Change is the only constant in today’s healthcare environment. Whether you’re growing your practice, entering new markets or undergoing a merger or acquisition, your practice and use of data is evolving and expanding. Access to the availability of primary patient electronic data will provide the foundation for learning and knowledge in all healthcare improvement activities. As we move from paper to electronic media, information and data will come from multiple sources and will be connected via portals or common access paths to be integrated at a single viewing point for analysis or interpretation.
That’s why data quality management has become a critical part of healthcare management. As you acquire new systems and replace old ones, your ability to protect the quality and integrity of data is vital.
As we move into a digital records system, access to and use of data becomes the foundation of learning and care delivery improvements. Developing practices to assure data accuracy and validity will become common practice in all clinical environments requiring new attention and discipline for management. Healthcare is new to these requirements and can learn from others who have a greater history with data disciplines. Data quality errors occur for many different reasons and can vary by hospital or clinical practice. While it is important for each organization to review its own processes to identify which errors are prevalent, there are certain errors that are common.
First, patient demographic data may be incomplete or just inaccurate. At the root of these problems is human error. At some point, data is manually entered by a person, and that information may or may not be verified. When free-form typing information, staff members can easily leave a field blank or fail to realize they have been provided incomplete information.
Next, outdated information plagues systems. As mentioned, demographic data is very fluid and changes frequently as individuals move, change jobs or organizations go through mergers and acquisitions. Information can become outdated quickly if processes are not in place to update or verify that information on a regular basis.
Finally, primary patient data often results from measurement or observation. Descriptions and standards exist to qualify results and serve to establish validity criteria for each measurement. When the result of such measurement is communicated as data in an electronic record, it is assumed to be valid and representative of the patient status. Too often failure to comply with the rules of measurement, or taking greater latitude for interpretations of intent, results in erroneous data and may misrepresent the patient’s actual history.
The new world of healthcare knowledge rests in management’s ability to enforce data discipline and ensure the accountability of everyone involved to deliver accurate, error-free and timely clinical data.