Inpatient Prospective Payment System (IIPS) Case Study

This project is based upon “Inpatient prospective payment system (IPPS)” which is used to refer to several payment methodologies for which means of determining insurance reimbursement is based on a predetermined payment regardless of the intensity of the actual service provided.


The Dataset : 


This database contains charges from various hospitals for the 100 most common inpatient services and 30 most common outpatient services across the country in 2011.

There are 3 cost fields at the end – “covered charges”, “total payments”, and “medicare payments.” Since “total payments” represents the out of pocket cost paid by the patient, that column was used.


Problem Statement : 


(1) What are the top 5 states with the average highest cost.

(2) What are the 5 procedures that cost on the average the highest?




Hive was used to make the query. To set up, the dataset (“inpatient.csv”) was copied to /home/cloudera/hive/inpatient.


NOTE: If you are using cloudera you can save your dataset in above path otherwise can choose path according to you.


There were 2 obstacles that were run into when using this data. The first was that the cost values are strings with “$” and embedded commas (for example: “$5,777.24”). Initially I tried reading these as floats but this resulted in all costs being “NULL.” This was overcome by initially reading costs as a string, and then making a second table (“inpatient_edited”) that contained just the 3 main columns of interest (procedure name, state, and total charge), using a regular expression to remove the dollar sign and commas and converting the result to a float. This can be seen in the INSERT OVERWRITE TABLE command used.

The second obstacle was sometimes the address would contain a comma in it. Those rows would have quotation marks around the address. When I initially tried using ROW FORMAT DELIMITED it would take those embedded commas as a separator, causing wrong values later for the state and total cost for that row. To get around this, I used a CSV serde jar file publicly available by clicking on the “csv-serde-0.9.1.jar” which is given in below link.


This was copied to the /home/cloudera/hive directory(Only while using cloudera) and included with the command

                                    ADD JAR hive/csv-serde-0.9.1.jar

and then in CREATE EXTERNAL TABLE adding the following lines:


ROW FORMAT SERDE „com.bizo.hive.serde.csv.CSVSerde‟
WITH serdeproperties(
“separatorChar” = “\,”,
“quoteChar” = “\””)


After doing this, the data could be processed. Using the inquiries:


SELECT state, avg(total_chargeF) AS highest_avg_cost FROM inpatient_edited GROUP BY state SORT BY highest_avg_cost DESC
SELECT procedure, avg(total_chargeF) AS highest_avg_cost FROM inpatient_edited GROUP BY procedure SORT BY highest_avg_cost DESC




We see that the 5 most expensive states are Alaska, Washington DC, Hawaii, California, and Maryland:

                            AK 14572.391745298972
                            DC 12998.029389880952
                            HI 12775.739504162542
                            CA 12629.668472019062
                            MD 12608.947661015507

and the 5 most expensive procedures are Septicemia, Infectious & Parasitic Diseases, Respiratory System Diagnosis w/ Ventilator Support 96+ Hours, Major Small & Large Bowel Procedures, and Spinal Fusion:

                            870 – SEPTICEMIA OR SEVERE SEPSIS W MV 96+ HOURS 44259.48536716254
                            853 – INFECTIOUS & PARASITIC DISEASES W O.R. PROCEDURE 40315.961397392806
                            207 – RESPIRATORY SYSTEM DIAGNOSIS W VENTILATOR SUPPORT 38588.92100171969
                            329 – MAJOR SMALL & LARGE BOWEL PROCEDURES W MCC 37765.59428459519
                            460 – SPINAL FUSION EXCEPT CERVICAL W/O MCC 27778.67117410379




The first line shows the DFS command used to copy the data into the part of HDFS used for pig work.

The same 2 obstacles of needing to remove commas embedded within quotes and removing “$” and “,” were addressed. To address removing embedded commas, “CSVLoader” from piggybank was used rather than the standard “PigStorage” with a “,” delimiter was used. REPLACE and REGEX_EXTRACT were used to get rid of the embedded “$” and “,” within the total_charge field (the “BF” and “CF” queries).
The “A = FILTER L BY state != ‘Provider State’” line was a simple way to filter out the header line. Finally, the last 3 lines (X, AC, S) are the commands to do the actual query by state. The 3 lines used to do the query by procedure are also shown.




MapReduce jobs were created to generate the average total cost per procedure and average total cost per state. Since the generated outputs were relatively small files (100 lines for the procedures query and 52 lines for the states query), python scripts were used to do the final sorting by cost rather than doing another MapReduce job.

CSV processing was handled by using an external jar file.

This jar will find in Downloads section using name opencsv-2.2.jar.

The resulting .jar file was copied to the lib directory of the Eclipse project and an “Add External Jar” was done for the project to point to this jar file. In the .java file, the following line was added:

and in the code itself, the following lines call this CSV code so that commas within quotes are properly handled:
CSVParser parser = new CSVParser();
String[] parts = parser.parseLine(value.toString());

The removal of “$” and “,” and conversion to float for the total_charge field are handled using standard java substring and replace commands for the string class.

The source files “” and “” show the MapReduce code to list average total cost by procedure and by state, respectively. The files “output_by_procedure.txt” and “output_by_state.txt” show the resulting outputs of the MapReduce jobs. For example, in “output_by_procedure.txt”, one can see the most expensive procedure entry:

                           870 – SEPTICEMIA OR SEVERE SEPSIS W MV 96+ HOURS 44259.496

and in “output_by_state.txt” one can see the most expensive state entry:

                             AK 14572.392

The files “” and “” are simple python scripts that take “output_by_procedure.txt” and “output_by_state.txt” and generate “output_by_procedure.txt.sorted” and “output_by_state.txt.sorted”, respectively. Looking at these files, the top 5 procedures can be seen as:

                              870 – SEPTICEMIA OR SEVERE SEPSIS W MV 96+ HOURS 44259.496
                              853 – INFECTIOUS & PARASITIC DISEASES W O.R. PROCEDURE W MCC 40315.984
                              207 – RESPIRATORY SYSTEM DIAGNOSIS W VENTILATOR SUPPORT 96+ HOURS 38588.934
                              329 – MAJOR SMALL & LARGE BOWEL PROCEDURES W MCC 37765.605
                              460 – SPINAL FUSION EXCEPT CERVICAL W/O MCC 27778.666


and the top 5 states as:

                              AK 14572.392
                              DC 12998.031
                              HI 12775.744
                              CA 12629.705
                              MD 12608.943

One can see that these match the results from Pig / Hive.



IIPS Codes

IIPS Dataset


I hope this tutorial will surely help you. If you have any questions or problems please let me know.

Happy Hadooping with Patrick..

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