai/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/Attribution_2018_2019/Attribution_result/InProcess_mc_2018/')
## READ MC and NPPES
nppes0 <- fread("E:/CT_APCD/shared/intermediate_data/APCD_modified/provider/nppes_1129.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',
'submitter_id',
"product_code",
NULL)
## Outpatient value set ### coomentted by Sai 05/25/2019
MC_OutP <- get_medical_vs(
vs.list = 'Outpatient',
year.list = 2016:2018,
select = col.select
)
MC_2018_OutP <- MC_OutP[mdy(first_service_dt) >= ymd('2017-10-01') &
mdy(first_service_dt) <= ymd('2018-09-30')]
MC_2017_OutP <- MC_OutP[mdy(first_service_dt) >= ymd('2016-10-01') &
mdy(first_service_dt) <= ymd('2017-09-30')]
rm(MC_OutP)
gc()
## modified provider file
provider <- fread('E:/CT_APCD/shared/intermediate_data/APCD_modified/provider/provider_1129.csv',
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
provider_facility <- fread('E:/CT_APCD/shared/intermediate_data/APCD_modified/provider/provider_facility.csv',
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
## modified eligibility file
elig.patient <- fread(paste0('E:/CT_APCD/Beth/data4/',
'Eligibility/Medicare_by_fiscal_yr/',
'MEDICAL_ELIG_ALL_FY12_17_medicar.csv'),
header = TRUE, colClasses = 'character',
encoding = 'UTF-8', select = c(
"FiscalYR",
"eligflg",
"INTERNAL_MEMBER_ID",
"birth_dt",
NULL)) ##
elig.patient <- elig.patient[FiscalYR == 2018 & eligflg == 1][, -c("FiscalYR",
"eligflg")]
elig.patient <- unique(elig.patient, use.key = FALSE)
names(elig.patient) <- tolower(names(elig.patient))
## identify patients whose ages more than 64
# patient_age0064 <- elig.patient[birth_dt >= 2018 - 64]$INTERNAL_MEMBER_ID # by Sai
## Get NPI list
realNPI_pcp <- provider[taxo %in% taxo_PCP]$NPI
realNPI_obgyn <- provider[taxo %in% taxo_OBGYN]$NPI
## Add NPI to raw dataset
MC_2018_OutP <- provider[MC_2018_OutP, on = c(PROVIDER_ID = 'rendering_provider_id')]
MC_2017_OutP <- provider[MC_2017_OutP, on = c(PROVIDER_ID = 'rendering_provider_id')]
## remove facility provider
MC_2018_OutP <- MC_2018_OutP[!provider_facility, on = 'PROVIDER_ID']
MC_2017_OutP <- MC_2017_OutP[!provider_facility, on = 'PROVIDER_ID']
## Include Medicare AD claims
MC_2017_OutP <- MC_2017_OutP[internal_member_id %in% elig.patient$internal_member_id]
MC_2018_OutP <- MC_2018_OutP[internal_member_id %in% elig.patient$internal_member_id]
## MC to MC_EM & MC_NotEM datasets
MC_2018_OutP_EM <- MC_2018_OutP[procedure_code %in% CPTList]
MC_2018_OutP_NonEM <- MC_2018_OutP[!procedure_code %in% CPTList]
MC_2017_OutP_EM <- MC_2017_OutP[procedure_code %in% CPTList]
MC_2017_OutP_NonEM <- MC_2017_OutP[!procedure_code %in% CPTList]
## to MC_year_EM_class datasets
MC_2018_OutP_EM_pcp <- MC_2018_OutP_EM[NPI %in% realNPI_pcp]
MC_2018_OutP_EM_obgyn <- MC_2018_OutP_EM[NPI %in% realNPI_obgyn]
MC_2017_OutP_EM_pcp <- MC_2017_OutP_EM[NPI %in% realNPI_pcp]
MC_2017_OutP_EM_obgyn <- MC_2017_OutP_EM[NPI %in% realNPI_obgyn]
MC_2018_OutP_EM_other <- MC_2018_OutP_EM[!NPI %in% c(realNPI_pcp, realNPI_obgyn)]
## Patient_Attribution matrix
Patient_classification <- data.table(internal_member_id =
unique(MC_2018_OutP_EM$internal_member_id))
Patient_classification[,
`:=` (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)),
In_2017_OutP_EM_pcp =
as.numeric(internal_member_id %in%
