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Mastering Patient Data Management: The Role of Master Data Management in Healthcare

A medical professional entering patient data on laptop computer. Photo courtesy of the National Cancer Institute.

By Scott Moore, Director of Presales, Semarchy

Patient data is exploding. Healthcare represents 30% of the global data share and is expected to climb to 36% by 2025. 

The sheer volume of these numbers creates almost limitless opportunities for expensive mistakes in patient care and missteps in compliance, not to mention the time and money wasted with old-fashioned data organization. 

This makes it extra important that healthcare organizations have robust patient data management systems in place. Human monitoring can’t possibly keep up with the volumes of data each organization sees, yet that data can play a critical role in ensuring positive patient outcomes.

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Understanding Patient Data Management

“Patient data management” refers to a system of collecting, storing, managing, and analyzing patient data. The data may come from many different sources, such as wearable devices, monitoring equipment, and doctor’s notes. 

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There are many types of patient data, including biometric monitoring information, patient health history, information relating to current medical conditions, genetic data, and lifestyle information. In managing patient care, it’s important for healthcare providers to have as complete a picture

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as possible. Sometimes, one data point may seem insignificant, but when put together with other data on the same patient, it may indicate a particular condition.

While the importance of patient data management is clear, healthcare organizations often face some common challenges, such as:

  • Rapid changes in data sources, types, and volumes.
  • Difficulty in having an up-to-date, full picture of patient data, especially if it is spread across multiple sources and systems. For example, humans are expected to reach more than 5,000 digital device interactions per day by 2025, with a huge number of those interactions being healthcare-related. 
  • Data silos within different healthcare operations. For example, one patient might have multiple different healthcare providers for different yet interrelated needs.

Data storage and data security issues. Patient data is protected by some of the strictest regulations governing privacy and security, yet healthcare is still frequently impacted by breaches. In 2022, hacking incidents and unauthorized accesses impacted thousands of patients.

The Role of Master Data Management (MDM) in Healthcare

Master Data Management (MDM) is an information management system that pulls data together into one centralized “source of truth.” It eliminates data silos so that the right people can access the right data at the right time.

In patient care, timely access to data can be critical. For example, an unconscious patient arrives at an ER. How quickly can responding providers download their history, medical conditions, allergies, and other pertinent information? Seconds may mean the difference between life and death. And if there are gaps in the data, there will be gaps in the care provided.

On the administrative side, patient data errors or inaccuracies can lead to billing mistakes. In an industry that follows each patient through all stages of the healthcare organization, inaccuracies can leave a poor impression.

Addressing Healthcare Master Data Management Challenges

How does MDM address the key challenges of patient data management? Some examples include:

1. Duplicate Records Contribute to Quality, Cost, and Safety Issues

What is the need for master data management in healthcare? Duplicate records are a clinical error condition (providers and/or payers do not have all the clinical or benefit information), which ultimately increases healthcare (unnecessary tests, contra-indications, overpayments, etc.) When duplicate records are created, early detection and correction by a “data steward” is critical. A data hub must provide the ability to create complex record survivorship rules, approval workflows, federated data stewardship, and the ability to extend the enterprise search – one of our clients decreased their duplicates creation by 66% when the enterprise search of the data hub was extended right to the point of registration. 

2. Updates That Change an Identity Create Serious Clinical Risk

Receiving updated patient data from source systems is a good thing, but demographic updates that incorrectly change the identity of a record is a serious clinical error that healthcare calls a “Potential Overlay.” Potential Overlays occur when a record (Winston Johns Sr., age 55) is updated in error and changes the identity (John Winston, age 33). This can result in a patient being admitted to the hospital, but the clinical staff is looking at someone else’s chart, which may lead to care mistakes. A data hub must be able to identify these conditions and alert HIM and patient safety staff in real-time.  

3. Sharing Identifiers May Indicate Bigger Problems

Different individuals sharing important identifiers such as social security, provincial health, driver’s license numbers, or insurance IDs can indicate potentially fraudulent activity or an error in how patient data was captured in the patient’s chart. Healthcare tourism is an increasingly complex issue. A data hub must be configurable to “watch” for shared identifiers and alert data integrity staff when the condition appears.  

