Data Study on US Hospital Charging Comparator

Posted on Feb 9, 2017
The skills the author demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

I. Introduction

The Centers for Medicare and Medicaid Services (CMS) released Annual Inpatient Charge Data for the 100 most common diagnosis and treatments for every hospitals in the country treating Medicare patients starting from 2011. According to the U.S. Department of Health and Human Services (HHS), the release of these charge data is a piece of the three-part initiative to increase transparency in the delivery of health care in the United States, encourage copetition, and provide consumers with more purchasing power. In attempt to help patients better explore the comparative price of procedures and estimate their medical services prior to receiving care, I implemented a Hospital Charging Comparator with RStudio's Shiny web application framework.

You can try out the application HERE.


II. Preparing the Data

CMS provided annual data from 2011, but 2014 was the first year for which data was available for all diagnosis groups. So I filtered the 100 most common diagnosis and treatments in 2014, and the application covered inpatient hospital charges and medicare payments of 112 procedures in 3,137 hospitals from 2011 to 2013 across the nation.


III. Building the Data Application

The application has 5 sections:

  • About: terminology illustration of CMS dataset
  • State Overview: distribution of average charges and medicare payments across states
  • Hospital Comparison: comparison of average charges and medicare payments of specific DRG (Diagnosis Related Group) across hospitals
  • DRG Comparison: comparison of average charges and medicare payments of different DRGs
  • Spending Exploration: exploration of medicare spending trending among differnet DRG


State Overview

Data Study on US Hospital Charging Comparator

In the State Overview tab, users can select the year and DRG, then the distribution maps and histograms will show the update result. The same data is also displayed as ordered bar charts with corresponding discharges. And the results are also listed in the table where users can filter to see charges of all DRGs in one state.

Data Study on US Hospital Charging Comparator

The graphs show immense variation of average hospital charges for a given DRG across the US. For example, the average charges for Major Joint Replacement within in Massachusetts is $ 34,742  for 11,333 discharges whereas the average charges for the same DRG in California is $ 90,207 for 32,930 discharges.


Hospital Comparison

The Hospital Comparison allows users to compare the average charges and medicare payments of selected hospitals in selected year. There is no limitation for the number of selected hospitals, but some hospitals did not provide data for all categories of procedures. The results are displayed in bar charts, and apparently there are wide variations between hospitals even in the same geographic area. For example, in the New York metro area, COPD (Chronic Obstructive Pulmonary Disease) at Bayonne Hospital Center, part of a chain called CarePoint Healthcare, comes in at $ 18,995, which is more than four times the prices at New York Community Hospital of Brooklyn and New York Methodist Hospital.

Data Study on US Hospital Charging Comparator


DRG Comparison

The DRG Comparison displays boxplots of average charges and payments for selected DRGs in selected year. And the average charges and medicare payments seem to increase as complication increases, for example, intracranial hemorrhage w mcc charges twice as much as intracranial hemorrhage w/o mcc.

Screen Shot 2017-02-06 at 02.35.14


Spending Exploration

Spending Exploration summarises 4-year average charges and payments data in bubble plot, users can select from top 5 to top 100 most expensive  DRGs (according to Medicare spending which is the product of total charges and average payments) and look at the distribution of payments by the average medical payment per discharge vs. the total number of discharges. The size of the bubble represents the total Medicare payments for selected DRGs.

Year trending of selected DRG in selected hospital is also included in this tab, users are able to identify the changes in both hospital charges and Medicare payments in certain hospital.

Screen Shot 2017-02-06 at 03.56.05


IV. Conclusion

Hopefully, this shiny app will help you understand US healthcare spending and be a useful tool while selecting your health care providers.

Possible future steps for this app would be combining with the quality of care provided by different hospitals. By aggregating all the data related to both charges and quality, the app would be a more accurate guide for patients.

R Code is available on HERE.


About Author

Related Articles

Leave a Comment

Google August 20, 2021
Google Every the moment inside a whilst we decide on blogs that we read. Listed beneath are the most current sites that we select.
Google August 17, 2021
Google Below you will obtain the link to some web pages that we think you ought to visit.
Google December 28, 2019
Google Always a massive fan of linking to bloggers that I enjoy but do not get a good deal of link really like from.
Google October 31, 2019
Google Wonderful story, reckoned we could combine a handful of unrelated data, nonetheless actually really worth taking a appear, whoa did one discover about Mid East has got far more problerms as well.
best tips for investing in stocks June 2, 2017
It's an awesome post for all the internet people; they will get benefit from it I am sure.
job description May 30, 2017
Paragraph writing is also a fun, if you be familiar with then you can write otherwise it is difficult to write.

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 H20 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