It’s quite ironic that while maintaining a massive amount of data in its arsenal, the healthcare industry still battles with misdiagnosis, mediocre quality of care, fraud, and operational inefficiencies. The reason is not far-fetched — coupled with strict healthcare data regulations, ethical issues, and the expensive cost of research, these data silos get lost in disconnected legacy systems.
A healthcare data warehouse or DWH is perhaps the crystal ball for the healthcare industry, especially in areas as diverse as drug discovery, cancer treatment, and disease prediction. This incredible turning point can help catch diseases early, identify patients at risk of developing specific diseases, and speed up administrative and clinical processes.
In this article, we explore everything you need to know about data warehousing, including its benefits, architectural models, and how you can implement it for your healthcare business. Let’s unpack!
What is Data Warehousing in Healthcare?
The global healthcare data warehousing market is estimated to reach approximately $8 billion by the year 2030, research says. So, it’s unsurprising that all the so-called big boys are leveraging a data warehouse in healthcare. The reason for this increased adoption include:
- Massive volumes of electronic data by hospitals, pharmacies, and project healthcare organizations;
- To prevent operational inefficiencies brought to light by the 2020 pandemic;
- Making sense of widespread data from wearable medical devices generating data;
- Digitization wave, with broad point-of-care use of EHR, EMR, and CPOE;
- The rising rate of misdiagnosis due to many serious illnesses presenting with constitutional or non-specific symptoms like fever, weight loss, pain, etc.
Data warehousing is promising to open new doors in predictive, prescriptive, and operational health.
7 Benefits of a Data Warehouse in Healthcare
The main benefits of a data warehouse in healthcare, hands-down, are keeping, analyzing, and extracting value from an ocean of data while maintaining access to historical data for predicting future trends and clinical outcomes. Here are 7 advantages of a data warehouse in the healthcare industry:
1. Promotes Data-Based Clinical Decisions
Helpful medical intervention is all about making the right diagnosis at the right time. Nowadays, it only takes a few clicks to harness and analyze relevant medical data, thereby making it easy to gain valuable insights that can inform prognoses and guide clinical decisions.
Instead of processing disorganized data from data lakes and siloed databases, you have structured, unified, pre-processed data at your disposal. For instance, by analyzing large amounts of decades spanning big data, machine learning algorithms can identify patterns and predict outcomes for rare disorders like amyloidosis.
2. Improves Healthcare Data Reporting and Analytics
A healthcare data warehouse is not just a centralized data repository but also an on-the-fly data analytics powerhouse for generating precise and timely reports.
Here’s one of the practical examples of data warehouses in healthcare. Clinicians can access the warehouse server to compare a patient’s current status with their medical history. You can also adequately monitor administrative health, like staff performance and pharmacy sales. Moreover, with this system in place, you can present data-based reports to key stakeholders, boost clinical research, or figure out problematic areas.
3. Streamlines Healthcare Insurance Claims and Payouts
Sorting out health insurance claims has always been one of the major challenges of hospitals and pharmacies. Using healthcare data warehousing, you can quickly process claim-related data. This way, healthcare organizations can determine if their insurance payout schemes are working, prevents fraudulent payouts, and identify other challenges.
4. Improves Individualized Patient Care
A healthy patient is the desired goal of every healthcare worker. Data warehouse for healthcare can be used to clearly compare and contrast symptoms while also determining the causes and most successful treatment plans. For instance, combining investigative data, follow-ups, and EMR/EHR details, provides a bird-eye view into every patient’s medical journey. Using this data, you can bridge the disconnect between healthcare workers and patients, achieve a higher standard of care, and foster patient satisfaction.
Plus, the cost of making the right diagnosis is quite high, even for insured patients. Using a data warehouse in healthcare data analytics, doctors can gain valuable insights into disease patterns and fine-tune treatment plans to fit patients’ needs. This way, unnecessary expenses are avoided, and patients are protected from invasive procedures they don’t need.
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5. Prioritizes HIPAA Compliance and Data Security
Protecting sensitive patient information has always been the top priority for healthcare providers, or they otherwise run the risk of getting in trouble with the law. To be HIPAA-compliant, healthcare organizations can utilize a data warehouse in healthcare to set up raw-level permissions preventing users from viewing certain data entries. They can also set up permissions at the business intelligence (BI) and data analytics level so only non-sensitive data is shared via reports and dashboards.
6. Eliminates Disparate Data and Improves Data Integrity
Similar to physical warehouses, data in a warehouse is arranged in a clear, concise, correct, and organized model. This is different from data lakes, which are just huge amounts of unorganized datasets. Typically, ETL and ELT processes maintain data integrity in warehouses, ensuring that data is transformed before reaching the target system or once it’s loaded.
7. Promotes Data-Based Resource Planning
One of the key benefits of a data warehouse to healthcare organizations is that they can drive useful insights on how to economize resources, enhance communication between departments, and improve their bottom line. Let’s face it, a hospital is a business, and the goal of every business is to make a profit while minimizing expenses. Data-based resource planning helps healthcare businesses to eliminate unnecessary spending and optimize income.
The Architecture of a Healthcare Data Warehouse
Healthcare data warehouse architecture is a complex framework that defines the data warehouse design. Before choosing a data warehouse architecture, it’s recommended to consider a number of factors, like the size of your healthcare organization, your business goals, and your area of specialization. It’s not in your organization’s best interest to pick an architectural model without advice from healthcare data warehouse vendors.
Now, let’s discuss the two most used data warehousing architectural models in healthcare — individual and enterprise data warehousing.
