This is the second half of my thoughts on the question “When will the IT industry provide reusable clinical and administrative data warehouses for the Medicaid enterprise?” The first posting discussed the current models and systems, and enumerated some barriers to creating a common data model. In this post I consider how to overcome the barriers to reach the goal of a comprehensive data model for analytics and administration for the HHS enterprise.
How might these barriers be overcome? How might a common data model be created and adopted?
The profit motivation of commercial business combined with the altruistic drive for health care solutions shown by standards organizations is the most powerful and likely path to a successful HHS data model standard. One example of this success is the Consolidated Clinical Document Architecture (C-CDA) developed by HL7 and adopted by CMS for Meaningful Use regulations.
Creating a common model is challenging in any industry, but has been accomplished by industry groups for accounting and government billing. Industry and vocational groups provide the most promising avenue for development of enduring standard data models. Business focused non-profit standards organizations coupled with government target-setting has been the most successful approach in the finance industry, and is the best path to a standard model for HHS.
However, healthcare industry standards organizations dealing with IT have less history and weaker accreditation associations than some other industries, e.g. accounting and finance. To the large healthcare IT organizations, and to the medical profession itself, there is still significant financial dis-incentives to promote interoperability. This leads to lackluster results in health and human services data model standards. Two promising standards come from Health Level 7 (HL7) and Observational Medical Outcomes Partnership (OMOP).
- HL7 – Reference Information Model (RIM)
- OMOP – Common Data Model (CDM)
These models are designed for a specific purpose to meet a specific need. Consequently they have limited scope, and are for the most part, very generalized. These models are hindered by their development process… development by committee, and also by the need for a solution that attempts to satisfy everyone at the cost of specificity. The result is a “one size fits all” data model containing compromises, including some designs using academic/institutional logic rather than direct business requirements.
Effects of establishing a common data model
If a common data model is established, would the healthcare IT industry respond?
Yes, the industry would attempt to deliver “best of breed” data sourcing and analytic solutions based on the data model standard. The financial incentive of high dollar sales and the potential future subscription revenues will motivate developers to meet the need. However, the market forces hindering this development are the limited number of customers combined with the high cost of development. With state and Federal governments being the primary customers, the opportunity for volume sales does not exist and the business requirements for each state procurement will remain unique, thus making the market even more challenging, particularly for smaller businesses. Procurement processes are lengthy and prohibitive to all but very well-funded sales organizations, which stifles the creativity and the competition that could otherwise be expected when data models become standardized. There might be easier ways for a developer to turn their capital and time into a return on investment.
What Teradata adds
The Teradata Healthcare Data Model (HCDM) is designed for the entire scope of an organization providing health and human services. It is based on the known needs of many healthcare customers over years of development. The customer requirements are augmented with information from successful industry standards. The customer needs provide concrete requirements for known current needs. The industry standards provide content that informs current implementation, and enables flexibility for potential or unplanned implementation.
In addition to the HCDM, Teradata provides advanced analytics for both structured and unstructured information with their Unified Data Architecture (UDA). The UDA has been evaluated and praised by industry thought leaders like Gartner and Forester.
The combination of structured and unstructured data, analytical capability, and a powerful database can result in unprecedented insight into population health, healthcare practice, and business action.