BPCI Advanced Analysis of Participants

An Analysis of BPCI Advanced Conveners and Non-Conveners

Install libraries, pull in dataset. The dataset is taken from this website https://innovation.cms.gov/initiatives/bpci-advanced and the link to the dataset is https://innovation.cms.gov/Files/worksheets/bpci-advanced-participants.xlsx

Bundled Payments for Care Improvement (BPCI) Advanced is a voluntary payment model developed by the Centers for Medicare & Medicaid Services (CMS) through the Center for Medicare and Medicaid Innovation (The Innovation Center). The Innovation Center is testing this model to evaluate its success to improve quality care and lower healthcare costs.

#install.packages("tidyverse")
#install.packages("plotly")
#install.packages("ggplot2")
#install.packages("dplyr")
library(tidyverse)

## -- Attaching packages --------------------------------------------------------------------------------------------- tidyverse 1.3.0 --

## v ggplot2 3.2.1     v purrr   0.3.3
## v tibble  2.1.3     v dplyr   0.8.3
## v tidyr   1.0.0     v stringr 1.4.0
## v readr   1.3.1     v forcats 0.4.0

## Warning: package 'ggplot2' was built under R version 3.5.3

## Warning: package 'tibble' was built under R version 3.5.3

## Warning: package 'tidyr' was built under R version 3.5.3

## Warning: package 'readr' was built under R version 3.5.2

## Warning: package 'dplyr' was built under R version 3.5.3

## Warning: package 'stringr' was built under R version 3.5.3

## Warning: package 'forcats' was built under R version 3.5.3

## -- Conflicts ------------------------------------------------------------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(plotly)

## Warning: package 'plotly' was built under R version 3.5.3

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

library(ggplot2)
library(dplyr)
participation <- read.csv("BPCIAparticipants.csv")
dim(participation)

## [1] 1086   10

summary(participation)

##         bpid                                orglegalname
##  1001-0000:   1   REMEDY BPCI PARTNERS, LLC       :228  
##  1006-0000:   1   NAVIHEALTH, INC.                :104  
##  1011-0001:   1   FUSION5, INC.                   : 91  
##  1019-0000:   1   STRYKER                         : 81  
##  1045-0000:   1   UNITED HEALTHCARE SERVICES, INC.: 64  
##  1048-0000:   1   SIGNATURE MEDICAL GROUP, INC.   : 39  
##  (Other)  :1080   (Other)                         :479  
##                             orgdbaname           role     participanttype
##  REMEDY PARTNERS                 :228   Convener   :835   ACH  :131      
##  NAVIHEALTH, INC.                :104   Participant:251   Other:771      
##  FUSION5                         : 91                     PGP  :184      
##  STRYKER PERFORMANCE SOLUTIONS   : 81                                    
##  UNITED HEALTHCARE SERVICES, INC.: 64                                    
##  SIGNATURE CARE MANAGEMENT       : 39                                    
##  (Other)                         :479                                    
##                              address1        address2            city    
##  1120 POST ROAD                  :164            :822   DARIEN     :228  
##  210 WESTWOOD PLACE, SUITE 400   :104   SUITE 300:106   BRENTWOOD  :124  
##  117 SOUTH MAIN STREET           : 91   SUITE 100: 36   CHICAGO    :102  
##  350 NORTH ORLEANS ST., SUITE 650: 70   N/A      : 22   ST. CHARLES: 91  
##  1120 POST RD                    : 64   SUITE 120: 19   MINNETONKA : 64  
##  9900 BREN ROAD EAST             : 64   SUITE 270: 13   LOS ANGELES: 55  
##  (Other)                         :529   (Other)  : 68   (Other)    :422  
##      state          zip       
##  CT     :230   Min.   : 1605  
##  MO     :135   1st Qu.: 6820  
##  IL     :130   Median :43215  
##  TN     :130   Mean   :42034  
##  CA     : 79   3rd Qu.:63141  
##  MN     : 68   Max.   :99508  
##  (Other):314

The dimensions are 1,086 records of 10 variables.

