Healthcare’s head is in the cloud — and that’s a good thing. Historically, patient data belonged to filing cabinets and isolated hospital systems. Extracting that data and sharing it across departments involved time-consuming paperwork and communication delays, which undermined the quality of care.
Cloud computing has changed the game and enabled diagnoses to happen in real time. With regulatory pressure intensifying, patients demanding more personalized care, and novel business models emerging from healthcare industry incumbents, healthcare cloud computing is no longer a nice-to-have. It’s a non-negotiable for patient-first, quality care.
Key benefits of cloud computing in healthcare
Cloud computing is poised to significantly improve healthcare in the coming years. By 2032, the global healthcare cloud computing market is slated to grow from $63.6 billion in 2025 to $197.5 billion. What’s behind the explosive growth of cloud-based solutions in the healthcare system?
Cost efficiency for providers
While many healthcare organizations perceive cloud systems as expensive, сloud-native healthcare IT solutions reduce costs by 20–40% compared to on-premises systems. Fewer infrastructure expenses, less on-the-ground maintenance overhead, elastic data storage, and other operational efficiency allow healthcare providers to optimize resource allocation and scale operations up and down based on real-time demand.
Agility and speed
By moving their assets to the cloud, healthcare organizations undergo a fundamental shift — and not just a technical one. They transition from siloed systems to a product-based, agile mindset, laying the groundwork for the infrastructure and tools for delivering digital healthcare services on a new scale and pace.
In particular, cloud platforms offer a breeding ground for implementing DevOps practices, CI/CD pipelines, and continuous feedback loops. Without these capabilities, the entire healthcare system can fall into long, reactive deployment lifecycles where updates to tech systems drag on for months and user feedback is lost in translation.
Stronger collaboration and centralized data access
Shared cloud databases and data lakes provide a unified gateway to structured and unstructured health data, whether it’s electronic health records, imaging, or lab results. Cross-functional teams, including clinical, operations, and IT, as well as various departments and external institutions such as partner hospitals, can access those unified datasets in real-time to coordinate their care efforts and co-author treatment plans.
Cloud-native APIs and FHIR-friendly architectures further support interoperability, allowing different systems such as patient remote monitoring platforms, EHRs, and telemedicine apps to inform treatment adjustments in real time. Additionally, cloud platforms serve as the backbone of federated learning, enabling different institutions to securely collaborate on research initiatives.
Faster time-to-insight
Without cloud computing, clinical data and patient information are stored across fragmented systems in heterogeneous formats. Instead of actually digging into insights, clinical data analysts have to spend around 80% of their time wrangling and preparing the data for analysis.
Cloud platforms integrate patient data, allowing data analysts to pull targeted datasets from a unified location and run on-demand queries. They don’t need to sift through irrelevant data points, fetch data from disconnected systems, or handle backups. Instead, they can access everything in one place to integrate and drill down into trends at scale.
Built-in security and compliance
Managing security and compliance for on-premises healthcare data requires significantly more effort than relying on built-in cloud safeguards, mainly because it’s 100% on the healthcare organization. Cloud providers offload a significant portion of the security and compliance burden from healthcare organizations, handling data security controls, monitoring, and protections.
Despite this shared security model, the ownership and control of healthcare data remain undeluted. Additionally, cloud services from major providers such as AWS and Google Cloud come with built-in compliance, including support for HIPAA, GDPR, CLIA, and other relevant standards.
Types of cloud computing in healthcare
In healthcare, cloud computing services usually vary by deployment model and service model. Each of these arrangements comes with a set of distinctive features, controls, and specific applications.
By deployment model
The type of deployment model defines the way the cloud infrastructure is set up, managed, and accessed. The choice is made based on the existing IT infrastructure, available expertise, and budget of a healthcare organization. Healthcare providers can choose between the four types of cloud setups:
- Private cloud — Hosted by the organization itself or a dedicated managed services provider, the private cloud infrastructure is typically used to keep highly sensitive patient information under watertight access controls. This deployment model is a popular option for the healthcare industry, as it provides complete control over stored data, as mandated by healthcare compliance requirements, and allows for granular customization of infrastructure and workflows. However, a private cloud usually requires more internal IT resources and a significant maintenance budget.
- Public cloud — Public cloud is favored by smaller clinics, health tech startups, and growing health systems for fast health tech product launches, especially those powered by AI and data analytics. Although it doesn’t offer the same level of data control as a private cloud, a public cloud can still check all the boxes in terms of data security and compliance, provided healthcare organizations sign a BAA with cloud service providers and properly configure their cloud use.
