Surprising Data on Unique Patient Identifiers
A new study on patient idenfiers surprised researchers in two ways. “First, an individual’s first name, last name, and date of birth is actually a fairly unique identifier,” says John Zech, the study’s lead author who works at the Icahn School of Medicine at Mount Sinai in New York City.1
The researchers analyzed a large Social Security database of 85.8 million individuals. Any given individual had a 98.81% chance of having a unique combination of first name, last name, and date of birth that they shared with no one else in the group.
The second surprise was that a large health information exchange (HIE) with approximately 11.6 million patients did not link many records with the same first name, last name, and date of birth.
In the Social Security database, an average of 1.0062 people shared each unique first name, last name, and date of birth. In HIE data, despite dealing with a significantly smaller population, that number was 1.1238.
“That means the HIE probably was not connecting records that belonged to the same patients, because those records had different data in other fields, such as address or phone number,” Zech says.
Improve Match Rates
Given that nearly 99% of individuals in a database of 85 million have a unique first name, last name, and date of birth in data sets with a smaller number of individuals (such as a hospital’s master patient index), one could expect it to be very rare for two different individuals to share the same first name, last name, and date of birth, Zech explains.
“Registration departments should try their very best to accurately obtain a patient’s correct legal name and date of birth,” he underscores. “It is important for matching patient records.”
Database engineers at hospitals should question their record-matching process if it gives a result in which many records with identical names and dates of birth are split across different patients. “They can use a calculation we give in the paper to check their matching process from this perspective,” Zech suggests.
If a hospital has a large percentage of records that share first name, last name, and date of birth, that might indicate they need to put in place a program to improve their match rates. This can be done by fine-tuning matching algorithms, manual processes, or both.
It can be difficult for registrars to find time to verify the spelling of a patient’s name or to check if an address on file has changed. “If these items are out of sync, however, patient records may not be correctly matched,” Zech warns.
Single Medical Record
To achieve the goal of a single medical record for every person, it is necessary to create a universal patient identifier. “No attempt to match records on demographic data collected at the point of care — no matter how carefully done — will be able to avoid some false-positive and false-negative links,” Zech says.
However, it is not clear that Americans currently want a single medical record for each patient. “Many have concerns about privacy,” Zech notes.
Federal law currently prohibits the use of federal funding to develop a universal patient identifier. “Until patients come to appreciate the benefits of a single medical record — and advocate for it politically — we will have to improvise as best we can to maintain and match patients’ records with the imperfect data we have available,” Zech says.
- Zech J, Husk G, Moore T, Shapiro JS. Measuring the degree of unmatched patient records in a health information exchange using exact matching. Appl Clin Inform 2016;11;7:330-340.
- John Zech, Icahn School of Medicine at Mount Sinai, New York City. Email: [email protected].
Experts recommend patient access departments fine-tune processes.
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