The Ultimate Guide to Reference Data Management in Healthcare

by | Aug 31, 2022 | Healthcare, Master Data Management

Reference data management in healthcare requires user interface management and reduces IT burden.

Data-informed decision-making increases operational efficiency and reduces organizational risk. In an enterprise system, reference data management in healthcare enables organization-wide consistency.

Industries that rely on vast quantities of information – such as healthcare systems – need a comprehensive method to store, categorize, and interpret this data across multiple applications. For example, a diagnosis like diabetes typically has a central code (i.e., reference input) that other system applications use to make sense of more complex information. 

Before constructing or personalizing your healthcare system’s reference data management protocol, evaluate a proposed software’s ability to facilitate data governance, system integration, and technological acquisition.

Key Takeaways:
  • Data-based decision-making reduces inconsistency and helps healthcare systems control costs, maximize profits, and improve value-based care. The enterprise suffers when the organization siphons data into disparate business units.
  • When creating a reference data management system in healthcare, executives should consider the importance of (1) data governance, (2) innovation planning, (3) content consistency, (4) management of data creep, and (5) integration of new tools.
  • Both payors and providers benefit from effective reference data management. The single source of truth established in such systems encourages operational efficiency, cost reduction, and optimized analytics. 

Reference Data Management in Healthcare

To understand reference data management, let’s first define “reference data.”

What Is Reference Data?

Reference data is any information you use to make sense of other information by categorizing inputs in a database or connecting that data to further enterprise inputs.

Reference data itself may be internally or externally defined.
Image Source: RDM | The Disruptive MDM / PIM / DQM List (

Examples of reference data include: 

  • Country codes
  • Exchange codes 
  • Postal codes 
  • Measurement units 
  • Products or pricing

Most reference data points remain consistent over time. For example, postal codes in the United States only change as population demographics shift over the long term. 

Other reference data points, such as country and currency codes, change more frequently. If your healthcare system relies on standardized codes that change often, an effective reference data management system must account for this “data creep.”

Reference data differs from master data. The former classifies and defines other data, while the latter is information about business enterprises. For instance, enterprise systems use reference data to create control logic and categorize data into understandable chunks to analyze master data. 

The Importance of Reference Data

Reference data plays an integral role in enterprise systems like healthcare providers. It impacts data quality, readability, and risk reduction. 

Uses of reference data include: 

  • Establishes data consistency: As the “base layer” of information about your company, reference data enables a uniform enterprise-wide understanding for designing common metrics. 
  • Enables data governance: Creating controls for data access facilitates accountability by key stakeholders and end-users. This promotes trust in the underlying data.
  • Source of truth: Use of a consistent reference data system contributes to a trusted single source of data enterprise-wide.
  • Improved data-informed decision-making: Creating a uniform source of truth relied upon by end-users throughout the organization enables the faster implementation of data insights

What Is Reference Data Management?

Managing reference data requires business leaders or IT systems administrators to manage classifications across enterprise systems and business units. This may include performing analytics on reference data, tracking changes to reference data over time, and distributing reference data across categories. 

Reference data management in healthcare facilitates value-based care and cost reduction.
Image Source: What is Data Management? | Summa Lai

An effective reference data management system:

  • Enables the mapping of complex data relationships for use by healthcare providers and patients 
  • Connects one data system with another for interoperability 
  • Automates certain data inputs to design new codes and code sets 
  • Compares inputs from disparate systems 

Key Factors for Designing a Reference Data Management System

A system for reference data management in healthcare organizations enables the efficient use of a large quantity of data. Important considerations include the following to select or design a reference data management system.

1. Capable of Mapping Reference Data

A reference data management system must be adaptable. The system must enable end-users to apply the reference codes to their application-specific or use-case-specific purposes. 

Similarly, the system must allow maximum accessibility to these users, including remote subscribers, consumers, and healthcare providers. A reference data management that is unwieldy or lacks a user-friendly interface is ineffective. 

    2. Provides for Management and User Experience

    Hospital systems and other healthcare providers need enterprise-wide consistency. A reference data management platform should provide flexibility for installing, configuring, and importing data with minimal IT costs. 

    At the same time, the data and system architecture must be secure. A reference data management system can provide role-based security, limiting access to the specific access for which end-users need the reference data.

    3. Designed with a Robust Architecture and Performance Ability

    Management systems may come with hierarchical management or interconnected sets of reference data. Hierarchical data structures allow for more complexity. 

    With this complexity, a semantic architecture allows the system to manage relationships within reference data sets across time.

    4. Reduced Burden on IT Systems

    Formal governance of reference data prevents data decay and miscommunications when mapping relationships. Workflow processes that eliminate end-to-end lifecycle management reduce the burden on existing IT infrastructure while having the added benefit of improving data quality. 

    5. Supports Interoperability Across Various Systems

    While reference data generally remains static, the master data or other information it decodes changes more frequently. Your selected reference data management program should enable the import and export of reference data through multiple formats (i.e., flat files, databases, CSV/XML files, and others). 

    6. Enables Automated Processes

    A specifically defined automated process enables reference data to collaborate with other key data forms. As this data increases in complexity, automation facilitates data consistency and maintains the ease of user interface. 

    Work with Gaine Solutions for Reference Data Management in Healthcare

    Effective data management transforms the healthcare experience for both the provider and patient. Gaine Solutions is designed with the healthcare industry in mind and designed a reference data management protocol for that purpose. 

    Contact us today to learn how Gaine can assist you in your data quality needs.

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