unique(MC_2017_OutP_EM_pcp$internal_member_id)),
In_2017_OutP_EM_obgyn =
as.numeric(internal_member_id %in%
unique(MC_2017_OutP_EM_obgyn$internal_member_id)))]
## patient attribution flow chart step1
Patient_classification[In_2018_OutP_EM_pcp == 1,
Attribution := 'PCP_2018']
## patient attribution flow chart step2
Patient_classification[In_2018_OutP_EM_pcp == 0 & In_2017_OutP_EM_pcp == 1,
Attribution := 'PCP_2017']
## patient attribution flow chart step3
Patient_classification[In_2018_OutP_EM_pcp == 0 & In_2017_OutP_EM_pcp == 0 &
In_2018_OutP_EM_obgyn == 1,
Attribution := 'OBGYN_2018']
## patient attribution flow chart step4
Patient_classification[In_2018_OutP_EM_pcp == 0 & In_2017_OutP_EM_pcp == 0 &
In_2018_OutP_EM_obgyn == 0 & In_2017_OutP_EM_obgyn == 1,
Attribution := 'OBGYN_2017']
## patient attribution flow chart step5
Patient_classification[In_2018_OutP_EM_pcp == 0 & In_2017_OutP_EM_pcp == 0 &
In_2018_OutP_EM_obgyn == 0 & In_2017_OutP_EM_obgyn == 0,
Attribution := 'Unattributed']
Patient_classification[!internal_member_id %in% c(unique(MC_2018_OutP_EM$internal_member_id),
unique(MC_2017_OutP_EM$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_2018 <- Patient_classification[Attribution == 'PCP_2018']$internal_member_id
patient_to_pcp_2017 <- Patient_classification[Attribution == 'PCP_2017']$internal_member_id
patient_to_obgyn_2018 <- Patient_classification[Attribution == 'OBGYN_2018']$internal_member_id
patient_to_obgyn_2017 <- Patient_classification[Attribution == 'OBGYN_2017']$internal_member_id
patient_unattr <- Patient_classification[Attribution == 'Unattributed']$internal_member_id
###################### FIND NPI for each Patient #############################
#### Part 1.1
MC_pcp_2018 <- MC_2018_OutP_EM_pcp[internal_member_id %in% patient_to_pcp_2018]
MC_pcp_2017 <- MC_2017_OutP_EM_pcp[internal_member_id %in% patient_to_pcp_2017]
MC_obgyn_2018 <- MC_2018_OutP_EM_obgyn[internal_member_id %in% patient_to_obgyn_2018]
MC_obgyn_2017 <- MC_2017_OutP_EM_obgyn[internal_member_id %in% patient_to_obgyn_2017]
MC_unattr_2018 <- MC_2018_OutP_EM[internal_member_id %in% patient_unattr]
fwrite(MC_pcp_2018,
paste0(dir_out, 'MC_pcp_2018.csv'))
fwrite(MC_pcp_2017,
paste0(dir_out, 'MC_pcp_2017.csv'))
fwrite(MC_obgyn_2018,
paste0(dir_out, 'MC_obgyn_2018.csv'))
fwrite(MC_obgyn_2017,
paste0(dir_out, 'MC_obgyn_2017.csv'))
fwrite(MC_unattr_2018,
paste0(dir_out, 'MC_unattributed_2018.csv'))
##### TieBreaker SS1 ####
## SS1.1 ##
group <- MC_pcp_2018
{
cat('start at ', as.character(Sys.time()), '\n')
tmp1 <- group[,
list(Freq = length(unique(medical_claim_header_id))),
by = list(internal_member_id, NPI)]
tmp2 <- tmp1[tmp1[, .I[Freq == max(Freq)], by = internal_member_id]$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_2018_UniqueVisits_EM <- out_unique[, Identify := 'Visits_EM']
Patient_PCP_2018_TieVisits_EM <- out_tie
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 <- 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))
## SS1.2 ##
group <- MC_pcp_2017
{ # 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]$V1]
}
Patient_PCP_2017_UniqueVisits_EM <- out_unique[, Identify := 'Visits_EM']
fwrite(out_unique[, Identify := 'Visits_EM'],
paste0(dir_out, 'Patient_PCP_2017_UniqueVisits_EM.csv'))
fwrite(out_tie,
