This monthly column will highlight and discuss emerging trends and challenges related to healthcare data and its ever changing life cycle.
Identifying patients correctly the first time is where data quality begins. Think of yourself as a patient for a moment. Are you correctly identified in your provider’s information system(s)? How many other patients with your name, date of birth, and other individually identifiable health information (IIHI) “look” like you in their databases?
Patient matching is a hot topic in the healthcare industry where many stakeholders are engaging to find a solution to correctly identify our patients. Lynne Thomas Gordon, AHIMA’s CEO, stated in support of the recent CHIME National Patient ID Challenge, “A solution would be a significant step toward ensuring the integrity of health information and, as a result, better patient care.”
I had the opportunity to discuss the challenges of identifying patients and the growing importance of data quality with several health information management (HIM) professionals recently. Their perspectives vary, but the key message is clear. Taking steps to ensure data quality is essential to improving patient care and maintaining the usefulness of the data for the future.
Richard Howe, PhD, executive director at North Texas Regional Extension Center, explained his view of the overarching problem, looking at a system that lacks a uniform approach to data quality control. “The whole idea of ‘interoperability’ is dependent upon correct patient matching—which in turn is dependent upon data quality. Issues stem from the fact that every hospital performs patient registration and data quality control on very different levels,” says Howe.
Theresa Mendoza, director of quality, BI, and data services at the Dallas Fort Worth Hospital Council (DFWHC) Foundation, is familiar with these issues. The DFWHC Foundation is a not-for-profit charitable organization that serves as a catalyst for continued improvement in clinical health and research in the Dallas-Fort Worth area, and receives data from over 80 facilities using a regional enterprise master patient index (REMPI).
To address data quality, the DFWHC Foundation has implemented a front-end tool that cleanses data before it is submitted to their database. For example, if a required data element such as date of birth (DOB) is missing, the tool blocks the data from being submitted. In fact, Mendoza attributes their high match rate to the cleansing tool. “You could have the best matching algorithms out there, but it won’t mean anything if the quality of the data coming into your system is bad,” says Mendoza.
Grant Landsbach, RHIA, data integrity MPI manager for SCL Health in Denver, CO, shared concerns related to health information exchange (HIE) and master patient indexes (MPIs). “They do not always match up which results in heavy reliance on sophisticated algorithms to assist with making the match,” says Landsbach.
“The lack of standards in these information sharing endeavors is a huge deal—we cannot use algorithms effectively between systems unless they are standardized. More and more people are not giving their social security numbers (SSNs) or are asking for them to be taken out of the system because of privacy and security concerns. We have to collect many different points of data to identify a patient and managing the duplicates and overlays is an iterative process,” Landsbach states.
Beth Just, MBA, RHIA, FAHIMA, president and CEO of Just Associates, Inc. and Karen Proffitt, MHIIM, RHIA, CHP, vice president of consulting services for Just Associates, Inc., see education, inconsistent naming conventions, searching, and quick registrations as top challenges for capturing and maintaining quality data.
Did you know that the patient access or registration area has one of the highest turnover rates where staff is paid entry level wages? A RAND study indicated average turnover in registration staff is every four months. Registration staff must be trained to understand that they are the first step in a patient’s care and that there are consequences in the event of a duplicate record creation. Just states, “Staff often just don’t understand,” says Just. “But once they ‘connect the dots’ their performance improves.”
“If naming conventions are in place and consistently used, you will have a better chance of identifying the patient correctly.” says Proffitt. For example, if a patient has no known address, a facility might use the facility’s address instead. Or if there is no Social Security Number, a facility could ust a standard stand-in of 999-99-9999.
Proffitt’s experience has shown that while system training guides exist, “searching procedures are not living and breathing documents and they are certainly not reinforced.”
“We rarely see a ‘best practice’ search procedure in place, let alone one that is followed,” Just says. Search capability varies in systems and most are “deterministic,” where they perform an exact match or exact match on partial data. Names with hyphenations or suffixes entered into the “Last Name” field will likely skew the search.
“Incomplete registrations create a patient safety risk where we have seen cases of adverse outcomes caused by the previous medical record not being available during an Emergency Department (ED) visit,” says Just. Duplicates are consistently created using a “quick registration” method. These are typically found in the ED or laboratory specimens where there are very few data elements, making the true identity of the patient hard to verify. Without complete data, the record will appear as a possible duplicate where it cannot be reconciled. It will remain in the database as a separate record which will create clutter and provide no value to subsequent patient care visits.
Extra care to ensure the quality of data from the very beginning of every encounter is crucial to the continued growth and success of data quality efforts, and holds the promise of improved care for our patients.