Data-driven Medicare Fraud Prediction

Posted on May 11, 2022

The skills the authors demonstrated here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.


Project Information

David completed 'Machine Learning with R, Intermediate Level', with Vivian Zhang at the NYC Data Science Academy, an intensive course from Oct-Nov, 2014.

  • Contributed by David Russo
  • This post is based on a final project submission, after having presented findings at a Demo Day on Dec 4th, 2014.


  • Demo Day Video


Doctor, are you going to finance it or shall I just bill Medicare? (John Louthan)

Doctor, are you going to finance it or shall I just bill Medicare? -John Louthan


Perpetrators of medicare fraud pick our very own pockets by steeling our tax dollars from our government's medicare system, thereby crippling our country's health care system. In 2009 the government developed a task force "to crack down on the people and organizations who abuse the system and cost Americans billions of dollars each year." The task force has successfully convicted more than 2,000 people of fraudulently billing Medicare for over $6 billion, but there are many more to be found, and analyzing the data is one step in the process.

To that end, the Centers for Medicare & Medicaid Services (CMS) has made a data set publicly available that contains "information on services and procedures provided to Medicare beneficiaries by physicians and other healthcare professionals." It is from the data in this set, covering all Medicare billings in the year 2012, that this project develops a method for flagging one type of potentially fraudulent doctor or supplier. I say "one type", because there are various ways in which the system is abused, and each would require a different approach to the analysis. To mention just a few:

  • A cancer doctor was convicted of billing medically unnecessary treatments to 1,200 patients to the tune of $62 M (Medicare alone), many patients of whom did not actually even have cancer.
  • A storefront wheelchair retailer in Los Angeles has a high percentage of customers in San Francisco, more than 350 miles away.
  • Random scam artists dig up doctor and patient id numbers and charge Medicare for anything and everything, embezzled through a fictitious medical organization.
  • During one particular office visit for one particular medical issue, many physicians bill patients for a handful of different services in addition to the one performed.

This project focuses on the latter method of fraud above, because it is one that has affected the contributor personally. I'm not covered by Medicare, so in my case the abuse has been against myself and my insurance company. Like the Medicare system, the health insurance industry also pays the price for these abuses, crippling the private side of the health care system as well.

Data Cleaning

Upon downloading, the data set is 9.2 M observations by  27 variables. Each observation provides information regarding each service that each provider has billed to Medicare in 2012. Keep in mind that some variables that are not relevant to this study, and therefore reduced, would be prominently relevant in the study of another type of Medicare fraud.

Variable Reduction:

  • Remove provider address variables except for state (not relevant)
  • Remove provider name variables (provider id is easier identifier)
  • Remove ‘credentials’ variable (‘provider type’ is more relevant)
  • Remove ‘place of service’ variable (redundant with ‘entity code’)
  • Remove statistical variables for amounts allowed and amounts submitted (statistics on amounts paid are most relevant)

Missing or Unusual Entries

  • Delete the first observation (unusual npi and other data missing)
  • Delete 7 observations with (dot) as the ‘entity’ rather than I or O

Data Transformation

The original data set is structured such that each observation represents a unique combination of healthcare provider (npi) and a particular service (hcpcs) performed by that provider. All variables represent relevant information pertaining to that combination. Therefore, the current data set gives us a summary of each service that each provider has performed in the year 2012.

More useful to this particular study would be a data set that sums the numerical quantities for each provider across all services they provided in that year, rather than breaking it out by service.

Copy the data.frame to a data.table, because taking the sums and sub-setting of such a large data.frame causes problems.

install.packages( "data.table" )
MPD_T <- data.table( MPD )
DOC_TOTALS <- MPD_T[ , list( num_services = sum( line_srvc_cnt ), num_patients= sum( bene_unique_cnt ), total_payment= sum( average_Medicare_payment_amt * line_srvc_cnt ), serv_per_patient= sum( line_srvc_cnt) / sum( bene_unique_cnt)), by = "npi" ]

Using dplyr package, join the non-numeric variables from the original data set into the transformed data set by common npi.

