“Introduction
For interoperability to keep pace with the shift to value-based care by public and private payers, the increasingly dated definitions, processes and use cases associated with interoperability must evolve.
Summary
Value-based care requires high-volume data exchange. It requires interoperability at a population level, which in turn requires automation, flexibility and scale. Intelligent, purpose-driven interoperability meeting these demands should add no additional provider burden via manual workflows as data is exchanged.
For this to occur across the delivery and reimbursement landscape, definitions and use cases need refinement and consensus, which is made more challenging by
the noise around interoperability’s successes, failures and needs.”
The foregoing excerpt from the 2016 Philips Wellcentive white paper by Mason Beard, Chief Product Officer, and Phillip Burgher, Director, Software Development entitled “The Case for Intelligent Interoperability” (https://www.wellcentive.com/resource/white-paper-case-intelligent-interoperability/) makes clear the urgency, meaning and required functionality of electronic health record (EHR) system “intelligent interoperability.” The white paper authors include the Health Information Management and Systems Society (HIMSS) definition of interoperability as “… the extent to which systems and devices can exchange data, and interpret that shared data .. [and that] For two systems to be interoperable, they must be able to exchange data and subsequently present that data such that it can be understood by a user.” Intelligent interoperability is essential for healthcare providers to effect a smooth transition from volume-based to value-based care, porovided that it includes these features:
“Features of intelligent interoperability that can help accomplish value-based care goals on top xof the exchange process include:
• Adaptable interfaces — A self-service administration toward rapid deployment of configurable interfaces and instant access to processing controls
• Mapping recommendation engines — Machine-learning algorithms that auto-recognize custom and standard clinical codes; can suggest more relevant mappings specific to CQMs [clinical quality measures]
• Collision detection — Fuzzy algorithms to auto-detect if multiple patients are assigned a unique ID due to downstream data architecture issues possibly resulting in collided charts
• 360-degree traceability — Complete history and audit trail of data origination, viewers, editors and end point. Allows for rollback and reversal while maintaining HIPAA compliance and payer audits”
The enterprise service bus and end-point connectivity features specified in the Cloud Healthcare Appliance Real-Time Solution as a Service (CHARTSaaS) reference architecture (RA) satisfy the intelligent interoperability definition, and the operation of a CHARTSaaS RA-compliant IT solution on a HIPAA-compliant cloud platform positions it perfectly to effect interoperability to/from user on-premise to/from cloud to/from remote site systems and sources of data. Please validate this proposition to your own satisfaction by reviewing the details of CHARTSaaS and the CHARTSaaS RA in these presentations, and then by imagining a CHARTSaaS-enabled IT solution:
Healthcare providers and their patients will benefit significantly from appreciating and then applying a CHARTSaaS RA-compliant IT solution. To do so will mitigate medical mistakes (currently the third leading cause of patient deaths, per Makaray and Daniel, http://www.bmj.com/content/353/bmj.i2139), thereby minimizing patient adverse events; and also will optimize clinical case outcomes while maximizing the cost-effectiveness of care and treatment and accelerating the accrual of medical knowledge.