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Healthcare data warehouse benefits, features, and use cases

Data Interoperability and Integration Data Management and Quality Quality Improvement

What do you picture when the word warehouse comes to mind? Probably a large building with sky-high ceilings full of neatly arranged goods. Or maybe you picture an assembly line of connecting spare parts together to make a useable good, like a car.

A healthcare data warehouse (HDW) isn’t far off from that physical image. However, you’ll want to take your existing warehouse image and add a couple million rows to better represent the volume of healthcare data consistently collected and stored from multiple sources.

That’s a lot of data, and you might wondering how it all fits together. In this article, we’ll take an inside look at the HDW and answer relevant questions like:

Data warehouses are powerful analytics tools used in several industries to make impactful, data-driven decisions. However, their specific use in healthcare translates to more than just improving sales, it directly correlates to delivering value-based care. Let’s unlock your data’s potential by understanding the ins and outs of HDWs.

What is a healthcare data warehouse?

In simple terms, a healthcare data warehouse is an organized central repository for all aggregated, usable healthcare information retrieved from multiple sources like EHRs, EMRs, enterprise resource planning systems (ERP), radiology, lab databases, wearables, and even population-wide data.

It's important to keep in mind that a HDW is only one key component of an integrated healthcare data platform which includes a connectivity layer, data lake, analytics and enrichment layer, data warehouse, and integrations and reporting features. These platform processes compose a data platform pipeline that collects and transforms raw data into usable analytics for your organization’s decision-making.

: This image shows the connection between key components within a healthcare data platform to understand how a healthcare data warehouse functions within a data platform.

Here’s how a HDW fits into the data platform pipeline:

  1. Data is collected in a connectivity layer which is built from various use cases, types, and fields. This layer determines exactly how much data you need and could mean that your platform could take days to collect your data volume.
  2. All collected data is stored in a data lake in its raw condition and is currently not primed for meaningful analytics.
  3. The data lake undergoes analytics and enrichment for conversion into usable metrics. This is usually done by a software provider and includes the active management of thousands of quality measures such as risk adjustment, risk suspecting, and cost utilization.
  4. Usable data is stored in a HDW as a central location for consistent, organized, and accurate data recovery.
  5. Usable data is pulled from the HDW for business intelligence dashboard reporting through a collaboration between analysts and providers.
  6. Specified applications and integrations are leveraged to build views that align with your organization’s needs.

HDWs are the fourth component of a healthcare data platform and are directly influenced by the components leveraged prior to it. In other words, the volume and type of data stored in a HDW are determined by the connectivity layer, data lake, and analytics enrichment processes.

It’s worth distinguishing the difference between a data lake and a HDW as you want these two components to remain separate from each other. A data lake represents raw data in its unusable form while a HDW is an organized storage location of usable data.

Keeping your HDW unstructured and unusable will result in inconsistency and breakability issues further down the data platform pipeline. Applications and integrations, therefore, will pull from potentially faulty data. That’s why many healthcare organizations partner with vendors like Arcadia to bridge the gap between an overflowing data lake and an effectively structured HDW within a holistic data platform framework.

There’s no doubt about it — building an efficient HDW takes precision, but, as you’ll see in the next section, the benefits can revolutionize your current care workflows.

What are the benefits of healthcare data warehousing?

Now that you know how a HDW functions within the data platform pipeline, you might be wondering, what are the practical benefits?

When leveraged correctly, HDW benefits look like:

  • Efficient reporting: HDWs can help your team create precise analytics reports quickly to get a bird’s eye view of performance. For example, your team can use dashboard reporting to efficiently compare your high-cost members based on cohort (admission, drug, condition, etc.), total members, and total cost.
  • Improved clinical decision-making: Discover the right insights at the right time by accessing a unified HDW. Rather than siloed databases, a HDW can connect all of your information in a supportive, evidence-based clinical decision framework.
  • Optimized insurance claims and payments: Your healthcare organization can review and efficiently process large sums of claims data, access its compensation services, prevent fraud, and ameliorate underlying roadblocks.
  • Elevated strategic planning: Take a comprehensive approach to resource planning with accessible data tools. Use data gathered from disparate sources to prodigy future issues and avoid them.
  • Enhanced patient satisfaction and outcomes: In your patients’ eyes, an accessible HDW means that they can receive timely and accurate treatment derived from advanced analytical models and experience measurable progress.
  • Personalized value-based care: Collaborate with other clinicians using a uniform HDW to ideate deeper insights and enhance current treatment plans for patients while avoiding unnecessary costs.

HDWs can help you identify and achieve your organization’s goals. Additionally, they can improve your day-to-day workflows, so that you can focus on delivering the highest quality care at the most opportune time.

What are the key features to look for in a healthcare data warehouse?

HDWs are inherently sensitive and require proper handling as they should meet security measures while integrating successfully with various data sources. Additionally, each HDW is tailored to a specific organization’s needs, so the key features will differ depending on the HDW’s purposed end-use.

