Top-Notch EMPI Solution: intelyMatch

IntelyHealth’s intuitive Enterprise Master Patient Index (EMPI) solution, intelyMatch, is designed to help healthcare organizations in delivering more efficient care and a more comprehensive view of each patient. Our patient matching solution can solve the problem of stale and siloed data by generating the best result and match the patient to the right data.

Everyone has moved and had to go through the painstaking process of determining which items are valuable and which items are better left behind and discarded as junk, right?

If not, you are lucky, but you probably have needed to spend at least one Saturday or Sunday per year making your best impression of Marie Kondo and parsing through everything you have accumulated.

More than likely, it is surprising how much junk accumulates in a short period. Things that provide no value except to clutter up your space and weigh you down.

Conceptually, this is very similar to the process of healthcare data management. Over time, continuously adding data inevitably results in some data becoming stale and siloed.

Some of the most common, detrimental scenarios arise in the Master Patient Index (MPI). Duplicate or fragmented records routinely get created for the same individual.

What is Master Patient Index for?

Master Patient Index in healthcare can be described as an electronic database of patients receiving healthcare services.

The data that falls under the Master Patient Index is demographic information, information from the facility’s financial system for the individual patient, etc. All this is within a single-source system.

Benefits of MPI in Healthcare

• Helpful in accurately identifying an individual with their healthcare record when utilizing the healthcare services at a facility.
• Maintaining systematic records of patients and their demographic information.
• Easy access regardless of time and place with updated information of these services.
• A medical MPI ensures that the medical staff gets access to accurate and updated information on a patient’s health record.
• Can help eliminate duplicate records or entries of one single patient.
• It is one important factor in maintaining cost-efficient and appropriate healthcare services.

What is Enterprise Master Patient Index?

Enterprise Master Patient Index (EMPI) and Master Patient Index (MPI) are often used interchangeably, but there are some differences between both.

The EMPI in healthcare is a database that encompasses information of patient records from multiple source systems and not just within a facility.

It links patients’ records from different facilities or healthcare providers and maintains them together to avoid repeated entries for a single patient.

Benefits of having EMPI in healthcare

• It assists in the identification of a patient’s health records from numerous source systems for each patient and links them together to avoid risks of incomplete data of a patient.
• It is essential in healthcare data management to eliminate the manual labor put on in matching patients using data.
• Medical EMPI helps make a master record of individual patients out of data, which is scattered across numerous sources on the inter-organizational level.
• Helps you get rid of excess duplicate data of one patient to avoid the risk of missing or improper information.

Purpose of Enterprise Master Patient Index

An Enterprise Master Patient Index persistently works on matching the data from all the sources available into the EMPI.

The matching algorithm performs the function of matching data; this evaluated and locates the matches even if the demographic data is missing or faulty.

The algorithm makes use of similar logic that a data integrity specialist would use for the comparison of records and check if it belongs to one single person or not.

We can count on this algorithm for the following:

• Assessing common errors in data entry could be as minor as misspelled names or conflicting addresses.
• Improper records especially, overlapping records of one person on the information of another person.
• Default values for phone numbers and in a similar context.
• Many EMPI can use semi-public information coming from third-party data aggregators, which may help in the identification of people.

These are automated systems still, few matches cannot be made automatically and need a human hand to accomplish the task more accurately.

All this enhances the healthcare data management process and helps in getting rid of data hoarding. For EMPI to get on live for the first time will result in loads of tasks, but it can be easily made possible by prioritizing them.

Why is there a need for Master Patient Index or Enterprise Master Patient Index?

Medical EMPI is vital today to track patient’s activity within or on an inter-organizational level for providing better care and services.

If a patient’s information is entered incorrectly in the patient record, demographic and clinical data may cause providers to miss critical elements of their patient’s prior care.

Inadequate and incorrect healthcare provider data leads to poor treatment plans and decreased quality of care and patient safety. Maintaining patient indexes and clinical records is quite a difficult task.

Generally, between 0.5–9 full-time resources are needed, depending on the size of the organization. Even then, this protocol pales in comparison to the efficiency of automated data maintenance software.

Analogize the moving scenario above with the task of migrating or merging healthcare IT platforms. Just as you want to find that medium of leaning out, you do not want to discard items only to need them again in the future.

What is required is a set of rules and protocols to streamline the process….

The same concept applies, identity as much useful and validated data and store it to alleviate the pains and processes of migrating host systems, but don’t bring over the wrong, data like that can only lead to decreased efficiency and less satisfactory outcomes for providers and patients.

You probably think that your databases look like the worst Hoarders episode to have ever aired on A&E, and most likely, you correct.

The good news, with the right solution, the process is straightforward and effortless. Not only that, but it can also be an economical, one-time effort with ongoing checks and protocols to make sure that you never get back to that unmanageable, out-of-control place.

Deploying machine learning in healthcare:

This beautiful day and age have taught machines how to execute the monotonous, time-intensive, and detail-oriented tasks that would be impossible for a human to ever, complete with any semblance of success and accuracy.

A machine learner also has a better memory than any individual user, so far less erroneous data or results slip through the cracks.

The machine learner can remember [almost] everything it has previously seen. Deterministic and probabilistic algorithms match potential duplicate data values and records.

Referential checks on data points via external database APIs identify the stale and obsolete data and determine which records the system should retain. This process efficiently consolidates Master Patient Indexes into one accurate Enterprise Master Patient Index (EMPI).

The same logic and practice are transferable to many healthcare provider data sets and can be useful when combining separate Health Plans Masters or reconciling a patient account between multiple systems.

Conclusion:

Keeping a clean database conducive to a high quality of care and accurate financial reimbursement is not a one-time task. It will take ongoing effort as maintaining data sets such as patient master index is a continual process.

New data is pervasively added, allowing the opportunity for new overlays and fragments. Luckily, available software solutions can eliminate the need for expensive and inefficient human capital.

IntelyMatch by IntelyHealth relies on cloud-based storage and leverages AI to index data from multiple host sources, validate and store it in an easily accessible and understandable format.

IntelyMatch applies to any data set and any data quantity; the worse data, the better the learner adapts.

The process will pick up on viable conjunctive concepts within the data and use that to parse through and identify problematic areas (duplicate records, invalid data, generic data, etc.) and produce a confidence score on estimated data accuracy.

IntelyHealth sits above the traditional healthcare IT platforms, which allows your data to be resistant to the familiar pitfalls of user errors, poor data management, and organizational changes.

You will no longer have to stress about the quality and accuracy of information your professionals use to make critical decisions.