paste0(dir_out, 'Patient_PCP_2017_TieVisits_EM.csv'))
info.summary <- rbind(info.summary,
data.table(matrix(c('MC_PCP_2017_Visits_EM',
length(unique(group$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$Freq)), nrow = 1)))
## SS1.3 ##
group <- MC_obgyn_2018
{ # from MC_xx_yyyy get out_unique, out_tie
tmp1 <- group[,
list(Freq = length(unique(medical_claim_header_id))),
by = list(internal_member_id, NPI)]
tmp2 <- tmp1[tmp1[, .I[Freq == max(Freq)], by = internal_member_id]$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)))
## SS1.4 ##
group <- MC_obgyn_2017
{ # 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]$V1]
}
Patient_OBGYN_2017_UniqueVisits_EM <- out_unique[, Identify := 'Visits_EM']
fwrite(out_unique[, Identify := 'Visits_EM'],
paste0(dir_out, 'Patient_OBGYN_2017_UniqueVisits_EM.csv'))
fwrite(out_tie,
paste0(dir_out, 'Patient_OBGYN_2017_TieVisits_EM.csv'))
info.summary <- rbind(info.summary,
data.table(matrix(c('MC_OBGYN_2017_Visits_EM',
length(unique(group$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$Freq)), nrow = 1)))
###### document summary ####
names(info.summary) <- c("visit",
"number_of_member",
"number_of_member_with_unique_npi",
"number_of_npi_tie",
"number_of_member_with_npi_tie",
"ratio_of_member_with_unique_npi",
"max_freq_in_npi_tie"
)
fwrite(info.summary,
paste0(dir_out, 'summarySS1.csv'))
######## SS1 complete #############
##### TieBreaker SS2 ####
info.summary <- fread(paste0(dir_out, 'summarySS1.csv'), header = TRUE,
colClasses = 'character', encoding = 'UTF-8')
## SS2.1 ##
Patient_PCP_2018_TieVisits_EM <- fread(paste0(dir_out, 'Patient_PCP_2018_TieVisits_EM.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
MC_PCP_2018_OutP_SS1 <- Patient_PCP_2018_TieVisits_EM[MC_2018_OutP,
on = c(internal_member_id = 'internal_member_id',
NPI = 'NPI')][Freq > 0]
group <- MC_PCP_2018_OutP_SS1
{
tmp1 <- group[,
list(Freq = length(unique(medical_claim_header_id))),
by = list(internal_member_id, NPI)]
tmp2 <- tmp1[tmp1[, .I[Freq == max(Freq)], by = internal_member_id]$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)
## SS2.2 ##
Patient_PCP_2017_TieVisits_EM <- fread(paste0(dir_out, 'Patient_PCP_2017_TieVisits_EM.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
MC_PCP_2017_OutP_SS1 <- Patient_PCP_2017_TieVisits_EM[MC_2017_OutP,
on = c(internal_member_id = 'internal_member_id',
NPI = 'NPI')][Freq > 0]
group <- MC_PCP_2017_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]$V1]
}
Patient_PCP_2017_UniqueVisits_OutP <- out_unique[, Identify := 'Visits_OutP']
fwrite(out_unique[, Identify := 'Visits_OutP'],
paste0(dir_out, 'Patient_PCP_2017_UniqueVisits_OutP.csv'))
fwrite(out_tie,
paste0(dir_out, 'Patient_PCP_2017_TieVisits_OutP.csv'))
tmp <- data.table(matrix(c('MC_PCP_2017_Visits_OutP',
length(unique(group$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$Freq)), nrow = 1))
names(tmp) <- names(info.summary)
info.summary <- rbind(info.summary, tmp)
## SS2.3 ##
Patient_OBGYN_2018_TieVisits_EM <- fread(paste0(dir_out, 'Patient_OBGYN_2018_TieVisits_EM.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
MC_OBGYN_2018_OutP_SS1 <- Patient_OBGYN_2018_TieVisits_EM[MC_2018_OutP,
on = c(internal_member_id = 'internal_member_id',
NPI = 'NPI')][Freq > 0]
group <- MC_OBGYN_2018_OutP_SS1
{
tmp1 <- group[,
