Mastering Data Management

Challenge your EMPI to do more for your interoperable health goals.

In this economy, it’s all about doing more with the resources you already have. Healthcare organizations aren’t immune to this trend, as they must maximize the value of existing technology assets and improve financial and clinical performance under tight fiscal constraints.

Those organizations with an embedded EMPI are challenging their existing infrastructure and systems to achieve more. Among these challenges, we’ve found the ones described below to be the most common:

Interoperability to Enable Communication Across the Continuum of Care

With the constant growth and evolution of the business, few if any healthcare organizations have homogenous technology environments. Consider the numerous specialty systems that exist within your health system or outpatient systems used by affiliated and referring providers.

To enable the communication of information across the continuum of care, you must be able to accurately reconcile identity across extended systems. This often requires capabilities beyond what the embedded EMPI can deliver.

An embedded EMPI’s core function is to interact with applications from the same vendor. An enterprise-class EMPI is technology-agnostic and able to aggregate identity data across information systems regardless of the unique approach for data type and format.

Quality and Depth of Identity Matching

An embedded EMPI is typically limited to simplistic methods for identification and does not extend beyond the patient. It usually employs a deterministic matching system, which can be problematic depending on the size and complexity of the environment.

An interoperable EMPI employs a probabilistic matching engine which is well suited for complex organizations with numerous disparate systems and databases. The probabilistic approach leverages statistical theory and data analysis to pinpoint variation and capture the nuances to a finer degree to establish more accurate links.

(My colleague, Scott Schumacher, explains probabilistic matching in a recent post.)

Extensibility to Manage Enterprise Master Data Beyond the Patient

The ability to track everyone who is part of your ecosystem, including patients, providers, business partners or members of a health plan, is the extensibility component of an enterprise solution that is not found in an embedded EMPI.

Accurately identifying providers across organizations allows you to analyze activity across facilities, associate providers to outcomes, track providers operating with more than one NPI, and validate credentials from third-party sources to detect illegal activity.

Embedded EMPIs do not provide this extensibility, and as a result cannot support true interoperability initiatives that expand and grow your business.

An interoperable EMPI creates system-wide views of entities that can be used by clinical and administrative systems to solve business challenges involving patients, providers, members and organizations that impact quality of care, billing/revenue cycle, provider management and health plan management.

Further, many vendors of interoperability platforms, integrated EMR solutions and clinical portals rely on the ability to effectively aggregate data across disparate systems to deliver their intended value. Many are now offering an interoperable EMPI as part of their solution, as they recognize that advanced functionality is required to enable effective data aggregation.

Smarter Analytics to Deliver Insight into Relationships

In addition to extending beyond the patient, many organizations seek to understand the clinical and business relationships that are hidden in the data.

Sophisticated analytics and data stewardship tools enable the healthcare organization to govern the data more proactively. For example, fraudulent activity can be detected through analytics tools, and records that are questionable matches can be evaluated and updated to prevent blind record overlay that could cause downstream issues.

Unreliable sources that may be delivering low-quality data into your data stream can be quickly detected and managed. Further, the ability to set tolerance thresholds that govern when matches are made is only available with a sophisticated EMPI, not the embedded EMPI.