Data quality: the key for integrated analytics

July 12, 2015 in Medical Technology

As healthcare delivery continues to evolve, healthcare organizations are often moving too quickly from EHR implementation to population health to risk-based contracts, glossing over (or skipping entirely) the crucial step of evaluating the quality of the data that serves as the foundation of their strategic initiatives. As these organizations adopt population health-focused tools and methodologies, an integrated analytics platform and a trusted, high quality, and objective data asset is critical for success under these new payment models.

Most healthcare analytics platforms rely heavily on claims data, which are highly structured but lack the rich context afforded by clinical data. Further, the few analytics programs that do leverage clinical data typically depend on vendor-supplied integration messages, such as a Continuity of Care Document (CCD). While CCDs offer a compact and convenient way to integrate clinical data, they also impose limitations through both design and implementation that make them insufficient for population health and performance analytics.

There are many downstream processes, including EHR configuration, data transport, aggregation, normalization, and reporting mechanisms, that through omission or commission can negatively impact data quality. Even defining a data quality gap properly presents an analytic challenge. Examples of data quality issues one might encounter in the EHR include:

  • Erroneous patient identifiers, such as a missing social security number, misspelled name, incorrect sex, or transposed date of birth. ?
  • A standard numerical metric, such as blood pressure, written in text in encounter notes rather than in appropriate structured fields. ?
  • Generic diagnosis codes entered quickly or out-of-habit instead of more specific and actionable diagnosis codes appropriate to the patient. ?
  • Crucial radiology images absent from reports resulting in insufficient information to consult or verify a diagnosis. ?
  • Inconsistent entry of standard codes, such as National Drug Catalog (NDC) for drugs, derailing bulk analysis. ?

Each of these cases involves very different causes, data elements, and outside standards, and each may result in one or more different types of gaps. Some arise as a result of standard reporting configurations that fail to transmit crucial information. Others are the result of clinical practices, which may in turn stem from EHR configuration, organizational workflow, or even user personalities.

Regardless of the cause, concerns about the quality of healthcare data generated in the clinical environment threaten to derail efforts to derive organizational and public value from healthcare data sets. ?

If the quality of clinical data is so important, how can the user gain confidence about his or her EHR data?

The first step towards gaining confidence in an integrated analytics program is to develop a structured way to capture clinical data quality gaps.  To address this challenge, consider the following three points at which data quality gaps can be introduced into your datasets:

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