This Programming Assignment is part of the R Programming course and consists of four parts. The dataset analyzed is about the death due to a specific outcome, such as heart attack, heart failure, or pneumonia.
best()
function aims to find the best hospital in any outcomes (heart attack,
heart failure or pneumonia) in the last 30 days.rankhospital()
returns the hospital name of a given state and a given ranking.rankall()
compares several states based on a given ranking.Download the file ProgAssignment3-data.zip file
containing the data for Programming Assignment 3 from the Coursera web
site. Unzip the file in a directory that will serve as your working
directory. When you start up R make sure to change your working
directory to the directory where you unzipped the data.
# Data acquisition.
# Creating a folder to host the data from the internet.
base::dir.create(path = "data")
# Downloading the file.
utils::download.file(url = "https://d396qusza40orc.cloudfront.net/rprog%2Fdata%2FProgAssignment3-data.zip",
destfile = "./data/ProgAssignment3-data.zip",
quiet = TRUE)
# Unzipping the file.
utils::unzip(zipfile = "./data/ProgAssignment3-data.zip", exdir = "./data")
# Printing the CSV file names from the zip file.
base::list.files(path = "./data", pattern = ".csv")## [1] "hospital-data.csv" "outcome-of-care-measures.csv"The data for this assignment come from the Hospital Compare web site
(http://hospitalcompare.hhs.gov) run by the U.S.
Department of Health and Human Services. The purpose of the web site is
to provide data and information about the quality of care at over 4,000
Medicare-certified hospitals in the U.S. This dataset essentially covers
all major U.S. hospitals. This dataset is used for a variety of
purposes, including determining whether hospitals should be fined for
not providing high quality care to patients (see http://goo.gl/jAXFX for
some background on this particular topic).
The Hospital Compare web site contains a lot of data and we will only look at a small subset for this assignment. The zip file for this assignment contains three files
# Loading hospital-data.csv
data_hosp <- utils::read.csv(file = "./data/hospital-data.csv")
# Loading outcome-of-care-measures.csv
data_outcome <- utils::read.csv(file = "./data/outcome-of-care-measures.csv",
colClasses = "character")A description of the variables in each of the files is in the
included PDF file named Hospital_Revised_Flatfiles.pdf.
This document contains information about many other files that are not
included with this programming assignment. You will want to focus on the
variables for Number 19 (outcome-of-care-measures.csv) and
Number 11 (hospital-data.csv). You may find it useful to
print out this document (at least the pages for Tables 19 and 11) to
have next to you while you work on this assignment. In particular, the
numbers of the variables for each table indicate column indices in each
table (i.e. “Hospital Name” is column 2 in the
outcome-of-care-measures.csv file).
Read the outcome data into R via the read.csv function and look at the first few rows.
# Print the first rows of "outcome-of-care-measures.csv" file.
# I have used DT package to turn it readable.
DT::datatable(head(data_outcome))There are many columns in this dataset. You can see how many by typing ncol(outcome) (you can see the number of rows with the nrow function). In addition, you can see the names of each column by typing names(outcome) (the names are also in the PDF document.
To make a simple histogram of the 30-day death rates from heart attack (column 11 in the outcome dataset), run:
# Convert character column into numeric one.
# The 11th column is the heart attack mortality in 30 days.
data_outcome[, 11] <- as.numeric(data_outcome[, 11])
## You may get a warning about NAs being introduced; that is okay
# Create a histogram
p <- ggplot2::ggplot(data_outcome,
aes(x = data_outcome[, 11])) +
geom_histogram(show.legend = FALSE) +
ggtitle("Heart Attack Histogram") +
xlab("Heart Attack Mortality in 30-days") +
ylab("Frequency \n Quantity of hospital")
# Converting into Plotly Graphic
plotly::ggplotly(p)Because we originally read the data in as character (by specifying colClasses = “character” we need to coerce the column to be numeric. You may get a warning about NAs being introduced but that is okay.
Write a function called best that take two arguments: the 2-character abbreviated name of a state and an outcome name. The function reads the outcome-of-care-measures.csv file and returns a character vector with the name of the hospital that has the best (i.e. lowest) 30-day mortality for the specified outcome in that state. The hospital name is the name provided in the Hospital.Name variable. The outcomes can be one of “heart attack”, “heart failure”, or “pneumonia”. Hospitals that do not have data on a particular outcome should be excluded from the set of hospitals when deciding the rankings.