4. Data Quality is Not Optional and Must Be Efficient.

As you can see from above, the right approach to MDM in healthcare is not optional for either providers or payers. A hallmark of healthcare information is an extremely high degree of data accuracy and integrity to ensure patient safety and build trust between care providers and patients. A 2022 report by global consulting firm Bain & Co. showed that healthcare providers are increasingly streamlining bloated tech stacks despite macroeconomic turbulence.  

Furthermore, the costs associated with provider data breaches are sharply on the rise. According to data from IBM, the average cost of such breaches now exceeds $4 million, marking an increase of 15% since 2020.  

A data hub must be able to address these challenges, and we see several areas where a Next Generation data hub can be effective: 

  • Data Enrichment: The data hub must be able to integrate with external reference data sources to bring additional data or standardized data back into the hub to increase match rate efficiency. Common examples in healthcare include obtaining a National Provider ID (NPI) from the NPPES NPI Registry and standardized addresses from various providers. 
  • Intelligent Data Stewardship: We will avoid the overused term “AI,” but Machine Learning is certainly a part of intelligent data stewardship. Organizations today are looking for the ability to create intelligent task assignments to specialized data stewards based on task types, location, client, etc. Machine Learning is desired to examine large volumes of tasks with common characteristics and make recommendations on changes to matching parameters or business processes. 
  • Source System Data Quality Compliance: Organizations are looking for data hubs to be able to assist them with advanced analytics and ML techniques that monitor the data quality of source systems over time and identify source system data quality issues that are decreasing the overall effectiveness of a data stewardship group.  

5. A Data Hub is About Far More Than Just Clean Data

Bipartisan Policy Center report published in February 2019 states:  

Patients without means/skills to manage their healthcare incur costs up to 21% more than those highly engaged. It’s time to bring the same 21st-century interoperability to healthcare as banking and cell phones.” 

Indeed, the most consequential changes we have seen over the past five years are how leaders in Health Information Management who understand this are re-casting the role of the traditional Enterprise Master Patient Index (EMPI). These changes are summarized in the table below: 

First Generation MDM Data HubA Next Generation Data Hub
Data Focused – “Build an E.H.R.”Service Focused –” Know Your Patient/Client”
Operational – “Keep the Data Clean”Operational & Analytical – “Data Insights”
One Business Owner – H.I.M.Multiple Business Owners – “Shared Governance”
Single Domain – Patient / ClientMultiple Domains – Patient, Provider, Public, etc.
Single Style – RegistryCoexistence – Registry, Physical, Reference, etc.
Internal Stakeholder Access OnlyExternal Stakeholders – Consent Models
Simple Data StewardshipComplex DS – ML, AI, Workflows, Alerts
On-Premises DeploymentCloud / Virtualized Rapid Deployment

Establishing a multi-domain MDM for healthcare strategy tied to an EMPI is vital for healthcare systems to identify and match patient data from diverse systems accurately. MDM in healthcare provides organizations with a consolidated and accurate view of their most valuable data assets. Used together with EMPI, the accuracy of matches improves drastically.  

The Benefits of Better Healthcare Master Data Management

Here’s how the right Master Data Management software can enable healthcare companies to leverage their data for optimal clinical and financial outcomes. 

1. Data Management to Improve Patient Care and Engagement 

With a single healthcare data management software solution for data integration, management, and analytics, you can: 

  • Protect confidentiality while utilizing data insights to optimize healthcare resources and outcomes. 
  • Build relationships with patients that enable you to focus on their needs, take health history into account, and speed proper diagnoses and treatment. 
  • Create continuity of care by ensuring that providers are all on the same page as clinical recommendations are carried out over time and across specialties. 

2. Achieve Interoperability 

With APIs for connecting all systems regardless of source, healthcare organizations can: 

  • Enable end-to-end empowerment: Give every provider, specialist, and staff member the insight they need to support every step of the patient data management journey.  
  • Accelerate Systems Interoperability: Coordinate data across all member and external organizations, pharmacies, insurance companies, and patient portals with the flexibility to provide enterprise-wide connectivity for those that need it. 
  • Streamline M&A: Onboard new providers and integrate patient and provider data into a single clinical record – all while reducing costs and accelerating timelines. 