add <- cbind( MPD$nppes_provider_gender, MPD$nppes_entity_code, MPD$nppes_provider_state, MPD$provider_type, MPD$npi )
add <- add )
names( add ) <- c( "gender", "entity", "state", "specialty", "npi" )

Reduce the add dataset to unique npi's by subsetting non-duplicates

add_reduce <- subset(add, !duplicated(add$npi))
DOC_TOTALS$npi <- as.character( DOC_TOTALS$npi )
library( dplyr )
DOC_INFO <- left_join( DOC_TOTALS, add_reduce, by="npi", copy=T )

Create a binary dummy variable, 1 = possible fraud and 0 = no fraud, based on being over or under the .90 quantile of serv_per_patient in each specialty(serv_tail_per_spec)

DOC_INFO$fraud <- ifelse(DOC_INFO$serv_per_patient > DOC_INFO$serv_tail_per_spec, 1, 0)
DOC_INFO$fraud <- factor(DOC_INFO$fraud)
(tb1 <- table(DOC_INFO$fraud))

88K (10%) of the npi's are tagged as potentially fraudulent on the basis of being in the .90 quantile for services per patient within their specialty in the year 2012.

Variables of the transformed data set:

  • Provider Identifier
  • Entity ( I or O for Individual or Organization )
  • Gender ( blank when entity = O )
  • State
  • Specialty ( of provider’s largest # of services )
  • Total Number of Services Provided
  • Total Number of Distinct Patients
  • Average Number of Services per Patient
  • Total Amount Paid from Medicare
  • 90th Quantile of 'Services per Patient' for each Specialty
  • Fraud Flag (1 or 0)

Descriptive Statistics

Study each provider’s average services per patient statistic

Min.      1st Qu.   Median   Mean    3rd Qu.     Max.
0.769     1.082     1.472    2.981   2.277       3669.000

Each provider has an average of 3 services per patient, which sounds reasonable, but the max shows that there is a provider with an average of billing 3,669 services per patient! WOW! Let's look at a plot showing the average services per patient for each provider.


OK, so the density of averages are down at the bottom, but there looks to be a lot of providers with a really high average number of services billed per patient. Let's do some calculations.

nrow(DOC_TOTALS[DOC_TOTALS$serv_per_patient > 5,]) / nrow(DOC_TOTALS)

* Only 12.5% of doctors are providing an avg of over 5 services/patient

nrow(DOC_TOTALS[DOC_TOTALS$serv_per_patient> 10,]) / nrow(DOC_TOTALS)

* Only 5% of doctors are providing an avg of over 10 services/patient

nrow(DOC_TOTALS[DOC_TOTALS$serv_per_patient> 20,]) / nrow(DOC_TOTALS)

* Only 1% of doctors are providing an avg of over 20 services/patient

So it's actually only a small percentage of these 880K providers that have billed an excessive number of services per patient. Only 1% billed over 20 services per patient. And yet the max is well over 3,000! Let's look at the details of that provider.

It's an Organization not an Individual, a Supplier not a Doctor, and they billed 51,372 times in 2012, but for only 14 patients, and for only 1 service (hemophilia drug/ treatment) - an average of 3,670 codes/patient. This looks suspicious.

Let's see if there is a common thread among the highest providers. We'll look at those providers that are in the 1,000, 200, and 100 services per patient "Clubs".


This plot shows that those providers with an average of 1,000 or more services billed per patient are all organizations and all suppliers.


This plot shows those providers with an average of 200 or more services billed per patient, still a very high amount, and as previously shown, relatively few providers put up these numbers. These providers are both organizations and individuals, and not just suppliers. They are from a number of specialties, but only 17% of all specialties are represented.


Among those in the 100 club, still only a fraction of 1% of providers, more specialties represented but only 35% of all specialties.

Predictive Modeling

Our data does not have a result variable telling us which providers were fraudulent. If it did, I would be able to develop models of variable relationships that produce those results, and I would be able to predict which new providers may also be fraudulent. So I will now assign a fraud variable to our data. A positive in this variable will indicate those providers that are suspicious enough to be investigated for fraud, rather than that those providers are actually fraudulent.

