Data-Driven Healthcare: How MDM Improves Patient Outcomes

Carol Johnson, a 58-year-old diabetic patient, is admitted to the nearest hospital with severe chest pain. Her details are entered into the hospital’s Electronic Medical Records (EMR) system. However, the lack of integrated records from her primary care physician leaves the cardiologist struggling to access her medical history and determine her pre-existing conditions. They prescribe additional diagnostic tests, adding to the financial strain. It becomes even more exhausting for her when mismatched supplies, caused by errors in the hospital inventory system, prolong her stay.

This is where Master Data Management (MDM) could have made all the difference.

With an effective MDM strategy, a single source of truth (SSOT) emerged. Powered by master data, the SSOT would have provided a complete view of Johnson’s medical history, medications, and supply needs. Her cardiologist could have quickly assessed her condition and determined the most effective course of action. Even the right supplies would have been readily available, thanks to an integrated inventory system. MDM would have transformed Johnson's experience from what could have been a frustrating and costly ordeal into an efficient journey toward recovery.

McKinsey has described MDM as a ‘critical component of any organization’s data strategy. These capabilities can make or break an organization’s efficiency and reliability—particularly in complex organizations with multiple business units, where data silos can lead to inefficiencies and errors.’

In healthcare, master data encompasses six key categories:

• Patient data (demographics, insurance information, identifiers, contact details, allergies, etc.)
• Provider data (doctor, nurses, affiliations, schedules, etc.)
• Organization/Facility data (location, facility type, ownership details, etc.)
• Product data (medical devices, pharmaceuticals, drug catalogs, etc.)
• Supply Chain data (medical supplies, equipment names, vendor information, pricing, etc.)
• Payor Data (insurance providers, billing details, etc.)

High-quality master data ensures that healthcare analytics are accurate, insightful, and capable of delivering better patient care. This applies to all types of analytics—descriptive (analyzing past trends), prescriptive (identifying optimal strategies), predictive (forecasting future outcomes), or discovery (highlighting areas for further exploration).

Repercussions of Poor-Quality Master Data

Flawed master data can affect the entire organization, from operational efficiency and regulatory compliance to strategic decision-making and customer satisfaction.

For instance, a large American healthcare network struggled with duplicate records and other data quality issues. The lack of an SSOT led to substantial rework in operational reporting, bred mistrust in reports among stakeholders, and hampered effective decision-making.

In another case, a retail supplier and buyer of medical equipment, with a growing customer base, product lines, partners, and vendors, faced challenges due to scattered data across information silos. As a result, time-to-market was negatively impacted, and customer and product management processes were delayed.

The consequences of poor-quality master data are extensive:

• Operational Inefficiencies: Inconsistent and fragmented data, coupled with a lack of a single source, complicates decision-making and delays treatment.
• Patient Safety Risks: Without standardized master data, incomplete patient information can result in misdiagnosis, incorrect treatment, and harmful drug interactions. Similarly, mismatched device data can lead to misinterpreted test results, increasing patient vulnerability.
• Financial Losses: Mismatched patient records cause billing errors, rejected insurance claims, and delayed reimbursements, resulting in financial losses for healthcare organizations. A survey from 2022 reported that healthcare providers across the United States spent an estimated $10.6 billion¹ overturning denials in medical billing.
• Reputation Damage: Patients lose trust in the hospital when delays or errors in their treatment occur due to poor data management. As a result, the organization's reputation suffers.
• Suboptimal performance: Without accurate master data, organizations struggle to evaluate and optimize their performance, limiting their ability to improve care quality and make effective data-driven decisions. 

Implementing MDM: A Strategic Solution

Resolving deep-rooted issues caused by inconsistent and inaccurate master data requires a well-structured MDM strategy.

The table below highlights various data management challenges, their impacts, and how MDM can serve as an effective solution.

Numerous success stories demonstrate the powerful benefits of leveraging MDM, such as enhanced efficiency, reduced waste, and increased revenue.

For example, in 2021, an American medical center² utilized an MDM-driven business intelligence (BI) solution to integrate data from sources like EHRs, financial systems, and supply chains. This integration streamlined operations and improved cost efficiencies by unlocking valuable insights from master data.

Additionally, a prominent healthcare provider adopted an MDM solution to consolidate provider data across systems, resulting in an 80% reduction in claim denials and a faster payment cycle. Teams were able to trust and harness this data for more accurate and actionable insights.

10 Steps to MDM Success: Building a Strong Data Foundation

Implementing MDM in healthcare isn't a one-size-fits-all solution. It's about crafting a tailored plan that fits the unique needs of each organization. And of course, safeguarding sensitive data is a top priority.

At Tech Mahindra, we stress the importance of identifying and securing the most critical data as a key part of the strategy. This is at the heart of our approach to helping healthcare organizations transform their data landscape.

Let’s walk through the key steps:

• Define Objectives: Establish clear goals for implementing MDM, such as improving data quality, ensuring regulatory compliance, and enhancing patient care.
• Secure Stakeholder Buy-In: Involve executives for strategic direction, data stewards for insights, and IT teams for technical support, from the start.
• Assess the Current Data State: Conduct a thorough ecosystem audit by identifying data sources, mapping system data flows, and evaluating data quality and accuracy.
• Establish Data Governance: Set up a governance framework to ensure data accuracy, consistency, and security by assigning ownership, standardizing data management processes, and using automated tools for quality checks.                                                   
• Select an MDM Solution: Evaluate vendors on data integration, quality management, scalability for future growth, security, and interoperability to select an MDM solution that fits your organization's needs.
• Develop a Data Model: Design a data model defining structure, attributes, relationships, hierarchies, and validation rules to effectively govern and manage master data.
• Design a Phased Implementation Plan: Implement MDM in phases, beginning with a pilot project focused on a single data domain. This approach minimizes risks, provides opportunities for adjustments, and helps build stakeholder confidence before expanding to additional domains.
• Implement Data Integration: Integrate MDM with existing systems using Application Programming Interfaces (APIs) and middleware for real-time data synchronization across tools like Customer Relationship Management (CRM), Enterprise Resource Planning (ERP), and Business Intelligence (BI).
• Train Teams: Upskill employees on its tool usage, importance, and data governance policies to build a data-driven culture.
• Monitor, Measure, and Improvize: Implement monitoring to track MDM performance and data quality metrics like accuracy, completeness, and timeliness. Use insights to identify improvements and refine processes over time.

Conclusion

MDM needs to be at the heart of healthcare organizations that are focused on providing outstanding patient care. MDM lays the groundwork for accurate, integrated, and actionable insights. By tackling data silos and ensuring compliance, it helps organizations gather, manage, and validate all kinds of master data. Investing in MDM now sets the stage for a future of innovation and excellence.