Handling ties. If there is a tie for the best hospital for a given outcome, then the hospital names should be sorted in alphabetical order and the first hospital in that set should be chosen (i.e. if hospitals “b”, “c”, and “f” are tied for best, then hospital “b” should be returned).
The function should use the following template.
best <- function(state, outcome) {
## Read outcome data
## Check that state and outcome are valid
## Return hospital name in that state with lowest 30-day death
## rate
}The function should check the validity of its arguments. If an invalid state value is passed to best, the function should throw an error via the stop function with the exact message “invalid state”. If an invalid outcome value is passed to best, the function should throw an error via the stop function with the exact message “invalid outcome”.
Here is some sample output from the function.
> source("best.R")
> best("TX", "heart attack")
[1] "CYPRESS FAIRBANKS MEDICAL CENTER"
> best("TX", "heart failure")
[1] "FORT DUNCAN MEDICAL CENTER"
> best("MD", "heart attack")
[1] "JOHNS HOPKINS HOSPITAL, THE"
> best("MD", "pneumonia")
[1] "GREATER BALTIMORE MEDICAL CENTER"
> best("BB", "heart attack")
Error in best("BB", "heart attack") : invalid state
> best("NY", "hert attack")
Error in best("NY", "hert attack") : invalid outcome
>Save your code for this function to a file named
best.R.
best <- function(state, outcome) {
## Read outcome data
data_outcome <- utils::read.csv(file = "./data/outcome-of-care-measures.csv",
colClasses = "character")
# Create a unique list of STATE in this dataset.
# IMPORTANT: There are 54 states, which is wrong.
# However, I found out this dataset contains DC and other territories.
states_outcome <- base::unique(data_outcome$State)
# List of outcomes
list_disease <- c("heart attack", "heart failure", "pneumonia")
## Check that state and outcome are valid
# Valid STATE and Valid OUTCOME
if ((state %in% states_outcome) & (outcome %in% list_disease)) {
# Subsetting the raw data.
data_tidy <- data_outcome %>% select(Hospital.Name, State, starts_with("Hospital.30.Day.Death"))
# Renaming columns to be readable.
colnames(data_tidy) <- c("hospital_name", "hospital_state","heart_attack", "heart_failure", "pneumonia")
# Uniforming the names with underscore to match during the select.
outcome_ <- sub(pattern = " ", replacement = "_", x = outcome)
# Converting character into numeric.
data_tidy$heart_attack <- as.numeric(data_tidy$heart_attack)
data_tidy$heart_failure <- as.numeric(data_tidy$heart_failure)
data_tidy$pneumonia <- as.numeric(data_tidy$pneumonia)
data_tidy %>%
select(hospital_name,hospital_state,all_of(outcome_)) %>%
filter(hospital_state == state) %>%
na.omit() %>% select(-hospital_state) %>%
arrange(across(all_of(outcome_)), hospital_name) %>%
head(1) %>%
select(hospital_name) %>%
as.character() -> message
}
# Something is wrong with the inputs
# The state input in not a valid one
if (!(state %in% states_outcome) & (outcome %in% list_disease)) {
return(base::cat(base::sprintf('Error in best("%s", "%s") : invalid state', state, outcome)))
}
# The outcome in not valid.
if ((state %in% states_outcome) & !(outcome %in% list_disease)) {
return(base::cat(base::sprintf('Error in best("%s", "%s") : invalid outcome', state, outcome)))
}
# Both state and outcome has typos
if (!(state %in% states_outcome) & !(outcome %in% list_disease)) {
return(base::cat(base::sprintf('Error in best("%s", "%s") : invalid state and outcome', state, outcome)))
}
## Return hospital name in that state with lowest 30-day death
## rate
return(message)
}best("TX", "heart attack")## [1] "CYPRESS FAIRBANKS MEDICAL CENTER"best("TX", "heart failure")## [1] "FORT DUNCAN MEDICAL CENTER"best("MD", "heart attack")## [1] "JOHNS HOPKINS HOSPITAL, THE"best("MD", "pneumonia")## [1] "GREATER BALTIMORE MEDICAL CENTER"best("BB", "heart attack")## Error in best("BB", "heart attack") : invalid statebest("NY", "hert attack")## Error in best("NY", "hert attack") : invalid outcomeWrite a function called rankhospital that takes three
arguments: the 2-character abbreviated name of a state
(state), an outcome (outcome), and the ranking
of a hospital in that state for that outcome (num). The function reads
the outcome-of-care-measures.csv file and returns a
character vector with the name of the hospital that has the ranking
specified by the num argument. For example, the call:
rankhospital("MD", "heart failure", 5)would return a character vector containing the name of the hospital
with the 5th lowest 30-day death rate for heart failure. The
num argument can take values “best”, “worst”, or an integer
indicating the ranking (smaller numbers are better). If the number given
by num is larger than the number of hospitals in that
state, then the function should return NA. Hospitals that
do not have data on a particular outcome should be excluded from the set
of hospitals when deciding the rankings.
