A culture of lab quality begins with data integrity

Feb. 22, 2018

It is the responsibility of all healthcare providers, and developers of healthcare information technology systems, to strive for data integrity. As an industry, we make all possible efforts to ensure the accuracy and consistency of the data we produce, maintain, and present. This data, from the point of order through result delivery, can affect patient care. Given the critical role of data integrity in healthcare decision-making, it must be an important consideration during the design, implementation, and usage of any system that stores, processes, retrieves, or presents health data.

A laboratory information system (LIS) is a central part of the healthcare information continuum. A detailed study by the Lewin Group for the U.S. Centers for Disease Control and Prevention (CDC) concluded that “laboratory medicine is an essential element of the healthcare system. It is integral to many clinical decisions, providing physicians, nurses, and other healthcare providers with often pivotal information for the prevention, diagnosis, treatment and management of disease.”1

With an LIS, the strength of the underlying database is a fundamental element of data integrity. For example, use of foreign key integrity constraints will prevent orphaned data from entering into the database tables. You can define integrity constraints to enforce vital business rules that are associated with the information in a database.2 The developers of the LIS can leverage database functionality to design the system in a manner that will protect the user from data entry mistakes at its very core. For instance, the database can be designed to disallow spaces at the beginning and ending of a patient ID, thus protecting the data from accepting invalid data entered by the user.

Backing it up

Another important feature of the database is the ability to restore data to a specific point in time. Many databases, for example, will write logs throughout the day, as laboratory data is entered. These incremental log files contain a complete history of database transactions. In a disaster recovery situation, it is possible to recover the data to the moment of failure with no loss of data by using a recent physical backup and rolling it forward in time by applying these logs.

Furthermore, it is critically important to perform regular backups to another device or system such as an external hard drive or to cloud storage. Backup utilities should be run at least daily to ensure a successful recovery, should a disaster occur. The IT staff could configure an external operating system partition to facilitate recovery of the system, if needed. These external backups should be stored in a secure location, and provisions should be made to rotate a copy of the data off-site on a regular basis to cover the possibility of a major catastrophe at the main site.

Interface issues

Data integrity also includes protections that prevent unintentional changes to information when interfacing data from one system to another. One way to do this in healthcare technology is to reduce unintentional manual transcription errors through use of electronic demographic and orders interfacing. Since Meaningful Use, it is most common for a lab order to be triggered from an electronic medical records (EMR) system. Interfaces with EMRs, practice management systems, and reference lab systems enable up-to-date and accurate insertion and updating of demographic and result information into the LIS as accurately as it was entered in the originating system. When the order is received at the LIS, a label is printed with demographic information from the EMR. The use of barcode printed labels reduces errors during the specimen tracking process from analyzer to storage.

Furthermore, data integrity must be insured between the LIS and medical devices such as interfaces. The LIS must be flexible enough to support instrument interfacing in a large variety of formats such as Excel, ASCII, CSV, ASTM, and HL7. It also must support the many transfer protocols such as TCP/IP, USB, file folder, and serial ports. Data imported from the instrument to the LIS must be evaluated for quality before posting to patient-facing systems through business logic and checksums, as the LIS will need to store values, graphs, corrective actions, and proof of review. The LIS must document QC failures and corrected reports so the documentation is readily available for inspections. The LIS must also assist with ensuring QC is acceptable and can disable release of results if certain criteria fail.

Laboratories conducting molecular diagnostic testing often employ a secondary review of results prior to releasing the patient results. Given the technical nature of these results, users need the ability to verify results and correct any invalid data. This may involve sequential documentation, where one laboratorian enters or accepts results and a second or a supervisor completes a final review before distributing the report. Since the specific review process will vary from lab to lab, users need the ability to customize the technical review workflow to accommodate their organization’s specific best practices.

Data mining

Laboratory data must be stored and maintained for long periods and must be easily retrievable for various purposes. Access to data over its lifecycle can be enabled through mining tools that provide insight from a population level to the patient level. For example, labs can drill down into specific diagnoses or abnormal test results to help their providers better understand how to manage chronically ill populations. These analytical capabilities make the lab a valuable partner for payers and providers that are seeking to improve care and reduce costs through value-based care efforts.

Data is protected from malicious processing through user controls and security. Administrators of the laboratory need accessibility and auditing of system changes such as modifications to tests, panels, reference ranges, patient demographics, and results. The management of user access and permissions should be logical and easy to maintain. Granular security measures include strong password settings, user permission settings, and audit trails that record details of systems and are accessed at the user level.

Working with an LIS that helps protect data and data integrity has a direct impact on quality and patient safety. This reduces potential liabilities and allows labs to make informed business decisions and protection for their most valuable asset, their laboratory data.


  1. Hallworth M. The ‘70 percent claim’: what is the evidence base? Ann Clin Biochem. 2011;48(Pt.6):487-488.
  2. Oracle Help Center. Data integrity. https://docs.oracle.com/cd/B19306_01/server.102/b14220/data_int.htm

Megan Schmidt serves as Vice President of Product Management for CompuGroup Medical Lab Division.