by Iryna VibleiMay 29, 2025

Data & Analytics Generative AI Healthcare & Life Sciences Innovation Platform

Why Healthcare Companies Are Slow to Adopt Modern Data Platforms (and How to Fix It)

7 min read

Healthcare data is exploding in size and complexity — but traditional systems are ill-equipped to manage it. Modern data platforms fix the most vexing problems, supporting real-time clinical decisions, personalized treatment, and population health analytics. They also meet HIPAA and GDRP requirements. If they are so great, why aren't more companies using them? The truth is, most healthcare companies want innovation, but they are constrained by:

Legacy systems
Regulatory pressures
Cultural inertia

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The blockers are real and deeply rooted — but solvable. Let’s look at how scalable, secure, and interoperable data platforms make patient-centric care possible.

5 big challenges

Healthcare companies juggle many priorities. They need to understand patients, manage budgets, scale operations, reduce risks, and launch data initiatives. Five challenges stand in the way.

Data is often siloed, with different standards across clinical, operational, and research sources.

It’s hard to integrate and analyze multi-modal data, such as genomics, imaging, or wearables.

On-premises data processing and storage costs are on the rise.

Managing patient data privacy and access is complex.

Provisioning cycles are long and lack flexibility.

Transform your data with a unified, governed approach

Move from reactive to predictive. Use real-time, up-to-date data instead of static information. The right data platform strategy scales with demand, protects sensitive information, and lets every team, from engineers to clinical researchers, work off one trusted source of truth.

Databricks, powered by Delta Lake, is the core of a modern healthcare data platform. It uses an ACID-compliant storage engine that follows the medallion architecture, with data organized into bronze, silver, and gold layers. This turns raw, fragmented data into structured, easy-to-audit datasets.

Success stories

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Open-care platform
A global healthcare company transformed its operations with a platform approach to support lifestyle and prescription programs and make it easier to share research data. Because the company used the platform in over 50 countries, it was unified with a highly configurable infrastructure and supported local regulations and privacy rules.

Coordinated care
A global healthcare and pharmaceutical company enhanced disease management using a digital platform. The solution included a coaching portal, patient app, and admin portal to provide coordinated, patient-focused care. This approach improved treatment adherence and equipped caregivers with helpful, data-driven tools.

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Databricks pipelines centralize internal sources like EHRs, lab systems, and clinical applications with external partners such as research institutions, digital health platforms, and third-party vendors. The result: reduced duplication, improved data quality, and a trusted foundation for real-time insights, advanced analytics, and AI innovation.

Governance is a pillar

Enterprise-grade governance is part of the platform's DNA. Unity Catalog manages metadata, tracks data lineage, and enforces role-based access control. It separates PHI from depersonalized analytics and ensures compliance with HIPAA and GDPR. Business lines and partner networks can safely collaborate without compromising compliance.

Real-time meets research-grade analytics

Modern data platforms handle both streaming and batch processing, enabling clinical decision support along with research and historical analysis. With tools like Delta Live Tables and event-driven architectures, you can quickly act on patient vitals, lab results, or medical device logs. On the user side, platforms have APIs, dashboards, and interactive notebooks to help data scientists, analysts, and clinicians use their data.

Built for scale and people

Modern platforms handle lots of data and support more users. They offer collaborative workspaces, self-service analytics, and automated deployments through CI/CD and infrastructure-as-code. When combined with training programs to improve skills, they foster a data-fluent culture, where people across the organization can work with and understand the data.

Maximize resources, accelerate innovation

Finally, it helps you make the most of your resources with a flexible, cloud-native infrastructure that separates storage and compute. It gives clinicians, researchers, and analysts secure, role-based, self-service access to governed data. Plus, it speeds up innovation by simplifying everything from data ingestion to deploying AI/ML models.

High-impact use cases

Federated learning for collaborative research
Train ML models without sharing sensitive patient data.

Real-time remote patient monitoring (RPM)
Stream and analyze data in real-time to support early interventions.

Precision and personalized medicine
Combine genomic, diagnostic, lifestyle, and environmental data to create personalized care plans.

Real-time operational intelligence
Track and manage KPIs like bed occupancy, staffing, supply chain, and readmissions.

Global public health and compliance reporting
Standardize data across systems and locations to support reporting and policy responses.

Digital decentralized clinical trials
Recruit, engage, and collect trial data remotely to reach more people.

The path forward

Healthcare and life science companies need better data infrastructure. A unified platform powered by Databricks makes this possible by giving IT and data leaders the tools to turn blockers into opportunities:

Legacy systems: Integrates seamlessly with existing infrastructure, enabling modernization without the need for a complete overhaul.
Regulatory pressures: Comes with enterprise-grade governance tools.
Cultural inertia: Fosters a data-driven culture and promotes collaboration between teams.

SoftServe is now a Databricks Select Consulting Partner! Together, we’re empowering businesses to scale smarter with lakehouse architecture, maximizing the value of their data and accelerating AI innovation.