Big data analytic of US Medicare Claims of healthcare service informatics and billing fraud

Big data analysis of health services and health informatics unique to public use files of US Medicare Claims data was the primary objective of this investigation. This study focused on the five most populated US states California, Texas, Florida, New York, and Pennsylvania of the 2021 US Medicare Claims datasets. To establish meaningful insights into the big data of US Medicare Claims in 2021, informative graphs, visualizations, regression analysis, and clustering data mining techniques were carried out.The data comprised two datasets. One dataset was called Medicare Physician & Other Practitioners - by Provider, and the other dataset was Medicare Physician & Other Practitioners - by Provider and Service, which comprised services related to a variety of chronic diseases. Fifteen aggregated HCPCS codes were calculated because there were over 400 HCPCS codes and that needed to be reduced for the analysis. The methodology used traditional data mining techniques that included graphing, visualization with trend lines, and linear regression to calculate residuals to identify outliers. That was, data visualizations were created to present the difference between submitted charges and payment amounts, linear regression, residuals, and subsequent residual outliers. Outliers were investigated for possible identified fraudulent providers or submitted claims.Big data exploration and insightful visualizations led to establish information about submitted claims, payment amounts, total services, provider type and specialty, Healthcare Common Procedure Coding System (HCPCS) codes, and total beneficiaries. The vast amount of health and healthcare informatics in the public use file showed the state of California with highest submitted charges of $45b. Across the top 5 US states the top three provider types by summitted charge was ambulatory surgical center ($23,305,959,194.92) followed by diagnostic radiology, and clinical laboratory. HCPCS codes were highest for repair (i.e. unplanned surgeries) and elective surgery. Sum of beneficiary per day service by 15 aggregated HCPCS codes showed that Patient Care Services ($0.35b), Misc. Testing ($0.21b), Emergency Services ($0.10b), and Vaccine Administration and Medication ($0.10b) were the most frequent HCPCS codes per day. Top three provider type by average risk score: nephrology (4,256,048), infectious disease (2,981,082), and hematopoietic cell transplantation and cellular therapy (2,930,108). Beneficiaries and submitted charges by state showed a linear progression of the total services by the submitted charges. California and Pennsylvania had recurrent and similar outliers in this regression analysis. Furthermore, provider types had clusters identified in the data for each of the five US states that were unique. Some provider types had higher frequency of total services that included pharmacy, independent diagnostic testing facility, and clinical laboratory.PowerBI proved to be a useful tool to use data mining techniques as no other software was able to load the US Medicare Claims Provider Services files and perform data analysis such as clustering. The dataset was limited to one-year coverage in 2021, data errors and technical challenges in PowerBI for more advanced regression analysis all introduced certain constraints in applying more advanced data mining algorithms. In conclusion, we were able to successfully data mine large public US Medicare Claim data using PowerBI and its functionalities such as DAX, measures, action filters, and visualizations.There were different outliers (related to National Provider Identifier) for each visualization with regression trend line for payment amount and total services; submitted charge (claim) and total services; total services and beneficiaries; total services and HCPCS codes; and provider with total beneficiaries. Moreover, 19 suspected fraud claims were identified for professional organizations. Thus, our results clearly showed that the variety of big data can be graphed with outliers. However, the different outliers found based on different data elements (variables and parameters) proved that a more sophisticated (perhaps artificial intelligence algorithms like random forest, boosted gradient or even deep learning) big data analytic is needed for further investigation of possible fraudulent healthcare claims.

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