The Broad Impact of Unique Patient Identifiers
Originally published by Just Associates
This is the first of three blogs focused on patient identity management best practices, which is the subject of a white paper series by patient matching experts with Just Associates. To access the series, click here.
Storing patient data electronically within an EHR hasn’t lived up to accessibility expectations, as much of the information remains fragmented and scattered across several disparate clinical systems. This adds another layer of complexity to the task of keeping unique identifiers and patient information in sync between inpatient and ambulatory environments.
Exacerbating the problem, many entities have separate revenue cycle and EHR systems for core hospitals and affiliated clinics. Even when some demographic data feeds transfer information between the systems, data discrepancies can be caused by gaps in configurations, manual updates and patient matching logic. This can lead to serious problems, such as important lab results not posting to the EMR.
There is also the burden a dirty MPI places on a facility’s limited resources. Along with the data integrity specialists focused on MPI error reconciliation, staff from clinical, ancillary and billing departments must get involved in making the proper corrections. These same resources are also typically needed to work through interface matching error queues to ensure documentation and test results are filed to the correct identifier and to review potential new MRNs that are created automatically in the system.
Overcoming these issues and succeeding in delivering the safest and highest quality care requires identifying better ways to arm clinicians and other providers with a patient’s total clinical picture at the point of encounter. To achieve that, the focus must be on reducing confusion around a patient’s history and clinical status—which is best accomplished through implementation of a well-designed patient identifier synchronization strategy.
The benefits of such a strategy extend beyond improved outcomes and lower risks to include improved revenue cycle results and the opportunity to redirect resources to other core areas.
Another benefit takes the form of clean data to exchange between HIEs and enterprise data warehouses (EDWs)—avoiding the whole “garbage in, garbage out” concept that often applies to the data these initiatives exchange with providers. Most MPI errors created at registration or within interface matching processes flow directly into these data sources via HL7 messaging. This results in a provider’s HIE query returning a proliferation of errors, causing many to simply refuse to use the exchange.
Ultimately, finding ways to keep MPIs clean and maintain the integrity of data exchanged via HIEs and EDWs will help hospitals and health systems participate more effectively in value- and quality-based care models. A commitment to a unique identifier strategy will go a long way toward improving patient matching accuracy to effectively manage the health of a population