Across the healthcare spectrum, clinics are having to adapt their traditional IT processes to handle increasing volumes of patient data, and with more challenging demands for breadth and depth. Without the right tools, they risk not being able to derive meaningful insights from clinical data which could result in making uninformed decisions.

That’s why advanced approaches and tools are necessary for streamlined data collection, storage, sharing, and analysis. When data management is done effectively, downstream analytic outputs help providers make the right decisions at the right time without any additional costs. 

In this guide, we’ll discuss how data management tools are solving healthcare’s greatest data difficulties, including:

• Data Storage
• Data Interoperability
• Data Safety 

The way that healthcare organizations store, share, and analyze data today has future consequences. For a healthier tomorrow, clinics should embrace the power of analytics and let it transform their processes. Let’s explore how they can do that in the next sections.

Data Storage

With a single patient generating nearly 80 megabytes of data each year in imaging and EMR data alone, healthcare data volumes are only expected to expand. Therefore, organizations need a reliable IT storage solution that can keep their patient data accessible without risking privacy concerns. 

The optimal storage solution should allow for:

• Data organization: File management can be a pain for clinics. Your online storage solution should be able to eliminate chart chasing and give you access to the patient data you need when you need it.
• Scalable growth: As your clinic grows, your data volume will inevitably increase. The amount of patient information and medical records will continue to balloon, so you’ll need to choose a solution that can handle new data input without causing growing pains.
• Reliable security. Ensuring the privacy and security of patient data and mitigating risk must be considered. Encryption, access protections, and row-level permission settings are critical to ensure your patient data is kept safe. 

To find the right data storage solution, many clinics turn to high-level analytics vendors that can provide what is known as a data warehouse. This organized repository stores all usable healthcare information retrieved from EHRs, insurance claims, HIEs, lab databases, and other critical data sources.

Data Sharing

Data storage isn’t the only problem clinics need to tackle. With siloed systems and multiple sources of truth, another common issue is data fragmentation or the occurrence of division within your documentation. 

At best, these divisions cause clinics to spend too much time interpreting data from scattered IT systems. At worst, they can cause poor clinical decisions. However, the right tools for data sharing can help your team avoid potential problems by increasing: 

• Employee efficiency. Tools like EHR integration keep data readily accessible. This enables employees to make the best use of their time and avoid provider burnout caused by hefty task loads.
• Care collaboration. Integrated EHR systems provide a platform for clinicians to seamlessly share and problem-solve through organized, transferable insights.
• Patient engagement and retention. Cleaner file management can lead to an increase in patient retention and engagement as additional patient nuances are taken into account. 

When leveraged properly, analytics tools can lead clinicians away from reactive medical treatments and toward preventive medicine. This means clinicians are no longer adjusting their treatment plans based on a limited pool of immediately available data. Instead, they are using aggregated EHR data from multiple sources at the point of care.

Data Safety

Data safety is the final hurdle healthcare organizations must jump through. Once a scalable data storage solution is established and data sharing is made more accessible, robust measures are needed to ensure that data quality remains high over time, so that clinicians can feel confident in their patient data. 

Managing data quality over time can be a risky endeavor, with the unexpected happening constantly. Just like driving a car, each one of us tries to drive as safely as possible, but other drivers on the road or even the weather could mean you end up in an accident. Data management needs to be approached with the same safety precautions to minimize harm in the event of a data crash. 

That’s where data seatbelts come in. A strong portfolio of safety checks should include: 

• Continuous monitoring. Your healthcare data is changing all the time. For each data batch, ask, “Did I get the data I expected? Was it in the right format? Did the information it contained align with previous batches?” For EHR data, this could look like measuring the volume over time of key clinical concepts.
• Disaster recovery and backups. Preventing a data crash requires reliable backup options. Make sure your team has a comprehensive data safety plan complete with redundant storage options for data restoration.
• Root cause analysis. If the data suggests any inconsistencies or promotes unexpected actions, conduct an analysis to make sure the data is standardized to reveal any underlying issues. Address these issues quickly to maintain data integrity.

A substantial benefit of good data management is that it builds trust in your organization’s data recovery processes. The best ongoing practices assume that something will go wrong, flag it when it does, and isolate that data from moving downstream and negatively impacting your operations.

Increasing data volumes demand improvements in existing healthcare IT systems. The adoption of strong data management tools and techniques can help healthcare organizations set the pace for improved capabilities. Additionally, smart data management can help your team improve its efficiency and increase capacity without increasing risk.