Handling ties. It may occur that multiple hospitals have the same 30-day mortality rate for a given cause of death. In those cases ties should be broken by using the hospital name. For example, in Texas (“TX”), the hospitals with lowest 30-day mortality rate for heart failure are shown here.
> head(texas)
Hospital.Name Rate Rank
3935 FORT DUNCAN MEDICAL CENTER 8.1 1
4085 TOMBALL REGIONAL MEDICAL CENTER 8.5 2
4103 CYPRESS FAIRBANKS MEDICAL CENTER 8.7 3
3954 DETAR HOSPITAL NAVARRO 8.7 4
4010 METHODIST HOSPITAL,THE 8.8 5
3962 MISSION REGIONAL MEDICAL CENTER 8.8 6Note that Cypress Fairbanks Medical Center and
Detar Hospital Navarro both have the same 30-day rate
(8.7). However, because Cypress comes before Detar alphabetically,
Cypress is ranked number 3 in this scheme and Detar is ranked number 4.
One can use the order function to sort multiple vectors in
this manner (i.e. where one vector is used to break ties in another
vector).
The function should use the following template
rankhospital <- function(state, outcome, num = "best") {
## Read outcome data
## Check that state and outcome are valid
## Return hospital name in that state with the given rank
## 30-day death rate
}The function should check the validity of its arguments. If an invalid state value is passed to rankhospital, the function should throw an error via the stop function with the exact message “invalid state”. If an invalid outcome value is passed to rankhospital, the function should throw an error via the stop function with the exact message “invalid outcome”.
Here is some sample output from the function.
> source("rankhospital.R")
> rankhospital("TX", "heart failure", 4)
[1] "DETAR HOSPITAL NAVARRO"
> rankhospital("MD", "heart attack", "worst")
3
[1] "HARFORD MEMORIAL HOSPITAL"
> rankhospital("MN", "heart attack", 5000)
[1] NASave your code for this function to a file named
rankhospital.R.
rankhospital <- function(state, outcome, num = "best") {
## Read outcome data
data_outcome <- utils::read.csv(file = "./data/outcome-of-care-measures.csv",
colClasses = "character")
# Adding the row number
data_outcome <- data_outcome %>% add_rownames()
# Create a unique list of STATE
states_outcome <- base::unique(data_outcome$State)
# List of outcomes
list_disease <- c("heart attack", "heart failure", "pneumonia")
## Check that state and outcome are valid
# Valid STATE and Valid OUTCOME
if ((state %in% states_outcome) & (outcome %in% list_disease)) {
# Subsetting the raw data.
# I want the death data of the last 30 Day all and other columns I will ignore.
data_tidy <- data_outcome %>%
select(rowname, Hospital.Name,
State,
starts_with("Hospital.30.Day.Death"))
# Renaming columns to be more readable.
colnames(data_tidy) <- c("rowname",
"hospital_name",
"hospital_state",
"heart_attack",
"heart_failure",
"pneumonia")
# Uniforming the colnames with underscore to match during the select.
outcome_ <- sub(pattern = " ",
replacement = "_",
x = outcome)
# Converting character into numeric.
# The read.csv has forced all columns to be character (colClasses = "character")
data_tidy$heart_attack <- as.numeric(data_tidy$heart_attack)
data_tidy$heart_failure <- as.numeric(data_tidy$heart_failure)
data_tidy$pneumonia <- as.numeric(data_tidy$pneumonia)
# Subsetting the input outcome given from the operator.
data_rank <- data_tidy %>%
select(rowname, #
hospital_name, # Selecting: Hospital name, Hospital State
hospital_state, # and outcome
all_of(outcome_)) %>% #
filter(hospital_state == state) %>% # Filtering the data based on state given by operator.
na.omit() %>% # Filtering only rows with data
arrange(across(all_of(outcome_)), # Arranging first by the outcome, then by hospital name
hospital_name) %>% #
mutate(Rank = 1:nrow(.)) # Add a column with the ranking of each hospital.
