SAS Medical Chief on How Analytics Assess Health Care Costs, Quality

by   |   November 18, 2013 5:00 am   |   0 Comments

SAS Claims Analytics screenshot 10 29 13 650x338 SAS Medical Chief on How Analytics Assess Health Care Costs, Quality

Above, a screenshot of a SAS visualization tool that shows a detailed analysis of diabetes costs by geography, site of service and age group. An analyst would be able to click on the items to drill into the data.

Graham Hughes SAS 200x200 SAS Medical Chief on How Analytics Assess Health Care Costs, Quality

Graham Hughes of SAS

Transparency in prices for health care goods and services is one of the big challenges in making health care more affordable. The costs of medical services are often not clear, and it is difficult to know how the costs of services delivered in one place compare to the same services delivered elsewhere.

On this episode of the Data Informed podcast, Graham Hughes, the chief medical officer of analytics provider SAS, talks about the difference analytics can make in examining the cost and quality of health care in the United States. Hughes discusses a new analytics program SAS has launched to collect and compare health care payer claims data. By collecting this information in a data warehouse, the software allows users, including state agencies, health care providers, researchers, and eventually consumers, to analyze and compare health care cost data within a county or state.

Hughes discusses the challenge of preparing data for this Claims Analytics system and the opportunities the system represents for improving the sophistication of health care analytics in the future, when new datasets are added and today’s problems with online government health insurance exchanges are fixed.

He also talks about the importance of data visualizations to making analytics of complex datasets accessible to different types of end-users.

And the interview covers a research program SAS is working on with the University of North Carolina Medical School. In that project, researchers plan to analyze the behavior and responses of diabetes patients to different communications, like reminding a patient to take medications via a text message.

In the conversation, there are references to a report earlier this year by Time Magazine about the opaque nature of medical bills, and a report by two research groups that gives failing grades to many states on their laws to create transparent prices in health care.

Michael Goldberg is the editor of Data Informed. Email him at Follow him on Twitter: @MGoldbergatDI.

Related articles and podcasts at Data Informed:

• Data Quality study Shows the Potential of Electronic Health Records

• IBM’s Data Science Project to Build Analytics to Predict Heart Failure

• Sentiment Analysis Tool Designed to Predict Veterans’ Suicide Risk

• Program Seeks to Merge Electronic Health Data and Genomic Data

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