The summary shows we have 835 Conveners, 251 Participants, 131 ACH, 771 Other, and 184 PGP. It also names the top 6 organizations by count of Bundled Payment Identification (BPIDs).

Select the variables we want to look at and assign them back to the participation dataset.

I decided to rename (mutate) the role so instead of “Convener” and “Participant” we have “Convener” and “Non-Convener” to match how our BPCI Advanced Team speaks about these roles.

participation <- participation %>%
  select(bpid, orglegalname, orgdbaname, role, participanttype, city, state) %>%
  mutate(role = case_when(role=="Participant"~"Non-Convener", role=="Convener"~"Convener"))

I created a histogram of the roles.

I used plotly to display the counts when you hover the columns.

roleplot <- qplot(data=participation, role, fill=role, geom_bar = "histogram", bins=20) + 
  scale_fill_brewer(palette= "Dark2") +
  xlab("Participant Role") +
  ylab("Number of Participants") +
  ggtitle(label="BPCI Advanced MY 1&2 Convener and Non-Convener \n  Participants by Role") +
  theme(legend.title=element_blank())

## Warning: Ignoring unknown parameters: geom_bar, bins

  ggplotly(roleplot)

Now I’ll plot the Participant Types - Acute Care Hospital (ACH), Physician Group Practice (PGP), or Other - for our 1,086 Participants.

First, I sort the sort the types in a order: ACH, PGP, Other.

participation$participanttype <- ordered(participation$participanttype,levels=c("ACH", "PGP", "Other"))
typesplot <- qplot(data=participation, participanttype, fill=participanttype, labels=c("ACH", "PGP", "Other"), geom_bar = "histogram", bins=20) +
  xlab("Participant Type") +
  ylab("Number of Participants") +
  ggtitle("BPCI Advanced MY 1&2 All Participants by Type") +
  theme(legend.title=element_blank()) +
  scale_fill_manual(values=c("darkgoldenrod1", "blueviolet", "cadetblue3"))

## Warning: Ignoring unknown parameters: labels, geom_bar, bins

ggplotly(typesplot)

Plot the number of Conveners by type (ACHs, PGPs, and Other).

allconveners <- participation %>%
  filter(role =="Convener")
allconvenersplot <- qplot(data=allconveners, participanttype, fill=participanttype, geom_bar = "histogram", bins=20) +
  theme(legend.title=element_blank()) +
   xlab("Participant Type") +
  ylab("Number of Participants") +
  ggtitle("BPCI Advanced MY 1&2 Conveners by Type") +
  scale_fill_manual(values=c("darkgoldenrod1", "blueviolet", "cadetblue3"))

## Warning: Ignoring unknown parameters: geom_bar, bins

ggplotly(allconvenersplot)

Plot the number of Non-Conveners by type (ACHs, PGPs, and Other).

nonconveners <- participation %>%
  filter(role =="Non-Convener")
nonconvenersplot <- qplot(data=nonconveners, participanttype, fill=participanttype, geom_bar = "histogram", bins=20) +
  theme(legend.title=element_blank()) +
   xlab("Participant Type") +
  ylab("Number of Participants") +
  ggtitle("BPCI Advanced MY 1&2 Non-Conveners by Type") +
  scale_fill_manual(values=c("darkgoldenrod1", "blueviolet", "cadetblue3"))

## Warning: Ignoring unknown parameters: geom_bar, bins

ggplotly(nonconvenersplot)

The charts above are also shown on my Tableau Public page here: https://public.tableau.com/profile/jennifer.paraboschi#!/vizhome/BPCIAdvancedParticipants/ConvenersandNon-Conveners?publish=yes

Mapping our Conveners

I mapped the Conveners in the US using Tableau Public. https://public.tableau.com/profile/jennifer.paraboschi#!/vizhome/BPCIAdvancedParticipants/LocationbyParticipantType?publish=yes