In the fast-paced world of healthcare, delivering high-quality patient care relies on accurate, accessible data. For instance, when a patient visits the emergency department, clinicians need a complete view of the patient’s medical history, including allergies, current medications, recent lab results, and prior diagnoses. If the patient has received care from multiple providers or organizations, this information will likely be scattered across various systems and locations. Without effective healthcare data management, the patient’s records could be incomplete, outdated, or lost altogether, putting the individual at risk.
Master data management, or MDM, is a proven way to improve healthcare data management and streamline operations. Implementing MDM can create a single, reliable, and easy-to-access source of critical healthcare information that allows clinicians to make informed decisions and deliver better patient outcomes. This guide will explore how MDM implementation can address common healthcare challenges, the steps to take for successful implementation, and best practices to keep in mind along the way.
A robust MDM solution can help healthcare organizations create a comprehensive database of critical data to improve operations and patient care. Azulity’s healthcare master data management services can help organizations get the most out of their data by facilitating MDM implementation and streamlining the process.
Importance of MDM Implementation in Healthcare
MDM in Healthcare: A Critical Function of Data Governance
Master data management, or MDM, is a vital area of data governance that helps healthcare organizations improve patient care and outcomes. MDM creates a single, accurate, consistent view of critical data to eliminate errors that can negatively impact patient care and organizational efficiency. MDM focuses on three data domains in healthcare: patient, provider, and location. Every health system needs to think about how to manage and cleanse that data so it is ready for use across numerous systems and applications.
For example, an organization can have financial and clinical systems that use the same data, which is managed or updated once and pushed into those systems. Consistent, accurate patient data gives organizations greater confidence in providing consistent care. Patient data errors can lead to care mistakes, such as delivering the wrong medication to a patient, or administrative issues, such as billing problems. Ultimately, MDM leads to more efficient and effective data processes by reducing redundant data effort and ensuring accurate data.
MDM Governance: Who’s in Charge Here?
MDM Governance
This includes the processes, people, and groups that create, manage, and oversee master data to ensure its quality, accuracy, and value.
Ownership and Stewardship
The technical and business stewards of data and business owners of data define and manage master data, guided by the governance process.
Tools
The software can automate, execute, and track data content and activities.
Getting Help with MDM Implementation
However, organizations can face challenges as they engage in MDM. Over time, it takes a coordinated effort from multiple people and departments as stakeholders identify and collect master data. Getting assistance from a trusted, experienced third party often helps organizations achieve their MDM objectives. These experts can also help an organization implement data governance best practices and apply them to master data.
It’s also helpful to establish roles for data quality managers and analysts who can understand and apply knowledge on data quality to critical domains. Healthcare organizations that implement MDM practices effectively will find themselves on solid ground as they engage in digital transformation initiatives that use data from various sources.
Types of MDM Implementation Styles
Registry MDM Implementation: Simplifying Data Management for Businesses with Disparate Systems
The Registry approach is dominant among organizations dealing with disparate data sources, mainly smaller and mid-sized ones. It works by placing data from all sources into one central repository where the data can be cleaned, consolidated, and aligned. Matching algorithms are used to identify and remove duplicates. An advantage of this approach is that the original data isn’t altered. Changes are made directly within source systems instead of a separate MDM repository. Anyone verifying the truth of data can use global identifiers to track it back to the original unaltered source.
For example, an organization might draw data from enterprise resource planning (ERP), customer relationship management (CRM), human resources (HR), accounting, and other systems. In the Registry approach, instead of each system drawing its own sometimes differing conclusions, the MDM plan provides an aligned opinion, prediction, or trend based on all of them. This is an excellent way to avoid compliance or regulatory repercussions that demand data be preserved in its original form.
While this low-cost approach can be implemented rapidly without much impact on other vital applications, some drawbacks exist. Creating a registry hub that can receive, cleanse, and consolidate data from various sources and types can be time-consuming. Modern platforms can automate these processes and shunt data and updates from one system to another, making them more feasible.
