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Live from Pervasive Software’s IntegratioNEXT 2010 Conference: Envisioning Healthcare Interoperability

Healthcare and healthcare information technology (HIT) continue to be a data integration challenge for payers, providers, and consumers of healthcare services. This post will explore the role of Pervasive’s Data Integrator software in the solution envisioned by one of the partners presenting at the iNExt, Misys Open Source Solutions (MOSS). However, first we need to take a step back and get oriented to the opportunity and the challenge.

Enabling legislation by the federal government in the form of the American Recovery and Reinvestment Act (ARRA) of 2009 provides financial incentives to healthcare providers (hospitals, clinics, group practices) that install and demonstrate the meaningful use of software to (1) capture and share healthcare data by the year 2011 (2) enable clinical decision support using the data captured in (1) by 2013 and (3) improve healthcare outcomes (i.e., cause patients to get well) against the track record laid down in (1) by 2015. Although the rules are complex and subject to further refinement by government regulators, one thing is clear. Meaningful use requires the ability of the electronic medical record (EMR/EHR) systems to exchange significant volumes of data and do so in a way that preserves the semantics (i.e., the meaning of the data).

Misys Open Source Solutions (MOSS) is jumping with both feet into the maelstrom of competing requirements, standards, and technologies with a plan to make a difference. MOSS first merged with Allscripts and then separated from them as Allscripts merged with Eclipsys. MOSS is now a standalone enterprise on a mission to harmonize medical records. Riding on Apache 2.0 and leveraging the Open Health Tools (OHT), MOSS is inviting would-be operators and participants in health information exchanges (HIE) to download its components and make the integrated healthcare enterprise (IHE) a reality. As of this writing, the need is great and so is the vision. However, the challenges are also formidable and extend from the requirements for patient identification, document sharing, record location, audit trail production and authentication, subscription services, clinical care documentation and persistence in a repository. Since the software is open source and comes at no additional cost, MOSS’s revenue model relies on fees earned for providing maintenance and support.

Whether the HIE is public or private the operator confronts the challenge of translating between a dizzying array of healthcare standards – HIPAA, HL7, X12, HCFA, NCPDP, and so on. With literally hundreds of data formats in legacy as well as modern systems out there, those HIEs that are able to provide a platform for interoperability are the ones that will survive and prosper by value-added services rather than just forwarding data. The connection with Pervasive is direct since the incoming data formats may be in HL7 version 2.3 and the outbound format in version 2.7. Pervasive is cloud-enabled and, not to put too fine a point on it, has more data conversion formats than you can shake a stick at. Pervasive is a contributor to the solution at the data integration, data quality, and data mining levels of the technology stack being envisioned by MOSS. Now and in the future, an important differentiator between winners and runner-ups amongst HIEs will be the ability to translate between diverse data formats. In addition, this work must be done with velocity and in significant volume, while preserving the semantics. This approach will add value to the content rather than just acting as a delivery service.

As noted in the companion post to this one, Pervasive has a cloud-enabled version of its data integration product, Version 10, Cloud Edition. DataRush, the parallel processing engine, continues to set new records for high performance parallel processing across large numbers of cores.  Significant new functionality in data profiling and data matching is now available as well, making Pervasive a triple threat across data integration, data quality, and when open source data mining from Knime is included, data mining.

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