Posted: September 19th, 2022

EXERCISE 4 HMGT 400

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# Exercise #4
##################
##################
sink(“C:/UMUC/week5exercise.txt”)
# install.packages(‘dplyr’)
library(dplyr)
# Step 2: Read your data
# Pl change the location of file
hosp <- read.csv("C:/UMUC/HMGT400HOSPITAL.csv", header=T, sep = ',') #Step 3: See the variables' names names (hosp) #Step 4: Generate new variables hosp$benefit <- (hosp$total_hosp_revenue-hosp$total_hosp_cost) hosp$medicare_discharge_ratio <- (hosp$total_hospital_medicare_discharg/hosp$total_hospital_discharges)*100 hosp$medicaid_discharge_ratio <- (hosp$total_hospital_medicaid_discharg/hosp$total_hospital_discharges)*100 # Step 5; Mean selected variable summarize (hosp, bed=mean(hospital_beds, na.rm=T), member=mean(system_member, na.rm=T), cost=mean(total_hosp_cost, na.rm=T), revenue=mean(log_hosp_revenue, na.rm=T), benefit=mean(benefit, na.rm=T), medicare_ratio=mean(medicare_discharge_ratio, na.rm=T), medicaid_ratio=mean(medicaid_discharge_ratio, na.rm=T)) mean(hosp$hospital_beds) mean(hosp$system_member) mean(hosp$total_hosp_cost) mean(hosp$log_hosp_revenue) mean(hosp$benefit) mean(hosp$medicare_discharge_ratio) mean(hosp$medicaid_discharge_ratio) sd(hosp$hospital_beds) sd(hosp$system_member) sd(hosp$total_hosp_cost) sd(hosp$log_hosp_revenue) sd(hosp$benefit) sd(hosp$medicare_discharge_ratio) sd(hosp$medicaid_discharge_ratio) # Step 6; SD selected variable summarize (hosp, bed=sd(hospital_beds, na.rm=T), member=sd(system_member, na.rm=T), cost=sd(total_hosp_cost, na.rm=T), revenue=sd(log_hosp_revenue, na.rm=T), benefit=sd(benefit, na.rm=T), medicare_ratio=sd(medicare_discharge_ratio, na.rm=T), medicaid_ratio=sd(medicaid_discharge_ratio, na.rm=T)) # Step 7; N for categorical variable # Step 7a ## Bed Size ##1) <50 ##2) 51-150 ##3) 151-250 ##4) 251-350 ##5) 351-450 ##6) 451-550 ##7) 551-650 ##8) >651
table(hosp$bedsize_cat)
# Step 7b
## Ownership
## 0) non-for-profit
## 1) for profit
## 2) Public
## 3) Other
table(hosp$own)
# Cost
mytable <- table(hosp$total_hosp_cost) summary(mytable) # Revenue mytable <- table(hosp$total_hosp_revenue) summary(mytable) # system_member mytable <- table(hosp$system_member) summary(mytable) # benefit mytable <- table(hosp$benefit) summary(mytable) # total_hospital_medicare_discharg mytable <- table(hosp$total_hospital_medicare_discharg) summary(mytable) # total_hospital_medicaid_discharg mytable <- table(hosp$total_hospital_medicaid_discharg) summary(mytable) # Step 8: Generate new variables hosp$benefit <- (hosp$total_hosp_revenue-hosp$total_hosp_cost) hosp$medicare_discharge_ratio <- (hosp$total_hospital_medicare_discharg/hosp$total_hospital_discharges)*100 hosp$medicaid_discharge_ratio <- (hosp$medicaid_discharge_ratio/hosp$total_hospital_discharges)*100 # Step 9: Generate Factor variables own1 <- factor(hosp$own, levels = c(0, 1, 2, 3)) bed_cat1 <- factor(hosp$bedsize_cat, levels = c(1, 2, 3, 4, 5, 6, 7, 8)) # Step 10: run regression models # 1st Model: ## 0) non-for-profit ## 1) for profit ## 2) Public ## 3) Other # Model 1a: Using bed as a continuous variable Benefit=function(beds, ownership) y=B0+B1beds+B2FP+B3Pbl+B4Ot+e model1a <- lm(benefit ~ hospital_beds + own1, data=hosp) summary(model1a) # Model 1b: Using bed as a categorical variable model1b <- lm(benefit ~ bed_cat1 + own1, data=hosp) summary(model1b) # Model 2: model2 <- lm(benefit ~ hospital_beds + own1 + system_member, data=hosp) summary(model2) # Model 3: model3 <- lm(total_hosp_revenue ~ hospital_beds + own1 + system_member + medicare_discharge_ratio + medicaid_discharge_ratio , data=hosp) summary(model3) # You may like to look at the plot to have better understaning. plot(hosp$benefit , hosp$hospital_beds, pch = 1, cex =.5, col = "blue", main = "Figure 1. Hospital Revenues and Hospital Beds", cex.main =.8, xlab = "Hospital Revenue ($)", ylab = "Hospital Beds(#)") abline (hosp$benefit , hosp$hospital_beds) hosp_sub <- subset(hosp, hosp$benefit>0 & hosp$benefit<200000000) plot(hosp_sub$benefit , hosp_sub$hospital_beds, pch = 1, cex =.5, col = "blue", main = "Figure 2. Hospital Benfit and Hospital Beds", cex.main =.8, xlab = "Hospital Revenue ($)", ylab = "Hospital Beds(#)") abline (hosp_sub$benefit , hosp_sub$hospital_beds) # Thank you, # Dr. Zare sink()

