Storage and Data Protection in Healthcare
One of the biggest challenges for healthcare is its data explosion. According to IDC’s Data Readiness Condition (DATCON) index in 2021, the healthcare data explosion will approach the 4ZB level and exceed 10ZB by 2025. Although healthcare and life science organizations currently manage on average 21PB of data (i.e., 25% less than the industry average), at the same time, they retain data almost 20% longer.
These data-retention rates are due to regulatory mandates. While organizations tend to retain patient and financial data the longest, they typically hold operational data for shorter periods. Regulations require some data to be retained for decades (e.g., patient health records). In contrast, other data types are retained for as little as 30 days (e.g., video surveillance data).
The growing plethora of data sources and types presents a challenge for IT organizations concerned about exhausting their resources on merely being able to keep up. Furthermore, healthcare faces a wide range of digital era stress tests alongside data proliferation — from aging populations to an increase in chronic conditions, the shift to value-based care (VBC), the rise of consumerism, and rampant cyberthreats.
These challenges drive health IT leaders to look at improved storage and data protection, alongside digital transformation (DX) initiatives, as a way forward to promote organizational resilience.
Storage and data protection underly all mission-critical workloads in healthcare. If this infrastructure experiences unplanned downtime, everything from clinical and operational performance at a single facility to the resiliency of an entire organization may be affected. Addressing data infrastructure initiatives can prove daunting. Large provider organizations can have more than 20,000 employees, with the average workforce in the range of 14,000 employees. Furthermore, clinical (e.g., electronic health record [EHR] and medical imaging), operational (e.g., claims and supply chain), and other data types (e.g., consumer and research) can vary between 7PB and 34PB per patient.
To effectively manage these massive amounts of data and plan for future requirements, health organizations must rely on a robust, secure, cost-effective, and elastic infrastructure. Typically, organizations already have an investment in existing infrastructure, but the cloud has gained much momentum, driven by its benefits alongside the ongoing transformation of skill sets, capacity, and agility of healthcare organizations. Legacy systems absorb significant resources — as high as 64% of IT budgets — and many organizations can no longer afford to do everything on premises.
Continuous advancements in functionality and services developed in the cloud have added to the interest in cloud solutions for healthcare environments. Cloud technologies have also come a long way in addressing healthcare regulatory needs, while application management tools make the cloud more secure, leading many healthcare organizations to adopt a private or hybrid cloud deployment model.
IDC data shows that 58.4% of healthcare organizations use cloud solutions to support production workloads and services, with private cloud preferred over public cloud. Furthermore, “on-premises enterprise private cloud” is the current model of choice (69.3%). More than half of the respondents (53.3%) now see “improved IT security” as the most significant benefit in a private cloud. Much of the apprehension in healthcare around cloud has largely dissipated with private cloud. With a private cloud, resources are shared within a single or an extended enterprise; the enterprise places restrictions on access and defines and controls the level of resources dedicated.
But the need for greater flexibility is driving interest among healthcare organizations toward hybrid cloud (i.e., usage of IT services including IaaS, PaaS, and SaaS apps and SaaS-SIS cloud services across one or more deployment models using a unified framework). Hybrid cloud can maintain sensitive protected health information (PHI) in a private environment while benefiting from public cloud scalability.
Compared with other industries, healthcare is traditionally considered a laggard in adopting IT and the use of digital mediums to transform. However, healthcare for many years faced mounting pressures that drive the need to prioritize digital transformation (DX), pressures that continue to manifest. In the United States, for example, the ongoing shift in reimbursement models from volume- to value-based programs drives increased demand for new revenue streams. These programs aim to improve care for individuals, improve the health of populations, and reduce costs. Therefore, organizations require quicker and more convenient access to clinical, claims, and operational data.
The aging healthcare workforce and projected shortages in specific medical disciplines (e.g., radiology and primary care), coupled with the rise of consumerism, further drive the need for new service models and care offerings (e.g., virtual care and teleradiology) powered by DX capabilities. Patients with one or more chronic conditions, aging populations, and growing healthcare expenditures further emphasize a need for healthcare organizations to undertake DX initiatives to address current and future needs.
