Is your Healthcare Data Quality Facilitating Optimal Patient Care? Pt. 2: Data Quality Gaps

In Pt. 1 of this Healthcare IT blog series, “Is your Healthcare Data Quality Facilitating Optimal Patient Care?” we discussed the importance of quality in Healthcare IT, specifically clinical data and the problems with data quality.  In this post, we will address where data quality gaps occur and how to address these gaps, and how to gain confidence in an integrated analytics program.

In order to gain confidence in an integrated analytics program, Healthcare companies need to develop a structured way to capture clinical data quality gaps.  In order to address this challenge, it is important to take into account the following three points at which data quality gaps can be imported into your datasets:

  1. Capture.  This is the point at which clinical professionals/automated systems enter data into the EHR.  In order for valid data capture to take place, a clinical event has to occur (ex: encounters/consultations with patients or a returned lab test) and the results of the clinical event have to be accurately entered into the system.
  2. Structure.  This is the process in which captured data is stored in an appropriate location and format.  The validity of structure is dependent upon both the way in which the data is entered as well as on the configuration of the EHR platform.  For example, if an integer is entered into a text field, the integer’s accessibility for analysis and reporting is reduced.  In addition, even if the integer is entered into a field that is structured, if the template is not configured or mapped correctly, the value may end up being stored in an inappropriate and incorrect location.
  3. Transport.  This is the way in which data is extracted from storage and subsequently made available to external systems for reporting or analysis.  If there is no direct database connection, then not all pertinent data will be included in an extract.  In fact, how records are selected for inclusion and which fields are extracted are both key factors in how the “Transport” mechanism impacts the outgoing data quality.

However, just highlighting where the data quality gaps occur is not enough, companies need to address the aforementioned gaps with focused initiatives.

Doing away with unstructured data. To begin the process, companies need to make sure that providers are making use of structured fields whenever possible.  However, this is harder said than done as the number of drop-down lists and checkboxes can get overwhelming.  In order to help alleviate this problem, work with providers when possible to configure templates that minimize the number of needed clicks, make sure numeric fields are obvious and intuitive, and make it easy to correctly select units.

Discovering the optimal structured element. This is harder than it sounds.  An example of this is when there are numerous fields and locations in which to enter vitals.  If this is the case, consider whether the workflow can be refined to the vitals data elements most likely intended for the visit and for future reporting.  A similar issue also arises when providers configure their EHR client. An example of this is when individuals may choose to create a specific, preferred template for a commonly used medication configuration.  Although this can be a time-saver, if the provider-user doesn’t tie the proper NDC code to the configured medication, NDC codes will be absent in future notations of the medication.  This absence results in gaps in downstream reporting.  If these gaps are detected early enough, then relatively simple workflow or template changes can correct the issue before it becomes unmanageable.

Understanding your reports.   A significant source of data quality gaps continue to be a mismatch between the systems used for reporting data from the EHR and the system used for analysis and measurement calculations.  In order to address this issue, evaluate the quality of the reporting mechanism as early as possible.  Focus on identifying common data quality gaps.  One might think if you use a major standard like HL7 or CCDs, then the standard will support the dataset you need right out of the box.  This often isn’t.  In order to adapt to this misconception, make sure your reporting system is configured to support a complete clinical dataset, and that you have transparency into how information is retrieved and packaged.

While it is possible to diagnose and correct data quality issues, the solutions to these issues demand both transparency and completeness from the dataset.  Lacking the ability to identify gaps directly and the ability to understand how data are structured reported makes even the most terrible data quality gaps hard to detect and fully understand.

The combination of levering the power of an integrated data set and understanding of this continuum of data flow, from capture to structure to transport, allows Healthcare organizations and provides to have an improved understanding of and confidence in their data.  This increased understanding will enable critical insight into the Healthcare organization’s patient populations, facilitating incentive revenues, improved quality measures, and improved quality of care for patients.

Gordian Dynamics helps Healthcare organizations understand these issues and work through the right process for data management. Contact us today to learn more about our services.