Consolidation MDM Implementation: Achieving a Single Version of the Truth
The Consolidation style creates what is known as a “golden record” of all organizational data in a single place. Like the Registry style, it combines data from multiple sources into a hub to develop a single version of the truth. Still, in this approach, a human is involved in verifying the accuracy of the golden record and analyzing it for errors. This leads to increased reliability over the Registry approach. It also means bringing experience to bear when evaluating the data, concluding, and making more informed decisions.
The golden record becomes the primary source of truth in the organization. As it is updated, any changes are pushed out to the sources—ERP and CRM systems, for example. This is particularly beneficial for organizations that rely heavily on analytics functions, as cleansing, matching, de-duplication, and integration functions can be done anywhere. MDM Consolidation implementations are more expensive than Registry ones but less expensive than the other types detailed here.
The ability to synchronize data with original data sources almost in real time means that users of ERP, CRM, and other mission-critical applications are not disadvantaged by long delays in receiving updates from the MDM hub. For these reasons, mid-sized organizations and those with a heavy analytics workload tend to favor this approach, using it to minimize the hassle involved in having multiple silos of information within the enterprise, each presenting its version of the truth.
Coexistence MDM Implementation: Achieving an Accurate Data Synchronization Process
The Coexistence style of MDM implementation enables the MDM hub and the original data sources to coexist fully in real-time. Because there is no delay in updating records from one system to another, the golden record remains accurate at all times—as do the related applications that feed the data—leading to efficiency, timeliness, and complete accuracy. This style is relatively simple for expanding businesses to upgrade from the Consolidation style, as it takes only minor modifications to link centrally controlled data with their sources.
The benefits of doing so include ease and rapidity of reporting and enhanced data management overall. The golden record cannot possess any disparities as long as the central MDM hub and related data sources remain consistent. Retaining all master data attributes in one place means overall data quality is enhanced, access is faster, and reporting becomes more facile.
Centralized MDM Implementation: Achieving Complete Data Mastery
The Centralized style of MDM implementation is sometimes called the Transaction style. It’s a step from the others detailed here, enabling link, cleanse, match, and enrich the data management algorithms for storing and maintaining all master data attributes. It’s all done centrally and transmitted to the sources that initially supplied the data. In this approach, the centralized master system acts as the central repository. Surrounding systems and applications subscribe to it to receive updates to remain consistent.
This turns the MDM into a full-fledged system of record, which can be the primary source for the entire supply chain and customer base. Data creation among suppliers and customers can be done even in highly distributed environments. It is now established as the system of origin for all information instead of being fed system first from other organizational applications. The master data is always complete, and accuracy is always ensured. It supports implementing advanced security and accessibility policies based on data attributes—even in organizations with multiple locations, geographies, and domains.
This architecture also increases data governance capabilities. As such, it is primarily used in large organizations with stringent data governance policies and deep enough pockets to afford the necessary investment of time and money. Implementations can be lengthy and often complicated, requiring a large implementation team and help from external providers and consultants. In most cases, organizations already have a consolidation or coexistence approach before they leap into the big leagues with a centralized MDM implementation.
Azulity: The Leader In Healthcare Master Data Management
Azulity specializes in healthcare master data management, bringing proven expertise in implementing healthcare data solutions and credentialing across the US. Our comprehensive platform ensures consistent patient, provider, location, and claims data synchronization across all systems and departments. Key features include healthcare MDM, provider MDM, reference data management, credentialing, and provider enrollment. We serve healthcare technology leaders – from CIOs and CDOs to VPs of data platforms and credentialing – helping them eliminate the costly problems of fragmented data systems. Book a call to learn more about our healthcare master data management services today!
Related Reading
- Data Quality Management in Healthcare
- How to Secure Patient Data
- Master Data Management Solutions
- Patient Data Management System
Successful MDM Implementation In Healthcare In 6 Easy Steps
1. Use Azulity: The Master Data Management Experts
Azulity specializes in healthcare master data management, bringing proven expertise in implementing healthcare data solutions and credentialing across the US. Our comprehensive platform ensures consistent patient, provider, location, and claims data synchronization across all systems and departments. Key features include healthcare MDM, provider MDM, reference data management, credentialing, and provider enrollment. We serve healthcare technology leaders – from CIOs and CDOs to VPs of data platforms and credentialing – helping them eliminate the costly problems of fragmented data systems. Book a call to learn more about our healthcare master data management services today!