However, there are common aspects worth paying attention to regardless of your team’s custom HDW model. A fully functioning HDW will include:

  • Data integration: HDWs field data from EHRs, ERP software, HR management systems, claims management systems, and large public health databases. This data integration includes big data and streaming data in addition to advanced medical data loading and querying. Make sure your HDW is equipped to standardize data from both internal and external sources.
  • Data quality: Successful HDWs include quality controls for data accuracy, completeness, and consistency. Built-in processes like data cleaning and validation help to promote standardization across data points.
  • Scalability: Your organization could start with a relatively simple HDW configuration, but with increased use, you will likely need to rely on a scalable solution. A robust HDW built with future data volume in mind will help you make sense of larger data loads.
  • Security and privacy: Correctly built HDWs have encryption and access controls to ensure the necessary security and privacy protocols are taken to protect patient data. Raw-level permissions by account or patient ownership keep HDWs aligned with US Federation laws such as HIPPA.
  • Interoperability: Efficient HDWs make fragmented workflows a thing of the past as they bring operational care teams together to collaborate for whole-person care. Your HDW should be set up with care coordination ease in mind.

If you decide to go with a single or multi-data platform provider, verify that they offer the above features. Or, if you decide to advise an in-house team, build your data platform with these outlined aspects. That way, you can maximize your analytical horsepower and gain the highest ROI for your time and efforts.

Common healthcare data warehouse models 

There are a few common HDW models across all providers. Based on their different approaches, they each have their own strengths and drawbacks. Finding the right one will help you bind data, or prepare it for analysis, when it makes the most sense for your organization.

Here are the most common models:

Enterprise data model

This image explains how an independent data mart works in a healthcare data warehouse setting.

This model represents a top-down approach that most analytics vendors promote as its usually used when organizations need additional analytic capacity.

The goal of this model is to create the perfect database from the start. This is done by determining in advance everything your organization would like to analyze to improve specified outcomes. For instance, you might decide to prioritize safety and cost-effectiveness and would then structure your HDW with that end goal in mind. 

Although a common model, the enterprise data model promotes an early-binding approach that can delay time-to-value. This means that once the data is bound, making changes is difficult and time-consuming. Therefore, within the ever-changing landscape of healthcare, enterprise models can pose significant challenges when new analyses are needed.

Independent data mart model

This image explains how an independent data mart works in a healthcare data warehouse setting.

This model advocates for a bottom-up approach and is commonly used for ad hoc analysis since it can build individual data marts for individual departments. For instance, if you need a specific report on patient safety or medical cost containment, your department can build it quickly.

Independent data mart models are inherently smaller and more focused than enterprise data models. They can help your team implement and measure needed metrics much more quickly than enterprise data models. However, independent data marts can also overwhelm systems quite easily and needlessly cause data redundancy issues down the line.

Healthcare data warehouse use cases

To visualize the potential impact a HDW can have on your organization, it can be helpful to take a step back and observe how it applies to real-life use cases. In fact, data analytics assist Account Care Organizations (ACOs) identify key clinical opportunities that may be hiding in plain sight. That said, here are two use cases to consider:

Providing Value-Based Care for Diabetic Patients

Diabetes afflicts 37.3 million or about 1 in 10 Americans. Measuring and monitoring this disease is especially important because if left untreated, it can be a catalyst for several other conditions such as kidney disease, blindness, stroke, or in the worst case, death. That’s why documenting patient conditions and comorbidities accurately and specifically is an absolute must.

How can a HDW help?

  • By leveraging SDoH (social determinants of health) factors like risk factors, trends, and pattern identification to cut down on administrative time.

  • By supporting disease management initiatives such as glucose level checks and medication adherence.

  • By providing insights to achieve prevention with appropriate care.

Diabetes is common and involves highly personalized treatment plans to be effectively monitored for intervention. With a robust HDW, your team can confront diabetic challenges head-on while paving the way to value-based care. 

Learn more about VBC use cases by reading Achieving quality and value: 5 Value-Based care strategies.

Improving end-of-life care

Adequate hospice care gives patients and their families the dignity they deserve during a patient’s final moments. Tracking and visualizing Medicare patients who have passed away can help organizations understand how to improve end-of-life care.

By tracking the cost per patient during their final thirty days, analysts found that:

  • Patients with adequate hospice care saw regularly increasing costs to the day of their death.

  • Patients with no hospice care saw costs spiral out of control.

  • Patients with inadequate hospice care saw a sharp increase in costs that stabilized in their final days.

The ramifications of this data suggest a relationship between adequate hospice care and a more comfortable, higher-quality end-of-life experience.

Healthcare data warehouse: Final thoughts

Every data point represents an individual and your organization’s ability to collect, streamline, and analyze each point directly impacts your quality of care. As healthcare changes rapidly, it’s essential that organizations have the right infrastructure to support future developments. Partnering with an experienced data vendor like Arcadia can not only help you organize your data pipeline but also detect and close risk gaps before they surface.