Healthcare Data Storage Trends and Challenges

The healthcare industry is at the forefront of the AI data revolution. Generating nearly a third of the world’s data, the industry is undergoing a dramatic digital transformation, reshaped and powered by machine learning.

The sector is facing pressing challenges around healthcare data storage. In particular, healthcare organizations have to balance the growing data deluge with the need to keep information secure and in observance of HIPAA standards.

Traditional on-premises solutions are no longer an option in a world where things like remote work, telehealth visits, and IoT wearables are becoming the norm. On the flip side, disparate cloud environments can lead to data siloes or inconsistencies and security vulnerabilities.

AI and Machine Learning Power Diagnostics

Artificial intelligence (AI) and machine learning are increasingly being integrated into healthcare data storage systems, supercharging them with capabilities that are fundamentally changing the level of care that can be provided. 

Previously, when a new patient walked into a hospital room, providers had to expend valuable time sifting through unfathomable quantities of data. Now, machine learning algorithms can make sense of years of healthcare records faster than their human counterparts and generate actionable insights for the entire care team.

“Generative AI agents synthesize data, and instead of reading 10 years of labs, the AI tells the doctor: ‘Patient's kidney function has declined by 12% since their new medication started,’” said Leah Gabbert, Global Industry Solutions Marketing Director at Nutanix. 

AI can anticipate disease occurrence before symptoms advance, and detect patterns that humans alone can’t discern. The results are truly life-saving. For example, specialized algorithms can help triage emergency room scans or even detect early signs of breast cancer. 

“AI models now ‘sniff’ the data stream for early markers of sepsis or stroke, triggering alerts before the patient even feels symptomatic,” said Gabbert.

AI Drug Discovery

In the world of pharmaceuticals, AI’s extraordinary ability to analyze colossal amounts of data with precision and speed is radically changing how drugs are being developed. Artificial intelligence can identify new disease targets, speed development with its own novel drug designs, and reduce the industry’s extremely high and costly failure rate. 

“AI leverages historical patient data to simulate how new drugs will perform, cutting drug development time from a decade to a few years,” said Gabbert.

Recursion is one start-up utilizing the technology in its research and currently has seven AI-discovered therapeutics in its pipeline, mostly targeting various cancerous tumors. 

Not only can machine learning help create life-sustaining medicine, but it can also make drugs cheaper in the process as well.

From a data storage perspective, AI uplevels the value of having data centralized and stored in a single repository where machine learning technologies can access and include it in their analyses.

Epic Alliance Manager Jon Kimerle summed up the AI imperative as it relates to data storage aptly in his article for Healthcare Business Today: “The adoption, implementation, and integration of AI workflows begins and ends with data. To start, training AI models requires that organizations have large amounts of data they can access easily and process quickly,” he wrote. “Many healthcare organizations today have an exponential amount of data at their fingertips.” 

“However, few know how to handle all of this information to fit their organizational needs and those of patients. For organizations to maximize the potential of AI, adequate data storage is a necessary yet often missing piece of the puzzle.”

From IoT and Wearables to Precision Medicine: The Growing Complexity of Healthcare Data

The healthcare data landscape is becoming increasingly complex, marked by a significant rise in the volume and diversity of data being generated and managed.

Today, the healthcare sector generates nearly an entire third (30%) of the world’s data, and this percentage is projected to grow. The global big data healthcare market grew to $132.32 billion in 2026, and is projected to reach around $644.8 billion by 2035, according to Towards Healthcare.

“The Internet of Medical Things (IoMT) is the primary driver of the ‘data deluge,’” said Gabbert. 

Vital healthcare data is no longer confined to traditional healthcare settings, as the growth of IoT and wearables means crucial information is now being generated at home as well.

“Remote patient monitoring (RPM) has turned the home into a clinical site, requiring data strategies that bridge the gap between consumer ISP speeds and clinical-grade reliability,” said Gabbert.

With a whole new set of technological tools at doctors’ disposal, essential health information can be fed constantly and in real-time, helping to better inform life-altering decision making.