2. Map Your Current Data Sources
First, get a handle on all sources of data across your organization. This includes patient records, interaction history, and billing information, just to name a few. Be thorough in your discovery. During this step, you may uncover datasets or flows you didn’t even realize existed, highlighting further the need to centralize, automate, and govern them more closely.
As you map your sources, document what you find. You may want to start with a list, but it’s also helpful to eventually create an accurate map that shows data sources and how datasets interact.
3. Define Your Goals and Project Scope
Next, define your data goals and/or the scope of the specific MDM project you’re taking on. For example: Are you in the process of adopting a new MDM tool? Or do you already have one and need to optimize further? Perhaps there are specific datasets and sources you want to prioritize and transform first as part of a pilot program. Maybe you need to focus on compliance first.
All of these are valid. The takeaway is that it’s necessary to define these goals and create a plan for the project that guides you toward execution.
4. Adopt a Quality MDM Platform
The sheer volume of data healthcare organizations handle today makes it possible to execute an MDM strategy with an automated, analytics-driven platform to support it. If you don’t have one already, research the top tools in your industry and decide on the best fit for you.
5. Train Employees to Maximize Value
Your MDM tools and strategy are only as powerful as the extent to which your employees can use them. As you implement new processes, tools, strategies, and regulations around data at your organization, communicate effectively with your employees and train them so they can use them in their daily work.
6. Measure and Optimize Over Time
A smart MDM strategy doesn’t stand still. Technology capabilities and tools evolve, as do compliance regulations, patient expectations, and work environments. Stay on the pulse of these changing conditions, measure essential metrics and KPIs for your MDM strategy, and be ready to make continual improvements and optimizations.
Related Reading
- Healthcare Provider Data Management
- Reference Data Management
- Healthcare Data Integration
- Healthcare IT Consultants
Best Practices For MDM Implementation In Healthcare
1. Equipping the Potential of Azulity
Azulity focuses on master data management in healthcare. Our experience helps organizations implement MDM to improve operations and patient outcomes. Our comprehensive platform ensures consistent patient, provider, location, and claims data synchronization across all systems and departments. Key features include healthcare MDM, provider MDM, reference data management, credentialing, and provider enrollment. We serve healthcare technology leaders – from CIOs and CDOs to VPs of data platforms and credentialing – helping them eliminate the costly problems of fragmented data systems. Book a call to learn more about our healthcare master data management services today!
2. Prioritize Change Management for MDM Implementation Success
Organization-wide change management is not just a ‘good to have’ but a ‘must have’ for the success of any MDM implementation. Teams are often reluctant to implement changes as they are wary of losing out on the familiarity and functionality of the current system. Many implementations fail simply due to the lack of stakeholder involvement and their sign-off on the purpose and need for MDM. Since ‘change’ in the legacy IT and data management system is at the foundation of implementing a robust MDM solution, organizations need to manage expectations and re-engineer processes that will consequently change. These include:
- Changes in organization structures
- Reinforcing communication between business and IT
- Actual infrastructure change (if at all) because of the new implementation
- Actual outcomes of the implementation that will impact vendors, analysts, consultants, and business users
- Change management ensures minimal process disruption. It also ensures that the stakeholders are aware of the changes and prepared for adoption.
3. Business Rules Management: The Key to Extracting Value from MDM
How can you ensure all relevant stakeholders extract the maximum value from the master data? The answer lies in documenting and building business rules (both external and internal) into the MDM solution. Building consistent business rules to provide accurate and periodic data to stakeholders and business users is crucial to decision-making. Establishing clear rules ensures data accuracy and quality, aiding the leadership in making the right business decisions.
While it is critical to capture existing laws, creating new regulations, and establishing processes to manage data’s evolving nature are also essential. Business rules aid the change management process as there is no dependence on an individual or role (or waiting for a change manager) to approve every change request. This reduces overall cycle time and brings significant efficiency across processes.
