We’ve all heard those hospital mix-up horror stories: a wrong diagnosis due to an incomplete patient profile, a drug interaction because prescriptions weren’t checked against patient records, etc. Problems like these arise when records aren’t fully updated, referenced or properly matched to patients, and they are bad news for healthcare providers and their patients alike. Issues surrounding correctly matching patients with their medical records, or “patient matching,” are certainly challenging, and those challenges grow as patients utilize additional healthcare services and expand their care to various providers. In the face of growing healthcare complexity, organizations must make accurate patient matching and patient data maintenance a priority to avoid errors that could negatively affect those in their care as well as produce financial and reputation consequences for the organization.

Measuring the Risks

The risks associated with patient matching failures are clearly detrimental to patient health and safety, but they also negatively impact hospital resources and reputations. The clinical risks include inadequate, ineffective or inappropriate care, which leads to readmissions, privacy violations and slower, weaker recoveries. Financially, patient matching failures result in billing errors that delay payments and increase customer frustration and dissatisfaction, which could lead to resource-draining lawsuits. They also equate to regulatory and compliance failures that can result in fines or reduced reimbursement funds.

Addressing Patient Data Management

There are three key patient data management challenges that plague healthcare organizations: data quality, data accessibility and timeliness of the data. If these challenges are not addressed, the result is a fragmented and inconsistent view of a patient, as opposed to the all-important “single view” that helps healthcare providers make more informed decisions and diagnoses.

Making patient data more accurate, timely, accessible and more meaningful demands a twofold approach: a combination of technology and the right data strategy, both of which must be deployed across the healthcare organization.

The Technology

Enterprise Master Data Management (MDM) technology will ensure that patient data is mastered for related business domains and subject areas, which creates that critical single view of a patient. The two vital components of the mastering process are data quality management and data integration technologies. The former de-duplicates, completes, validates and corrects patient data. The latter delivers or pulls patient data to or from the right systems, in the right forms and at the right time.

Data Strategy

The second element to making patient data more accurate, timely and accessible is an effective data strategy that establishes an enterprise-wide culture of data governance and trust. This means ensuring that all applicable stakeholders, people and processes participate in maintaining the highest possible level of data quality, accessibility, timeliness and meaningfulness throughout the enterprise. Admittedly, this is no small task, but it is necessary for both short and long term success.

Enterprise MDM and a culture of data governance and trust go hand-in-hand and are interdependent. Both are needed to ensure patient matching success and reduce costly and dangerous errors. Today’s healthcare systems are growing increasingly complex, and if patient data isn’t effectively managed and organized, an organization puts itself at risk, from both a financial and patient safety perspective.