- Hybrid cloud — Many healthcare organizations use a combination of private and public clouds with secure integration in between. This approach enables companies to share workloads between clouds, securing sensitive patient data in a private cloud, while the public cloud is dedicated to new applications, analytics, and AI tools. Additionally, hybrid environments help healthcare facilities avoid costly, large-scale migrations, allowing them to keep legacy systems in place.
- Community cloud — Designed to meet the shared needs of a group of healthcare organizations (e.g., multiple hospitals in a regional health network), community cloud bundles the elements of private cloud and collaborative infrastructure. This deployment model enables shared data access and exchange, facilitates easy collaboration on clinical research, and supports care coordination across facilities.
By the service model
While the deployment model defines the way and place where the infrastructure is hosted, service models refer to the specific kind of cloud-based services healthcare organizations consume. In healthcare cloud computing, there are three main types of service models:
- Infrastructure as a Service (IaaS) — IaaS is the foundation upon which healthcare cloud IT is built. Healthcare organizations leverage IaaS when they need a fundamental virtualized infrastructure for storage-heavy applications, development environments for new applications, or the capabilities for running legacy applications that aren’t yet cloud-native.
- Platform as a Service (PaaS) — This service model builds on top of IaaS by offering a managed platform with development tools, databases, and other services needed to build, run, and manage applications. In healthcare, organizations choose PaaS when they need to supercharge the entire application lifecycle, without having to double down on managing the backbone structure. PaaS allows healthcare providers, payers, and researchers to rapidly develop healthcare applications, deploy data analytics platforms, or simplify database management.
- Software as a Service (SaaS) — This service model refers to cloud-hosted applications that are entirely maintained by the vendor and accessed via subscriptions. In healthcare, SaaS is a popular option for EHRs, telemedicine platforms, billing systems, and patient portals.
Below is a comparison of responsibilities across on-premises infrastructure and the three main cloud service models.
| Responsibility | On-premises | IaaS | PaaS | SaaS |
|---|---|---|---|---|
| Customer data | Customer | Customer | Customer | Customer |
| Configurations and settings | Customer | Customer | Customer | Customer |
| Identities and users | Customer | Customer | Customer | Customer |
| Client devices | Customer | Customer | Customer | Shared |
| Applications | Customer | Customer | Shared | Shared |
| Network controls | Customer | Customer | Shared | Provider |
| Operating system | Customer | Customer | Provider | Provider |
| Physical hosts | Customer | Provider | Provider | Provider |
| Physical network | Customer | Provider | Provider | Provider |
| Physical data center | Customer | Provider | Provider | Provider |
Core use cases for cloud solutions in healthcare, real-world examples
When healthcare providers, payers, and insurers strategize their cloud transformation, they typically focus on high-impact cases that rely heavily on data and analytics. Such cases have the shortest time to impact, the biggest value potential, and play into the organization’s current implementation capacity. Here are six examples of comparable use cases we've seen consistently deliver results:
Electronic health records and electronic medical records
Both EHRs and EMRs generate massive volumes of patient data. Managing that data on-premises would require healthcare organizations to put thousands of dollars into servers, security, maintenance, and upgrades. Cloud-based elastic patient records offer a more cost-efficient solution thanks to scalable storage, reduced IT overhead, and greater interoperability.
Not only that, but cloud-based EHR/EMR systems can also automatically switch over to the wireless network if the internet connection fails. So, even in rural areas, healthcare professionals will have remote access to critical patient data.
Example: Mount Sinai
On a mission to facilitate generative AI innovation and reduce data remediation costs, Mount Sinai moved its Epic EHR system to a multi-cloud environment. This migration marked the biggest production instance of Epic running on Microsoft Azure and allowed the hospital to achieve faster data processing times and easier experimentation with new technologies.
Home healthcare
Real-time remote care models, such as telehealth, remote patient monitoring, and virtual follow-ups, require a remote infrastructure that can process healthcare data in real-time. As traditional setups aren’t built for it, cloud infrastructures are becoming the go-to option. The on-demand elasticity of cloud technology also gives healthcare providers the flexibility to onboard as few or as many patients as needed without overspending on infrastructure.
Moreover, cloud-based, out-of-the-box compliance and FHIR-native infrastructure enable health data to flow seamlessly across platforms, apps, and devices — a must for care teams working outside healthcare facilities.