3. Reduce the Cost of Healthcare Data Management 

Say goodbye to managing multiple data systems and sources. With Semarchy’s Unified Data Platform you can: 

  • Integrate any data source: Reduce data redundancy by eliminating the IT, administrative, and financial burden of managing multiple data sources and software systems.  
  • Improve business planning and agility: Improve capacity planning, forecasting, and budgeting while reducing direct and indirect auditing costs with centralized management of all data.  
  • Achieve a holistic view of financial and clinical health: Prevent costly change management programs and embrace intelligent, informed growth and expansion with increased traceability and transparency for improved business continuity, forecasting, and planning.
     

4. Improve Compliance and Security 

Drive consistency in your data to reduce errors and increase safety with tools to: 

  • Adapt to changing regulations and requirements: With a centralized data platform, you can maintain compliance and adapt quickly to changing requirements as they are published without impacting operational systems, including HIPAA and GDPR.  
  • Achieve best-in-class security: Duplicate records contribute to quality, cost, and safety issues, while data breaches are expensive and can cause lasting damage. Moreover, organizations can create complex record survivorship rules, approval workflows, federated data stewardship, and a flexible, configurable healthcare data management system to “watch” for shared identifiers and alert data integrity, HIM, and patient safety staff in real-time.  
  • Ensure quality of care compliance: Prevent inaccuracies with a single source of identity data for all patient records, manage all compliance documentation and record keeping for all entities, industry, and quality standards, ensure data validation and easily governed semantic consistency across the enterprise – all in a secure, single accessible database. 

5. Support Clinical and Financial Health for Your Entire Organization 

With the Semarchy Unified Data Platform, you can develop applications for any business vertical with customizable workflows and interfaces driven by your data and optimized to support your goals:  

  • Enable quality care experiences with data: When you know what patients are experiencing, you can provide just-in-time care to support better health. Healthcare and financial administrators can investigate care quality and manage population health or measure financial performance with ease.  
  • Support customer preferences and privacy: Self-service portals, online appointment scheduling or billing, smartphone alerts, virtual consultations via video chat, and patient self-monitoring are all part of enhanced client engagement. Achieving this requires a single trusted view of all client data that supports consumer preferences and enforces individual privacy and consent directives.  
  • Improve business planning and agility: Improve capacity planning, forecasting, and budgeting while preventing costly change management programs. Embrace intelligent, informed growth and expansion with increased traceability and transparency for improved business continuity, forecasting, and planning.  

Key Components of MDM in Healthcare

Some key components of MDM in healthcare help organizations better handle patient data management. Those include:

  • Data integration – This is the process of pulling together data from different sources to give users a unified view. For example, data may come from medical devices, monitoring equipment, hospital records, and more.
  • Data quality – The quality of data is about its accuracy, consistency, and completeness. Additionally, it’s about its usefulness. Does your data quality help healthcare providers make better decisions or do some aspect of their job better, more efficiently, or faster? 
  • Data governance – Every successful MDM program requires a robust data governance program. This oversees the management of data to maintain integrity across the system. The data should be usable and available to those who need it, while it should only be available to those authorized to view it. As part of data governance, data security must play a key role.
  • Data stewardship – This identifies who in the organization takes responsibility for data assets. Someone needs to oversee and ensure that high-quality standards are maintained, and data usage and processes are consistent.

A high-quality MDM software solution is needed to implement Master Data Management. The software should seamlessly facilitate the key components highlighted above.

5 Healthcare Master Data Management Best Practices

Here are five best practices to get your Healthcare Master Data Management project started.

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1. Define Your Scope: Examples of Data Management in Healthcare

The first place to start is with the anticipated context and scope of your project. Will you start small, and eventually increase your MDM use cases? Will your MDM solution need to be in multiple places, or in one location? 

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To get started, you may want to prioritize from a list of goals, and then add on as you meet key targets. Examples might include:

  • Create interoperability for continuity of care
  • Encourage care compliance
  • Support customer preferences with new offerings

The right MDM strategy will maximize your organization’s efficiency and connections with patients, providers, and partners. This will give all stakeholders an up-to-date, holistic view of your patient, which is critical to delivering a consistent quality of care. 

