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 connecting spare parts together to make a usable 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 be 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.
In simple terms, a healthcare data warehouse (HDW) is an organized central repository for all aggregated, usable healthcare information retrieved from multiple sources like:
It's important to keep in mind that an 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.
Here’s how an HDW fits into the data platform pipeline:
HDWs are the fourth component of a healthcare data platform, and the prior components directly influence it. In other words, the connectivity layer, data lake, and analytics enrichment processes determine the volume and type of data stored in an HDW.
It’s worth distinguishing between a data lake and an HDW, as you want these two components to remain separate. A data lake represents raw data in its unusable form, while an 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.
Building an efficient HDW takes precision, but the benefits can revolutionize your current care workflows, as you’ll see in the next section.
Now that you know how an HDW functions within the data platform pipeline, you might wonder about its practical benefits.
When you leverage an HDW correctly, you’ll experience benefits like:
HDWs can help you identify and achieve your organization’s goals. Additionally, they can improve your day-to-day workflows so you can focus on delivering the highest quality care at the most opportune time.
HDWs are inherently sensitive and require proper handling to 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 proposed 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:
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 generate the highest ROI for your time and efforts.
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:
This model represents a top-down approach that most analytics vendors promote, as organizations typically use it when they need additional analytic capacity.
The goal of this model is to create the perfect database from the start by determining everything your organization would like to analyze in advance to improve specified outcomes. For instance, you might decide to prioritize safety and cost-effectiveness and 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.
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 using this model.
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 cause data redundancy issues down the line.
To visualize the potential impact an 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) in identifying key clinical opportunities that may be hiding in plain sight. That said, here are two use cases to consider:
Diabetes afflicts 38.4 million Americans — a staggering 11.6% of the population. 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 an 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 effectively monitor it for intervention. With a robust HDW, your team can confront diabetic challenges head-on while paving the way to value-based 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.
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.