The Center for Medicare and Medicaid Services (CMS) manages health care for over one hundred million patients. A large majority of these are Medicare members, for which the CMS is responsible for administering health care benefits through medical claims data. For this population, comfortability with their health care physician, quality of life, cost, and longevity are all of concern. Indeed, Medicare members’ financial and health concerns burden not only the members themselves, but families and taxpayers as well.
The recent emergence of big data analytics (BDA) has provided solutions to issues like these in many other patient populations. The CMS has recently conducted some internal BDA and has shown efficacy in fighting fraud and lowering hospital readmissions. However, the agency cannot possibly conduct research to addresses all Medicare members’ needs in such an encapsulated manner.
Other government agencies that house similar data provide easy access, and even some analytical discovery tools, to outside researchers in varying settings. The CMS has partners to help manage data and its availability to researchers, however, large barriers to BDA in Medicare still exist. In addition to conventional challenges with big data characteristics, the CMS faces regulatory and legal hurdles in health care policy and data privacy.
Additionally, extremely rigorous application processes and prohibitively high costs limit researchers’ access to Medicare data for discovery and early-stage research purposes. These barriers need to be overcome to expand the CMS’s success in other areas to addressing all the health needs of Medicare members.
Table of Contents
1. Introduction
1.1 Medicare
1.2 Big Data Analytics
1.3 Statement of the Problem
2. Methods
3. Results
3.1 BDA Research Workflow
3.2 Data acquisition
3.3 Storage
3.4 Research data subsets
3.5 Data manipulation
3.6 Reporting and Visualization
3.7 Virtual research data center
4. Discussion
Objectives and Topics
This paper examines the significant barriers preventing external researchers from effectively accessing and utilizing Medicare data for Big Data Analytics (BDA). It identifies the current limitations within the CMS data ecosystem and investigates how policy, cost, and procedural hurdles hinder the potential for improved health outcomes and cost reduction in the Medicare program.
- Analysis of the BDA research workflow within the context of Medicare claims data.
- Evaluation of the "Five Vs" of Big Data as applied to Medicare health care information.
- Identification of regulatory and legal constraints, specifically regarding HIPAA and CMS data access policies.
- Comparison of Medicare data accessibility with other governmental research data initiatives.
- Recommendations for streamlining data delivery and reducing barriers to foster research innovation.
Excerpt from the Book
Research data subsets
From the much larger data storage management system, a researcher typically designs a scientific study based on a subset of the entire population. For example, a researcher is interested in studying the data of Medicare members that have died in the past year that were also treated for a cardiac incident in the hospital inpatient setting within the last five years. In this type of study, there is no need for the scientist to retrieve Medicaid data. Nor is there a need to retrieve any data prior to 2012 whatsoever. Additionally, claims with CPT codes outside cardiology or point of treatment codes outside the hospital will not be needed. Truly, this example shows how selection criteria can dramatically shrink the amount of data to work with, and in turn, the resources and workspace to perform further activities on this data. We describe this cohort-specific set the research data subset (RDS). A researcher will frequently work with an RDS of volume they can manage locally (in their own lab or university), however as explained in more detail later, the CMS may also provide a virtual workspace for the scientist to manage their RDS.
Summary of Chapters
Introduction: Outlines the significance of Medicare within the US health care system and introduces the conceptual framework of Big Data Analytics (BDA) and the existing problems with data access.
Methods: Describes the systematic literature search process used to identify relevant peer-reviewed articles and industry resources for this study.
Results: Provides a detailed examination of the BDA research workflow, analyzing the stages of data acquisition, storage, subsetting, manipulation, visualization, and the role of the Virtual Research Data Center.
Discussion: Synthesizes the findings, highlighting how policy and cost barriers currently stifle research creativity and suggesting future steps for simpler data access protocols.
Keywords
big data, analytics, Medicare, CMS, informatics, bioinformatics, health care, claims data, BDA, research workflow, HIPAA, data access, veracity, volume, value
Frequently Asked Questions
What is the primary focus of this paper?
The paper primarily focuses on identifying the major barriers that outside researchers encounter when attempting to access and utilize Medicare claims data for Big Data Analytics purposes.
What are the key thematic areas covered?
The key themes include the Medicare administrative environment, the "Five Vs" of Big Data in health care, CMS data usage policies, the technical workflow of data research, and regulatory hurdles like HIPAA.
What is the central research goal?
The goal is to analyze why existing BDA efforts in Medicare are restricted and to provide recommendations that could improve access for the scientific research community to lower costs and improve patient outcomes.
Which scientific methodology does the author employ?
The author conducts an advanced literature review using PubMed to synthesize findings on BDA trends in health care and evaluates administrative and policy challenges faced by researchers.
What topics are discussed in the main body of the work?
The main body details the BDA research workflow, including specific stages like data acquisition, storage, the creation of Research Data Subsets (RDS), and the implementation of the Virtual Research Data Center.
Which keywords best describe this work?
Key terms include big data, analytics, Medicare, CMS, informatics, bioinformatics, veracity, and health care policy.
How do CMS policies specifically affect the research workflow?
CMS policies create significant delays and high costs, particularly during the Research Data Subset (RDS) creation stage, which prevents timely and exploratory research.
How does the cost of accessing Medicare data compare to other sectors?
The paper notes that while organizations like the NIH provide genomic data at no cost, the CMS imposes high fees—often ranging from $3,000 to $40,000—which acts as a major barrier for researchers.
- Quote paper
- Eugene Roberts (Author), 2017, Big Barriers to Big Data Analytics in Medicare, Munich, GRIN Verlag, https://www.hausarbeiten.de/document/352261