Exercise #4

Linear Regression Model

If you have chosen to work with RStudio, please run the following model and complete the following tables.

1st Model:

Run a linear model and predict the difference between hospital beds (use the bed-tot) and hospital’s ownership on hospital net-benefit? Discuss your finding, do you think having higher beds has positive impact on the hospital net benefit? What about the ownership?

Model 1a

Hospital Characteristics

Coef.

St. Err

Hospital beds

Ownership

For Profit

Non-for profit

Other

N

R-Squared

2nd Model:

Now, estimate the impact of being a member of a system on hospital net benefit? And discuss your finding (nor more than 2 lines)? Is it significant?

Model 2

Hospital Characteristics

Coef.

St. Err

Hospital beds

Ownership

For Profit

Non-for profit

Other

Membership

System Membership

N

R-Squared

3nd Model:

Now, include the ratio of ratio-Medicare-discharge and ratio-Medicaid-discharge in your model? How do you evaluate the impact of having higher Medicare and Medicaid patients on hospital revenues?

Model 3

Hospital Characteristics

Coef.

St. Err

Hospital beds

Ownership

For Profit

Non-for profit

Other

Membership

System Membership

Socio-Economic Characteristics

Medicare discharge ratio

Medicaid discharge ratio

N

R-Squared

Based on your finding please recommend 3 policies to improve hospital performance, please make sure to use the final model for your recommendation.

Discuss your findings.

##################
# Exercise #4
##################
##################
sink(“C:/UMUC/week4exercise.txt”)
# install.packages(‘dplyr’)
# library(dplyr)
# Step 2: Read your data
# Pl change the location of file
hosp <- read.csv("C:/UMUC/HMGT400HOSPITAL.csv", header=T, sep = ',') #Step 3: See the variables' names names (hosp) #Step 4: Generate new variables hosp$benefit <- (hosp$total_hosp_revenue-hosp$total_hosp_cost) hosp$medicare_discharge_ratio <- (hosp$total_hospital_medicare_discharg/hosp$total_hospital_discharges)*100 hosp$medicaid_discharge_ratio <- (hosp$total_hospital_medicaid_discharg/hosp$total_hospital_discharges)*100 # Step 5; Mean selected variable # summarize (hosp, bed=mean(hospital_beds, na.rm=T), #member=mean(system_member, na.rm=T), # cost=mean(total_hosp_cost, na.rm=T), # revenue=mean(log_hosp_revenue, na.rm=T), # benefit=mean(benefit, na.rm=T), # medicare_ratio=mean(medicare_discharge_ratio, na.rm=T), # medicaid_ratio=mean(medicaid_discharge_ratio, na.rm=T)) mean(hosp$hospital_beds, na.rm=T) mean(hosp$system_member, na.rm=T) mean(hosp$total_hosp_cost, na.rm=T) mean(hosp$log_hosp_revenue, na.rm=T) mean(hosp$benefit, na.rm=T) mean(hosp$medicare_discharge_ratio, na.rm=T) mean(hosp$medicaid_discharge_ratio, na.rm=T) sd(hosp$hospital_beds, na.rm=T) sd(hosp$system_member, na.rm=T) sd(hosp$total_hosp_cost, na.rm=T) sd(hosp$log_hosp_revenue, na.rm=T) sd(hosp$benefit, na.rm=T) sd(hosp$medicare_discharge_ratio, na.rm=T) sd(hosp$medicaid_discharge_ratio, na.rm=T) # Step 6; SD selected variable #summarize (hosp, bed=sd(hospital_beds, na.rm=T), # member=sd(system_member, na.rm=T), # cost=sd(total_hosp_cost, na.rm=T), # revenue=sd(log_hosp_revenue, na.rm=T), # benefit=sd(benefit, na.rm=T), # medicare_ratio=sd(medicare_discharge_ratio, na.rm=T), # medicaid_ratio=sd(medicaid_discharge_ratio, na.rm=T)) # Step 7: Generate new variables #hosp$benefit <- (hosp$total_hosp_revenue-hosp$total_hosp_cost) #hosp$medicare_discharge_ratio <- (hosp$total_hospital_medicare_discharg/hosp$total_hospital_discharges)*100 #hosp$medicaid_discharge_ratio <- (hosp$medicaid_discharge_ratio/hosp$total_hospital_discharges)*100 # Step 8; N for categorical variable # Step 8a ## Bed Size ##1) <50 ##2) 51-150 ##3) 151-250 ##4) 251-350 ##5) 351-450 ##6) 451-550 ##7) 551-650 ##8) >651
table(hosp$bedsize_cat)
# Step 8b
## Ownership
## 0) non-for-profit
## 1) for profit
## 2) Public
## 3) Other
table(hosp$own)
mytable <- table(hosp$total_hosp_cost) summary(mytable) # 10-7 mytable <- table(hosp$total_hosp_revenue) summary(mytable) # 10-12 mytable <- table(hosp$benefit) summary(mytable) # 10-13 mytable <- table(hosp$medicare_discharge_ratio) summary(mytable) mytable <- table(hosp$medicaid_discharge_ratio) summary(mytable) # Step 9: Generate Factor variables own1 <- factor(hosp$own, levels = c(0, 1, 2, 3)) bed_cat1 <- factor(hosp$bedsize_cat, levels = c(1, 2, 3, 4, 5, 6, 7, 8)) # Step 10: run regression models # 1st Model: # Model 1a: Using bed as a continuous variable model1a <- lm(benefit ~ hospital_beds + own1, data=hosp) summary(model1a) # Model 1b: Using bed as a categorical variable model1b <- lm(benefit ~ bed_cat1 + own1, data=hosp) summary(model1b) # Model 2: model2 <- lm(benefit ~ hospital_beds + own1 + system_member, data=hosp) summary(model2) # Model 3: model3 <- lm(total_hosp_revenue ~ hospital_beds + own1 + system_member + medicare_discharge_ratio + medicaid_discharge_ratio , data=hosp) summary(model3) # You may like to look at the plot to have better understaning. plot(hosp$benefit , hosp$hospital_beds, pch = 1, cex =.5, col = "blue", main = "Figure 1. Hospital Revenues and Hospital Beds", cex.main =.8, xlab = "Hospital Revenue ($)", ylab = "Hospital Beds(#)") abline (hosp$benefit , hosp$hospital_beds) hosp_sub <- subset(hosp, hosp$benefit>0 & hosp$benefit<200000000) plot(hosp_sub$benefit , hosp_sub$hospital_beds, pch = 1, cex =.5, col = "blue", main = "Figure 2. Hospital Benfit and Hospital Beds", cex.main =.8, xlab = "Hospital Revenue ($)", ylab = "Hospital Beds(#)") abline (hosp_sub$benefit , hosp_sub$hospital_beds) # Thank you, # Dr. Zare sink()

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