At the foundation of quality improvement in healthcare is data. Thus, it is important that the data is working for you, so there are some vital aspects to the data that must be supported throughout an organization: data governance and data integrity.
Data Governance is Data Quality
The healthcare industry depends heavily on information obtained from patients, medical notes, medical history and medical journals. But, no one wants to be bombarded by everything all at once; there is no need to have insurance documentation in a clinical visit with a doctor, nor is it necessary for the billing department to know that you are allergic to peanuts when generating statements. This is centric to governance, which refers to the control placed over data when accessing and using it. Governance also manages the integrity and security of data to ensure the right people have the right information at the right time and in the right format to enable high-performance yet a protection of the data.
When data governance is applied to clinical data, it tends to weed out inaccuracies and lessens the financial risk that healthcare organizations are taking on. Everything is in its proper place, and there are guidelines overseeing the appropriate use of very sensitive information.
However, when data is missing, inaccuracies rise. This doesn’t just apply to specific patient data, but also reporting data points that influence how an organization handles population health management and reimbursement programs. Many hospitals and clinics can be penalized if they aren’t meeting specific standards, but they may be paying these fees unnecessarily due to poor data governance and inaccuracies.
Data Integrity Stands out from within Data Governance
As stated in the description of data governance, integrity is an essential feature, but in the case of healthcare, it stands in its own category of importance and should be handled separately. Although it may seem that data integrity is self-defining, small discrepancies between processes and procedures can cause conflict and affect care and cost to the patient. For example: if a patient is triaged before being registered into a hospital, an insurance carrier might kick back billing due to inconsistencies. This leads the hospital to seek payment directly from the patient or to resubmit the paperwork with more details.
This only begins to scratch the surface of data integrity. There are many characteristics to it that must be dealt with on some level or another. To give you just a taste of what healthcare providers face, here is a small list of qualities that are addressed:
- Verification and validation
- Automatic alerts for medical treatment variances
- Data quality
- User-friendly system interface
- Strong workflow structure
- Consistent documentation for workflow
- Capturing of the right data
- Flexibility within the system for growth of changes
- Templates to help users obtain all necessary information
- Adequate training
Honestly, the list is much longer and much more specific for those assigned to manage the data integrity for their healthcare organization. In fact, each of these items probably has a vast array of definitions and subcategories below them. Sufficeth to say this is why integrity of data has its own classification, and requires strict dedication to its protection and adherence.
Quality improvement in healthcare relies on focus to both categories of data control for quite a few reasons, one of which is the fact that much of the care we receive is not found on a single plain of care, but at several different doctor’s offices, specialists, therapists and other healthcare professionals. Coordination of care no longer entails walking an x-ray to another doctor’s location or waiting for copies of your medical record to be delivered. Rather, your history is able to be pulled up electronically, and can contain details that help to strengthen healthcare treatment and with much more efficiency.
No one wants to just be a number when it comes to receiving medical help, nor do any of us want to have subpar aid when trying to get better. As such, it is necessary to support the qualities that go into data and its availability to be accessed and yet protected. Such demands are not for the faint of heart, yet are truly a demand in this day and age of fast-paced technology.