Hospital Quality Analysis
Shambhavi Malik
25/08/2020
Synopsis
We analyse the Hospital Data of US and analyse it to rank the hospitals according to states and overall.
Data Processing
The zip file containing the data for this project can be downloaded here:
The data for this project comes from the Hospital Compare web site 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 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 project. The zip file for this assignment contains three files:
• outcome-of-care-measures.csv: Contains information about 30-day mortality and readmission rates for heart attacks, heart failure, and pneumonia for over 4,000 hospitals. • hospital-data.csv: Contains information about each hospital. • Hospital_Revised_Flatfiles.pdf: Descriptions of the variables in each file (i.e the code book).
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 project. We 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).
We first need to unzip this file in the current working directory and read it using read.csv().
unzip("hospital_data.zip")## Warning in unzip("hospital_data.zip"): error 1 in extracting from zip fileoutcome<-read.csv("outcome-of-care-measures.csv",colClasses = "character")Then we analyse the dataset and look at its attributes and first few entries.
dim(outcome)## [1] 4706 46head(outcome)## Provider.Number Hospital.Name Address.1
## 1 010001 SOUTHEAST ALABAMA MEDICAL CENTER 1108 ROSS CLARK CIRCLE
## 2 010005 MARSHALL MEDICAL CENTER SOUTH 2505 U S HIGHWAY 431 NORTH
## 3 010006 ELIZA COFFEE MEMORIAL HOSPITAL 205 MARENGO STREET
## 4 010007 MIZELL MEMORIAL HOSPITAL 702 N MAIN ST
## 5 010008 CRENSHAW COMMUNITY HOSPITAL 101 HOSPITAL CIRCLE
## 6 010010 MARSHALL MEDICAL CENTER NORTH 8000 ALABAMA HIGHWAY 69
## Address.2 Address.3 City State ZIP.Code County.Name Phone.Number
## 1 DOTHAN AL 36301 HOUSTON 3347938701
## 2 BOAZ AL 35957 MARSHALL 2565938310
## 3 FLORENCE AL 35631 LAUDERDALE 2567688400
## 4 OPP AL 36467 COVINGTON 3344933541
## 5 LUVERNE AL 36049 CRENSHAW 3343353374
## 6 GUNTERSVILLE AL 35976 MARSHALL 2565718000
## Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack
## 1 14.3
## 2 18.5
## 3 18.1
## 4 Not Available
## 5 Not Available
## 6 Not Available
## Comparison.to.U.S..Rate...Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack
## 1 No Different than U.S. National Rate
## 2 No Different than U.S. National Rate
## 3 No Different than U.S. National Rate
## 4 Number of Cases Too Small
## 5 Number of Cases Too Small
## 6 Number of Cases Too Small
## Lower.Mortality.Estimate...Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack
## 1 12.1
## 2 14.7
## 3 14.8
## 4 Not Available
## 5 Not Available
## 6 Not Available
## Upper.Mortality.Estimate...Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack
## 1 17.0
## 2 23.0
## 3 21.8
## 4 Not Available
## 5 Not Available
## 6 Not Available
## Number.of.Patients...Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack
## 1 666
## 2 44
## 3 329
## 4 14
## 5 9
## 6 22
## Footnote...Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack
## 1
## 2
## 3
## 4 number of cases is too small (fewer than 25) to reliably tell how well the hospital is performing
## 5 number of cases is too small (fewer than 25) to reliably tell how well the hospital is performing
## 6 number of cases is too small (fewer than 25) to reliably tell how well the hospital is performing
## Hospital.30.Day.Death..Mortality..Rates.from.Heart.Failure
## 1 11.4
## 2 15.2
## 3 11.3
## 4 13.6
## 5 13.8
## 6 12.5
## Comparison.to.U.S..Rate...Hospital.30.Day.Death..Mortality..Rates.from.Heart.Failure
## 1 No Different than U.S. National Rate
## 2 Worse than U.S. National Rate
## 3 No Different than U.S. National Rate
## 4 No Different than U.S. National Rate
## 5 No Different than U.S. National Rate
## 6 No Different than U.S. National Rate
## Lower.Mortality.Estimate...Hospital.30.Day.Death..Mortality..Rates.from.Heart.Failure
## 1 9.5
## 2 12.2
## 3 9.1
## 4 10.0
## 5 9.9
## 6 9.9
## Upper.Mortality.Estimate...Hospital.30.Day.Death..Mortality..Rates.from.Heart.Failure
## 1 13.7
## 2 18.8
## 3 13.9
## 4 18.2
## 5 18.7
## 6 15.6
## Number.of.Patients...Hospital.30.Day.Death..Mortality..Rates.from.Heart.Failure
## 1 741
## 2 234
## 3 523
## 4 113
## 5 53
## 6 163
## Footnote...Hospital.30.Day.Death..Mortality..Rates.from.Heart.Failure
## 1
## 2
## 3
## 4
## 5
## 6
## Hospital.30.Day.Death..Mortality..Rates.from.Pneumonia
## 1 10.9
## 2 13.9
## 3 13.4
## 4 14.9
## 5 15.8
## 6 8.7
## Comparison.to.U.S..Rate...Hospital.30.Day.Death..Mortality..Rates.from.Pneumonia
## 1 No Different than U.S. National Rate
## 2 No Different than U.S. National Rate
## 3 No Different than U.S. National Rate
## 4 No Different than U.S. National Rate
## 5 No Different than U.S. National Rate
## 6 Better than U.S. National Rate
## Lower.Mortality.Estimate...Hospital.30.Day.Death..Mortality..Rates.from.Pneumonia
## 1 8.6
## 2 11.3
## 3 11.2
## 4 11.6
## 5 11.4
## 6 6.8
## Upper.Mortality.Estimate...Hospital.30.Day.Death..Mortality..Rates.from.Pneumonia
## 1 13.7
## 2 17.0
## 3 15.8
## 4 19.0
## 5 21.5
## 6 11.0
## Number.of.Patients...Hospital.30.Day.Death..Mortality..Rates.from.Pneumonia
## 1 371
## 2 372
## 3 836
## 4 239
## 5 61
## 6 315
## Footnote...Hospital.30.Day.Death..Mortality..Rates.from.Pneumonia
## 1
## 2
## 3
## 4
## 5
## 6
## Hospital.30.Day.Readmission.Rates.from.Heart.Attack
## 1 19.0
## 2 Not Available
## 3 17.8
## 4 Not Available
## 5 Not Available
## 6 Not Available
## Comparison.to.U.S..Rate...Hospital.30.Day.Readmission.Rates.from.Heart.Attack
## 1 No Different than U.S. National Rate
## 2 Number of Cases Too Small
## 3 No Different than U.S. National Rate
## 4 Number of Cases Too Small
## 5 Number of Cases Too Small
## 6 Number of Cases Too Small
## Lower.Readmission.Estimate...Hospital.30.Day.Readmission.Rates.from.Heart.Attack
## 1 16.6
## 2 Not Available
## 3 14.9
## 4 Not Available
## 5 Not Available
## 6 Not Available
## Upper.Readmission.Estimate...Hospital.30.Day.Readmission.Rates.from.Heart.Attack
## 1 21.7
## 2 Not Available
## 3 21.5
## 4 Not Available
## 5 Not Available
## 6 Not Available
## Number.of.Patients...Hospital.30.Day.Readmission.Rates.from.Heart.Attack
## 1 728
## 2 21
## 3 342
## 4 1
## 5 4
## 6 13
## Footnote...Hospital.30.Day.Readmission.Rates.from.Heart.Attack
## 1
## 2 