When you hear that duplicate patient records can reach 10 percent or more in many health systems, it is not an abstract statistic. It is a direct hit to access, throughput, and staff workload. Every duplicate chart means more time spent searching, re entering data, fixing billing issues, or untangling clinical histories that never quite line up.
If you run or help run an outpatient clinic, especially a busy therapy practice, you live at the sharp end of that problem. Phones ring, referrals arrive, portals and email bring their own stream of updates, and your team has to decide, often in seconds, whether this person is already in your system. Record linkage in healthcare is the discipline that tries to answer that question reliably.
At its simplest, record linkage in healthcare means correctly identifying and connecting multiple records that belong to the same patient, even when those records sit in different systems or come from different sources. It is closely related to patient identity matching and patient identity resolution, and in practice the terms overlap. The goal is a unified, trustworthy view of each patient, regardless of which front door they used to reach you.
For clinics that already think in terms of a unified front office, this concept fits naturally with platforms such as Solum Health, which positions itself as a unified inbox and AI intake automation layer for outpatient facilities. When patient communications, pre visit forms, and intake data converge in one place, you have a much better shot at accurate record linkage from the start.
Although the vocabulary can sound technical, the underlying logic of record linkage follows a clear sequence. The details vary by vendor and health system, but most approaches share the same building blocks.
Raw demographic data is messy. Names are entered in different formats, addresses are inconsistent, dates are missing leading zeros, and phone numbers may or may not include a country code. Before matching, systems standardize these fields. They format names, addresses, phone numbers, and dates in consistent ways, remove extra spaces and characters, and normalize common abbreviations. Standardization does not magically fix every error, but it raises the floor on match quality.
Once data is standardized, the system compares key fields across records. Typical inputs include name, date of birth, address, phone number, email, internal patient identifiers, and sometimes insurance identifiers. The goal is to evaluate how similar two records are across all of these dimensions at once.
Most organizations use one of three broad strategies: deterministic matching, probabilistic matching, or a hybrid.
Matching algorithms typically assign a similarity score to each record pair. If the score is above a high threshold, the records are automatically linked. If it is below a low threshold, they are treated as different patients. Pairs in the middle range are flagged for manual review. This combination of automation and human oversight respects both efficiency and patient safety.
Once match decisions are made, organizations either merge duplicate records into a single canonical record or maintain separate source records that are linked through an index. In both patterns, the objective is the same: a consistent way to answer the question, who is this, whenever a workflow touches patient data.
If your technology stack already focuses on tight EHR PM system integration, record linkage becomes part of that integration strategy. Data that enters through patient portals, referral feeds, or a unified inbox, such as the model described in call queue analytics for medical practices, is then consistently reconciled against the core record set.
What is the main purpose of record linkage in healthcare?
The main purpose of record linkage is to make sure all records about a patient, across all connected systems, are identified as belonging to that same person so clinical, operational, and financial decisions rest on accurate information.
What typically causes duplicate patient records?
Duplicate records usually come from ordinary variations and errors, for example spelling differences, nicknames, address changes, data entry mistakes, or incomplete registration. When systems cannot confidently match these variations, they create new records instead of attaching to an existing one.
Is record linkage the same as patient identity matching?
Record linkage uses patient identity matching decisions, but it is a broader process. Matching decides whether two records refer to the same person at a moment in time, while linkage uses those decisions to connect and manage records across the life of the patient.
How do deterministic and probabilistic matching differ in practice?
Deterministic matching uses fixed rules and expects exact agreement on specific fields. Probabilistic matching calculates a likelihood that records belong together based on patterns across many fields. Deterministic methods are more rigid and easier to explain, probabilistic methods are more flexible in the face of messy data.
Does record linkage really affect patient safety?
Yes. Poor record linkage can lead to clinicians viewing the wrong chart, missing allergies, or overlooking prior results. Strong linkage, supported by good processes and tools, reduces those risks and improves the reliability of every decision that depends on accurate patient identity.
If you want to move from theory to practice, here is a concise action plan. First, know your duplicate rate and where your data enters. Second, tighten standards at the front door, in registration and communications. Third, confirm that your matching logic reflects both clinical risk and operational realities. Fourth, connect your unified inbox, AI intake, and integration strategy, for example by aligning efforts across Solum Health style tools and your core EHR. Finally, revisit your governance twice a year. Record linkage is not a one time project, it is a quiet foundation that either supports or undermines everything else you are trying to automate.
