From pharmaceutical and biotech companies to care providers, the healthcare sector demands evidence-based decision-making now more than ever. The reality is simple: employers, consumers, payers, and providers need actionable data – and they need to be able to share it. Of course, sharing potentially sensitive healthcare data can, on its face, seem like an insurmountable challenge. That’s where data aggregation comes into play. In a nutshell, data aggregation is the process of combining information from multiple databases to produce cohesive, shareable information.
When it comes to healthcare data, the benefits of sharing are plentiful. Additionally, these benefits extend far past the needs of big companies and hospitals; they lead to better healthcare for payers and consumers alike.
Aggregate vs. Individual data – what’s the deal?
In the healthcare sector, individual data – or “patient data” – is created by one patient’s interaction with the healthcare system. Individual data includes his or her name, medical history, family medical history, diagnosis, age, and treatment outcome. This type of data is necessary, of course, for treatment on the individual level. With an understanding of the patient’s history, family history, and past diagnoses, doctors can make more intelligent and informed decisions about his or her future treatment, too.
Now, imagine this principle on a larger scale. What if those same doctors had access to not only their patient’s data, but data from hundreds or even thousands of similar cases? Of course, the patient’s individual data is important, but anonymized aggregate data can help care providers see patient data from a completely new angle, with a plethora of supplemental information. That’s the beauty of aggregate health data. In fact, aggregate data can even be lifesaving when utilized properly by providers.
At the end of the day, aggregate may not provide everything providers need to treat patients, but can offer crucial insight for strategic planning and creating health systems.
The benefits of data collaboration in the world of healthcare
On the one hand, it’s easy to see why data aggregation and collaboration is increasingly important for healthcare systems and providers. But what are the granular implications? On the surface, the simple answer is “more data equals better healthcare,” but the reality is far more intricate. Here are a few practical ways data sharing can positively influence healthcare:
- It builds and maintains confidence in data quality, reliability and balance. A survey that polls 10 people is fine, but one that asks one thousand people the same question is demonstrably better. By simply increasing the sample size, data sharing increases data quality.
- It facilitates better healthcare products. With more knowledge comes better product development. Not only is the data more reliable, but it can help product developers and create healthcare products that suit the needs of a larger population. Additionally, supply and demand statistics ensure that enough of a given product or medication are on the market.
- It allows superior monitoring of healthcare trends. Remember the individual vs. aggregate patient story above? Now imagine a scenario where doctors can identify potential diagnoses based on healthcare trends, past treatment for similar conditions, and more. By monitoring trends in healthcare, providers can more effectively monitor patients too.
- It maintains transparency and trust. Transparency is the key to building trust, and data transparency between providers, product creators, pharmaceutical professionals, and patients is paramount to building lasting trust between the healthcare system and its consumers.
Turning healthcare data into insight
With the rise of Electronic Health Records (EHR), healthcare data is being tracked more than ever – but how can healthcare professionals turn it that data into actionable insight? Is it being used to improve a system designed to treat patients, or is it simply another way for big hospitals and pharmaceutical companies to outsmart the competition? This is where data transparency truly shines. The more players in the healthcare sector share data, the less focused on the competition they become. On the flipside, this allows them to become more focused on the thing that matters most – making smart, data-driven decisions for the benefit of patients and consumers.
The key is measuring value. Sure, data can help companies understand trends within the industry. More importantly, though, data sharing highlights top-performing organizations in the U.S. healthcare system and allows them to adapt their own processes to those of higher-performing institutions. With the same information and transparency, consumers can identify which organizations best suit their needs and to provide the best care. The result? Low-performing healthcare organizations will either improve their performance or simply won’t survive.
Read more: Curing Data Challenges in Healthcare
Healthcare data best practices
Data in the healthcare sector includes clinical data, information about hospital staff, patient data, financial information, HR data, supply chain statistics, and much more. With so much data floating around, it’s important to understand a few tried and true methods for using it effectively:
- Focus on acquiring new data feeds quickly, not perfectly. Don’t get us wrong, data quality is important. However, when dealing with large quantities of data it’s best to focus on getting it in one place than scrubbing it first. In other words, assimilate the data into your warehouse, then scrub it when it’s needed. This late-binding architecture is effective because it focuses on gathering all of the necessary sources before cleaning it up.
- Make sure context matches the definitions. Keep in mind that multiple sources for data may lead to misleading contextual information. Let’s say you’re gathering information about a specific type of surgery. One source (from the surgeons) defines the length of stay one way, while data from administrative offices defines it another way. Be wary of these differences and account for them when you scrub data.
- Remember that your data architecture is temporary – and flexible. Sure, the word “architecture” typically refers to something immovable. In reality, your data architecture is always subject to improvement. With a late-binding data structure, analysts have the freedom to adjust the structure before the data itself is finalized. Simply put, a flexible structure lets you mix sources by different means, allowing for greater flexibility and better outcomes.
At times, creating an effective data architecture for healthcare may seem challenging. After all, there’s a lot of information to collect, scrub, organize, display, and apply. The right data management suite will allow you to combine multiple data sources flawlessly and import them into an interactive, secure dashboard that can communicate those data insights to stakeholders.
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