Healthcare Data Warehouse
Turn Healthcare Data into Valuable Insights
In healthcare IT since 2005, ScienceSoft devises digital pathways to make data handling easier and faster. Providing a full range of data warehousing services, ScienceSoft helps healthcare organizations and companies build robust data warehouses from scratch or enhance their existing systems.
Data Warehouse in Healthcare: the Fundamentals
A healthcare data warehouse is a centralized repository for healthcare organization’s data retrieved from disparate sources, processed and structured for analytical querying and reporting. A DWH can help improve clinical outcomes, optimize staff management and procurement, reduce operating costs.
Compared to a regular database, an enterprise data warehouse (EDW) consolidates data not from one source but from multiple healthcare IT systems (e.g., EHR, CRM, patient portal). The main purpose of a DWH is not to simply store raw data, but to keep it in a structured format for further analysis. A healthcare data warehouse can be integrated with a data lake, ML, and BI software. Implementation costs for a healthcare DWH start from $70,000 for a small healthcare provider and may reach $1,000,000 for a complex technology-rich solution for a large company.
Healthcare Data Warehouse Solution Architecture
ScienceSoft creates enterprise data warehouses, which become a central component of a healthcare BI solution comprising the following elements:
- Data source layer – healthcare data from internal and external data sources (ERP, EHR/EMR, CRM, claims management system, pharmacy management systems, etc.).
- Staging area – intermediate temporary storage, where healthcare data undergoes the extract, transform and load (ETL) or the extract, load and transform (ELT) process.
- Data storage layer – includes centralized structured storage. It may also have data marts – healthcare DWH subsets oriented to a specific business line (HR, accounting, etc.) or department (radiology, intensive care, pediatrics, etc.).
- Analytics and BI – business analytics, data mining, data reporting and visualization tools.
The functionality of medical data warehouse solutions ScienceSoft delivers differs from customer to customer. Here, we’ve outlined the features commonly requested by healthcare organizations we work with:
- Ingesting structured, semi-structured, unstructured healthcare data (from EHR systems, ERP, HR management systems, public medical databases, claims management systems, etc.).
- ETL/ELT-based healthcare data integration.
- Full and incremental healthcare data extraction/load.
- Controlled healthcare data loading/management.
- Healthcare data transformation of varying complexity (data type conversion, summarization, etc.).
- Healthcare data loading and querying using SQL.
- Big data ingestion.
- Streaming data ingestion.
- Integrated, historical, summarized, subject-oriented healthcare data storage.
- Protected Health Information (PHI) storage.
- Metadata storage.
- Options of healthcare data storage environments (cloud, on-premises, hybrid).
Database performance and reliability
- Elastic scaling of storage and compute resources.
- High-performance query processing due to healthcare data indexing, materialized view support, result-caching.
- ML capabilities to dynamically manage performance and concurrency.
- Automated data backup across various regions and zones within the cloud environment for fault tolerance and disaster recovery.
Security and compliance
- Granular row and column level security control.
- Multi-factor authentication.
- Healthcare data encryption at rest and in transit (including backups and network connections).
- Dynamic healthcare data masking.
- Ongoing threat detection and vulnerability assessment.
- Compliance with healthcare regulations (HIPAA, FDA, HITECH, MOHAP requirements, etc.).
The healthcare customers I work with agree that analysis of various information consolidated in a data warehouse provides important insights, especially for value-based care model. In my projects, I suggest using a DWH to measure patient outcomes (based on length of hospital stay, readmissions, etc.), care costs (e.g., used resources, medical staff time), and assess the care value.
To maximize the value and cost-efficiency, ScienceSoft recommends setting up the following integrations for enterprise data warehouse in healthcare:
Enables training ML models on structured healthcare data from the clinical data warehouse. ML-powered analytics helps predict clinical outcomes, deliver personalized care, analyze medical images, make informed business decisions, and more.
Enables visualization of data stored in DWH, automated reporting (hospital annual report, average hospital stay report, etc.), and creates interactive dashboards (with patient demographics data, insurance claims, etc.).
Healthcare Data Warehouse Investments
The cost of healthcare data warehouse implementation ranges from $70,000 to $1,000,000 depending on the project scope. Based on ScienceSoft’s experience in medical DWH implementation, the approximate timeframes are from 3 to 12 months.
Healthcare data warehouse key cost drivers:
- Number of healthcare data sources (ERP, EHR/EMR, CRM, claims management system, pharmacy management systems, etc.).
- Healthcare data disparity (for example, difference in data structure, format, and use of values) across various source systems.
