The question was put to me “When will the IT industry provide reusable clinical and administrative data warehouses for the Medicaid enterprise?” It was a thought-provoking question that was well worth considering.
Currently the healthcare insurance and provider industries are guided to target reporting that is defined by government regulation and motivated by financial incentives. These specific measures do lead to the creation of tracking and reporting system scoped to meet the requirements for reimbursement from government programs. This does not lead to a comprehensive data warehouse model or analytic platform for the Medicaid enterprise.
Federal government reporting regulations and the new procurement rules for “modularity” do not strictly require a comprehensive data warehousing platform. With an overwhelming task of re-doing MMIS systems, States are struggling with merely defining “modules” and ensuring the basic required functionality is being achieved. Though the business and policy people recognize the need and value for analytics, those are not “requirements” and, thus, are pushed off for future scoping. Unfortunately, that means most of the early modular MMIS EDW RFP’s have been dominated by traditional reporting measures and pre-written applications for tracking statistics, rather than requirements which will set the States up for future flexibility and growth.
The RFP’s lack serious and specific requirements that focus on the future extensibility, data normalization and virtual views of data within a data warehouse platform. Thus, the vendors responding to these RFP’s, in order to provide competitive bids, must often use technologies and infrastructures which are minimally capable of providing reports and data aggregation.
There are existing data models or standards (for example, MITA, National Human Services Interoperability Architecture (NHSIA) and National Information Exchange Model (NIEM), that attempt to serve as a foundation for a state based HHS data warehouse. While these are a start, they fall short of what is needed to define a data model.
• MITA specifications serve as a good “ruler” to measure the content and capability of a data model for HHS. Running MITA scenarios against the data model in the form of a “data scenario” can provide valuable insight on how the model is designed to work.
