3 Master Data Management Best Practices for Healthcare Payors to Pursue
The shift to value-based care started a chain reaction which resulted in a higher standard of accuracy and reliability across data used within the healthcare payor industry. When reimbursement depends upon the effectiveness of care outcomes and the efficient use of members’ premium dollars, your organization needs a single data authority to guide careful decision making and long-term strategy. Despite relative advancements, insurers still struggle with master data management maturity.
Just last year, a survey conducted by TransUnion found that only 37% of payor organizations were confident about the demographic information contained within their data. Even with the current data deficiency, 87% of executives in a Harvard Business Review expressed their faith in the importance of a strong master data management program.
What do payor organizations need to do to bring their technology, processes, and people in line with leading-edge innovation? Here are three master data management best practices that can propel your organization ahead of the curve.
1.) Embrace Multi-Domain MDM
Though many organizations have done the work of breaking down data silos within specific divisions or business functions, some justify leaving partitions between the different branches. The reasoning is that it’s easier to maintain compliance and cybersecurity hygiene. However, there are some insights lost when no one is able to easily cross-compare disparate data.
For example, a payor organization might have a dedicated system that contains the entirety of member data (eligibility, demographics, claims, etc.) and a separate system for the administrative side (billing, precertification, credentialing, etc.) . Plenty of standard reporting can operate within these larger silos, but the potential for analytics under a single multi-domain repository surpasses the scope and depth these separate systems are otherwise capable of on their own.
When insight is truly holistic, it’s easier for payor organizations to make connections across an expanse of different functions, improving the capabilities of real-time operations. Since data sharing is an essential part of creating a whole-person view of members and demographic groups, it’s important for MDM compliant technology and processes to be in place if you’re going to create a single source of truth. Everything from creating a reliable data exchange framework to fostering a culture of data stewardship can achieve this end.
2.) Incorporate Machine Learning Tools
The Healthcare Data Priorities: Insights from Providers and Payers report estimates that healthcare data grows by an astounding 48% annually. If stakeholders and data stewards were expected to input that data manually, their master data would perpetually fall short of the timeliness that is essential for reliable decision making. With new policies and laws – the 2021 Consolidated Appropriations Act (H.R. 133) is a major one – there are even noncompliance risks for not keeping provider directories updated every two days.
Unless you want to fall behind, it’s essential for payor organizations to integrate machine learning into their MDM strategies. Here are a few ways that machine learning can improve your overall performance:
- Running clustering analysis can help to discover natural patterns and groupings in data, discovering irrelevant or conflicting information based on pre-established patterns in claims or eligibility data.
- Machine learning algorithms can more rapidly trace and map data lineage, both as a way of detecting any errors in the master data and remaining compliant with HIPAA regulations.
- Data ingestion and onboarding of a wide range of data categories can be automated with machine learning algorithms, increasing accuracy and turnaround times.
This is just a sampling of the possibilities. Implementing machine learning and artificial intelligence in healthcare payor organizations can create a self-governing master data model that prevents noncompliance and frees up your stakeholders for other mission-critical tasks.
3.) Connect Your MDM Strategies to Your Business Goals
Master data management should not be removed from the business itself. Like all methodology or workflows, they are not practiced for their own benefit, but for the benefit of the business. As a result, you should be including other members of your team in the MDM process from planning to ongoing adherence.
With a cross-disciplinary group of decision makers, it’s essential to review the metrics that will empower your organization to make the most progress towards your quarterly and long-term business goals. From there, you need to determine the blind spots within your master data that is preventing the full potency of your analytics. Often, this requires some clever reverse engineering, but it can lead to a more comprehensive and consistent view along the way. Ask questions like these to pinpoint your blind spots:
- Are there customer segments with lower conversion rates?
- Are there certain processes and workflows that are obviously inefficient?
- Do you struggle to keep fraud in check?
- Is your business unable to identify and track members across channels?
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