Master Data Management in Health care

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Master Data Management (MDM) is the practice of cleansing, rationalizing and integrating data into an enterprise-wide “system of record” for core business activities [1]. It is a discipline used to bring order and control to data. Master data is foundational to all business activities and does not change often.

It can be divided into two categories [2]:

  • Identity Data - such as patient, provider and location identifiers
  • Reference Data - which includes common linkable vocabularies such as ICD-9, DRG, SNOMED, LOINC, RXNorm and Ordersets.

The Need for MDM

Matching data to the wrong patient is not only unusable but also dangerous. In addition to providing inadequate care, inefficiency and risking patient safety, the healthcare organization's reputation and resources are also at risk [3]. With the implementation of niche systems such as a Lab information System (LIS) or a Radiology Information System (RIS) and other custom applications, as well as with the focus on interoperability and Health Information Exchanges (HIE), it is imperative that we send the right patient identifiers across systems. Also, more organizations are using analytics to help gain insights to drive care coordination and population health management. Analytics need a clean set of data to be useful and hence it is extremely critical that master data be managed [4]. Among all the data generated in Healthcare, Patient data is the natural data to start with. However from an analytical perspective, provider, location and other master data are extremely important too. Every organization will have to determine the value it will derive from the management of a certain set of data before designating it as master data and including it in its MDM program.

Master Patient Index (MPI)

Master Patient Index (MPI) is the concept that is used to manage Patient data. It includes assigning a unique identifier for each patient that can then be used by other systems and applications to refer to a patient. An organization has to decide on a matching approach as it works to consolidate the patient records from the various systems to create a clean master system of record. The most common accepted approach is algorithm based, where an MDM system matches on the patient's identifiable attributes such as name, date of birth, address, SSN etc. to find duplicate or similar records. The algorithm can use either a probabilistic approach or a deterministic approach.

  • A probabilistic matching algorithm assigns a likelihood score to the records, to indicate whether they refer to the same entity based on the acceptance of a certain volatility in the data. The higher the score, the greater the likelihood there is a match between records.
  • The Deterministic approach matches a subset of the key attributes and if they are an exact match then it indicates that the records refer to the same entity.

The probabilistic matching approach allows for the greatest flexibility and provides the highest accuracy when properly configured. The quality of data across systems however usually contributes to false positives and false negatives [5].

Processes Needed for MDM

One of the biggest issues with Master Data in healthcare is data quality which includes duplication, fragmented data, lack of standardization and incomplete information. This occurs due to multiple producers and consumers of the data both horizontally and vertically without management over the key data that is being generated in these systems. To implement a MDM program, healthcare organizations have to put certain key processes/initiatives in place: [6]

  • Data Governance - encompasses the management and ownership of data within an organization. It includes the people, processes and technology needed to make sure the data is secure, accessible, available and used in an appropriate way. Data Stewards who are essentially embedded in the business, understand the workflow and are empowered to make decisions about the data, are people who enforce standards and help make governance a reality in an organization. The Data stewards are responsible for the data quality of the domain data.
  • Data Integration - involves the process of making sure all systems are using the Master Data from the system of record. Organizations can cascade their Master Data to other ancillary systems in either a transactional mode or a batch mode. A transactional approach is more real time whereas the Master Data is updated, the information is sent to ancillary systems and at all times all systems are synced. An example of this would be, any time a new patient is created/updated in the EHR (if that is identified as the producer of the patient information), a real time HL7 message is sent to the LIS or RIS and other systems to make sure that the most updated patient information is available at all times. In a Batch mode, data is extracted from the designated source system on a periodic basis and uploaded/updated to the ancillary systems. In this approach, there is a lag between systems and hence this is not the preferred way but is still better not having a process in place.
  • Data Remediation - involves the process of addressing data quality as well as matching issues. Not all information can be matched using algorithms and sometimes manual intervention is needed to address issues.


In summary, in order to treat data as a corporate asset and to be able to derive insights from it, a Master Data Management program is crucial for any organization to put into place. Today, there are many vendors in the market that have the technology to help kick start the infrastructure however an MDM program impacts not only systems but also people and processes and in order to be successful, an organization needs to be committed to address all parts of the MDM three legged stool.


  1. MDM in the Context of Data Governance for Healthcare Management :
  2. Master Data Management in Healthcare: 3 Approaches
  3. Prescription for Reducing Health Risks : One Dose Technology, One Dose Data Strategy
  4. Healthcare Data Management for Providers
  5. Master Data Management within HIE Infrastructures: A Focus on Master Patient Indexing Approaches
  6. Master Data Management within HIE Infrastructures: A Focus on Master Patient Indexing Approaches

Submitted by Bhavna Mehta