list(Freq = length(unique(medical_claim_header_id))),
by = list(internal_member_id, NPI)]
tmp2 <- tmp1[tmp1[, .I[Freq == max(Freq)], by = internal_member_id]$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)
## SS2.4 ##
Patient_OBGYN_2017_TieVisits_EM <- fread(paste0(dir_out, 'Patient_OBGYN_2017_TieVisits_EM.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
MC_OBGYN_2017_OutP_SS1 <- Patient_OBGYN_2017_TieVisits_EM[MC_2017_OutP,
on = c(internal_member_id = 'internal_member_id',
NPI = 'NPI')][Freq > 0]
group <- MC_OBGYN_2017_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]$V1]
}
Patient_OBGYN_2017_UniqueVisits_OutP <- out_unique[, Identify := 'Visits_OutP']
fwrite(out_unique[, Identify := 'Visits_OutP'],
paste0(dir_out, 'Patient_OBGYN_2017_UniqueVisits_OutP.csv'))
fwrite(out_tie,
paste0(dir_out, 'Patient_OBGYN_2017_TieVisits_OutP.csv'))
tmp <- data.table(matrix(c('MC_OBGYN_2017_Visits_OutP',
length(unique(group$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$Freq)), nrow = 1))
names(tmp) <- names(info.summary)
info.summary <- rbind(info.summary, tmp)
###### document summary ####
fwrite(info.summary,
paste0(dir_out, 'summarySS2.csv'))
###### SS3 #####
info.summary <- fread(paste0(dir_out, 'summarySS2.csv'), header = TRUE,
colClasses = 'character', encoding = 'UTF-8')
## SS3.1 ##
Patient_PCP_2018_TieVisits_OutP <- fread(paste0(dir_out, 'Patient_PCP_2018_TieVisits_OutP.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
MC_PCP_2018_OutP_SS2 <- Patient_PCP_2018_TieVisits_OutP[MC_2018_OutP,
on = c(internal_member_id = 'internal_member_id',
NPI = 'NPI')][Freq > 0]
group <- MC_PCP_2018_OutP_SS2
{
tmp1 <- group[,
list(Freq = length(unique(first_service_dt))),
by = list(internal_member_id, NPI)]
tmp2 <- tmp1[tmp1[, .I[Freq == max(Freq)], by = internal_member_id]$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)
## SS3.2 ##
Patient_PCP_2017_TieVisits_OutP <- fread(paste0(dir_out, 'Patient_PCP_2017_TieVisits_OutP.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
MC_PCP_2017_OutP_SS2 <- Patient_PCP_2017_TieVisits_OutP[MC_2017_OutP,
on = c(internal_member_id = 'internal_member_id',
NPI = 'NPI')][Freq > 0]
group <- MC_PCP_2017_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]$V1]
}
Patient_PCP_2017_UniqueDays_OutP <- out_unique[, Identify := 'MostDays_OutP']
fwrite(out_unique[, Identify := 'MostDays_OutP'],
paste0(dir_out, 'Patient_PCP_2017_UniqueDays_OutP.csv'))
fwrite(out_tie,
paste0(dir_out, 'Patient_PCP_2017_TieDays_OutP.csv'))
tmp <- data.table(matrix(c('MC_PCP_2017_Days_OutP',
length(unique(group$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$Freq)), nrow = 1))
names(tmp) <- names(info.summary)
info.summary <- rbind(info.summary, tmp)
## SS3.3 ##
Patient_OBGYN_2018_TieVisits_OutP <- fread(paste0(dir_out, 'Patient_OBGYN_2018_TieVisits_OutP.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
MC_OBGYN_2018_OutP_SS2 <- Patient_OBGYN_2018_TieVisits_OutP[MC_2018_OutP,
on = c(internal_member_id = 'internal_member_id',
NPI = 'NPI')][Freq > 0]
group <- MC_OBGYN_2018_OutP_SS2
{
tmp1 <- group[,
list(Freq = length(unique(first_service_dt))),
by = list(internal_member_id, NPI)]
tmp2 <- tmp1[tmp1[, .I[Freq == max(Freq)], by = internal_member_id]$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)