# The num input has two conditions:
# 1. numeric
# 2. character
# Checking if the num input is character:
if (is.character(num)) {
# However, there are 2 conditions in this case of character:
# 1. best -> I need to show the best hospital
# 2. worst -> I need to show the worst hospital
# IN CASE: the num variable is the character's worst.
if (num == "worst") {
# TAIL will give the worst hospital in that outcome.
data_rank %>%
select(-hospital_state) %>%
rename(" " = rowname,
"Hospital.Name" = hospital_name,
"Rate" = all_of(outcome_),
"Rank" = Rank) %>%
tail(1) %>%
select(Hospital.Name) %>%
as.character() -> output
# Return the hospital with the worst outcome.
return(output)
}
# IN CASE: the num variable is the character's best.
else if (num == "best") {
# HEAD will select the best hospital in that outcome.
data_rank %>%
select(-hospital_state) %>%
rename(" " = rowname,
"Hospital.Name" = hospital_name,
"Rate" = all_of(outcome_),
"Rank" = Rank) %>%
head(Rank == num) %>%
select(Hospital.Name) %>%
as.character() -> output
# Return the hospital with the best outcome.
return(output)
}
# The script only accepts characters: best and worst.
# Any other characters will throw an error.
else {
return(cat("Please, check the num input."))
}
}
# Checking if the num input is a numeric variable and the num input and if it is a single number.
# should be a single value (not a vector).
if (is.numeric(num) & length(num) == 1) {
# We have also two conditions here:
# 1. If the num input is out of the boundaries
# 2. A single number between 1 and the number of hospital.
# IN CASE the num input is between 1 the number of rows of the dataset.
if (num >= 1 & num < nrow(data_rank)) {
# Filter will select the exactly num rank.
data_rank %>%
select(-hospital_state) %>%
rename(" " = rowname,
"Hospital.Name" = hospital_name,
"Rate" = all_of(outcome_),
"Rank" = Rank) %>%
filter(Rank == num) %>%
select(Hospital.Name) %>%
as.character() -> output
# Return the hospital in the "num position" in that outcome.
return(output)
}
# The num input is wrong because is negative, zero or above the number of hospital.
else {
# Following the instructions. For num above of the number of hospital return NA.
return(NA)
}
}
# Warning the operator about a problem in num input.
else if (length(num) >= 2) {
return(print("Please, make sure your num input is a single value."))
}
}
# Something is wrong with the inputs
# The state input in not a valid one
else if (!(state %in% states_outcome) & (outcome %in% list_disease)) {
return(base::cat(base::sprintf('Error in best("%s", "%s") : invalid state', state, outcome)))
}
# The outcome in not valid.
else if ((state %in% states_outcome) & !(outcome %in% list_disease)) {
return(base::cat(base::sprintf('Error in best("%s", "%s") : invalid outcome', state, outcome)))
}
# Both state and outcome has typos
else if (!(state %in% states_outcome) & !(outcome %in% list_disease)) {
return(base::cat(base::sprintf('Error in best("%s", "%s") : invalid state and outcome', state, outcome)))
}
}rankhospital("TX", "heart failure", 4)## [1] "DETAR HOSPITAL NAVARRO"rankhospital("MD", "heart attack", "worst")## [1] "HARFORD MEMORIAL HOSPITAL"rankhospital("MN", "heart attack", 5000)## [1] NAWrite a function called rankall that takes two
arguments: an outcome name (outcome) and a hospital ranking
(num). The function reads the
outcome-of-care-measures.csv file and returns a 2-column
data frame containing the hospital in each state that has the ranking
specified in num. For example the function call
rankall(“heart attack”, “best”) would return a data frame
containing the names of the hospitals that are the best in their
respective states for 30-day heart attack death rates. The function
should return a value for every state (some may be NA). The first column
in the data frame is named hospital, which contains the
hospital name, and the second column is named state,
which contains the 2-character abbreviation for the state name.
Hospitals that do not have data on a particular outcome should be
excluded from the set of hospitals when deciding the rankings.
