Radiology Workflow Project
Evan Willoughby
Last compiled on May 31, 2024 at 10:01 AM - CDT
1 Load Packages
Make sure to have the necessary packages installed on your computer.
library(readxl)2 Read in Data
We are going to read in the data from the Radiology Workflow from the hospital for the years 2016-2017.
dat <- read.csv('https://raw.githubusercontent.com/tmatis12/datafiles/main/RadDat_IMSE.csv')
head(dat)## Unique.Identifier PatientAge Radiology.Technician
## 1 1 75 65
## 2 2 87 65
## 3 3 35 16
## 4 4 51 24
## 5 5 67 37
## 6 6 54 7
## CatalogCode Ordering.Physician PatientTypeMnemonic
## 1 DX Abdomen 2 vw w/single chest 4 IP
## 2 DX Abdomen 2 vw w/single chest 4 IP
## 3 DX Abdomen 2 vw w/single chest 150 IP
## 4 DX Abdomen 2 vw w/single chest 130 IP
## 5 DX Abdomen 2 vw w/single chest 173 IP
## 6 DX Abdomen 2 vw w/single chest 349 IP
## Priority OrderDateTime ExamCompleteDateTime FinalDateTime
## 1 Routine 12/27/16 10:32 12/27/16 11:19 12/28/16 14:32
## 2 Routine 1/13/17 11:44 1/13/17 12:32 1/14/17 16:00
## 3 Routine 1/2/17 17:19 1/2/17 18:00 1/3/17 7:44
## 4 Routine 11/13/16 10:13 11/14/16 9:34 11/14/16 16:40
## 5 STAT 12/13/16 3:22 12/13/16 4:04 12/13/16 3:19
## 6 Routine 1/17/17 5:38 1/17/17 7:47 1/17/17 10:55
## Ordered.to.Complete...Mins Ordered.to.Complete...Hours Loc.At.Exam.Complete
## 1 47 0.7833333 GTU
## 2 48 0.8000000 GTU
## 3 41 0.6833333 3W
## 4 1401 23.3500000 4W
## 5 42 0.7000000 Emergency Ctr
## 6 129 2.1500000 3E
## Exam.Completed.Bucket Section Exam.Room
## 1 8a-8p DX DX Rm 1
## 2 8a-8p DX DX Rm 1
## 3 8a-8p EC DX DX Rm 5 (EC)
## 4 8a-8p DX DX Rm 1
## 5 12a-8a EC DX DX Rm 5 (EC)
## 6 12a-8a DX DX Portable2.1 Select Columns
Select only the columns related to the patient Age, Radiology
Technician, Priority, Time to Complete(min), and Location of
Patient.
dat2 <- subset(dat,select=c("PatientAge", 'Radiology.Technician', 'Priority','Ordered.to.Complete...Mins','Loc.At.Exam.Complete'))
colnames(dat2) <- c('PatientAge', 'RadTech', 'Priority', 'CompletionTime', 'Location')
head(dat2)## PatientAge RadTech Priority CompletionTime Location
## 1 75 65 Routine 47 GTU
## 2 87 65 Routine 48 GTU
## 3 35 16 Routine 41 3W
## 4 51 24 Routine 1401 4W
## 5 67 37 STAT 42 Emergency Ctr
## 6 54 7 Routine 129 3E2.1.1 Convert to Factors
Now we want to convert some of the variables to factors
dat2$RadTech <- as.factor(dat2$RadTech)
dat2$Priority <- as.factor(dat2$Priority)
dat2$Location <- as.factor(dat2$Location)
str(dat2)## 'data.frame': 43632 obs. of 5 variables:
## $ PatientAge : int 75 87 35 51 67 54 34 65 67 40 ...
## $ RadTech : Factor w/ 72 levels "2","3","4","5",..: 63 63 15 23 36 6 38 1 1 33 ...
## $ Priority : Factor w/ 2 levels "Routine","STAT": 1 1 1 1 2 1 1 1 1 1 ...
## $ CompletionTime: int 47 48 41 1401 42 129 42 1068 49 47 ...
## $ Location : Factor w/ 25 levels "3E","3W","4E",..: 16 16 2 4 14 1 1 1 7 2 ...2.1.1.1
2.2 Select Rows
dat2 <- dat2[dat2$Location=='3W',]
head(dat2)## PatientAge RadTech Priority CompletionTime Location
## 3 35 16 Routine 41 3W
## 10 40 34 Routine 47 3W
## 15 71 57 Routine 1214 3W
## 17 39 23 Routine 90 3W
## 18 66 23 Routine 90 3W
## 20 53 9 Routine 80 3W3 Data Visualization
Now lets visualize the data on 3W
3.1 Histogram of Completion Time
The histogram on 3W has many outliers
hist(dat2$CompletionTime,main='Histogram of Completion Time on 3W', xlab='minutes', ylab='count',col='blue')
3.2 Plot of Age by Completion Time
A plot of patient age by completion time is given below
plot(dat2$PatientAge, dat2$CompletionTime, main='Plot Age by Completiomn Time', xlab='Age', ylab='Time(min)', col='red')
4 Descriptive Statistics
Generate some descriptive statistics for 3W
4.1 Mean Patient Age
The mean patient age is approximately 52 years
mean(dat2$PatientAge)## [1] 52.097694.2 Summary of Completion Times
dat3 <- dat2[dat2$CompletionTime>0,]
summary(dat3$CompletionTime)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.0 38.0 70.0 1463.1 402.5 29637.0Note that the completion time has outliers. Filter data such that only completion times less than a day are considered.
dat4 <- dat3[dat3$CompletionTime<24*60,]
summary(dat4$CompletionTime)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2 34 56 152 113 14065 Complete Code
library(readxl)
dat <- read.csv('https://raw.githubusercontent.com/tmatis12/datafiles/main/RadDat_IMSE.csv')
head(dat)
dat2 <- subset(dat,select=c("PatientAge", 'Radiology.Technician', 'Priority','Ordered.to.Complete...Mins','Loc.At.Exam.Complete'))
colnames(dat2) <- c('PatientAge', 'RadTech', 'Priority', 'CompletionTime', 'Location')
head(dat2)
dat2$RadTech <- as.factor(dat2$RadTech)
dat2$Priority <- as.factor(dat2$Priority)
dat2$Location <- as.factor(dat2$Location)
str(dat2)
dat2 <- dat[dat$Location=='3W',]
hist(dat2$CompletionTime,main='Histogram of Completion Time on 3W', xlab='minutes', ylab='count',col='blue')
plot(dat$PatientAge, dat$CompletionTime, main='Plot Age by Completiomn Time', xlab='Age', ylab='Time(min)', col='red')
mean(dat2$PatientAge)
summary(dat3$CompletionTime)
dat4 <- dat3[dat3$CompletionTime<24*60,]
summary(dat4$CompletionTime)