What is Master Data Management? A Healthcare Perspective.

by | Apr 20, 2022 | Healthcare, Life Sciences

Master data management in healthcare concept on a laptop.

Market demand for tools to ensure data quality and compliance is growing. The global market for master data management systems in healthcare and other industries had a combined value of $11.3 billion in 2020 and will rise to $27.9 billion by 2025.

Two globalized trends account for the bulk of growth in this field. During and after the Covid-19 pandemic, the widespread adoption of remote work in offices across industries has compounded the difficulty of managing data in multiple versions and has highlighted the need for real-time authoritative data at the organizational level. At the same time, recent developments in technology – particularly in the Internet of Things (IoT) – have opened new possibilities for better and more cost-effective data management.

Changes in big data management practices present unique obstacles in healthcare. The industry’s complex and ever-evolving compliance standards and privacy liabilities make better data management both more desirable and harder to implement. In this guide, you’ll learn what master data management is and how it can help healthcare organizations handle emerging industry challenges.

Key Takeaways

  • Master data management is an IT-enabled practice that establishes a master data version as an organization’s single source of truth.
  • The challenges of reconciling multiple inconsistent copies of the same data create the need for master data management in healthcare.
  • Master data management helps healthcare organizations improve overall operational efficiency, mitigate risk, and deliver a higher standard of care to patients.

What is Master Data Management?

Master data management (MDM) refers to the IT process of establishing and maintaining a single master source of critical business data. Most businesses create and store data in different systems and databases. As the data recorded in different business operations contain a high degree of overlap, inconsistencies in multiple copies of the same data inevitably emerge over time


Video: What is Master Data Management?


MDM Data Quality Goals

Organizations practice MDM to resolve these inconsistencies in a single source of truth or master data version that has precedence when different data versions conflict. Technology research firm Gartner identifies five data quality goals for MDM.

  • Uniformity
  • Accuracy
  • Stewardship
  • Semantic Consistency
  • Accountability


Data quality types in master data management.
Image source: https://www.syntio.net/en/labs-musings/data-engineers-guide-to-data-governance-part-3-3


To achieve these goals, organizations need to align people, processes, and technology (PPT) according to a clearly defined set of roles and practices.

  • People: MDM-specific roles include an executive data owner at the level of master data and possible subordinate roles for subset data owners in categories such as employees and customers.
  • Processes: Data owners implement MDM processes to define how data is captured, stored, transmitted. Processes determine the hierarchy among data sources and the semantics for validating and distributing data.
  • Technology: Establishing master data – a golden record of an organization’s critical information – requires a dedicated software platform into which MDM processes move data.

3 Uses of Master Data Management in Healthcare

Over the next decade, MDM will play an expanding role in many industries to meet the challenges of data growth. At the same time, nearly twice as many healthcare organizations remain unprepared for major technological disruptions as those ready to change with the times.


Healthcare organizations ready for disruptions.
Image source: https://www.gartner.com/en/industries/healthcare-providers-digital-transformation


Here’s a list of three healthcare-specific instances where MDM can provide critical capabilities and enhance overall performance.

1. Duplicate Records

Healthcare data inherently contains many possible entry points for duplicate records. Provider, payer, and patient data overlap significantly. When organizations lack the ability to update information across silos in real time, duplicate records can set redundant processes in motion, causing errors to propagate through the system, including:

  • Overpayments: Redundant solicitations to payers result in automated overpayments, adding manual hours to the billing workload.
  • Repeat Tests and Appointments: Barriers to data sharing between providers at different points of care can result in patients being directed to repeat tests and regularly scheduled appointments.
  • Contraindications: In the U.S., medical errors cause 7,000 to 9,000 deaths annually and cost the healthcare system more than $40 billion. Failure to identify drug contraindications ranks among the top seven causes of serious medical error.
  • Patient Overlays: Overlays occur when inconsistent data entries cause separate records to merge. Overlaid records can result in healthcare professionals using the wrong records to make treatment decisions.

In each of these instances, duplicate records introduce costly inefficiencies and, occasionally, serious harm into healthcare operations. MDM-enabled data hubs can mitigate these risks through probabilistic matching and early detection of data discrepancies.

2. Data Visibility

As the amount of data collected in business operations grows, so does the potential value of the insights it contains. With the multibillion-dollar data analytics market commanding a predicted 12% CAGR for the next five years, organizations are finding that a better understanding of the data they already have yields at least three bottom-line advantages:

  • 69% chance of better performing strategies
  • 8% increase in revenues
  • 10% overall cost reduction

To deliver accurate, actionable insights, data analytics depend on a reliable authoritative data source. In the healthcare industry, organizations can use MDM capabilities to achieve visibility into the data they collect across disparate systems and enable valuable functions.

MDM in healthcare can also help:

  • Identify population-level trends
  • Give providers access to comprehensive records to indicate optimal treatment at the point of care
  • Monitor irregularities in treatment and performance standards
  • Automate hospital intakes processes
  • Integrate patient data from smart devices

3. Compliance

Healthcare has extremely complex data governance and compliance requirements. Federal and state laws concerning privacy controls change regularly. For organizations that operate at a national or international level, ensuring compliance and mitigating the risk of legal liabilities is costly and time consuming.

MDM can help large healthcare organizations navigate this unpredictable landscape in several important ways:

  • Define internal policies for FOIA requests, the No Surprises Act, data retention, and audits.
  • Align outgoing data with state-specific requirements.
  • Filter sensitive data passed into marketing databases for HIPAA and GDPR compliance.

Take an Ecosystem-Level Approach to Healthcare MDM with Coperor by Gaine

Coperor is an industry-first MDM platform designed by healthcare professionals specifically for use by healthcare professionals. Coperor integrates master data and affiliate data in real time to give organizations access to a single, trustworthy source of data across complex networks of contracted partners.

To request a demo or quote, contact Gaine today.


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