The more information care providers have about a patient, the better the care they can provide.
Standard protocols like FHIR, which are supported by national regulations, make it easier to collect patient data from labs, hospitals, diagnostic centers, etc. But identifying whether different records belong to the same person or contain duplicated data can present a challenge. This can be solved by a Master Patient Index with an appropriate record linkage algorithm.
In this article we will discuss:
The Master Patient Index (MPI) is a software used in healthcare organizations to uniquely identify each patient and link their multiple records together. This helps to ensure that all healthcare providers have access to accurate information about patients.
Record Linkage is the process of matching duplicate records. The linkage algorithm (method) is the heart of the MPI. It calculates the similarity of two records and decides whether they are the same.
Hint: If you are seeking a comprehensive tool for managing patient records, the term "Master Patient Index" is a useful keyword to search for. If you are more interested in understanding how it works, just keep reading the article and google "Record Linkage" later.
The implementation of MPI follows a specific pipeline, consisting of several key steps:
Now let's examine each step in more detail.
First, we clean the data.
Ensure that identifiers (like address, gender, SSN, etc.) are formatted consistently for more accurate results in the end. This can be done by removing punctuation, converting to uppercase, and standardizing values.
Normalize terminologies: while Gender may be represented as "Male/Female" in one dataset and "Man/Woman" in another, it should be coded the same way.
If a value is invalid and has been entered just to fulfill a required field (e.g. "Baby boy" in the First Name field or "00000000" for a social security number), it is recommended that the value is reset to "NULL" as the algorithm may be sensitive to such entries.
The ideal situation occurs when your data source provides clean data in the same format (same names for attributes), but this is not always the case and usually you have to convert your data to the same format.
We recommend using HL7 FHIR for standard data formats, because we are seeing significant adoption of FHIR and it looks like FHIR will become a lingua franca for healthcare data exchange in the near future. Our Aidbox Record Linkage module is implemented on top of FHIR: https://docs.aidbox.app/mdm/mdm-module.
Once the data has been cleansed & normalized, we can start the process of blocking records. If an Master Patient Index has one million records, a straightforward comparison of all records would require one trillion comparisons, but you can significantly reduce this number.
This usually involves running the dataset through a series of filters, each time using a different attribute or method to identify possible matches, but there is a tradeoff between performance and accuracy and it’s important to choose the right key for blocking.
Blocking is typically done by dividing the records into smaller groups – or "blocks" – based on certain criteria (filters), for example using weak similarity (like first name, gender, year of birth, ZIP code etc.).
As you can see on the example scheme below, one blocking operation may generate candidate blocks using the Last Name attribute and the first letter of the First Name, or it could make blocks using other attributes, like Phone Number or ZIP:
Matching is then done inside the block, or between two blocks within the same attribute:
It is worth noting that different runs of this operation may uncover different true matches, so taking the combination of the results may potentially uncover a majority of the true matches.
The aim is to minimize the number of candidates, therefore the guidelines should be:
The "heart" of the Master Patient Index is the Scoring & Matching process, and in this step it is crucial to determine whether two records are likely to refer to the same individual. Algorithms may classify records into two groups – Match or Not Match – or a third group, Possible Match, where the decision can be delegated to humans.
Matching can be done using different methods:
You can use this table to compare the methods quickly and easily:
The Aidbox Record-Linking Module uses the Probabilistic method (Fellegi-Sunter algorithm-based model), which is a type of Bayesian classifier, and compares the values of specific attributes such as first name, DOB, or SSN. If the attributes appear to be the same, this increases the match probability, and if the attributes do not match, the match probability decreases.
So, we use a system that helps us decide how likely it is that the two records are the same. We start by guessing how likely it is that any two records picked randomly match. Then we compare the records to see if they have things in common, like the same birth date, SSN or phone number.
So here is a quick and easy way to explain how the algorithm works: it compares records and accurately evaluates the evidence, so we can determine the likelihood of a match:
If this total evidence score meets or exceeds a configurable threshold, known as the cutting value, the records are considered a match and can be matched. If the score falls below the threshold, the records are considered to be separate individuals and are not matched in the MPI.
A common practice is to examine the distribution of scores and choose a similarity method that will result in a desired rate of false positives or false negatives. This can help to balance the need for accuracy with the need to minimize the workload of human reviewers.
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As a default choice, Fellegi-Sunter looks to be the most appropriate option. It requires an expert to set up and configure, but can provide a good outcome.
If you have fewer trusted data sources with intimate relationships, simple deterministic rules will be enough, but there is no way to evaluate their accuracy.
After the records are matched, it is important to decide whether you should link or merge the records.
This is useful in cases where data is continuously aggregated from different sources, as it allows the Master Patient Index to keep track of the relationships between records without having to continuously resolve merge conflicts.
This involves combining the data from multiple records into a single record that contains all of the relevant information about a patient. This is useful when there is only a single patient record and most interactions with the MPI involve a set of records.
However, it can be more complex to manage merge conflicts and false-positive links in this type of system.
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If data are continuously aggregated from different sources, a linking strategy looks preferable because there is no need to continuously resolve merge conflicts, while false-positive links could be simply reverted by unlinking.
On the other hand, this system doesn’t only deal with a single patient record, and most interactions deal with sets of records.
In real world systems, it is common to use a hybrid approach, combining both linking and merging. This is because the problem of the tradeoff between maintaining data integrity & allowing for flexible querying on the one hand, and creating a complete & accurate picture of the data on the other is unresolvable.
Hybridizing makes the whole system more complicated, but it leads to the desired outcome in the end. For example, a system might link matched records first to create a relationship between them, and then merge the linked records into a single, consolidated record.
Alternatively, a system might merge matched records first and then link the resulting consolidated records.
The tradeoff when choosing between linking and merging depends on the specific goals and requirements of the system. If the focus is on maintaining data integrity and allowing for flexible querying, linking may be preferred. If the focus is on creating a complete and accurate picture of the data, merging may be preferred.
To experiment with implementing a Master Patient Index and record linkage, try the free version of Aidbox. It offers a complete environment to test these functionalities, providing all necessary tools without any limitations.
There are several key points to keep in mind when implementing an Master Patient Index:
Our Aidbox Record Linkage Module uses the probabilistic method based on the Fellegi-Sunter algorithm, as it has been highly effective in achieving accurate and efficient results. This algorithm has been extensively studied, optimized for use with relational databases (SQL) and performed exceptionally well on large datasets.
Authors:
Nikolai Ryzhikov, CTO at Health Samurai
Pavel Suverov, PM at Health Samurai
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