The fraud flag will be essentially based on a provider's average services per patient being in the upper 0.90 quantile, but the descriptive statistics above show that I need to do this with respect to specialty. Therefore, here I create a data frame showing the cutoff for each specialty of the upper 0.90 quantile of serv_per_patient.

docMelt<- melt( DOC_INFO, id = "specialty", measure.vars = "serv_per_patient" )
upper.10 <- dcast( docMelt, specialty ~ variable, quantile, .90 )

Merge the quantile values into the DOC_INFO dataframe

DOC_INFO <- merge(DOC_INFO, upper.10, by="specialty")

Create the binary dummy variable “fraud”

Serv/patient/specialy > .90 quantile --> investigate for fraud

Serv/patient/specialy< .90 quantile --> do not investigate for fraud

DOC_INFO$fraud <- ifelse( DOC_INFO$serv_per_patient> DOC_INFO$serv_tail_per_spec, 1, 0 )

Now I can model on the fraud variable.

Separate into training (60%) and test (40%) sets:

trainIndex <- createDataPartition(DOC_INFO$fraud, p = 0.6, list = FALSE, times = 1)
Train <- DOC_INFO[trainIndex, ]
Test <- DOC_INFO[-trainIndex, ]

The following models are variations on Decision Tree and Logisitc Regression model types.

Model 1: Decision Tree

treemodel_1 <- rpart( fraud ~ total_payment + specialty + num_services + num_patients + gender + entity + state, data = Train, control = rpart.control( minbucket = 1 ) )
fancyRpartPlot( treemodel_1 )


Here are the results of the summary, showing which variables, in bold, are statistically significant. When editing the model using only those three variables, the results are the same, so the reduced version would be used.

root 528385 52841 0 (0.90, 0.10)

2) num_services< 6181.5 498178 42204 0 (0.92, 0.08)

4) num_services< 1745.05 399669 28171 0 (0.93, 0.07) *

5) num_services>=1745.05 98509 14033 0 (0.86, 0.14)

10) num_patients>=1061.5 70477 5451 0 (0.92, 0.08) *

11) num_patients< 1061.5 28032 8582 0 (0.69, 0.31)

22) specialty=Addiction Medicine,Allergy/Immunology,Ambulance Service Supplier, … (0.86, 0.14) *

23) specialty=All Other Suppliers,Ambulatory Surgical Center,Anesthesiology, … (0.41, 0.59)

46) num_patients>=790.5 6236 2424 0 (0.61, 0.39)

92) num_services< 2415.5 4181 654 0 (0.84, 0.16) *

93) num_services>=2415.5 2055 285 1 (0.14, 0.86) *

47) num_patients< 790.5 4087 449 1 (0.11, 0.89) *

3) num_services>=6181.5 30207 10637 0 (0.65, 0.35)

6) num_patients>=3302.5 16552 3167 0 (0.81, 0.19) *

7) num_patients< 3302.5 13655 6185 1 (0.45, 0.55)

14) specialty=Allergy/Immunology,Ambulance Service Supplier,Clinical Laboratory,… (0.72, 0.28)*

15) specialty=All Other Suppliers,Ambulatory Surgical Center,Anesthesiology,… (0.22 0.78) *

Tree Model 1 Prediction

newdoc1 <- data.frame( total_payment = 100000, specialty = 'Anesthesiology', num_services = 150, num_patients = 50, gender = 'M', entity = 'O', state = 'CA')
predict( treemodel_1, newdata = newdoc1 )


0                                1

0.9295142            0.07048583

93% confidence in no-fraud

Model 2: Decision Tree

I began to think that since I had to manufacture the fraud variable that I am modeling/predicting for, I should only use variables that did not actually go into calculating the outcome. So here I remove 'num_services' and 'num_patients':

treemodel_2 <- rpart( fraud ~ total_payment + gender + entity + state + specialty, data = Train, control = rpart.control( minbucket = 1 ) )

n= 528385

1)root 528385 52841 0 (0.8999953 0.1000047) *

Only one node results, branches end, so no other variables are significant.

