Patient Matching Underlies All of Your Projects
It’s well accepted that the healthcare focus on the triple aim (improving experience, improving population health, and reducing costs) has spawned thousands of initiatives and projects across all healthcare organizations spanning all aspects of the healthcare community. Commonly, I see organizations focused on broad efforts like improving the patient experience, measuring quality and performance, and enterprise clinical integration.
While each of these broad efforts has specific goals focused back to the triple aim, they all share a foundational assumption that frankly should be challenged: they all assume each project has access to patient data that is uniquely identified and correctly matched. In other words, they all assume to know who the patient is – and to have a complete view of each patient’s data – from the billions of bits and bytes of patient data they have stored in all their systems.
According to Black Book Research, this assumption (that these healthcare organizations know who their patient is) is off by an average of 18%. In a recent study, Black Book Research established that the average hospital has 18% duplication of patient records. Therefore, each of these triple-aim-focused initiatives where projects are designed to quantitatively improve healthcare by reducing costs, improving experience, and improving health are fundamentally be flawed by the lack of unified data about patients.
So for the example projects I mentioned above:
Improving Patient Experience – Patient safety is commonly referred to as the key to a positive patient experience. Fundamentally, when patients receive the appropriate care and get better they tend to report a good patient experience. However, patient safety is based on the appropriate care at the appropriate time. Using information about a patient that is incomplete, as is the case for 1 out of 5 patients based on the Black Book Research statistic, puts that care at risk, and thus the patient’s safety and ultimately their positive experience are in jeopardy.
Measuring Quality and Performance – Organizations are focused on measuring quality for a variety of reasons: for example, to prevent overuse, underuse, and misuse of healthcare services. With patient records at an average of 18% duplication, measuring the use or misuse of healthcare services is fundamentally flawed because organizations cannot accurately see all of a patient’s encounters.
Clinical Integration – The American Medical Association (AMA) describes clinical integration as “the means to facilitate the coordination of patient care across conditions, providers, settings, and time in order to achieve care that is safe, timely, effective, efficient, equitable, and patient-focused.” In order to coordinate care safely and timely, healthcare organizations need accurate and complete information about their patients. With the general duplication statistic, we know that 1 out of 5 times care providers are seeing an incomplete picture of clinical information about a patient.
So while there are no shortage of projects designed to improve healthcare at any organization, there also needs to be a focus on foundational issues such as patient identity to ensure the success of all of these initiatives. It isn’t that organizations have not tried to address the issue of patient duplication. Many organizations have invested significantly in new EMRs or EMPIs to attempt to fundamentally resolve the issue. However, as we know from the average 18% duplicate rate, they have not addressed the issue in its entirety. Therefore there needs to be a new and different approach to address this issue – and one that respects the investments made in the legacy approaches used by EMPI and EHRs.
Verato Auto-Steward™ is a cloud-based plug-in that integrates with your EHR or EMPI technology to resolve its toughest matches using the power of Referential Matching. Verato Auto-Steward automatically resolves 50-75% of the potential duplicates that your EHR or EMPI has flagged as tasks for data stewards or HIM staff to manually resolve. This enables your organization to reduce duplicates, reduce clinical costs, improve revenue cycle, reduce the costs of data stewardship processes, and improve care and patient safety.