number of cases is too small (fewer than 25) to reliably tell how well the hospital is performing
## 3
## 4 number of cases is too small (fewer than 25) to reliably tell how well the hospital is performing
## 5 number of cases is too small (fewer than 25) to reliably tell how well the hospital is performing
## 6 number of cases is too small (fewer than 25) to reliably tell how well the hospital is performing
## Hospital.30.Day.Readmission.Rates.from.Heart.Failure
## 1 23.7
## 2 22.5
## 3 19.8
## 4 27.1
## 5 24.7
## 6 23.9
## Comparison.to.U.S..Rate...Hospital.30.Day.Readmission.Rates.from.Heart.Failure
## 1 No Different than U.S. National Rate
## 2 No Different than U.S. National Rate
## 3 Better than U.S. National Rate
## 4 No Different than U.S. National Rate
## 5 No Different than U.S. National Rate
## 6 No Different than U.S. National Rate
## Lower.Readmission.Estimate...Hospital.30.Day.Readmission.Rates.from.Heart.Failure
## 1 21.3
## 2 19.2
## 3 17.2
## 4 22.4
## 5 19.9
## 6 20.1
## Upper.Readmission.Estimate...Hospital.30.Day.Readmission.Rates.from.Heart.Failure
## 1 26.5
## 2 26.1
## 3 22.9
## 4 31.9
## 5 30.2
## 6 28.2
## Number.of.Patients...Hospital.30.Day.Readmission.Rates.from.Heart.Failure
## 1 891
## 2 264
## 3 614
## 4 135
## 5 59
## 6 173
## Footnote...Hospital.30.Day.Readmission.Rates.from.Heart.Failure
## 1
## 2
## 3
## 4
## 5
## 6
## Hospital.30.Day.Readmission.Rates.from.Pneumonia
## 1 17.1
## 2 17.6
## 3 16.9
## 4 19.4
## 5 18.0
## 6 18.7
## Comparison.to.U.S..Rate...Hospital.30.Day.Readmission.Rates.from.Pneumonia
## 1 No Different than U.S. National Rate
## 2 No Different than U.S. National Rate
## 3 No Different than U.S. National Rate
## 4 No Different than U.S. National Rate
## 5 No Different than U.S. National Rate
## 6 No Different than U.S. National Rate
## Lower.Readmission.Estimate...Hospital.30.Day.Readmission.Rates.from.Pneumonia
## 1 14.4
## 2 15.0
## 3 14.7
## 4 15.9
## 5 14.0
## 6 15.7
## Upper.Readmission.Estimate...Hospital.30.Day.Readmission.Rates.from.Pneumonia
## 1 20.4
## 2 20.6
## 3 19.5
## 4 23.2
## 5 22.8
## 6 22.2
## Number.of.Patients...Hospital.30.Day.Readmission.Rates.from.Pneumonia
## 1 400
## 2 374
## 3 842
## 4 254
## 5 56
## 6 326
## Footnote...Hospital.30.Day.Readmission.Rates.from.Pneumonia
## 1
## 2
## 3
## 4
## 5
## 6names(outcome)## [1] "Provider.Number"
## [2] "Hospital.Name"
## [3] "Address.1"
## [4] "Address.2"
## [5] "Address.3"
## [6] "City"
## [7] "State"
## [8] "ZIP.Code"
## [9] "County.Name"
## [10] "Phone.Number"
## [11] "Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack"
## [12] "Comparison.to.U.S..Rate...Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack"
## [13] "Lower.Mortality.Estimate...Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack"
## [14] "Upper.Mortality.Estimate...Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack"
## [15] "Number.of.Patients...Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack"
## [16] "Footnote...Hospital.30.Day.Death..Mortality..Rates.from.Heart.Attack"
## [17] "Hospital.30.Day.Death..Mortality..Rates.from.Heart.Failure"
## [18] "Comparison.to.U.S..Rate...Hospital.30.Day.Death..Mortality..Rates.from.Heart.Failure"
## [19] "Lower.Mortality.Estimate...Hospital.30.Day.Death..Mortality..Rates.from.Heart.Failure"
## [20] "Upper.Mortality.Estimate...Hospital.30.Day.Death..Mortality..Rates.from.Heart.Failure"
## [21] "Number.of.Patients...Hospital.30.Day.Death..Mortality..Rates.from.Heart.Failure"
## [22] "Footnote...Hospital.30.Day.Death..Mortality..Rates.from.Heart.Failure"
## [23] "Hospital.30.Day.Death..Mortality..Rates.from.Pneumonia"
## [24] "Comparison.to.U.S..Rate...Hospital.30.Day.Death..Mortality..Rates.from.Pneumonia"
## [25] "Lower.Mortality.Estimate...Hospital.30.Day.Death..Mortality..Rates.from.Pneumonia"
## [26] "Upper.Mortality.Estimate...Hospital.30.Day.Death..Mortality..Rates.from.Pneumonia"
## [27] "Number.of.Patients...Hospital.30.Day.Death..Mortality..Rates.from.Pneumonia"
## [28] "Footnote...Hospital.30.Day.Death..Mortality..Rates.from.Pneumonia"
## [29] "Hospital.30.Day.Readmission.Rates.from.Heart.Attack"
## [30] "Comparison.to.U.S..Rate...Hospital.30.Day.Readmission.Rates.from.Heart.Attack"
## [31] "Lower.Readmission.Estimate...Hospital.30.Day.Readmission.Rates.from.Heart.Attack"
## [32] "Upper.Readmission.Estimate...Hospital.30.Day.Readmission.Rates.from.Heart.Attack"
## [33] "Number.of.Patients...Hospital.30.Day.Readmission.Rates.from.Heart.Attack"
## [34] "Footnote...Hospital.30.Day.Readmission.Rates.from.Heart.Attack"
## [35] "Hospital.30.Day.Readmission.Rates.from.Heart.Failure"
## [36] "Comparison.to.U.S..Rate...Hospital.30.Day.Readmission.Rates.from.Heart.Failure"
## [37] "Lower.Readmission.Estimate...Hospital.30.Day.Readmission.Rates.from.Heart.Failure"
## [38] "Upper.Readmission.Estimate...Hospital.30.Day.Readmission.Rates.from.Heart.Failure"
## [39] "Number.of.Patients...Hospital.30.Day.Readmission.Rates.from.Heart.Failure"
## [40] "Footnote...Hospital.30.Day.Readmission.Rates.from.Heart.Failure"
## [41] "Hospital.30.Day.Readmission.Rates.from.Pneumonia"
## [42] "Comparison.to.U.S..Rate...Hospital.30.Day.Readmission.Rates.from.Pneumonia"
## [43] "Lower.Readmission.Estimate...Hospital.30.Day.Readmission.Rates.from.Pneumonia"
## [44] "Upper.Readmission.Estimate...Hospital.30.Day.Readmission.Rates.from.Pneumonia"
## [45] "Number.of.Patients...Hospital.30.Day.Readmission.Rates.from.Pneumonia"
## [46] "Footnote...Hospital.30.Day.Readmission.Rates.from.Pneumonia"Plot of the 30-day mortality rates for heart attack
To make a simple histogram of the 30-day death rates from heart attack (column 11 in the outcome dataset).
outcome[, 11] <- as.numeric(outcome[, 11])## Warning: NAs introduced by coercionhist(outcome[, 11],col = "salmon")
Because we originally read the data in as character (by specifying colClasses = “character”) we need to coerce the column to be numeric.
Finding the best hospital in a state
This function called best takes 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 are 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 are sorted in alphabetical order and the first hospital in that set is chosen (i.e. if hospitals “b”, “c”, and “f” are tied for best, then hospital “b” should be returned).
The function also checks 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”.
