Avoiding Duplicates of Data: What’s an Enterprise Master Patient Index?

TechTarget defines an enterprise master patient index (EMPI) as a “database that is used to maintain consistent and accurate information about each patient registered by a healthcare organization.”

Although the term is defined for a healthcare organization, it is important to note that the same index is applicable to any ERP system with duplicate problems or application that stores people data.

An example of this outside the healthcare industry is in police department databases, as there is data stored on a large number of people, arrest records, connections to other arrests, family history, and more.

More on what an EMPI is

Person data or patient data is often contained in separate systems. In healthcare, there are records on different patients from a wide variety of different places and occurrences. Records may exist from hospital visits, primary care doctors, outpatient clinics, or even rehab centers. The same applies to police departments. Relevant data may exist in arrest records from different counties, government bodies like the FBI, and more.

An EMPI and similar technology aggregate data from all these different systems.

An EMPI also uses machine learning algorithms to ensure patients or people are listed once – avoiding duplicates and confusion within an organization on where the up-to-date information is located.

Mindbreeze InSpire is Helping Organizations with Sensitive People Data

Let’s use the example of John Smith being booked by a police department. Officers will need to access up-to-date, relevant, and real-time information on the person. However, there may be multiple people named John Smith in the system and the department’s applications need a way to understand if they are different people or duplicates. In addition, there could be alternate spellings input such as “John Smyth” or “Jon Smith.”

How can organizations ensure they are looking at the right records and avoid duplicates?

Mindbreeze uses a variety of plug-ins and out-of-the-box features to make this possible for companies. Here are a few explained.

Zone Boosting ensures the correct records are at the top of search results. Zone boosting is another way to change the order of the search results. Boost factors can be configured for so-called zones.  A zone is nothing more than a piece of document metadata. If you want documents that are found based on certain metadata to be ranked higher in the search results, you can define a boost factor for this metadata

The Mindbreeze Relevance Model calculates a relevance count using parameters like the recency of a search, term frequency, term proximity, and term inverse zone frequency.

Entity Recognition can be used to extract metadata from the document content or from other metadata properties of the documents which may be used for more efficient searches afterward.

Wasted time and effort are a major cost of having duplicates in your database. Also, it leads to inconsistent and incorrect findings by the user.

Learn more about how Mindbreeze can help your organization see people data effectively and accurately by contacting us today.