- Complexity of healthcare data (for example, big data, streaming data).
- Volume of healthcare data to be processed.
- Healthcare data security requirements.
- Number of healthcare data tables and columns used for analysis.
- Healthcare data warehouse performance requirements (velocity, scalability, fault tolerance, etc.).
Here is the pricing for healthcare organizations of different size:
$70,000 – $200,000*
For companies with 200 – 500 employees.
$200,000 – $400,000*
For companies with 500 – 1,000 employees.
$400,000 – $1,000,000*
For companies with more than 1,000 employees.
*Monthly software license fee and other regular fees are NOT included.
Ballpark timelines for each stage of healthcare data warehouse implementation
A typical ScienceSoft's project on healthcare data warehouse software implementation covers the following stages and timelines:
- Healthcare data warehouse goals elicitation: 3-20 days.
- Healthcare data warehouse solution conceptualization and tech stack selection: 2-15 days.
- Business case and project roadmap creation: 2-15 days.
- System analysis and healthcare data warehouse architecture design: from 15 days.
- Healthcare data warehouse solution development and stabilization: from 2 months.
- Healthcare data warehouse solution launch: from 2 days.
- After-launch support, maintenance, and evolution: as requested.
Healthcare DWH Financial Benefits and Success Factors
Relying on 18+ years of experience in healthcare IT and DWH implementation, ScienceSoft helps consolidate disparate data into a structured repository to achieve:
Improved care outcomes. In our project catering to 200 US healthcare facilities, implementation of a DWH with BI reports (e.g., on missed medications, hospital readmissions) helped providers to timely spot the issues in care processes and resolve them.
Optimized staff management due to advanced analytics of employee schedules, recruitment needs, etc., and data-driven HR decisions.
Decreased healthcare operating costs due to informed business and clinical decision-making.
Optimized healthcare asset management. When ScienceSoft implemented a DWH and BI functionality for a provider of diagnostic imaging services, the Customer started tracking the efficiency of its services, streamlined its operations, optimized business processes. It resulted in quicker response time, improved staff productivity, enhanced customer experience.
Improved patient retention due to personalized care delivery and better patient experience.
In data warehousing since 2005, ScienceSoft’s seasoned IT consultants have defined the set of factors that can help maximize the ROI for DWH projects.
Healthcare DWH scalability and flexibility
If the amount of data you store is likely to increase, you need a scalable DWH solution to instantly upload any type (structured, semi-structured, unstructured) and volume of healthcare-related data.
Security and data protection measures
To ensure full security of the patient data stored in the DWH, you need to employ robust cybersecurity measures (e.g., data encryption, MFA), and conduct regular checkups such as vulnerability assessment and pentesting.
Well-established data quality management
Conduct a comprehensive DWH system analysis and design future-proof data governance practices before collecting data from diverse sources. It will help avoid common issues like different encoding formats, conflicting key fields, etc.
Key Steps for a Healthcare DWH Implementation
1. Data warehouse feasibility study to assess project viability with respect to business objectives and user needs.
2. Discovery phase to elicit and analyze high-level data warehouse requirements.
3. Data warehouse conceptualization and platform selection considering the number of data flows, security requirements, etc.
4. Project planning to define project timeframes, milestones, deliverables, risk management strategy, costs and TCO.
5. Healthcare data warehouse architecture design.
6. Development and stabilization of a medical DWH.
7. Data warehouse launch.
8. Healthcare data warehouse support and evolution throughout the DWH solution lifetime.
Consider Professional Services for Healthcare DWH Development
Since 2005, ScienceSoft has offered medical IT solutions and provided a full range of data warehousing services. Our robust DWHs integrate various types of medical data and support the decision makers with high-quality healthcare insights.
ScienceSoft Is a Leader in Healthcare IT Services Market in 2022 SPARK Matrix
ScienceSoft is featured as a leading healthcare IT services provider, along with Athena Health and Oracle Cerner. This achievement is a result of 18 years of tireless pursuit of technological innovation, made possible by ScienceSoft’s passionate team of healthcare IT experts who always strive to make a difference for patients and caregivers alike.
ScienceSoft as a Reliable Partner
President & CEO
bioAffinity Technologies hired ScienceSoft to help in the development of its automated data analysis software for detection of lung cancer using flow cytometry. Our project required a large amount of industry specific methodology and algorithms to be implemented into our new software connected to EHR/LIS systems, which the team handled well. They are reliable, thorough, smart, available, extremely good communicators and very friendly.
