Rooting out Errors: Problematic Data Fields
Errors in patient matching continue to plague the healthcare industry, potentially affecting clinical decision making and patient safety, impacting a patient’s privacy and security, and increasing costs to patients, providers, and payers. What’s even more concerning, patient identification errors can grow exponentially as the information proliferates through data sharing across healthcare organizations.
In a soon-to-be-published data discrepancy research project conducted by Just Associates in collaboration with The College of Saint Scholastica, key findings indicate that data discrepancies are worsening. Identifying an immediate solution is becoming more imperative.
In an effort to discover the underlying causes of matching errors, the study analyzed patient data for patterns that may lead to conclusions about what causes duplicates to be created, thus stirring the conversation for increased standardization and regulation of data.
The study found that the underlying cause of duplicate records - using a multi-site dataset of 398,939 patient records with confirmed duplicates – was multi-tier. The middle name data field element had the greatest proportion of discrepancies between duplicate pairs, accounting for 58.1%. The social security number (SSN) was discrepant in 53.4% of duplicate pairs, while first name discrepancies occurred in 22.6% of duplicate pairs and last name discrepancies occurred in 23.7% of duplicate pairs.
So what does this information mean and what can be done now to start addressing the issues presented by duplicate patient records?
Organizations must have a patient identity integrity program that includes performance improvement measurements that monitor the percentage of error rates and duplicate records within its electronic master patient index (MPI). Policies and procedures must ensure that key demographic data are accurate and used to link records within and across systems.
To lessen the burden, healthcare organizations should partner with colleagues in patient access to establish standard policies and procedures. This includes establishing patient searching protocols, standard name entry conventions, and questions that registrars can ask the patient to determine if they have ever been to their organization before.
For example, capturing the full middle name would substantially help to verify the patient’s identity and is particularly useful with twins and patients with common names. Additionally, creating a separate searchable field to store the patient’s last four digits of the SSN would greatly improve duplicate record prevention. This is a common technique utilized in financial institutions that has improved client identification immensely. While many patients are reluctant to share their full SSN, sharing the last 4 digits of their SSN will ultimately improve patient matching.
At a national level, the ONC’s Patient Matching Initiative report states that people and process improvement are definitely needed to improve patient identity data integrity. Technology is also vital. As databases continue to expand, manpower alone will not be able to resolve this issue.
Also needed are effective tools to fix errors that do occur. Utilizing more advanced record matching algorithms is key along with other technology solutions such as smart cards and biometrics. Advanced algorithms are tolerant of both multiple data entry errors and changes in patient’s demographic data.
Also leading the way in creating initiatives are task forces focused on establishing the “gold standard” for principles, models and best practices related to Information Governance in healthcare. While no amount of advanced technologies and record matching tools will completely eliminate human error, standardization will enable proper techniques for monitoring, trending and identifying errors that occur in healthcare organizations which will improve interoperability across the care continuum.
