Radiology Workflow Project
Siddharth Uttekar
Last compiled on May 31, 2024 at 8:15 PM - IST
1 Load Packages
Be sure to have the package readxl 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 year 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 and location of patient
dat<-subset(dat,select=c("PatientAge","Radiology.Technician","Priority","Ordered.to.Complete...Mins","Loc.At.Exam.Complete"))
colnames(dat)<-c("PatientAge","Radtech","Priority","Completion Time","Location")
head(dat)## PatientAge Radtech Priority Completion Time 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
Convert some of the variables to factors
dat$Radtech<-as.factor(dat$Radtech)
dat$Priority<-as.factor(dat$Priority)
dat$Location<-as.factor(dat$Location)
str(dat)## '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 ...
## $ Completion Time: 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 ...2.2 Select Rows
Keep only those observations on 3 West
dat1<-dat[dat$Location=="3W",]
head(dat1)## PatientAge Radtech Priority Completion Time Location
## 3 35 16 Routine 41 3W
## 10 40 34 Routine 47 3W
## 15 71 57 Routine 1214 3W
## 17 39 23 Routine 90 3W
## 18 66 23 Routine 90 3W
## 20 53 9 Routine 80 3W3 Data Visualization
Now let us visualize the data for 3W
3.1 Histogram of Completion Time
The histogram for 3W has many outliers
hist(dat1$`Completion Time`,main="Histogram of Completion time on 3W",xlab="in minutes",ylab="Count",col="blue")
3.2 Plot of age by Completion time
A plot of completion time with age is given below
plot(dat$PatientAge,dat$`Completion Time`,xlab="Age",ylab="Time in minutes",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(dat1$PatientAge)## [1] 52.097694.2 Summary of Completion Time
summary(dat1$`Completion Time`)## 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 time less than a day are considered
dat2<-dat1[dat1$`Completion Time`<24*60,]
summary(dat2$`Completion Time`)## 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)
dat<-subset(dat,select=c("PatientAge","Radiology.Technician","Priority","Ordered.to.Complete...Mins","Loc.At.Exam.Complete"))
colnames(dat)<-c("PatientAge","Radtech","Priority","Completion Time","Location")
head(dat)
dat$Radtech<-as.factor(dat$Radtech)
dat$Priority<-as.factor(dat$Priority)
dat$Location<-as.factor(dat$Location)
str(dat)
dat1<-dat[dat$Location=="3W",]
head(dat1)
hist(dat1$`Completion Time`,main="Histogram of Completion time on 3W",xlab="in minutes",ylab="Count",col="blue")
plot(dat$PatientAge,dat$`Completion Time`,xlab="Age",ylab="Time in minutes",col="red")
mean(dat1$PatientAge)
summary(dat1$`Completion Time`)
dat2<-dat1[dat1$`Completion Time`<24*60,]
summary(dat2$`Completion Time`)