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Home»Health Insurance»Good health and good data: Recognizing the link
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Good health and good data: Recognizing the link

AwaisBy AwaisApril 6, 2026No Comments5 Mins Read0 Views
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Healthcare today relies heavily on technology in the patient journey. Patient onboarding, visits staged through a digital front door, electronic records sharing, and online claims submission are distinct elements of the patient experience that are all made simpler and more convenient by providers’ investments in technology. They also demonstrate how the healthcare journey can fall apart when data is faulty or incomplete.

Patient data is at the core of healthcare processes; maintaining its quality is strategic to overall care and far more than a compliance task.

Ignoring the obvious is costly

When claims are denied, hospital revenue takes a hit. That’s an obvious connection, but what is not so obvious is that claims are often declined simply due to bad data. Errors like a misspelled name, an incomplete address, or a duplicate patient record can perpetuate problems and snowball into costly headaches. Effects are long-term, with impact well beyond payment delays: compliance gaps may occur, possibly triggering audits and ultimately eroding patient trust.

Federal programs like Medicare further prove the need for data accuracy. Their requirements are exacting and claims based on addresses without a ZIP+4® code may be outright rejected. Multiplied by thousands of patient transactions, the cost to healthcare operations is staggering. Consider the additional risk to patient care when mismatched records lead to fragmented or otherwise imperfect care.

Addressing the address

Data processes that protect accuracy serve as a linchpin to patient matching and data sharing, and must take on greater importance for healthcare providers. This is reflected in Project US@, a federal initiative to develop a unified, industry-wide specification for representing the patient address.

Standardizing address formats may not sound exciting, but the process matters immensely. Turns out, healthcare systems must be able to distinguish between ‘123 Main St’ and ‘123 Main Street’ to avoid issues when matching patient records and coordinating care. Address accuracy provides a base of clean, correct data; it’s a critical value that can be achieved with integration of a USPS® CASS™-certified address verification engine that painlessly standardizes ‘St vs Street’ and appends the correct ZIP+4®.

Safe and correct are two different things; both matter

HIPAA and other healthcare industry regulations frame the necessary protections for patient data — but they do not ensure the data itself is accurate. That is in many ways a much more complex and in-depth requirement, going beyond encrypted records, firewalls, and other security protocols. True compliance validates that the person protected by health data security processes really is who you think they are.

Workflows must include data verification, a process that checks more than data validity, i.e., is an address correct. Verification ensures the data connection, that this address belongs to this person. It’s a greater level of validation, central to patient trust and safety, as well as the data integrity intended by various compliance rules.

Good data improves care and the cost of care

The healthcare industry has long considered data quality as a back-office function, a clerical activity intended to keep files in order and ensure every field in a patient record contains text. Whether the text is correct is another matter entirely. In practice, data quality operations belong front and center, given they simultaneously encompass patient safety and financial strategy.

When data is wrong, everyone pays in some way, including patients, healthcare providers, and insurers. Who doesn’t want to avoid this? Data quality operations should be embedded into every stage of the healthcare revenue cycle, including address verification, record deduplication, and a multitude of data enrichments. It’s clear that strong address and identity verification are imperatives, but healthcare providers should also be aware that data quality tools can further be supported by AI/ML (artificial intelligence/machine learning)-based data modeling that helps keep revenue cycle management in check while preserving patient safety. Ultimately, these same data processes support both providers and patients, instilling confidence in the information being used as the foundation for care.

Digital health is only getting more digital

We’ve all heard the phrase ‘garbage in, garbage out.’ It has meaning in many applications, but perhaps none is more essential than healthcare. Precision simply is the key to data interoperability between and within healthcare systems. Even systems that are inherently smart cannot compensate for bad data. Healthcare is at a turning point in this regard: as care systems integrate technical developments like AI-driven diagnostics, remote care, and personalized medicine, excellence in care becomes even more reliant on the quality of data.

No matter what defines the next big thing in digital care, healthcare leaders must ensure their data operations are based on clean, correct, and compliant patient information. Data quality may not be glamorous, but it is indispensable — from patient intake to final reimbursement. And it may be the overlooked cure to so many of the healthcare sector’s ailments.

About the Author

Bud Walker, Chief Information Officer, Melissa

As Chief Information Officer, Bud Walker leads Melissa’s strategic vision and global expansion, helping organizations unlock the potential of their customer data. Connect with Bud on LinkedIn or via email at [email protected].

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