Distrust of data can stop or delay action on a performance improvement agenda and it's very difficult to overcome that distrust. Tracing and correcting errors is costly and often imperfect. The amount of operational inefficiencies due to data quality issues, such as untangling an error in patient identification, is legend.

In short, problems in data accuracy and validity can impair the value of the information that health care is investing so much to digitize.

TDWI estimates that poor data quality costs U.S. businesses $600 billion a year. There's no estimate of the cost of data quality problems in health care-but even the most conservative guesses for quality problems in the nation's largest industry would indicate there's real money at stake here.

The explosive growth of digital information-with weak information governance-has raised the stakes. Information integrity is a foundational building block of effective information management and it's arguably the most underdeveloped block.

Framing the challenge

Information integrity is broadly defined as the trustworthiness and dependability of information. More specifically, it's the accuracy, consistency and reliability of the information content, processes and systems. The concept is larger than "data quality," which focuses on guarding against and correcting "bad" data. Information integrity builds the foundations for good data, which includes:

  • The information content including data elements and their underlying definitions, relevant data content standards in whatever form they are held (e.g., numeric, text, structured, unstructured) and the metadata.
  • The processes used to capture and transform data for use. Data entry, decision support, vocabulary mapping, coding, and claims adjudication process are familiar examples of health information processes that have the potential to impact information integrity, positively and negatively.
  • The human, IT, organizational and regulatory environment systems and subsystems that work together to impact information integrity. A major software upgrade is a good example of a system change that carries risk. The transition from ICD-9 to ICD-10 is a classic case study in how all parts of the system must come together to ensure that integrity. Information integrity will always be an issue in health care because the industry is so information-intensive.

Data quality issues certainly preceded the change in medium from paper to computer, but research on unanticipated consequences of health IT and experiences of those who extract data for analysis and exchange suggest that they are growing rather than diminishing.
Scoping the issues

For a quick assessment of your organization's information integrity readiness, consider the following six questions:

1. Do you use data quality benchmarks for critical applications such as patient registration and identity management, CPOE, other clinical data capture applications, coding?

2. Does the organization maintain a data dictionary of expected meaning and acceptable representation of data for at least critical data sets and, if so, is the dictionary accessible to those who need to reference it in their work?

3. Are data mapped across systems to ensure that there is consistency in meaning and attributes?

4. Do documentation improvement efforts lead to systemic improvements or do they stop with a case-by-case focus?

5. Are defensible protocols used to monitor, audit and trend data quality, and is there staff training on the importance of their contribution?

6. Do you test all upgrades and interfaces with test databases and test scripts to make certain there is no information distortion?

Health information is undergoing profound change, not only in its medium but also in the demands being placed on it. Without adequate information governance, counterforces work against information integrity.

First, there is the sheer pace of change. Most health care organizations have adopted health IT on a compressed schedule. This has left little time or resource to put in place needed information integrity protection mechanisms. Ideally, some form of information governance would precede major systems change, but this does not happen in most organizations. So, there is some infrastructure catch-up that is needed at most.

Complexity also works against information integrity. The sheer number of systems supporting the high stakes and high-speed information and communications demands-in the absence of information content standards-inevitably impedes information integrity. Further, data doesn't stay in one place and its movement presents a changing data quality target.

And complexity doesn't diminish. In fact, systems continue to evolve; software and systems upgrades and conversions have been shown to be a high-risk time for information integrity.

And finally, there's human error, accidental failures, deliberate fraud and other situations that have the potential to profoundly impact the integrity of health information.

Practical steps

Consider whether your organization is underinvesting in information integrity. Your answers to the questions posed earlier will help you judge. If you're reactive to data quality problems, more deliberate action may be needed. If staff distrust and challenge data, particularly aggregate data for performance improvement or business intelligence, tighter evaluation is probably needed. If procedures for auditing, error correction and staff training are not sufficiently standardized, improvements are in order.

The following actions can go a long way to advance information integrity in your organization are:

  • First, identify this as an important element of the organization's strategic information agenda. It may be reflected in principles for information asset management described earlier in this series or it may be set as a tactic to achieve the organization's goal. No matter how it is accomplished, information integrity must get on the agenda.
  • Assign responsibility for information integrity and set incremental goals, focusing first on applications with the highest requirements for accurate and reliable information. As with all change management, a few small wins will go far in sustaining progress.
  • Formalize a data dictionary function and a process for its development and dissemination. Understand the data and its characteristics within and among applications and the standards-or lack thereof-so they are available to the users who need them.
  • Invest in developing the competencies and awareness of staff that capture, manage and use information.
  • Evaluate and measure information integrity actions, and assess those findings against available comparative and benchmark measures.

The value of information accrues from its ability to fulfill the purposes set forth by people. At the most fundamental level, information integrity is the basis for the trust that users have in data.
If your organization is putting out fires attributable to information integrity, consider short- and long- term plans for improving information integrity. It's a safe bet that those efforts will pay off now and in the future.

This article originally appeared in Health Data Management magazine.

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