Data Integrity and HIOs

The convergence of advanced health information technologies, a greater focus on collaborative care and population health management, and a growing federal focus on initiatives driven by wide-spread information sharing has given rise to the health information exchange organization (HIO). While the benefits of HIOs are numerous and significant (e.g., point of care access to accurate patient information for improved care management and coordination; streamlined clinical workflows; enhanced organizational efficiencies), maintaining data integrity is a particularly thorny challenge.

Limited resources often result in HIOs implementing problematic data integrity practices that threaten an initiative’s long-term success. More importantly, questionable data integrity can compromise the safety of patients whose care is dependent upon the information housed within an HIO’s databases.

To avoid these “dirty data” repercussions, HIOs must operate on a zero error tolerance model. A good starting point for doing so is eliminating the following five data integrity practices that we consider to be the most dangerous.

  1. Relying on “Out-of-the-Box” Algorithms
    Many HIOs take a hands-off approach to record matching algorithms, relying on those that come standard within their health information system as the primary means of identifying duplicate records. The problem with this is that even highly weighted potential duplicates are not always the same person. This in turn creates a “needle in the haystack” problem of multiple individuals with the same name and date of birth (DOB) that grows exponentially as databases grow. In fact, relying too heavily on out-of-the-box algorithms and auto-merging to manage duplicates often results in overlaid records, which can ultimately be more dangerous than duplicates.
  2. Excluding HIM from Algorithm Implementation
    An HIO’s enterprise master patient index (EMPI) requires complex data mapping from all participating organizations’ EHRs and downstream systems, a task that is often left up to information technology (IT) teams. However, while IT professionals are well-equipped to direct data to where it needs to be, they may not be sufficiently knowledgeable on what constitutes critical data or where it resides in the patient record. For example, IT may limit the data mapped to common fields like first name, last name, DOB and gender, an oversight that can create serious challenges in preventing and eliminating duplicate and overlaid records once the data is moved into the HIO.
  3. Failure to Manage Ongoing Data Integrity
    Data integrity often begins and ends with removing duplicate records from the EMPI prior to a new system implementation, a strong practice that is nonetheless insufficient to mitigate the long-term risk of duplicate or overlaid records. Duplicates and overlays are created every day. They will continue to pollute systems — registration and all downstream clinical systems — unless action is taken to not only eliminate existing problems, but to prevent their creation in the first place.
  4. Lack of Standard Interfaces and Automation
    Believing the myth that there are no truly effective means for automating data mapping, integrity audits and duplicate reconciliation and merging, many HIOs resort to primarily manual processes that are error-prone, inefficient and require time and resources many do not have. As for interfaces, while it is true that multiple standards make pure automation of interfacing impossible at this time, Patient Identification segments do feature standard fields for key patient demographic data. Thus, as long as automated record-matching algorithms are strict enough, transactional interfaces for information exchange can also be automated.
  5. Failing to Establish Strong Governance Processes
    Many HIOs establish weak data governance out of fear that the strong policies necessary to fully regulate the integrity of data being shared across the organization will hinder participation. Weak governance may encourage broader participation, however it ultimately harms the HIO by allowing dirty data to enter the system and create a broadening circle of issues as information moves from one system and participating organization to another. It also impacts provider confidence in the information they access via the HIO, and can result in other issues such as Health Insurance Portability and Accountability Act (HIPAA) violations when weak restrictions result in unauthorized personnel accessing patient data.

Data Integrity and HIOsData Integrity Best Practices
HIOs can avoid these dangers by implementing strong data integrity best practices at the outset that help ensure patient information is clean and accurate and care decisions are not negatively impacted. Given the significance of data integrity within the HIO environment, it is important that HIM professionals play a leading role in implementing best practices across HIOs.

Much of the knowledge and expertise needed to successfully identify and correct duplicate or overlaid records resides with HIM professionals, who understand the content of the patient record and the meaning of the numerous data fields. Thus, they tend to have a better understanding of the causes of duplicate records.

To that end, HIM should be involved in all aspects of data integrity, starting with ensuring that record matching algorithms are strict enough to eliminate false positive matches, thereby preventing overlaid records caused by the algorithms. By taking an expert role and providing IT with the guidance it needs to ensure the correct data fields are being used within algorithms, HIM can mitigate the long-term risk of incorrect or inappropriate record matching and of overlaid record creation within the individual organization and across the HIO.

HIM should also:

  1. Work closely with IT to ensure that data fields are correctly mapped across systems
  2. Design, implement and manage the processes by which duplicate and overlaid records are assessed and their cause and source determined
  3. Report data integrity issues to the HIO’s data governance committee with specific recommendations on how to reduce or eliminate errors
  4. Identify users who may require additional training or corrective action and design appropriate education programs
  5. Play a key role in instituting data governance policies, including representation on the HIO’s data governance committee

When it comes to successfully maintaining data integrity, the key is consistency. Strong data integrity practices driven by HIM professionals ensure participants are sharing accurate patient information that is free of duplicate and overlaid records.

Grant Landsbach, RHIA, is data integrity/MPI manager for the Sisters of Charity of Leavenworth (SCLHS) and Exempla Health System, headquartered in Denver. Beth Haenke Just, MBA, RHIA, FAHIMA is founder and CEO of Just Associates, a healthcare data integration consulting firm focused on data integrity, data migration and health information exchange.

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