4 Master Data Management Best Practices in Healthcare

Organizations in all industries are struggling to keep up with the rapidly accelerating proliferation of digital data occurring in nearly all day-to-day operations. The healthcare industry currently accounts for 30% of data created each day globally and will outpace projected growth in manufacturing, financial services, and media over the next three years. To meet the coming data management challenges, healthcare organizations must prioritize implementing master data management best practices through combined processes of policies and technologies. 

This guide will teach you what master data management is and how healthcare organizations can implement it most effectively. 

Key Takeaways:

• The runaway growth of healthcare data has made master data management (MDM) a critical priority for healthcare organizations.
• MDM is a subfield of data management that focuses on creating an authoritative version – or golden record – of an organization’s data to improve its reliability and utility. 
• Healthcare organizations can apply MDM best practices in domains, data governance, and goal alignment to achieve long-term, scalable solutions that improve patient outcomes and reduce costs. 

What Is Master Data Management?

Master data management (MDM) is a specialized branch of data management that standardizes the capture, storage, and exchange of data types – referred to as domains in MDM – critical to an organization’s operations. An organization’s master data comprises the catalog of standardized data contained in an organization’s data domains. Data domains vary by industry and organizational structure, but they always represent objects or assets rather than transactions or activities.

For example, common MDM domains by industry would include:

• Retail: Products, customers, suppliers, employees, and purchase orders
• Healthcare: Hospitals/clinics, care providers such as doctors and nurses, patients, services, equipment, and suppliers
• Public Sector: States, cities, agencies, services, employees, and citizens
• Contract Services Such as Finance and Insurance: Agents or brokers, policies, services, assets, and clients

Organizations typically create and store data from different domains in separate IT systems. MDM focuses on improving data quality and reliability by establishing consistency standards for data elements and identifiers throughout an organization.

What Is Master Data?

Master data is an organization’s definitive data set or golden record for information in each domain. MDM systems and practices create master data through processes that resolve inconsistencies, duplications, and gaps in data persisted in different IT systems, such as customer relationship management (CRM) platforms, data warehouses, and enterprise resource planning (ERP) systems. 

Image Source: https://www.conducivesi.com/mdm-in-healthcare

For example, formats for data entry fields may differ across systems resulting in incomplete or ambiguous record matches, as illustrated above. MDM standardizes data formatting throughout an organization to eliminate these discrepancies, creating an authoritative master list or single source of truth that improves operations and yields more reliable analytics. 

4 Healthcare Master Data Management Best Practices

For national hospital networks or small private practices, implementing MDM systems and practices can help organizations create more consistent, reliable data to improve the quality of care and gain actionable insights. Among polled physicians, 95% agree that better data quality and interoperability ultimately lead to better patient outcomes. To achieve this, healthcare organizations can apply established MDM best practices. 

Image Source: https://cloud.google.com/blog/topics/healthcare-life-sciences/google-and-harris-poll-healthcare-interoperability-survey

1. Maximize Data Domains

When setting up an MDM system and defining its data domains, administrators and IT personnel may feel inclined to minimize the number of domains contributing to the organization’s master data for efficiency and simplicity. While this approach may result in faster initial system implementations, drawing master data from fewer sources and domains will limit your MDM system’s capacity to disclose insights through analytics. For example, the definition of providers in healthcare has expanded in recent years to include data from peripheral services that contribute to overall patient health, such as social workers, substance abuse counselors, and even behavioral science and community health organizations. Organizations that broaden the scope and number of their data domains will have a richer master dataset to submit to analytical tools and AIs for insights to drive decision-making. 

2. Implement Data Governance

Effective MDM involves more than purchasing a software-as-a-service (SaaS) platform and hooking it up to your IT systems. Any system your organization implements ultimately relies on inputs for data standards and domains chosen by decision-makers. Data governance focuses on creating policies and assigning responsibility roles for data management tasks in an organization. In large organizations, filling these roles may require establishing dedicated positions for a chief data officer and a subordinate data governance team. Nevertheless, in any implementation style, defining responsibility for data quality and availability in each of your organization’s domains helps ensure consistent policy implementation and greater transparency in data management processes.

3. Configure Your MDM Platform According to Organizational Goals

Getting the specific results, your organization wants out of MDM systems and technologies requires communication and collaboration between leaders and IT teams. Where traditional commercial enterprises would focus on KPIs and quarterly goals, healthcare organizations may use data management for various goals, such as delivering better patient care, reducing unnecessary costs and wasted resources, or applying predictive analytics to organizational data to inform policies and decisions. Whatever the immediate results your organization seeks from an MDM plan, all responsible parties from the administrative and IT/data governance sides must align their understanding of MDM priorities and purposes. 

4. Build in Scalability

Older MDM platforms tended to be rigid and difficult to restructure once deployed. Today’s cloud-native SaaS platforms – in almost any industry – have more scalable and open-ended architectures. When choosing an MDM service, look for features that support long-term scalability as your organization grows and responds to new changes and challenges. Capabilities for adjusting data models, adding attributes, and integrating natively with other systems and services – thereby eliminating lost time and resources to custom integrations – are essential to any major MDM investment.

Establish a Single Source of Truth in Your Organization Through Healthcare MDM with Coperor by Gaine

Coperor is an industry-first, scalable MDM platform designed specifically for the unique challenges of the healthcare industry. With capabilities for integrating data across your organization and contracted partners in a data model that manages hundreds of objects and thousands of industry-specific attributes, Coperor is the premiere MDM solution for organizations of all sizes and needs.

To learn more and schedule a live demo, contact Gaine today.