As a result, healthcare DX is climbing. In an IDC survey, 94.1% of respondents indicated that their organization has begun or is well underway in applying health IT to advance DX initiatives.
A recent IDC survey of over 260 healthcare and life science organizations revealed that only 60% felt they had a handle on the growth and management of their data while understanding what data required protection and how to go about protecting it. The growth of the digital footprint presents highly lucrative targets for cybercriminals and poses a critical concern for healthcare IT.
Consider the potential vulnerabilities combined with the black-market value of electronic health records. The value of health records can add up to $1,000 per patient due to its richness as a source of data and personal health information (PHI).
Leaders should understand how storage and data protection offerings can advance cybersecurity strategies. The end state should be a realization of maximal cyber-resilience with dynamic monitoring and continual improvement. Integrated solution offerings evaluated in light of HIPAA-HITECH, SOC2, and HITRUST standards will be well positioned to support cybersecurity initiatives.
All offerings must manage and protect workloads around the clock in a healthcare data environment. Healthcare leaders should also seek to include KPIs related to cybersecurity training, system updates, role-based access controls, regular risk assessments, and data recovery mechanisms.
“Next-generation clinical documentation” marks a shift in electronic health records (EHRs) from traditional systems of record for routine data entry to nontraditional systems (or platforms) of engagement for value-added activities. These activities range from clinical decision support (CDS) to patient engagement, revenue optimization, and interoperability.
Remote patient monitoring (RPM) technologies collect data passively from a body area network (BAN) to monitor vital signs and physiological data outside traditional care settings. Examples include implantable sensors, continuous glucometers, blood pressure monitors, pulse oximeters, trackers, wearables, smartphones, and edge gateways.
The resulting influx of data that streams continuously from the edge can be life-changing when aggregated to enable reliable, integrated access by patients, providers, and life science companies. As a result, medical intervention becomes timelier and patient relationships are strengthened through such things as personalized care plans and joint/emergency decision making.
Next-generation sequencing (NGS) determines the sequence of DNA or RNA genetic material to study genetic variation. This genetic variation could be associated with diseases or other human biological phenomena, making it critical for healthcare and life science organizations to understand, which will continue to drive precision medicine, research, and the future of drug development.
NGS has already significantly increased the global amount of raw and processed genome sequencing data from research in genetics, oncology, microbiology, and reproductive health. But NGS requires proper infrastructure — high-performance computing (HPC) to deliver the compute power for complex data analysis and interpretation, and highly scalable storage to manage the genomic data while ensuring compliance and usability.
Medical imaging transformation relies heavily on scalable storage and data protection to drive operational and diagnostic imaging workflow performance improvement. Imaging workflows can be complicated due to the need to quickly retrieve and reliably search vast amounts of stored and archived images with metadata across EHRs, picture archiving and communications systems (PACSs), and vendor-neutral archives (VNAs) in standardized formats.
Medical images can also quickly inundate IT infrastructure due to large file sizes: radiography (10MB per image), computed tomography (250MB–1GB per exam), magnetic resonance imaging (10–300MB per exam), ultrasound (30–50MB per exam), and digital pathology (2–3GB per slide). In addition, new imaging techniques and longer-term studies are creating richer, more detailed, and voluminous scans as well as longitudinal image libraries that add even more data.
AI shows vast potential to transform healthcare in countless application areas — but with the right data infrastructure. AI holds much promise to impact clinical and operational use cases cited here, to AI-assisted/automated diagnosis, conversational chatbots, virtual triage, care automation, symptom checkers, prescription auditing, analytics (e.g., medical imaging, population health, claims, and third-party data), robot-assisted surgery, drug discovery, gene analysis, medical device studies, care/service automation, and fraud detection.
Cutting-edge use cases include AI-enabled command centers for intelligent capacity planning and AI-embedded modern control towers for supply chain management. However, AI-driven predictive analytics in operational workflows represents the majority of use case adoption today.