## SS3.4 ##
Patient_OBGYN_2017_TieVisits_OutP <- fread(paste0(dir_out, 'Patient_OBGYN_2017_TieVisits_OutP.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
MC_OBGYN_2017_OutP_SS2 <- Patient_OBGYN_2017_TieVisits_OutP[MC_2017_OutP,
on = c(internal_member_id = 'internal_member_id',
NPI = 'NPI')][Freq > 0]
group <- MC_OBGYN_2017_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]$V1]
}
Patient_OBGYN_2017_UniqueDays_OutP <- out_unique[, Identify := 'MostDays_OutP']
fwrite(out_unique[, Identify := 'MostDays_OutP'],
paste0(dir_out, 'Patient_OBGYN_2017_UniqueDays_OutP.csv'))
fwrite(out_tie,
paste0(dir_out, 'Patient_OBGYN_2017_TieDays_OutP.csv'))
tmp <- data.table(matrix(c('MC_OBGYN_2017_Days_OutP',
length(unique(group$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$Freq)), nrow = 1))
names(tmp) <- names(info.summary)
info.summary <- rbind(info.summary, tmp)
###### document summary ####
fwrite(info.summary,
paste0(dir_out, 'summarySS3.csv'))
########## SS4 ##########
info.summary <- fread(paste0(dir_out, 'summarySS3.csv'), header = TRUE,
colClasses = 'character', encoding = 'UTF-8')
## SS4.1 ##
Patient_PCP_2018_TieDays_OutP <- fread(paste0(dir_out, 'Patient_PCP_2018_TieDays_OutP.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
MC_PCP_2018_OutP_SS3 <- Patient_PCP_2018_TieDays_OutP[MC_2018_OutP,
on = c(internal_member_id = 'internal_member_id',
NPI = 'NPI')][Freq > 0]
group <- MC_PCP_2018_OutP_SS3
{
tmp1 <- group[,
list(RecentDate = max(as.Date(first_service_dt, '%m/%d/%Y'))),
by = list(internal_member_id, NPI)]
tmp2 <- tmp1[tmp1[, .I[RecentDate == max(RecentDate)], by = internal_member_id]$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)
## SS4.2 ##
Patient_PCP_2017_TieDays_OutP <- fread(paste0(dir_out, 'Patient_PCP_2017_TieDays_OutP.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
MC_PCP_2017_OutP_SS3 <- Patient_PCP_2017_TieDays_OutP[MC_2017_OutP,
on = c(internal_member_id = 'internal_member_id',
NPI = 'NPI')][Freq > 0]
group <- MC_PCP_2017_OutP_SS3
{
tmp1 <- group[,
list(RecentDate = max(as.Date(first_service_dt, '%m/%d/%Y'))),
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]$V1]
}
Patient_PCP_2017_UniqueRecentDate_OutP <- out_unique[, Identify := 'RecentDate_OutP']
fwrite(out_unique[, Identify := 'RecentDate_OutP'],
paste0(dir_out, 'Patient_PCP_2017_UniqueRecentDate_OutP.csv'))
Patient_PCP_2017_TieRecentDate_OutP <- out_tie
fwrite(out_tie,
paste0(dir_out, 'Patient_PCP_2017_TieRecentDate_OutP.csv'))
tmp <- data.table(matrix(c('MC_PCP_2017_RecentDate_OutP',
length(unique(group$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$RecentDate))), nrow = 1))
names(tmp) <- names(info.summary)
info.summary <- rbind(info.summary, tmp)
## SS4.3 ##
Patient_OBGYN_2018_TieDays_OutP <- fread(paste0(dir_out, 'Patient_OBGYN_2018_TieDays_OutP.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
MC_OBGYN_2018_OutP_SS3 <- Patient_OBGYN_2018_TieDays_OutP[MC_2018_OutP,
on = c(internal_member_id = 'internal_member_id',
NPI = 'NPI')][Freq > 0]
group <- MC_OBGYN_2018_OutP_SS3
{
tmp1 <- group[,
list(RecentDate = max(as.Date(first_service_dt, '%m/%d/%Y'))),
by = list(internal_member_id, NPI)]
tmp2 <- tmp1[tmp1[, .I[RecentDate == max(RecentDate)], by = internal_member_id]$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)