Tree Model 2 Prediction

newdoc2 <- data.frame( total_payment = 100000, gender = 'M', entity = 'O', state = 'CA', specialty = 'Anesthesiology')
predict( treemodel_2, newdata = newdoc2 )

0                                1

0.8999953        0.1000047

90% confidence in no-fraud

Model 3: Decision Tree

Here I use the same variables as in Model 2, but I decrease the Complexity parameter from .01 to .005, which allows for less worthwhile splitting to be admitted.

treemodel_3 <- rpart( fraud ~ total_payment + gender + entity + state + specialty, data = Train, control = rpart.control( cp = .005 ) )
fancyRpartPlot( treemodel_3 )


Here the summary shows that 'total_payment' and 'specialty' variables are statistically significant.

1) root 528385 52841 0 (0.89999527 0.10000473)

2) total_payment< 158519.1 462316 38800 0 (0.91607472 0.08392528) *

3) total_payment>=158519.1 66069 14041 0 (0.78747976 0.21252024)

6) specialty=Addiction Medicine,Allergy/Immunology,Ambulance Service, 64220 12837 0 (0.80010900 0.19989100) *

7) specialty=All Other Suppliers,Anesthesiology,Audiologist, … 1849 645 1 (0.34883721 0.65116279) *

Tree Model 3 Prediction

predict( treemodel_3, newdata = newdoc2 )

0                           1

0.9160747    0.08392528

92% confidence in no-fraud

Model 1: Logistic Regression

logmodel_1 <- glm( fraud ~ total_payment + gender + entity + state + specialty, data = Train, family = 'binomial' )
summary( logmodel_1 )

Estimate         Std. Error      z value     Pr(>|z|)

(Intercept)                        -9.164e+00   8.444e+01    -0.109      0.913573

total_payment                  8.122e-07    1.913e-08       42.460     < 2e-16 ***

genderF                             -1.367e+00   2.561e-01      -5.339       9.34e-08 ***

genderM                           -1.034e+00   2.560e-01      -4.041       5.33e-05 ***

Total payment and gender are strongly significant, which is different than the tree models. Many specialties showed significance, but many did not. State did not show significance.

Logistic Model 1 Prediction

logmodel_1.pre <- predict( logmodel_1,type = 'response' )
pre_1 <- ifelse( logmodel.pre_1 > 0.5, 1, 0 )
table( pre_1, Train$fraud)

pre2              0                 1

0            475,203      52,612

1              341              229


90% overall success

Now run the model on the Test data set

log_pred_test1 <- predict( logmodel_1, newdata = Test )
pre_1 <- ifelse( log_pred_test1 > 0.5, 1, 0 )
(table1 <- table( pre_1, Test$fraud ))

pre2           0                 1

0           316,875    35,131

1            153            96


90% overall success on Test data set as well

Model 2: Logistic Regression

Reduction of variables based on previous results, using only 'total_payment' and 'gender'

logmodel_2 <- glm( fraud ~ total_payment + gender, data = Train, family = 'binomial' )
summary( logmodel_2 )

Estimate              Std. Error         z value           Pr(>|z|)

(Intercept)           -2.375e+00    1.931e-02      -122.974     <2e-16 ***

total_payment    6.322e-07       1.581e-08     39.997         <2e-16 ***

genderF                -4.050e-02     2.094e-02     -1.934          0.0531 .

genderM                2.066e-01      1.981e-02     10.429         <2e-16 ***

Shows that all are significant.

Logistic Model 2 Prediction

logmodel_2.pre <- predict( logmodel_2, type = 'response' )
pre_2 <- ifelse( logmodel_2.pre > 0.5, 1, 0 )
(table2 <- table( pre_2, Train$fraud ))

pre2                0               1

0   475,254    52,686

1            290          155


90% overall success

Now run the model on the Test data set

log_pred_test2 <- predict( logmodel_2, newdata = Test )
pre_2 <- ifelse( log_pred_test2 > 0.5, 1, 0 )
(table2 <- table( pre_2, Test$fraud ))

pre2                 0               1

0    475,254    52,686

1            290           155


90% overall success on Test data set as well


About Author

Related Articles

Leave a Comment

No comments found.

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI