Welcome, thank you for visiting my website. I am a passionate revenue cycle analyst with a strong focus on communication and tangible results. Below are some samples of my revenue cycle analysis, data visualization and source code using both Python and R programming languages.



SOURCE CODE

import matplotlib.pyplot as plt


year = [2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023]


CIGNA = [ 29,24,18,23,15,17,10,8,6]

HUMANA = [ 32,26,20,25,16,19,14,10,8]

AETNA = [ 35,28,22,26,19,21,17,12,9 ]


plt.plot ([],[], color='b', label = 'CIGNA')

plt.plot ([],[], color='y', label = 'HUMANA')

plt.plot ([],[], color='g', label = 'AETNA')


plt.stackplot(year, CIGNA, HUMANA, AETNA, colors = ['b', 'y', 'g'])


plt.legend()

plt.title('Claim Adjustment Reduction')

plt.xlabel('year')

plt.ylabel('Millions')

plt.show()



SOURCE CODE

import matplotlib.pyplot as plt

import numpy as np


x = np.array(["M54", "M64", "MA33", "MA41"])

y = np.array([30, 48, 17, 37])


plt.barh(x, y, color = '#af4cab', height = 0.3)

plt.title("Remittance Advice Remark Codes")

plt.xlabel("Denials Per Thousand Claims")


plt.show()



SOURCE CODE

import matplotlib.pyplot as plt


tob_countA = [73, 76, 71, 85, 74, 80, 89, 77, 83, 79, 85, 80]

cab_countB = [58, 61, 56, 68, 59,65, 71, 62, 67, 63, 69, 65]

month = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]


plt.plot(month, tob_countA, 'green')

plt.plot(month, cab_countB, 'blue')


plt.xlabel("Month")

plt.ylabel("Revenue (Thousands)")


plt.title("Monthly Revenue")

plt.legend(['Billed Amount', 'Reimbursement'])

plt.grid(axis = 'y')


plt.show()




SOURCE CODE

x <- c(10,20,30,40)

pie(x, init.angle = 90)


mylabel <- c("Cigna", "Humana", "Aetna", "Molina")

colors <- c("lightblue", "orangered", "seagreen", "purple4")


pie(x, label = mylabel, main = "Insurance Payors", col = colors)

legend("bottomright", mylabel, fill = colors)



SOURCE CODE

import matplotlib.pyplot as plt

import matplotlib.pyplot as plt


x = np.array(["CO 16", "CO 11", "CO 22", "CO B9", "CO 41", "CO 29",

"CO 97", "CO 16"])

y = np.array([30, 18, 21, 12, 35, 27, 12, 28])


plt.bar(x, y, width = 0.3, color = "#4CAF50")


plt.title("Denial Root Cause")

plt.xlabel("Denial Code")

plt.ylabel("Claims Per Thousand")


plt.show()




SOURCE CODE

import matplotlib.pyplot as plt

import numpy as np


x = np.array([5,7,8,2,17,10,9,4,11,12])

y = np.array([32,40,52,48,50,26,9,27,27,45])


plt.scatter(x, y, color = '#fe2038')


font1 = {'family':'arial','color':'black','size':13}

font2 = {'family':'arial','color':'black','size':13}


plt.grid()


plt.title("Pre-Adjudication Rejections", loc = 'center', fontdict = font1)

plt.xlabel("Electronic Submission Batches", loc = 'center', fontdict = font2)

plt.ylabel("Rejected Claims Per Thousand", loc = 'center', fontdict = font2)


plt.show()



SOURCE CODE

import matplotlib.pyplot as plt


labels = 'Commercial', 'Medicaid', 'Medicare', 'Other'

sizes = [15, 30, 45, 10]


fig1, ax1 = plt.subplots()

ax1.pie(sizes, labels=labels, autopct='%1.1f%%',

startangle=90)

ax1.axis('equal')


plt.show()



SOURCE CODE

colnames <- c("Q1","Q2","Q3","Q4")

rowname <- c("Income","Expenses","Profit")

ratio <- matrix(c(10,6,4,14,8,6,16,11,5,12,7,5),

c(3),

byrow=FALSE)


colnames(ratio) <- colnames

rownames(ratio) <- rowname

barplot(ratio,

beside=TRUE,

main="Income Statement",

xlab = "Business Quarters",

ylab = "Millions",

col=c("springgreen4","purple4","orangered"),

border="black")


legend(x = "topright", box.col = "black",

bg ="white", box.lwd = 3,

legend=c("Income", "Expenses", "Profit"),

fill = c("springgreen4","purple4","orangered"))