Handling ties. The rankall function
should handle ties in the 30-day mortality rates in the same way that
the rankhospital function handles ties.
The function should use the following template.
rankall <- function(outcome, num = "best") {
## Read outcome data
## Check that state and outcome are valid
## For each state, find the hospital of the given rank
## Return a data frame with the hospital names and the
## (abbreviated) state name
}NOTE: For the purpose of this part of the assignment
(and for efficiency), your function should NOT call the
rankhospital function from the previous section.
The function should check the validity of its arguments. If an
invalid outcome value is passed to rankall,
the function should throw an error via the stop
function with the exact message “invalid outcome”. The num
variable can take values “best”, “worst”, or an integer indicating the
ranking (smaller numbers are better). If the number given by
num is larger than the number of hospitals in that state,
then the function should return NA.
Here is some sample output from the function.
> source("rankall.R")
> head(rankall("heart attack", 20), 10)
hospital state
AK <NA> AK
AL D W MCMILLAN MEMORIAL HOSPITAL AL
AR ARKANSAS METHODIST MEDICAL CENTER AR
AZ JOHN C LINCOLN DEER VALLEY HOSPITAL AZ
CA SHERMAN OAKS HOSPITAL CA
CO SKY RIDGE MEDICAL CENTER CO
CT MIDSTATE MEDICAL CENTER CT
DC <NA> DC
DE <NA> DE
FL SOUTH FLORIDA BAPTIST HOSPITAL FL
> tail(rankall("pneumonia", "worst"), 3)
hospital state
WI MAYO CLINIC HEALTH SYSTEM - NORTHLAND, INC WI
WV PLATEAU MEDICAL CENTER WV
WY NORTH BIG HORN HOSPITAL DISTRICT WY
> tail(rankall("heart failure"), 10)
hospital state
TN WELLMONT HAWKINS COUNTY MEMORIAL HOSPITAL TN
TX FORT DUNCAN MEDICAL CENTER TX
UT VA SALT LAKE CITY HEALTHCARE - GEORGE E. WAHLEN VA MEDICAL CENTER UT
VA SENTARA POTOMAC HOSPITAL VA
VI GOV JUAN F LUIS HOSPITAL & MEDICAL CTR VI
VT SPRINGFIELD HOSPITAL VT
WA HARBORVIEW MEDICAL CENTER WA
WI AURORA ST LUKES MEDICAL CENTER WI
WV FAIRMONT GENERAL HOSPITAL WV
WY CHEYENNE VA MEDICAL CENTER WYrankall <- function(outcome, num = "best") {
## Read outcome data
data_outcome <- utils::read.csv(file = "./data/outcome-of-care-measures.csv",
colClasses = "character")
# Adding the row number
data_outcome <- data_outcome %>% add_rownames()
# Create a unique list of STATE
states_outcome <- base::unique(data_outcome$State)
# List of outcomes
list_disease <- c("heart attack", "heart failure", "pneumonia")
# Subsetting the raw data.
# I want the death data of the last 30 Day all and other columns I will ignore.
data_tidy <- data_outcome %>%
select(rowname, Hospital.Name,
State,
starts_with("Hospital.30.Day.Death"))
# Renaming columns to be more readable.
colnames(data_tidy) <- c("rowname",
"hospital_name",
"hospital_state",
"heart_attack",
"heart_failure",
"pneumonia")
# Uniforming the colnames with underscore to match during the select.
outcome_ <- sub(pattern = " ",
replacement = "_",
x = outcome)
# Converting character into numeric.
# The read.csv has forced all columns to be character (colClasses = "character")
data_tidy$heart_attack <- as.numeric(data_tidy$heart_attack)
data_tidy$heart_failure <- as.numeric(data_tidy$heart_failure)
data_tidy$pneumonia <- as.numeric(data_tidy$pneumonia)
# Checking if the operator has inserted a valid outcome.
if (outcome %in% list_disease) {
# Creating a group and ranking by state.
data_rank_state <- data_tidy %>%
group_by(hospital_state) %>%
mutate(ranking = order(order(across(all_of(outcome_)), hospital_name)))