## SS4.4 ##
Patient_OBGYN_2017_TieDays_OutP <- fread(paste0(dir_out, 'Patient_OBGYN_2017_TieDays_OutP.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
MC_OBGYN_2017_OutP_SS3 <- Patient_OBGYN_2017_TieDays_OutP[MC_2017_OutP,
on = c(internal_member_id = 'internal_member_id',
NPI = 'NPI')][Freq > 0]
group <- MC_OBGYN_2017_OutP_SS3
{
tmp1 <- group[,
list(RecentDate = max(as.Date(first_service_dt, '%m/%d/%Y'))),
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]$V1]
}
Patient_OBGYN_2017_UniqueRecentDate_OutP <- out_unique[, Identify := 'RecentDate_OutP']
fwrite(out_unique[, Identify := 'RecentDate_OutP'],
paste0(dir_out, 'Patient_OBGYN_2017_UniqueRecentDate_OutP.csv'))
Patient_OBGYN_2017_TieRecentDate_OutP <- out_tie
fwrite(out_tie,
paste0(dir_out, 'Patient_OBGYN_2017_TieRecentDate_OutP.csv'))
tmp <- data.table(matrix(c('MC_OBGYN_2017_RecentDate_OutP',
length(unique(group$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_2018_UniqueRecentDate_OutP <- fread(paste0(dir_out, 'Patient_PCP_2018_UniqueRecentDate_OutP.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
Patient_PCP_2017_UniqueRecentDate_OutP <- fread(paste0(dir_out, 'Patient_PCP_2017_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_OBGYN_2017_UniqueRecentDate_OutP <- fread(paste0(dir_out, 'Patient_OBGYN_2017_UniqueRecentDate_OutP.csv'),
header = TRUE,
colClasses = 'character',
encoding = 'UTF-8')
Patient_Unique <- rbind(Patient_PCP_2018_UniqueVisits_EM, Patient_PCP_2018_UniqueVisits_OutP, Patient_PCP_2018_UniqueDays_OutP, Patient_PCP_2018_UniqueRecentDate_OutP,
Patient_PCP_2017_UniqueVisits_EM, Patient_PCP_2017_UniqueVisits_OutP, Patient_PCP_2017_UniqueDays_OutP, Patient_PCP_2017_UniqueRecentDate_OutP,
Patient_OBGYN_2018_UniqueVisits_EM, Patient_OBGYN_2018_UniqueVisits_OutP, Patient_OBGYN_2018_UniqueDays_OutP, Patient_OBGYN_2018_UniqueRecentDate_OutP,
Patient_OBGYN_2017_UniqueVisits_EM, Patient_OBGYN_2017_UniqueVisits_OutP, Patient_OBGYN_2017_UniqueDays_OutP, Patient_OBGYN_2017_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_2018_TieRecentDate_OutP,
Patient_PCP_2017_TieRecentDate_OutP,
Patient_OBGYN_2018_TieRecentDate_OutP,
Patient_OBGYN_2017_TieRecentDate_OutP,
fill = TRUE
)
fwrite(Patient_Tie,
paste0(dir_out, 'PatientTie.csv'))
#### Taxonomy/Specialty Freq Research ####
Patient_classification2 <- nppes0[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$Specialty), decreasing = TRUE)),
paste0(dir_out, 'PatientToSpecialty.csv'))
fwrite(Patient_classification2,
paste0(dir_out, 'PatAttri_detail.csv'))
fwrite(Patient_classification2[Attribution == 'Unattributed',
Identify := 'Unattributed'][Identify == "", Identify := 'Tie'],
paste0(dir_out, 'PatAttri_detail.csv'))
#### Research for the Unattributed Par
## taxoClass <- fread('E:/CT_APCD/Hongfei/files/taxonomy classification.csv',
## header = TRUE,
## colClasses = 'character',
## encoding = 'UTF-8')
## NPI_freq_unattr <- head(sort(table(MC_unattr_2018$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_2018$NPI)
## taxo_freq_unattr_NPI <- head(sort(table(nppes0[NPI_unattr, on = c(NPI = 'NPI')]$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_2018$NPI))
## taxo_freq_unattr_uniqueNPI <- head(sort(table(nppes0[uniqueNPI_unattr, on = c(NPI = 'NPI')]$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')