4. Choosing the Right MDM Solution Improves Outcomes
MDM delivers business value to an organization’s bottom line by providing a reliable, relevant, and accurate view of business-critical master data. So, choosing an MDM solution that aligns with the business requirements is critical. Organizations often get short-changed on their MDM investment by not setting the demands and expectations of the solution upfront. A successful MDM solution must provide a unified and complete functionality for data integration, quality, profiling, mastering, and governance.
Checking the boxes in this checklist can aid in selecting the best-fit partner/solution:
- The MDM partner is from the desired industry vertical and, therefore, has the necessary data, domain depth, and knowledge.
- The MDM partner/vendor has the required MDM technical skill set, expertise, and implementation experience.
- The MDM solution offered is scalable as the organization grows, and the partner can provide support according to the forecasted growth and business plans.
- The MDM solution supports flexible deployment options like on-premise and on-cloud implementation.
- All architectural styles are supported, and broad data integration connectors are offered.
The MDM solution is built on a future-proof, next-gen technology. Azulity checks all these boxes for you!
5. Data Governance: The Foundation for MDM Implementation in Healthcare
The volume of big data has grown exponentially, thanks to electronic health records (EHRs) and interoperability, making it easier for organizations to access information. The electronic environment has further increased the number of privacy and security risks, prompting organizations to anticipate and proactively mitigate them. Data governance is at the core of every MDM implementation, but even more so in healthcare, as the data can impact a patient’s life, ensuring its accuracy and privacy is critical.
Effective data governance can ensure that internal decision-making is consistent and effective, lower the risk of compliance failures, and improve overall data security. Further, healthcare and life sciences companies procure data from several vendors, and governance becomes essential for data onboarding. Establishing a Data Governance Council establishes a systematic approach via a governance framework design, an organizational structure aligned to business priorities, ownership, and roles and responsibilities assigned to suit the MDM strategy.
Book a Call to Learn More About Our Healthcare Master Data Management Services
Master data management implementation (MDM) refers to establishing and integrating a master data management system with existing data systems in an organization. This process involves defining a structure for the master data, cleansing any existing master data before migration, and establishing ongoing processes for maintaining the data. The goal of MDM implementation is to create a single, accurate, and trustworthy source of data that can be shared across the organization to eliminate data silos and improve overall operational efficiency.
Why Does MDM Implementation Matter?
MDM implementation matters because it gives organizations a precise and controlled approach to managing critical data. It helps reduce the chaos associated with fragmented and siloed data by creating a single, accurate, and complete source of data that can be shared across various systems and departments within an organization. This process is essential for highly regulated industries like healthcare, where data accuracy can significantly impact organizational performance and patient outcomes.
What Are the Steps Involved in MDM Implementation?
While every MDM implementation will differ depending on the organization and the specific tools being used, there are several common steps involved in the process, including:
1. Establish a Plan
Like any other project, MDM implementation requires careful planning. An organization should start by defining the implementation’s goals, the stakeholders involved, and the resources available for the project. This step may also include establishing a timeline for the process.
2. Audit Existing Data
Before migrating data to a new system, it is crucial to assess its current state to identify problems that could affect MDM performance. This step will help address any existing issues.
3. Define a Structure for the MDM
Next, the organization should define a structure for the MDM system that considers its implementation goals. This step includes determining how the master data will be organized, what attributes will be included, and how data will be categorized.
4. Migrate Data
After auditing the existing data and defining a structure for the MDM system, the next step is to migrate the data. This process involves cleaning the data to remove duplicates or inaccuracies and moving it to the new system.
5. Establish Ongoing Management Processes
Finally, MDM implementation involves defining ongoing processes for managing the master data. This includes figuring out how to keep the data up to date, who will be responsible for managing the data, and how to handle any data changes over time.
Azulity: The Leader In Healthcare Master Data Management
Azulity focuses on master data management in healthcare. Our experience helps organizations implement MDM to improve operations and patient outcomes. Our comprehensive platform ensures consistent patient, provider, location, and claims data synchronization across all systems and departments. Key features include healthcare MDM, provider MDM, reference data management, credentialing, and provider enrollment. We serve healthcare technology leaders – from CIOs and CDOs to VPs of data platforms and credentialing – helping them eliminate the costly problems of fragmented data systems. Book a call to learn more about our healthcare master data management services today!