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
outcome_data <- read.csv("outcome-of-care-measures.csv", colClasses = "character")
if (!state %in% outcome_data$State)
{message("invalid state")}
else if(outcome == "heart attack") {
col <- 11
}
else if (outcome == "heart failure") {
col <- 17
}
else if (outcome == "pneumonia") {
col <- 23
}
else{stop("invalid outcome")}
sub<-subset(outcome_data,State==state)
minmort<-min(as.numeric(sub[,col]),na.rm = TRUE)
lowestmort<-subset(sub,as.numeric(sub[,col])==minmort)
output<-lowestmort[,"Hospital.Name"]
return(output[order(output)])
}Ranking hospitals by outcome in a state
This 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 are 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 are broken by using the hospital name.
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”.
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
#Read the data
data <- read.csv("outcome-of-care-measures.csv", colClasses = "character")
#Create a database for use
fd <- as.data.frame(cbind(data[, 2], #Hospital name
data[, 7], #State
data[, 11], #heart attack
data[, 17], #heart failure
data[, 23]), #Pneumonia
stringsAsFactors = FALSE)
colnames(fd) <- c("hospital", "state", "heart attack", "heart failure", "pneumonia")
#Check whether the state value is valid
if (!state %in% fd[, "state"]) {
stop("invalid state")
} #Check whether the outcome is valid
else if (!outcome %in% c("heart attack", "pneumonia", "heart failure")) {
stop("invalid outcome")
} #Check whether the rank value is numeric
else if (is.numeric(num)) {
#Extract the data for the called state and outcome
si <- which(fd[, "state"] == state)
ts <- fd[si, ]
ts[, eval(outcome)] <- as.numeric(ts[, eval(outcome)])
#Sort the data frame
ts <- ts[order(ts[, eval(outcome)], ts[, "hospital"], na.last = NA),]
#Check whether the called rank exceeds the record number
output <- ts[, "hospital"][num]}
else if (!is.numeric(num)) {
if (num == "best") {
output <- best(state, outcome)}
else if (num == "worst") {
si <- which(fd[, "state"] == state)
ts <- fd[si, ]
ts[, eval(outcome)] <- as.numeric(ts[, eval(outcome)])
ts <- ts[order(ts[, eval(outcome)], ts[, "hospital"], na.last = NA, decreasing = TRUE), ]
output <- ts[, "hospital"][1]
}
else {
stop("invalid rank")}
}
return (output)
}Ranking hospitals in all states
This 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 checks 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.
rankall <- function(outcome, num = "best") {
#Load the data
data <- read.csv("outcome-of-care-measures.csv",
colClasses = "character")
#Check whether the outcome value is valid
if (!(outcome %in% c("heart attack", "heart failure",
"pneumonia"))){
stop ("invalid outcome")}
if (is.numeric(num) == TRUE) {
if (length(data[,2]) < num) {return(NA)}
}
colnum <-
if (outcome == "heart failure") {17}
else if (outcome == "heart attack") {11}
else {23}
data[, colnum] <- as.numeric(data[, colnum])
data[, 2] <- as.character(data[, 2])
#Create an empty vector
output <- vector()
#Get a complete list of states
states <- levels(factor(data[, 7]))
for (i in 1:length(states)) {
statedata <- subset(data, State == states[i])
selected_columns <- as.numeric(statedata[, colnum])
statedata <- statedata[!(is.na(selected_columns)), ]
orderdata <- statedata[order(statedata[, colnum],
statedata[, 2]), ]
if (num == "best") {
ranknum = 1
}else if (num == "worst") {
ranknum = nrow(orderdata)
}else {
ranknum = num
}
hospital <- orderdata[ranknum, 2]
output <- append(output, c(hospital, states[i]))}
#Convert the output from vector to data frame
output <- as.data.frame(matrix(output, length(states),
2, byrow = TRUE))
colnames(output) <- c("hospital", "state")
rownames(output) <- states
return(output)
}