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OVERVIEW

Improving outcomes and reducing the cost of care with data
 

The healthcare industry is under immense regulatory and competitive pressure to transform. AI-powered advanced analytics for clinical decision support  can dramatically improve patient outcomes, help providers achieve regulatory benchmarks like reducing avoidable readmissions, and lower the cost of care.

A longitudinal patient record is the  key to building a data-driven healthcare organization. By unifying patient and operational data across silos, healthcare providers can:

  • Put patients at the center of their healthcare journey and ensure a continuum of care.
  • Use advanced analytics like population health and social determinants of health for clinical decision support.
  • Reduce the administrative burden and give clinicians more time with their patients.

Establishing a longitudinal patient record

Healthcare providers need to unify data across silos and democratize access to data for non-technical users:


Hybrid and multi-cloud access to data across the organization, and interoperability with a wide range of tools and platforms.


A consistent set of governance, security, and access control policies to ensure health and patient data remains private.


Self-service access to data using natural language, so clinicians and administrators can see, understand, and act on insights.

Key use cases

  • Reduce avoidable readmissions
  • Improve population health
  • Personalize patient engagement
  • Clinical decision support

Reduce avoidable readmissions

In Fiscal Year 2023, Medicare penalized over 2,000 hospitals for high readmissions rates, resulting in a total of $320 million in payment reductions. Readmissions is a global challenge that impacts the patient experience and increases the cost of care.

Providers can leverage a longitudinal patient record and advanced models like population health and social determinants of health to determine the likelihood of complications and readmissions and take action to mitigate the risks.

Improve population health

Healthcare organizations are shifting from fee-for-service to a value-based care model, requiring them to manage the health of entire populations instead of individual patients.

By unifying data across silos and leveraging advanced analytics and AI, providers can identify at-risk cohorts, address disparities, and deploy proactive intervention at scale.

Personalize patient engagement

Patient centricity is no longer a buzzword–it’s table stakes. Patients expect a continuum of care, personalized communication and convenience, and a healthcare experience that puts them in control.

Unifying patient data across silos and architecting for data interoperability enables providers to deliver a holistic profile that follows the patient across providers and platforms.

Clinical decision support

Clinical decision support is critical for improving patient outcomes and reducing the cost of care. Clinicians require timely, evidence-based insights derived from sources including the longitudinal patient record, population health data, and social determinants of health to make better decisions, proactively identify risks, personalize treatment plans, and ultimately improve the quality and efficiency of care.

By unifying patient data with evidence-based guidelines and leveraging AI, clinicians can ensure diagnostic accuracy, streamline treatment planning, and reduce medical errors, leading to improved patient safety and outcomes, and reduced costs.

CUSTOMERS

Improving outcomes and reducing costs with data and AI.

Cloudera has supported us at every step in creating a unified platform, a single source of information, to help improve healthcare in Murcia. Thanks to its flexibility, we can deliver new AI use cases to improve healthcare processes and better federate our data, contributing and collaborating with the European Health Data Space.

—Miguel Ángel Gambín Marín, Head of Applications Service at Servicio Murciano de Salud

Murciano de Salud

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