Difference between revisions of "Master Data Management in Health care"

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(Approaches for MPI)
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== Approaches for MPI==
 
== Approaches for MPI==
 
In order to implement an MPI, 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 an algorithm based approach, where an MDM system matches the patients identifiable attributes such as name, date of birth, address, SSN etc.  The algorithm can use either a probabilistic approach or a deterministric approach.  
 
In order to implement an MPI, 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 an algorithm based approach, where an MDM system matches the patients identifiable attributes such as name, date of birth, address, SSN etc.  The algorithm can use either a probabilistic approach or a deterministric approach.  
*A probabilistic matching alogorithm assigning 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.   
+
*A probabilistic matching alogorithm 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 an subset of the key attributes and if they are an exact match then it indicates that the records refer to the same entity.(5)   
 
*The Deterministic approach matches an subset of the key attributes and if they are an exact match then it indicates that the records refer to the same entity.(5)   
  
Both the algorithms have their own pros and cons and most times, the quality of data across systems is what contributes to the variation in results.
+
Both the algorithms have their own pros and cons and most times, the quality of data across systems is what usually contributes to the variation in results.
  
 
==Processes for MDM  ==  
 
==Processes for MDM  ==  

Revision as of 03:11, 19 April 2016

Definition

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. This data is foundation to all business activities and does not change often. It is not transactional in nature. Master Data 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.

Master Patient Index and the need for MDM

Mater 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. 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 (4). With the implementation of niche systems such as Lab information System or a Radiology Information System and other custom applications as well as with the focus on interoperability and 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 data set to be useful and hence it is extremely critical that master data be managed (3).

Amongst all the data generated in Healthcare, patient is the most critical to start with but provider, location and other master data are extremely important too from an analytical perspective. Every organization will have to determine the value it will derive from management of a certain set of data before designating it as master data and including it in its MDM program.

Approaches for MPI

In order to implement an MPI, 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 an algorithm based approach, where an MDM system matches the patients identifiable attributes such as name, date of birth, address, SSN etc. The algorithm can use either a probabilistic approach or a deterministric approach.

  • A probabilistic matching alogorithm 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 an subset of the key attributes and if they are an exact match then it indicates that the records refer to the same entity.(5)

Both the algorithms have their own pros and cons and most times, the quality of data across systems is what usually contributes to the variation in results.

Processes for MDM

One of the biggest issues with master data in Healthcare is Data Quality which include duplication, lack of standardization, 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: (5)

  • Data Governance - Data governance encompasses the management and ownership of data within an organization. It includes the people, processes and technology need 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
  • Data Remediation


References

  1. MDM in the Context of Data Governance for Healthcare Management http://www.damachicago.org/wp-content/uploads/2012/01/DAMA-Spring2013-DG-and-MDM.pdf
  2. Master Data Management in Healthcare: 3 Approaches https://www.healthcatalyst.com/master-data-management-in-healthcare-3-approaches
  3. Healthcare Data Management for Providers https://www.informatica.com/content/dam/informatica-com/global/amer/us/collateral/white-paper/healthcare-data-management_white-paper_2117.pdf
  4. Prescription for Reducing Health Risks : One Dose Technology, One Dose Data Strategy http://www.business2community.com/health-wellness/prescription-reducing-health-risks-one-dose-technology-one-dose-data-strategy-0773304#Ff7PodT41d7Hdm9b.97
  5. Master Data Management within HIE Infrastructures: A Focus on Master Patient Indexing Approaches https://www.healthit.gov/sites/default/files/master_data_management_final.pdf