Data Warehouse in today’s world are helping companies across industries to make data-driven decisions. For the healthcare industry experiencing a massive shift towards consumerism and advanced analytics, a single & comprehensive view of data is becoming imperative. It is one of the many reasons why leading healthcare organizations are beginning to implement enterprise healthcare data warehouses in their structure.
It helps them to constantly improve patient outcomes and reduce costs. Further, the effective use of the data gathered over the years helps increase operational efficiency, optimize infrastructure investments and improve customer experience.
If you are someone who wants to harness the power of data with a dedicated data warehouse to deliver value-based care, this post will serve as your one-stop reference. It will help you learn how to unlock your healthcare data’s potential to drive action where it matters the most.
It is a centralized repository for electronic health records and clinical data retrieved from disparate sources, processed, analyzed, and distributed to facilitate data-driven decisions in minutes.
They help transform the organizational data into accessible, actionable information to drive quality improvement across clinical outcomes, patient experience, and operational efficiency. An experienced data analytics company can help you develop data and metrics as valuable assets to improve standardization and transparency.
A database is the core unit of a business intelligence solution.
1. A Data Source Layer
Contains medical data generated from internal and external data sources (EHR, EMR, ERP, CRM & claims management system)
2. A Staging Zone
This is the temporary intermediary storage. All the medical data is processed through the extract, transform, and load (ETL) framework.
3. Data Storage Layer
This layer is typically structured and includes centralized storage. It features data marts such as clinical subsets oriented to a specific line of business (accounting, HR, inventory) or specialty (pediatrics, radiology, intensive care unit).
4. Business Intelligence & Analytics
Data mining, reporting, visualization tools, and business analytics
Data Security and Regulatory Compliance
Choosing the suitable DWH model for your practice is essential. It helps develop and map the capabilities that impact the future adaptability & time-to-value of healthcare systems.
Providers can opt for an enterprise-wide data model when they need additional power to keep up with data sets residing in every corner of the organization.
It is one of the most highly recommended models by analytics vendors. It takes a top-down approach to model a comprehensive database that determines, in advance, the KPIs to analyze for improved patient safety, satisfaction and treatment outcomes.
However, this model is the best choice if you’re building a new system from scratch. But, in the context of healthcare, you need to create a secondary system that extracts data from existing systems and makes it all work in sync. This is not only downright difficult but also tedious, time-consuming and expensive. However, with the right skills and experience, it is possible.
The design of this model takes a bottom-up approach. You start small with building individual data marts as and when you need them. Accordingly, if you want to analyze insurance claims or revenue cycle, you need to develop an individual data mart for that particular process. This model brings in data from different sources that apply to a specific area.
One can start implementing the model and tracking data much faster. The independent data mart approach works more quickly and efficiently than the typical 2-5 year life cycle of the enterprise-wide data model.
A data storage solution can be implemented on-premise, within the four walls of your healthcare organization, or located in the cloud. But, with the markets for analytics-as-a-service (AaaS) and infrastructure-as-a-service (IaaS) gaining momentum, it could bring down the burden of an immediate up-front cost for providers looking to unlock the potential of big data.
It enables healthcare organizations to systematically monitor and measure different chronic conditions, care delivery processes, payments, and standard operating procedures. With the volume and type of clinical data constantly increasing and evolving, it has become different for organizations to store, share and analyze their data on time.
Enterprise data warehouse in healthcare is a single-point platform that offers an end-to-end view of data stored across different systems. It includes – public health records, claims, cost accounting systems, inventory, supply chain and more.
For your business, it can;
We can help you quickly design & deploy a custom solution that allows optimum use of your healthcare data to deliver improved patient care
A Data Lake
While an enterprise DWH for healthcare can store highly structured data, a data lake works as cost-effective storage of semi-structured and unstructured data (manual patient records, image-based test reports, practitioner’s notes, etc.)
The machine learning models (for instance, healthcare demand forecasting) get created based on the information stored in the data lake.
Business Intelligence Software
A self-service BI system enables healthcare organizations to be agile and self-reliant in visualizing, analyzing and reporting the medical data structured in the EDW. This facilitates quick and easy transfer of analytics insights to key decision-makers.
Security & Regulatory Compliance
Store and process confidential medical data within highly secure infrastructures (Google Cloud, AWS, Microsoft Azure), ensure dynamic data masking and all-time encryption, multi-factor authentication, penetration testing, restricted data access, and vulnerability assessment.
Agility & Scalability
To upload any type and amount of medical data (structured, semi-structured, unstructured) instantly and address the objectives of new data analytics.
Proof of Concept
Validate your healthcare data warehouse solution with a PoC. Give it a spin to get the hang of its true potential, along with feedback from real-life users.
There are plenty of options available on the market. However, the following three DWH platforms are shortlisted for their above-average performance and excellent customer satisfaction reviews.
|Amazon Redshift||Azure Synapse Analytics||Oracle Autonomous Database|
|Ideal For||It has been optimized for datasets that range from a few hundred gigabytes to a petabyte-scale DWH which makes it best for big data warehousing||Apt for implementing a DWH without inviting the additional cost & maintenance issues of an on-premise implementation. It is highly recommended for advanced data analytics||Designed for all business sizes and best suited for, data lake, analytical reporting, and read-intensive databases. An ideal choice for hybrid healthcare|
|Key Benefit||Makes it easy and cost-effective to analyze healthcare data efficiently using your existing BI tools.||As a cloud-based solution, it is ready-to-implement. It enables you to design your DW structure immediately & with absolute ease.||Robust, reliable and easy to integrate with other tools. It efficiently extracts, loads & transforms data across multiple apps.|
|Computing Pricing||$0.25 – $13.04/hour||$1.20–$360/hour||$1.3441/CPU/hour|
Rishabh Software has helped several healthcare organizations expand and revolutionize their care-delivery systems with end-to-end data warehousing services. Because data accuracy is paramount to running any healthcare facility efficiently, we’ll work with you to implement a reliable EDW that drives informed decisions and profitable actions in the long-term.
We provide our clients with healthcare data warehouse that comprises of:
Setting up a data warehouse requires extensive planning and testing with the scale & volume of data it will change. It is vital to understand the entire ecosystem of data flow to ascertain what fits your requirement. As a business owner, you quite often would get confused by the number of options and technologies used; therefore, it is always better to consult an experienced data analytics company to define business purpose in warehousing, data science, ETL and more.
Partner with us for a rapid transformation that drives better decision-making at every touchpoint.