The need for data integrity is critical in industries that deal with large amounts of information electronically. In the health care industry, the integrity of quality control management systems is absolutely essential to protecting the integrity of staff and patient data. Backdating, swapping signatures, cheating on skills-based tests? All of these activities compromise data integrity and put patients at risk.
It can be challenging for a lab or hospital to know if their quality control software protects against fraudulent user activity. Unless you are a software developer or an IT expert, it can be hard to discover flaws or defects. A staff member looking to cheat on critical tasks required for accreditation may not be noticed by his peers or superiors. A supervisor may never notice a technician modifying records to look like they were created in the past. And who would notice if an administrator approved and signed off on a procedure on behalf of a medical director who hadn’t even seen it?
Most of the time, unethical behavior, criminal or not, combines motive and opportunity. Many folks may have the motive to cheat or act immorally, but if the opportunity does not present itself easily, it’s often a good deterrent for all but the most determined.
That’s where data integrity is so important. A solid foundation of data integrity designed into a quality management system should deter most people from acting upon their motivation through a series of controls.
Data integrity means that all data must be correct and accurate in terms of its title, date, form, content, and purpose. It must comply with the legislation governing data and it must adhere to these basic databased principles:
1. Data Model: The model describes how to organize your data and business processes. The model must be consistent and comprehensive throughout your company. It should also contain accurate information about what information you have and how you use it.
2. Data Validation: Data validation involves ensuring that the information presented about a healthcare provider’s accreditations, competency, awareness, and acceptance of the latest best practices and other information can impact the efficacy of patient safety standards in healthcare facilities.
3. Data Quality Control: This means ensuring that all of your systems are working correctly to maintain all your records’ accuracy, completeness, and timeliness.
The impact of data integrity in healthcare
Data integrity has been a hot topic in the healthcare industry for quite some time. The U.S. Healthcare Information and Management Systems Society (HIMSS) recently ranked 11 global healthcare organizations on their data management systems, including seven in North America — making it the largest global health care industry survey of its kind.
HIMSS surveyed more than 25,000 employees at 24+ organizations worldwide to determine their views on how well the health information systems in those organizations are helping organizations deliver better patient care, reduce costs, and improve patient satisfaction.
Below are sobering numbers regarding perception of data integrity:
Overall, 48% of respondents believed that their organization does not have a single point of contact for identifying and resolving data integrity issues:
American Cancer Society (ACS) — 64%
The American College of Cardiology (ACC) — 65%
The American Diabetes Association (ADA) — 60%
The Centers for Medicare & Medicaid Services (CMS) — 58%
The National Institutes of Health (NIH) — 50%
Patient Safety Institute/Nursing Home News — 48%
The U.S. Department of Veterans Affairs (VA) — 47%
American Hospital Association (AHA) — 45%
These numbers raise some important questions:
How certain are we that the data we store are secure? Are there safeguards in place to ensure data safety?
How confident are we that our results really represent what happened? Do our assumptions about what events occurred match reality?
How reliable are our records and reports? Do they match up with what we claim to have done?
Without the proper staff in place to audit a system, its functionality, and the integrity of the data, it may be extremely difficult to catch the more nuanced flaws in a quality management system.
A well-functioning quality management system should keep this process on rails – acting like bumper lanes in bowling, keeping the whole process from falling into the gutter. It should deliver rock-solid security and process management that alleviates these questions’ concerns.
When the basic tenets of data integrity are violated, especially in quality management, it is either by incompetence or design. Quality management systems, at the very least, should meet the essential levels of data integrity in order to ensure that the hospital or lab is fully accountable and that patients are being provided with the best care.
