timely data. States, in turn, have found themselves under pressure to gather and evaluate data supporting both the traditional fee-for-service program and managed care. That's because states need reliable data to understand how best to improve the quality of managed care, which means monitoring, evaluating and influencing a managed-care plan's performance.
A few years ago, states began building decision-support systems (DSS) to help make sense of the vast data they were collecting and to speed up the decision-making process. A DSS consists of hardware, analytical software and a database that gives the user timely access to data that hasn't been duplicated, is consistent, has been integrated and has passed through standard integrity controls.
In 1996, HCFA surveyed the states and, out of 42 responses, found that 17 states already had a DSS in place or were building one. Another 22 states planned to have a DSS within the next two years. The survey found that technical analysts were the most frequent DSS users, in terms of creating and analyzing reports, while senior staff were least likely to use the system.
The typical DSS contained about two to three years of claims history -- that's about 13 million to 15 million claim lines per year -- but some systems were gargantuan. California, with more than 5 million recipients, had nearly 185 million claim lines, units of measure for stored Medicaid data, for each year in storage.
Most states used that data to develop ad-hoc reports. But nearly every state used its DSS to look at patterns of treatment, to profile hospitals and doctors and to measure the performance of providers. They compared fee-for-service coverage with managed care, ran "what-if" analyses of potential policy changes and analyzed trends with cost and use of health care.
Only Arkansas, Illinois, Mississippi, Michigan, Minnesota, New York, Ohio and Wisconsin have opted to pursue decision support through a data-warehouse strategy, although other states have data-warehouse projects in the development or planning stages.
There's nothing whiz-bang about data warehousing. The typical warehouse consists of data stores, which are databases containing the operational data; data conversion and extraction procedures that translate and convert disparate data into a standardized format; the warehouse itself, which is a powerful database optimized for online analytical processing; business-intelligence tools, which provide the interface for users to query data-warehouse information; and warehouse-administration tools for monitoring and support.
Data warehouses and other DSS differ in scale, performance and price. Data warehouses are often hundreds of gigabytes in size -- some a terabyte or more -- often running on powerful servers, sometimes with parallel processors to boost their performance. None of this is cheap, however. The hardware, software and technical expertise needed to make a warehouse run can cost tens of millions of dollars. But, according to Medicaid consultant Smith, they are worth it. "Medicaid is a huge program that dwarfs almost all others in state government. It deserves the best technology you can bring to it."
Apparently that's the philosophy in Illinois. In July, Gov. George H. Ryan announced that the state was building a $28 million data warehouse to help manage the state's $6 billion medical-assistance programs. The warehouse, which will be built by Bull, will be used by the Department of Public Aid to administer Medicaid and a children's program called Kidcare -- which both provide health coverage to nearly 1.4 million people each month. The department processes 25.7 million claims per year and tracks nearly 50,000 medical providers.
To improve its ability to analyze trends, the department will store five years' worth of data in the warehouse. "We're going to use the warehouse to try and understand the characteristics of a problem," said Theron Aflaksen, deputy director of administrative operations. "It may lead us to alter how we reimburse or who we have for providers."
The system, which will begin operation