1. Individual Data Warehousing
An individual data warehouse, otherwise known as an individual data mart, is a DWH architectural model focused on a single functional area of a healthcare organization and contains different subsets of data stored in a data warehouse. While a data mart can be termed as an isolated digital repository of data, it’s best considered as a condensed version of a data warehouse designed for use by specific units or departments in an organization. Examples are pharmacy, outpatient, radiology, and more.
The best thing about an individual data warehouse is that healthcare units can start analyzing specific data, say rare diseases, and upscale as necessary. However, data marts draw data from only a few sources compared to the enterprise DWH model. This architectural model provides a suitable solution for small to midsize healthcare providers that want to tackle specific problems as soon as possible.
Individual data warehouses can exist in three forms: independent, dependent (as part of an enterprise model), and hybrid data marts.
2. Enterprise Data Warehousing
The healthcare enterprise data warehouse model, EDW for short, is an all-encompassing option for healthcare providers that want to store and analyze large amounts of data from multiple sources. This architectural model is quite complex, consisting of the following layers:
Data Source Layer
As the name implies, the data source layer consists of both internal and external data entries from different sources relevant to the research process. In hospitals, data can come from ERP systems, EMRs/EHRs, pharmacy management systems, lab portals, insurance claim management software, clinical sources, surveys, polls, CRM systems, and more.
True to its name, the staging layer or the staging zone temporarily stores the large stream of disparate data coming from the data source layer. At this point, data aggregation, anonymization, and normalization happen using either the ETL (extract, transform, and load) or ELT (extract, load, and transform) methods. Both have the same end game — to sort only well-structured, top-quality data without duplicates, inconsistencies, and errors.
Data Storage Layer
This layer is the central point of the enterprise data warehousing model. Bytes and bytes of structured data loaded via ETL/ELT processes are stored in the cloud, SQL database, or hybrid database, waiting to be analyzed. One of the main benefits of an enterprise data warehouse in healthcare is that its storage layer can also carry individual data marts, think dependent and hybrid models. In fact, it’s recommended to sort and store data in this layer into individual data marts for use by different healthcare departments
Data Reporting and Analytics Layer
This layer is also known as the business intelligence (BI) layer, where you can deploy analytical toolsets as well as integration with third-party apps like machine learning, BI, and data lake software. In this final layer, you can analyze relevant data using statistical analysis, data mining, visualizations, graphs, charts, and report summaries.
Here’s an illustrative diagram of what an enterprise data warehouse for healthcare looks like:
How to Implement a Data Warehouse for Healthcare Organizations
Even in its simplest form, healthcare data warehousing is highly technical. Implementing it may seem tough, especially for healthcare executives without a tech background. Here is our tried and true healthcare DWH implementation roadmap from start to finish.
Stage 1 — Strategic Planning
The planning stage is perhaps the most crucial step in the data warehouse building process. It centers around critical thinking and executive meetings where the following tasks are carried out:
- Defining and establishing stakeholder needs
- Identifying loopholes and potential hindrances in the data management process
- Analyzing the IT infrastructure in place, if any
- Creating aims, objectives, and key performance indicators
- Setting a budget based on the scope and size of the DWH
- Planning a blueprint and ensuring it aligns with the stakeholder needs
Stage 2 — Architectural Design
In the architectural design stage, your contracted team of developers will craft the framework of the data warehouse while defining data integration and overall storage format. Here’s a checklist of tasks to accomplish in this stage.
- Determine your data integration strategy
- Construct a conceptual data model or any data model of choice
- Aggregate facts to form OLAP cubes
- Locate data sources and plan data transformation using ELT or ETL processes
- Define and design the preferred data model
- Design required integrations
- Set tracking duration and validation procedures
Stage 3 — Data Warehouse Development
This is the stage where the actual data warehouse development takes place. It involves developing the predetermined infrastructure, a series of coding, and implementing DWH software as well as end-user apps. At Langate, we use phased delivery schedules to keep our clients in the loop throughout the development process.
Stage 4 — Product Testing and Deployment
In the product testing and deployment phase, schema and data models are implemented on your preferred data storage layer. Typically, deployment can be on-premise or on the cloud. For on-premise infrastructure, you’ll need a dedicated team of developers working to debug and fix functional problems that may arise. Cloud-based deployment solutions can include Databricks and Snowflake.
Stage 5 — Continued Post-Migration Testing and Maintenance
Post-migration testing and maintenance involve frequently updating the warehouse based on changes and feedback from end users. For instance, a new medical metric may come up, necessitating new columns in the data pipeline.
Consider Langate as Your Trusted Partner
If you are looking for expert developers to build a new healthcare data warehouse or update your legacy system, you’re at the right place. At Langate, we have a history of developing and fixing data warehouse architectures for big healthcare providers. Our team can deploy an in-depth data pipeline from different sources using our well-planned strategy, outstanding data mapping, and ML/BI integration options. We take pride in our work and can fit all your healthcare disparate data like pieces of a jigsaw puzzle. Every member of our team is passionate about bringing innovative technologies to the healthcare industry. We believe in health for all and that everybody has a right to choose life.
Healthcare data warehouse systems provide a strong, data-based, consolidated, and accurate solution for making informed clinical and health administrative decisions. With careful planning and implementation, your healthcare data warehouse can provide vital information on disease patterns, helpful treatment options, early disease detection, and the best treatment options for cancer. At the administrative arm of healthcare, DWH can help to detect insurance and healthcare fraud, ineffective personnel management, and many more. In the end, a solid plan can save your organization money and make this powerful tool a reality.