# The num input has two conditions:
# 1. numeric
# 2. character
# Checking if the num input is character:
if (is.character(num)) {
# However, there are 2 conditions in this case of character:
# 1. best -> I need to show the best hospital
# 2. worst -> I need to show the worst hospital
# IN CASE: the num variable is the character's worst.
if (num == "worst") {
# MAX will give the worst hospital in that outcome.
data_rank_state %>%
filter(!is.na(across(all_of(outcome_)))) %>%
filter(ranking == max(ranking, na.rm = TRUE)) %>%
select(hospital_name,hospital_state) %>%
arrange(hospital_state, all_of(hospital_name)) -> output
# Return the hospital with the worst outcome.
return(output)
}
# IN CASE: the num variable is the character's best.
else if (num == "best") {
# MIN will select the best hospital in that outcome.
data_rank_state %>%
filter(ranking == min(ranking, na.rm = TRUE)) %>%
select(hospital_name,hospital_state) %>%
arrange(hospital_state, all_of(hospital_name)) -> output
# Return the hospital with the best outcome.
return(output)
}
# The script only accepts characters: best and worst.
# Any other characters will throw an error.
else {
return(cat("Please, check the num input."))
}
}
# Checking if the num input is a numeric variable and the num input and if it is a single number.
# should be a single value (not a vector).
if (is.numeric(num) & length(num) == 1) {
# We have also two conditions here:
# 1. If the num input is out of the boundaries
# 2. A single number between 1 and the number of hospital.
# IN CASE the num input is between 1 the number of rows of the dataset.
if (num >= 1 & num < nrow(data_rank_state)) {
# Filter will select the exactly num rank.
data_rank_state %>%
filter(ranking == num) %>%
select(hospital_name,hospital_state) %>%
arrange(hospital_state, all_of(hospital_name)) -> output
# Creating the rows of state with NA.
merge(x = data_tidy %>% select(hospital_state) %>% unique() %>% arrange(hospital_state),
y = output,
all = TRUE) -> output
# Return the hospital in the "num position" in that outcome.
return(output)
}
# The num input is wrong because is negative, zero or above the number of hospital.
else {
# Following the instructions. For num above of the number of hospital return NA.
return(NA)
}
}
# Warning the operator about a problem in num input.
else if (length(num) >= 2) {
return(print("Please, make sure your num input is a single value."))
}
}
# Something is wrong with the inputs
# The outcome in not valid.
else if (!(outcome %in% list_disease)) {
return(base::cat(base::sprintf('Error in rankhospital(outcome = "%s", num = "%s") : invalid outcome', state, num)))
}
}head(rankall("heart attack", 20), 10)## hospital_state hospital_name
## 1 AK <NA>
## 2 AL D W MCMILLAN MEMORIAL HOSPITAL
## 3 AR ARKANSAS METHODIST MEDICAL CENTER
## 4 AZ JOHN C LINCOLN DEER VALLEY HOSPITAL
## 5 CA SHERMAN OAKS HOSPITAL
## 6 CO SKY RIDGE MEDICAL CENTER
## 7 CT MIDSTATE MEDICAL CENTER
## 8 DC <NA>
## 9 DE <NA>
## 10 FL SOUTH FLORIDA BAPTIST HOSPITALtail(rankall("pneumonia", "worst"), 3)## # A tibble: 3 x 2
## # Groups: hospital_state [3]
## hospital_name hospital_state
## <chr> <chr>
## 1 MAYO CLINIC HEALTH SYSTEM - NORTHLAND, INC WI
## 2 PLATEAU MEDICAL CENTER WV
## 3 NORTH BIG HORN HOSPITAL DISTRICT WYtail(rankall("heart failure"), 10)## # A tibble: 10 x 2
## # Groups: hospital_state [10]
## hospital_name hospital_state
## <chr> <chr>
## 1 WELLMONT HAWKINS COUNTY MEMORIAL HOSPITAL TN
## 2 FORT DUNCAN MEDICAL CENTER TX
## 3 VA SALT LAKE CITY HEALTHCARE - GEORGE E. WAHLEN VA MEDICAL CE~ UT
## 4 SENTARA POTOMAC HOSPITAL VA
## 5 GOV JUAN F LUIS HOSPITAL & MEDICAL CTR VI
## 6 SPRINGFIELD HOSPITAL VT
## 7 HARBORVIEW MEDICAL CENTER WA
## 8 AURORA ST LUKES MEDICAL CENTER WI
## 9 FAIRMONT GENERAL HOSPITAL WV
## 10 CHEYENNE VA MEDICAL CENTER WY