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Healthcare Data Management
Architecture, Tech Stack, Costs
In data analytics since 1989 and in healthcare IT since 2005, ScienceSoft builds compliant and secure data management solutions optimized for analytics, process automation, and scientific research.
Contributors
Healthcare IT Consultant, ScienceSoft
Head of Data Analytics Department, ScienceSoft
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The State of Healthcare Data Management
According to recent surveys, healthcare organizations find data management and its components essential to their business success. For instance, 2024 The Current State of Healthcare Analytics Platforms by HIMSS and Arcadia, found that 93% of healthcare executives view access to high-quality multi-source data as key to their organizations' performance, and 56% agree that accurate data helps improve quality of care. At the same time, there is a gap between the acknowledged importance and the actual state of data management solutions. The same survey shows that only 57% of gathered healthcare data is used in decision-making, and according to 2023 The State of Healthcare Data Management by Verato, only 14% of healthcare organizations are satisfied with the quality of their identity data. In addition, only 34% of organizations rate their data protection frameworks as optimized, according to 2023 New Challenges in Health Data Management by Informatica.
Custom Development: Bridging the Gap Between the Desired and the Actual
The Informatica report highlights that one major reason for insufficient data security is the low technology capacity for integration and compatibility with the existing systems and workflows. This is a challenge we often tackle in data management projects for our clients in healthcare. The diverse facets of each organization’s data ecosystem — such as stakeholders, data sources, regulatory standards, and medical specialty — demand flexibility and customization that off-the-shelf software simply can't provide. That is why most of our data management solutions for healthcare are either fully custom or include custom components. This approach consistently pays off, allowing our clients to fully utilize their data without unnecessary constraints caused by security, compliance, integration, and performance discrepancies.
Sample Architecture of a Healthcare Data Management Solution
Healthcare data management is the systematic approach to collecting, processing, storing, and sharing clinical data according to regulatory standards (e.g., HIPAA, GDPR) and each organization’s internal needs. In most cases, the healthcare data management framework is designed to support reporting and analytics, but depending on architecture, it can also facilitate process automation, machine learning, and research.
Below, ScienceSoft’s data engineers provide a sample architecture of a data management solution that covers software blocks and information flows of a comprehensive data management solution for a large healthcare provider that uses its data for traditional BI reporting, AI-supported diagnostics, and remote patient monitoring.
A large healthcare provider gathers data from a variety of sources, including internal software (e.g., EHR/EMR, CRM, patient apps, revenue cycle management software) and external systems (e.g., HIE databases, Medicare/Medicaid, Wareed). It also gathers real-time sensor readings from remote patient monitoring (RPM) software.
Due to the high diversity and unstructured nature of the data use cases, the provider uses two data processing layers:
Batch processing layer
- The cost-effective raw data storage (a.k.a. data lake) keeps data in its initial format (e.g., XML, JSON, DICOM) until it is needed for analytics.
- The batch processing block prepares data for analytics (e.g., removes outliers from sensor readings or matches patient records from different systems) at defined intervals (e.g., every 24 hours).
- Batch processing is optimal for data that doesn't require immediate actions and is needed for building a comprehensive picture of processes and events (e.g., to analyze financial performance, healthcare outcomes, lab management operations or to conduct research).
Real-time (stream processing) module
- The real-time message ingestion engine directly receives data that must be monitored and analyzed continuously (e.g., patient sensor readings and financial transactions).
- The stream processing and analytics module ensures low-latency response to events as they happen (e.g., alerts on abnormal sensor readings, automated inventory stock replenishment, fraudulent transaction blocking).
Cleaned data from both layers lands in the analytics storage — a centralized repository that organizes data according to the chosen data model; it is usually represented by a data warehouse (DWH) or a big data database. This location is optimized for scheduled reporting and ad hoc data exploration via BI and analytics tools.
Advanced analytics (e.g., predictive modeling, fraud detection, medical image analysis) are enabled by the machine learning (ML) engine. The ML training module continuously improves the accuracy of the engine’s output based on historical data from the data warehouse.
The data governance framework defines the policy for enforcing data quality, integrity, security, and privacy throughout the data lifecycle. As a rule, the policy is compliant with relevant regulations such as HIPAA. The governance measures may include but are not limited to data backup and recovery, data encryption at rest and in transit, multi-factor authentication, role-based access, data privacy controls (e.g., data masking, anonymization), and more.
Technology and Tools to Build a Healthcare Data Management Solution
See How Our Clients Benefit from Data Management Systems Developed by ScienceSoft
Estimate the Cost of Your Healthcare Data Management Solution
The cost of implementing a healthcare data management solution may vary from $70,000 to $1,000,000+, depending on the required scope of the system. To see detailed cost ranges and learn about significant cost factors, visit our dedicated page about healthcare data warehousing.
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