“We’ve moved from ‘snapshot’ data, like blood pressure taken at the office, to ‘streaming’ data, like continuous glucose monitors and smart-watches,” said Gabbert.

Of course, with data sources now evolving, IT infrastructures must be reconfigured and modernized to meet the moment. Edge architectures, in particular, are becoming indispensable for their speed, security and compliance.

Armed with a new wealth of data and growing diversity, AI algorithms can help fuel significant efforts in precision medicine. Precision medicine is when healthcare is personalized based on an individual's genetic makeup or unique lifestyles, leading to better health outcomes.

“Precision medicine is moving from ‘experimental’ to ‘standard of care,’ but it requires incredible data scales… like a ‘Unified Data Platform’ that can store genomic sequences, alongside EHR notes and pathology slides,” said Gabbert.

The more sources of biological data for AI algorithms to parse, the better personalized care can be. That can be a game-changer for many patients that struggle with the one-size-fits-all system that is currently commonplace.

This staggering growth is the result of a number of digital transformation drivers in healthcare over the past several years, including:

• Electronic Health Records – The digitization of patient records has led to a substantial increase in the volume of data healthcare providers handle. Electronic health records (EHRs) encompass a comprehensive range of patient information, from medical histories to ongoing treatment details. While they improve accessibility and can enhance patient care, EHRs also present significant challenges in terms of securing patient health information, and of data storage and management, particularly in maintaining data accuracy and integrity

• Advancements in Medical Imaging – Medical imaging technologies, such as MRIs, ultrasounds and CT scans, produce high-resolution images that contribute to large datasets. The storage and management of these images are challenging due to their size and the need for high-speed access and analysis. Efficiently storing and retrieving these diagnostic-quality images quickly for care purposes is often crucial but requires sophisticated healthcare data storage systems.

• Patient-Generated Data – The explosion of wearable technology like Fitbits and health apps has generated unprecedented amounts of patient-generated health data. This data type offers valuable insights into patient adherence and health outside clinical settings, but it also adds to data management complexity. Integrating this unstructured data with structured clinical data to form a holistic view of patient health is a challenge that healthcare providers increasingly face.

• Length of Storage Requirements – Deciding how long to store old data is a decision every enterprise has to make as part of its data storage strategy. For healthcare organizations, however, this decision is defined by legal and regulatory requirements.

Government regulations mandate holding onto records and images from radiology studies for 7-10 years, depending on state or geographic location, according to Gabbert.

“And, perhaps even longer if that patient is a minor, depending on various governmental and compliance requirements,” Gabbert explained. “These studies create diagnostic-quality images that radiologists can access and read from anywhere, but these are enormous files that need to be stored and secured.”

Further, the nature of interoperability and record-sharing between providers requires easy retrieval of these images to be digitally shared.

In these and similar scenarios, high capacity and accessibility are both essential but difficult to achieve with traditional, siloed and on-premise solutions. Unified and cloud-based alternatives are needed to meet modern demands for length of storage and accessibility over that period.

Managing the Sprawl – Healthcare is no exception to the massive IT infrastructure sprawl that’s happening in every industry and sector in the era of AI. At this scale, effective data storage is simply not possible without a defined strategy and the right tools in place to streamline and centralize this continued and accelerating sprawl. 

Compliance with Healthcare Regulations – Data storage in healthcare is highly regulated to ensure patient privacy and security. Compliance with regulations like HIPAA in the U.S. is non-negotiable but adds layers of complexity for data storage systems. These regulations dictate how healthcare data is stored, accessed, and shared, which requires robust systems and protocols.

Integrating Diverse Data Types – Perhaps the most significant challenge in healthcare data management is integrating diverse data types from EHRs, medical imaging, patient-generated data, and even operational data (such as physical surveillance or asset tracking) into one cohesive system. This integration is critical for comprehensive patient care but demands advanced technology solutions and effective data governance strategies.