2. Ensure Governance and Data Quality in Healthcare

Ensuring the proper governance and data quality in healthcare is often an important reason for implementing a Master Data Management (MDM) solution

The right master data management strategy can alleviate the heavy lifting by painting a fuller, clearer picture of an organization’s data ecosystem. By aligning a strong “HOW” for your healthcare organization with intelligent data stewardship, you can leverage your data assets and create a single, trusted view of patient data. This in turn will streamline intakes, quality reporting, admissions, billing, and other clinical or revenue-driven initiatives.

3. Address Compliance Head On

Your MDM solution must meet current requirements regarding healthcare regulations and privacy, and be flexible enough for future needs. The right MDM strategy will enable you to leverage your data to improve care and protect confidentiality while adapting to changing regulations and requirements.

With a centralized data hub, you can maintain compliance with new regulations. You can also adapt quickly to changing requirements as they are published without impacting operational systems, including HIPAA and GDPR compliance and governance requirements.  

4. Research Deployment Options for Master Data Management in Healthcare 

Do you need a solution installed on-prem, in the cloud, or a hybrid between the two? 

Optimizing resources and costs is key. You don’t want to purchase a solution that you can’t support internally for the foreseeable future.  

The most effective MDM strategy enables you to integrate and consolidate systems to create a single view, and adapt to changing business, clinical, or technical requirements without disruption or added cost. 

5. Prioritize Tools to Drive Innovation and Reduce TCO 

Enterprise EHR vendors promised a “single view of patient data,” but challenges remain when it comes to external sources of data and interoperability initiatives. Driving innovation and enhancing decision-making require a single source of truth for analytics while improving internal operational efficiencies.

With a centralized data hub, you can seamlessly integrate data, and reduce its redundancy by eliminating the IT, administrative, and financial burden of managing multiple sources and software systems. 

With the right data hub and management strategy, healthcare administrators can investigate care quality, and finance can implement customizable workflows and interfaces driven by your data. This will eliminate manual processes, and bring clinical quality to your patients and efficiency to your business.  

Case Study: MDM in Action

Master Data Management can help all sectors of healthcare improve how they manage patient and organizational data. A global pharmaceutical company turned to Semarchy when it needed a solution to help them pull together multiple data forms and assets across 170 countries.

The organization includes over 100,000 employees, 70 manufacturing sites, and 20 R&D sites. This meant that disparate data silos were a common issue, and gaining a complete overview of data was a challenge.

Semarchy’s xDM solution brought Master Data Management to the company, enabling them to save time on data management and, importantly, gain a 360-degree view of their customers across all systems, channels, and interactions. They soon offered a better level of personalized service and a consistent experience across the organization.

Semarchy for Healthcare Master Data Management

The healthcare industry is under pressure to cut costs without cutting any corners on patient care and experiences. Effectively tapping into their vast data resources can provide a solution that helps everyone.

Semarchy offers a unified healthcare data management software solution to ensure continuity of care and accurate patient data and to enable efficient, effective decision-making. Key features include:

  • A unified view of patient data.
  • Automated compliance and a robust data governance system.
  • Visibility of data across the organization.
  • Actionable reporting and analytics.
  • Clear data ownership.
  • Faster response times for data requests.

Healthcare organizations can expect a system that adapts to their organization and helps them operate with less regulatory risk. Real-time business intelligence helps organizations to identify opportunities quickly, while consolidated patient data ensures your providers have the best available information to treat patients.

Conclusion

The healthcare industry is under pressure to cut costs without cutting any corners on patient care and experiences. Effectively tapping into their vast data resources can provide a solution that helps everyone.

Semarchy MDM provides healthcare organizations with a solution to manage the high volumes of patient data they have available. It’s difficult to get a clear picture when data exists in disparate systems and channels, but MDM brings that data into one unified view.

For healthcare organizations, this means better decision-making and, ultimately, better patient outcomes and a more robust profit margin. Managing your data well can be critical to putting your organization ahead.

Ready to implement MDM for your healthcare organization? Your first step is to talk to Semarchy about your needs. Contact us here