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Data lakes in healthcare: Use cases for large datasets

Healthcare data lakes are customized data repositories that enable health systems and payer organizations to store, access, and analyze large amounts of information in one centralized place.

Data lakes are growing in popularity in and outside of healthcare. Still, their goal is similar no matter the sector: break down data silos and provide actionable insights to inform decisions. For payers, data lakes can help plan economic performance and much more.

Data lakes also:

    • Provide a single, comprehensive data source to support advanced reporting and analytics
    • Help users better access and utilize data at the point of care, for better health outcomes
    • Help providers and payers develop complete patient profiles
    • Help identify high-risk patients for care management and community programs

Healthcare data lake use cases

There are several use cases for healthcare data lakes, most notably data integration, increased speed of data access, creating a single-source-of-truth for analyzing complex health issues, and improving patient interactions and outcomes. Other uses for data lakes in healthcare include monitoring provider performance against benchmarks, tracking population and patient levels to drive quality measure improvement, ensuring chronic conditions are accurately assessed and coded annually, and mitigating social determinants of health (SDOH) impacts on care.

Secure data integration

The data generated from a single patient interaction is copious, broad, and substantial. Health systems that collect health records and other payer-generated data, genomic research, patient-reported data, family medical history, exercise or diet regimen, and data from smart devices are all examples of data that populates a data lake. A data lake provides an ideal, secure platform to assimilate vast volumes of data with widely varying content.

Data can be sorted to make it more accessible and actionable, and data lakes may be transformed from storage pools to robust databases to distribute vast volumes of information.

Data lakes also eliminate the need for costly insecure on-premise infrastructure to store data. Data lake storage provides healthcare organizations with a secure and compliant solution with administrative, physical, and technical measures to ensure confidentiality, integrity, and availability of electronically protected health information (ePHI). Utility-based cloud services are being used safely and effectively by providers, payers, and others to process, store, and transfer ePHI subject to HIPAA.

Increased speed of data access (data automation)

Employing a data lake strategy means healthcare organizations can collect and standardize data, such as claims, clinical information, patient registries, structured and unstructured data, and data from EHRs and EMRs no matter how it’s collected or enters the health system. All information can be merged and analyzed to generate a holistic view of the patient, which can help with better outcomes, better economics, and improving medical decision-making and care quality.

This allows for quick judgements during the treatment encounter and aids in overall care coordination. Previously, clinicians used manual forms and records to track patients and diagnostic tests. In addition, data accessibility and visibility challenges reduced care delivery options. However, using efficient data processing, machine learning, assisted automation, self-service data preparation, and intelligent data discovery, a new care culture can be developed where insights are gleaned from the acquired data and relevant care interventions are deployed quickly.

For example, by regularly monitoring patient vital signs, providers can deliver better care to each patient. Likewise, data collected by multiple monitors may be analyzed in real time, and notifications issued to providers, so they are aware of changes in each patient’s condition.


Data lakes quickly produce more insights from medical devices, patient records, provider notes, and claims data. Healthcare systems can employ data lakes to customize and integrate large volumes of information for their patient’s specific requirements. With complete datasets available, providers can lead data-driven decisions at the point of care. In addition, healthcare delivery is more efficient and timely with real-time access to comprehensive clinical and claims data.

New data channels such as this allow payers and providers to spot health patterns, correlations, and trends that can significantly influence integrated patient care.

Improving patient outcomes

Because of the previous difficulties in accessing unstructured data, much critical unstructured data stays unexplored – or has until the implementation and utilization of healthcare data lakes. Organizations can use data-driven insights to improve care quality, cut expenses, and avoid resource waste.

Data can highlight the importance of “upstream” social determinants of health factors that relate to healthcare delivery in improving health and reducing health disparities. SDOH have a significant impact on people’s health, well-being, and quality of life and data in congruence with these factors may be used to determine opportunities for providing better care or moving patients toward healthier outcomes.

Another example of data lakes driving better patient outcomes is a repository with integrated cardiovascular patient data that enabled better collaborations in the development of predictive statistical models to detect and manage patients with diabetes.

Data lakes for better care

Like the “ripple effect” caused by a rock being tossed into a lake, data lakes present similar ripples as data enters and moves throughout an organization. The factors listed here ultimately result in better patient care, better treatments and medication matching, and more representation throughout the healthcare environment.

To learn how your organization can achieve some of the benefits listed here, check out Converged Healthcare Data Explorer.

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By Inovalon