Founder and CEO
Thanks to ScienceSoft’s practical healthcare IT expertise, we created a musculoskeletal therapy platform that can be fully customized and reflect the needs of each program member. I am excited to see AKLOS Health change the approach to physiotherapy and offer each member a truly bespoke experience that’s based in science.
Deyarat Trading Co.
We highly appreciated our cooperation with ScienceSoft. Their consulting assistance was action-driven and brought a bunch of practical action points to us. It’s a pleasure to work with experts who are knowledgeable, self-motivated and sincerely interested to do the best of their ability.
Healthcare DWH Implementation: Success Stories by ScienceSoft-stories
Implementation of a DWH and analytics solution for 500+ nursing homes
Challenge: The existing data analytics solution had restricted tuning capabilities and didn’t offer standardized and comprehensive reporting.
Results: ScienceSoft created a new DWH based on Microsoft SQL Server, developed a robust analytical system, established standardized reporting, and enhanced solution security.
DWH and BI implementation for 200 healthcare centers
Challenge: The need to revamp an inefficient Java application used by the healthcare centers for data management and enable quality population health analytics with prompt reports.
Results: ScienceSoft built a Microsoft SQL Server data warehouse that consolidated the data from 200 separate databases, facilitating analytics reports on medication inventory, clinical services, patient data, and more.
DWH and BI implementation for a provider of diagnostic imaging services for 800+ facilities
Challenge: The need for clear reporting according to HHS requirements and comprehensive analysis of the business performance.
Results: ScienceSoft implemented a DWH and a BI solution that provide reports and dashboards on modality utilization, clinical outcomes, financial indicators, order payments, and more.
Data De-Identification and Sharing Software for a Gulf HIE Platform Provider
Challenge: The need to de-identify data and securely share it.
Solution: The platform was successfully launched within 12 months and met all the requirements for PHI storage and complied with the Gulf healthcare data protection regulations.
DWH Vendors ScienceSoft Recommends for Healthcare
When listing the healthcare data warehouse vendors, we drew on our 18 years of experience in DWH and considered reputable rankings like Gartner’s Magic Quadrant and Forrester’s Wave. Here are the vendors suitable for midsize and large healthcare organizations:
Best for: big data warehousing
- Integration of all healthcare data types (structured, semi-structured, unstructured) for storing and SQL-querying.
- Integrations with the AWS ecosystem (including S3, AWS Glue, Amazon EMR) and third-party tools (Power BI, Tableau, Informatica, Qlik, Talend Cloud).
- Federated queries support.
- ML capabilities for optimized performance under varying workloads.
- Separate scaling of compute and storage.
- Healthcare data encryption and fine-grained access control.
- On-demand pricing – $0.25 – $13.04/hour.
- Reserved instance pricing offers saving up to 75% over the on-demand option (a 3-year term).
- Data storage (RA3 node types): $0.024/GB/month.
Azure Synapse Analytics
Best for: advanced data analysis
- SQL-querying of structured, semi-structured, unstructured healthcare data.
- Native integrations with a data lake, operational databases, BI and ML software.
- Integration with third-party BI tools, including Tableau, SAS, Qlik, etc.
- Separate billing for compute and storage.
- Healthcare data encryption, dynamic healthcare data masking, column- and row-level security.
- Compute on-demand pricing – $1.20–$360/hour.
- Compute reserved instance pricing allows saving up to 65% over the on-demand option (a 3-year term).
- Data storage: $122.88/TB/month.
Oracle Autonomous Data Warehouse
Best for: hybrid healthcare DWHs
- Querying across multiple healthcare data types (structured, semi-structured, unstructured).
- Built-in connectivity to Oracle Cloud Infrastructure Object Storage, Azure Blob Storage, Amazon S3.
- Integration with Oracle Analytics Desktop and third-party BI tools (Microsoft Power BI, Tableau, MicroStrategy, Qlik, etc.).
- Healthcare data encryption, privileged user and multifactor access control.
- Independent scaling of storage and compute.
- Compute costs: $1.3441/CPU/hour
- Data storage: $118.40/TB/mo (in the public cloud).
ScienceSoft is a global IT consulting and software development vendor with offices in the US, the UAE, and Europe. With 100+ successful healthcare IT projects, we provide a full range of data warehousing services. Being ISO 13485-certified, ScienceSoft designs, develops, and tests high-quality medical IT solutions according to the requirements of the FDA and the Council of the European Union. We rely on mature quality and security management systems to guarantee our customers’ satisfaction with project outcomes and complete safety of their data.
More from ScienceSoft