The problem with health data is that it may reside in multiple places and take various formats, burdening doctors trying to understand it and get a complete picture. A data warehouse comes as a lifesaver, providing a centralized location for well-organized data that is consistent and standardized, making it easy to analyze.
To help you navigate the sea of medical information, this blog post explains the basics of data warehousing in healthcare, its advantages, implementation specifics, and several common scenarios where industry players utilize it.
Data warehousing has gained popularity in recent decades as a solution for large corporations grappling with redundant data flow between transactional and decision support systems. Whenever a new user group required collecting, cleaning, and combining the same raw data over again, this process replication made a centralized data repository an urgent need. Around the turn of the millennium, US-based Intermountain Health and Northwestern Medicine became some of the early adopters of enterprise data warehouses in healthcare.
Generally, a data warehouse is a storage system that converges an organization's data, enabling analysis, visualization, and reporting through standard business intelligence functions. In the healthcare industry, it brings together such sources as EHR, laboratory and pharmacy systems, ERP, patient surveys, CRM, financial records, and many more. By merging large quantities of diverse information, a medical data warehouse helps overcome silos and establishes a single source of truth.
Industry stakeholders may have different pain points and reasons for establishing data warehouses. In addition to combining fragmented information and equipping hospitals with analytics and reports, these are valuable tools for examining historical data over an extended period. They help monitor trends in patient outcomes and evaluate the effectiveness of interventions.
Data warehouses can also serve health insurance companies by offering reliable shared data for determining service rates and provider reimbursement schedules. Moreover, data integration between healthcare payer and provider systems can improve alignment between the two parties.
Clinical research organizations may require data warehouses, too. These repositories can support their studies and contribute to medical advancements in cures and preventive measures by providing researchers with a rich source of aggregated data.
While there are ETL- and ELT-based variations (more on that later), a healthcare data warehouse model commonly includes several vital components that work together to make it functional.
Now, let's examine some essential features of a data warehouse.
Several fundamental qualities define the data stored in the warehouse, making it distinct from the operational systems feeding it. Namely, this data should be complete, reflecting a long time horizon, orderly, and free of such inconsistencies as measurement units and naming conventions. While operational systems can quickly record transactions and provide a business snapshot on request, data warehouses tend to store extensive historical data, updated periodically, for analysis of trends and comparisons between periods.
Two kinds of processes guarantee data integrity: extract-transform-load and extract-load-transform. The difference is in the phase where data modification occurs: ETL transforms data before it reaches the warehouse, while ELT does it after loading. ETL is more suitable for smaller data volumes because it takes longer, whereas prioritizing ELT is the best alternative for big data projects where processing speed is crucial.
There are three environment choices for data storage: cloud (including multi-cloud setups), on-premise, and a hybrid version of these two. The general direction is for organizations to keep data in the cloud and hybrid environments, with 53%-54% of respondents in Yellowbrick's recent survey of IT managers and executives seeing this as a top 1 trend in data warehousing for their company. According to the research, only 18% keep all their data warehouses on-premise.
In the cloud environment, an essential feature of a data warehouse that supports disaster recovery when information-related incidents happen is geographically-spread automated backing up of data. It is equally critical to provide strong cybersecurity through such measures as authentication rules, data encryption, and vulnerability checks, as well as ensure regulatory compliance, e.g., by following HIPAA requirements in the US.
This storage component can be part of an overall data warehousing system, allowing for the loading of unstructured data without needing modification. Healthcare institutions often utilize data lakes for inexpensive temporary data storage in mixed formats, such as medical images and sensor readings from wearable devices. Afterward, they can bring that data to a unified form, ingest and process it in the warehouse, or feed it to ML models.
The primary aim of a data warehouse is to gather diverse information and arrange it in a single, organized repository, but how does it benefit a healthcare institution? The key advantages are in the business intelligence that this comprehensive cross-organizational view enables, ultimately leading to:
How can a data warehouse create value for medical institutions in practice? The following few scenarios make the benefits outlined above a bit more tangible:
Of course, answering the pricing question for implementing a healthcare data warehouse is easier after estimating the scope and complexity of a given project. The overall investment can vary from less than USD 100,000 to 7-digit numbers. Still, understanding some major cost drivers will make you better prepared. First, consider the volume of data and its sources involved. Next, evaluate how dissimilar your data is. Think about how you will host it: on-premise or in the cloud. Other price factors include performance requirements and information security needs.
Providers of data warehousing in healthcare often tailor pricing options to an organization's size. For smaller teams of less than 500 employees, the solutions range from $50,000 to $250,000, providing cost-effective storage and analysis capabilities. For medium-sized institutions with 500 to 1000 employees, the price is typically between $150,000 and $500,000, ensuring scalable data solutions that accommodate these organizations' growing needs. And for larger enterprises with over 1000 employees, robust data warehousing services are available within the range of $300,000 to $2,000,000, providing comprehensive and sophisticated data management.
According to different market researchers, the data warehouse industry players with the largest market share (at least 10%) are Snowflake, SAP Business Warehouse, Google BigQuery, and Amazon Redshift. Looking at a leading producer's customers by industry, we can also see that the hospital and healthcare sector is among the most prominent buyer segments, following IT, software, and financial sectors.
However, when a small or medium-sized healthcare organization is looking for a data storage solution, we recommend considering a wider circle of providers. Custom healthcare software development companies with qualified teams of developers and data specialists can help you get a tailored and cost-effective data warehouse addressing your specific needs.
Just like the costs of individual projects vary widely, so do the timeframes: it can be a relatively small 3-month or extensive year-long undertaking. While the phases may differ too, they usually include:
Our analysis would be incomplete without essential tips from our experts. So, here is what you should consider to build an efficient data warehouse for your organization.
When planning a clinical data warehouse undertaking, it's important to consider flexibility and scalability ahead of time. With a well-designed, scalable system, you can expand computing power quickly, store more source data, and generate more analytics and reports without requiring additional development, thus helping you maximize the project's value.
Specific operation-enhancing techniques will be necessary to optimize system performance and speed up query response time, including indexing, caching, and parallel task execution.
It is another success factor to consider from the very beginning. Defining data governance practices accounting for the potential source variety can help prevent many problems in advance.
Integrating the data warehouse with ML can fuel advanced AI-based analytics, leveraging data to train models for profound insights such as clinical result forecasting and drug prescription.
Finally, it is worth repeating the priority of ensuring information security, as it affects not only patient safety but also medical institutions that can be liable for data privacy breaches. Typical ways to protect personal data from unauthorized use include encryption, anonymizing, and permission controls at different levels.
The global healthcare sector is experiencing rapid growth in the data storage market due to digitalization and the increasing volumes of data. The industry players invest in data warehouse development to achieve operational excellence, increase efficiency and strengthen their analytical capabilities.
Whether you are just starting to consolidate your organization's data or looking to enhance your existing system, CleverDev Software's healthcare sector specialists are ready to assist you. When developing our custom solutions, we focus on the ultimate user's needs while ensuring the utmost security and total compliance — the prerequisites that form an effective healthcare data warehouse.
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