Implementing Data Lakes for Health and Human Services

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Enhance Opportunities for Revenue Growth

✓ Drive higher margins with better revenue management, sharper market insights, and increased value-based care earnings.

Improve Patient Care and Outcomes

✓ Unlock deeper patient insights, improve satisfaction, and elevate care quality.

Enable New Analytics Capabilities

✓ Empower teams with advanced analytics, AI-driven insights, and accessible data for faster, smarter decisions.

Modern healthcare organizations face a growing wave of data from diverse sources — electronic health records, claims systems, scheduling platforms, and patient engagement tools. 

A centralized data lake creates powerful information synergy by bringing these streams together in one place. The interaction between different data types reveals patterns and connections that isolated systems might miss.

This post explores the outcomes you can achieve by setting up a data lake, the distinctions between data lakes and warehouses, and key principles for building an effective healthcare data architecture.

Strategic Benefits of Building Healthcare Data Lakes

A well-structured data lake creates multiple opportunities for healthcare organizations to advance their operations, improve care quality, and boost financial performance. Potential outcomes include:

Revenue opportunities:

  • Improved billing and revenue cycle management
  • More precise market and competitive intelligence
  • Increased value-based care revenue

Improvement in patient care and outcomes:

  • Deeper customer insight and satisfaction analysis
  • Healthcare quality improvement

Net new analytics capabilities:

  • Advanced analytics capabilities, including predictive modeling and AI applications
  • Democratized data access for organizational decision-making

What’s the Difference Between a Data Lake and a Data Warehouse?

Data lakes and data warehouses each play distinct roles in health and human services organizations. Data warehouses store structured information in organized schemas — specific client data, claims information, and standardized reports that follow predetermined formats. This structured approach works well for routine analytics and compliance reporting. 

Data lakes, on the other hand, accept raw data in any format, including clinical notes, client intake forms, service documentation, images, and unstructured text. This flexibility makes data lakes ideal for advanced analytics projects like identifying social determinants of health or predicting client needs.

However, in our experience, these concepts are increasingly merging in modern data organizations. While data lakes and warehouses have traditionally been thought of as separate systems, modern data science platforms make it possible to rapidly load and iterate on datasets from multiple sources while allowing curation of select datasets for reporting and audit consistency. The dichotomy between the two is less severe than it used to be — enabling faster results for organizations that have adopted modern approaches.

Foundational Principles for Data Lakes in Health and Human Services

Keywell has built data lakes for health and human services organizations, and we’ve learned that these systems require careful planning and architecture.

Here are the top principles to consider when you’re establishing a data lake:

  1. Build on a flexible platform: The platform or environment you build on must be flexible enough to integrate seamlessly with diverse healthcare systems while facilitating secure data exchange. This is critical for complex healthcare data environments.
  2. Establish security controls: To protect sensitive patient information, implement security and privacy protocols such as data masking and row/column-level security for users who should not be able to access PHI or should have limited access to it.  Ensure that all access is granted at the user (not service) level for auditability purposes.
  3. Support diverse data: Your healthcare data arrives in many formats, from FHIR standards to unstructured documents and files. It is important to select tools and methodologies that can support and govern mixed data and information formats. 
  4. Design clear zones: Your data lake architecture should maintain distinct zones for raw and production-ready data, following a medallion structure to represent different stages of data refinement. This creates a clear lineage between original sources and final outputs. When you employ modern extract, load, transform (ELT) tools, you streamline this process and make data transformations transparent and traceable.
  5. Enable data discovery: Round out your foundation with a comprehensive data catalog. Your users can make better decisions when they clearly understand available data elements. If possible, democratize the ability to define and add descriptions so the data dictionary is a helpful part of the knowledge base.

Smart Data Strategy for Health and Human Services

Healthcare organizations generate vast amounts of valuable data every day. A thoughtfully implemented data lake — built with strong security, governance, and access controls — helps organizations maximize their information assets.

Ready to make your healthcare data work harder for you? The Keywell team brings proven experience building successful data lakes. Contact us to learn more.

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