Telehealth Delivery – The rapid adoption of telehealth that started during the pandemic has introduced a new dimension to healthcare data management. Virtual visit and health monitoring data records are now being maintained at unprecedented levels. As a result, challenges are emerging around the secure transmission and efficient storage of these data types, as well as their integration with existing health records. Telehealth's rise has necessitated data storage solutions that not only can manage the vast amounts of data generated by this delivery mode, but can ensure data is both securely stored and readily accessible for providers and patients who need it in real-time.

As healthcare continues to evolve in the digital age, these healthcare data storage drivers and challenges collectively show us the pressing need for a more strategic approach to storage.

In the next section, we’ll take a wider-lens look at the overarching data storage trends driving healthcare organizations toward unified data storage solutions.

Current Trends in Data Storage

The healthcare industry isn’t the only sector undergoing a significant transformation when it comes to data storage and the need for more secure, strategic data management systems. New innovations are shaping the future of data storage at the highest levels, changing the way organizations in every industry collect, manage, store, and share their data.

Cloud Migration – The shift toward cloud-based solutions in healthcare has been massive over the past several years, and for good reason. Cloud storage in healthcare offers remarkable flexibility, enabling providers to scale storage resources as needed. The benefits of cloud in healthcare is that it offers much-needed capabilities for centralized storage and remote access. Amish Patel, CTO of Fortune 20 healthcare company Elevance Health, shared with McKinsey the transformative impact of centrally storing and operating data and digital assets on the cloud. “By leveraging cloud-native approaches…we have achieved agility, scalability, and resiliency in our member-facing applications. This ‘single-pane-of-glass’ approach to standardization [has] significantly enhanced member experiences and streamlined processes, providing us with a competitive advantage.” This competitive advantage is something healthcare organizations will increasingly need in the future, as patients behave more like consumers and seek personalized, high-touch experiences from their providers.

Big Data and Analytics – Big data analytics plays a pivotal role in healthcare data storage by enabling the extraction of valuable insights from vast datasets. With the increasing availability of EHRs and other data, including third-party data, big data analytics can identify patterns and trends that inform smarter clinical decision-making. Analytical approaches aid healthcare organizations in everything from minimizing organizational risk, predicting patient outcomes, improving diagnostics, and enhancing population health management. By leveraging big data, healthcare providers can gain a deeper understanding of patient needs, leading to more personalized and effective care at scale. To achieve these capabilities, organizations need unified storage solutions that provide central access to their datasets as they’re needed to perform analyses and make decisions.

Interoperability – IT teams are expected to leverage more than one infrastructure, including a mix of public, private, and hybrid clouds as well as traditional data centers. Add to that the complex nature of healthcare ecosystems, with internal and external partners all needing their systems to communicate, and it’s easy to see why this is such a need. Modern data storage strategies must be designed so that data can be accessed as needed across critical systems without compromising its integrity or its security at any time. Unified data storage systems with high integration capabilities are a key piece to this puzzle.

The Solution: Unified Data Services Platform for Healthcare – Unified data services platform has emerged as the top solution for storing and securing exponentially growing unstructured data that now exist within healthcare organizations—and for leveraging it strategically. Software-Defined, unified storage solutions consolidate all types of unstructured data, storage protocols and synthesize various data storage challenges into a single, unified storage platform that offers simplified management, scale with performance, flexible consumption with single license.

As healthcare continues to evolve digitally and processes become increasingly data-driven, adopting unified data storage systems is now becoming a competitive imperative for healthcare providers to keep pace with these changes.

Nutanix Unified Storage is one such software-defined data service platform that helps healthcare organizations to consolidate management and protection of siloed block, file, and object storage into a single platform. Powered by data services like analytics, ransomware protection, lifecycle management, and data protection, NUS empowers healthcare IT and storage generalists to adapt to fast-changing applications' demands and to focus on strategic data management rather than day to day storage. 

These benefits allow for a more holistic, efficient, and secure way of managing the diverse and ever-growing data in healthcare, paving the way for improved patient outcomes and care. 

While the IT world continues to drown in an onslaught of healthcare data, the era of machine learning promises to make that information even more actionable and impactful. Whether it’s enabling drug discovery, optimizing diagnostics or fueling precision medicine, artificial intelligence is reshaping healthcare and saving lives one byte of data at a time.