Example: Our client, a U.S. global firm investing across vital industries
One of our clients (under NDA) set out to venture into virtual care delivery, needing a transition from a traditional to a cloud-based infrastructure to achieve this. Our team delivered a scalable, HIPAA-compliant, and cloud-based backend hosted on AWS to enable real-time data exchange between patients, healthcare professionals, pharmacies, and IoT devices.
Medical image management
Cloud-based storage for radiology images is another growing trend in the healthcare sector. By moving medical images like CTs, MRIs, and PET scans to the cloud, healthcare providers get a scalable storage for terabytes or petabytes of imaging data without splurging on costly on-premise hardware.
Cloud-based medical image storage also facilitates better cross-site collaboration, enabling clinicians and radiologists to access cloud-based image archives in real-time. Additionally, cloud environments come with an AI-ready infrastructure, which allows healthcare organizations to easily pair imaging data with diagnostic tools, thereby streamlining image analysis, triage, and prioritization.
Example: Our client, a US-based healthcare provider
A healthcare client (under NDA) turned to us with a challenge: they wanted to change the way medical images were analyzed and shared across healthcare professionals. Based on an outdated architecture, the existing system was fragmented and limited in scalability. Our team developed a cloud-based, AI-powered image analysis solution with built-in quality control measures, which led to a 7% increase in analysis accuracy.
Clinical trial data management and research acceleration
Clinical trials often falter as early as patient recruitment, because pharma companies simply cannot plug into the varied datasets needed to identify the right candidates. That data — clinical, genomic, or real-world — is typically scattered across different locations. By migrating this deluge of data to the cloud, pharmaceutical companies gain a unified, secure environment where they can consolidate, clean, and query multiple data sources in real-time.
Consolidated in one place and analyzed by AI, this aggregated data can then inform the candidate selection process, optimize site selection, and even simulate trial outcomes.
Example: Medable
Now touted as the leading trial platform, Medable earned its stripes by providing a unified, cloud-based platform that integrates real-world data (RWD), electronic health records, and patient outcomes. Complemented with AI analytics, Medable’s platform allows CROs to cut the trial launch timeline from 6 months to under 3.
Generative AI, AI-powered diagnosis, and other AI solutions
The cloud is also the name of the game when it comes to implementing innovative healthcare solutions powered by the latest technologies, such as generative AI, conversational AI, AI agents, and others. At the heart of such applications are various artificial intelligence algorithms that require robust computing muscle to train and run. Maintaining that kind of infrastructure on-premise is prohibitively expensive, but in the cloud, it’s readily accessible and relatively easy to set up.
As AI-powered tools thrive on prompt access to various data sources, cloud environments are also crucial for enabling timely and secure data access across different locations. To that end, cloud platforms are geared up with ready-to-use APIs, development frameworks, and training environments that slash the time and effort put into building and deploying AI-powered healthcare solutions.
Example: Hippocratic AI
Focused on non-clinical, patient-facing tasks, Hippocratic AI’s solution is based on a unique cloud-enabled, AI-based constellation architecture. The solution features a primary large language model (LLM) paired with over 20 task-specific support models for cross-checking and validating task-specific outputs. The company uses multi-cloud environments, such as AWS, GCP, and Azure, to deploy its LLMs and other AI agents.
Population health management
If you want a more data-driven approach to managing population health data, aggregating clinical, claims, and SDoH data in the cloud will do the trick. A cloud-native infrastructure unites vast datasets under one roof, allowing researchers to run analyses across populations to detect health trends, forecast outbreaks, and elevate community-level interventions.
It also opens the door for cross-organizational data sharing, enabling public health agencies, payers, and providers to join forces around shared patient populations.
Example: Persistent Systems
Persistent Systems, a global enterprise modernization leader, launched a generative AI-powered PHM solution using Azure OpenAI Service and Dynamics 365 Customer Insights. This solution identifies social determinants of health by pulling unstructured data from EHRs.
Top challenges of cloud transformation in healthcare and ways to overcome them
Despite its undeniable benefits, the cloud computing technology can still backfire if not handled with due caution. Here are common stumbling blocks to watch out for:
Data interoperability between legacy and cloud
The healthcare industry still relies on on-premise systems, such as EHRs, PACS, LIMS, and many others. When you integrate cloud solutions, cloud-native systems struggle to communicate with the legacy IT estate due to different data formats. For example, PACS might store imaging data in a proprietary DICOM format, whereas cloud-native imaging systems may operate on DICOMweb or FHIR ImagingStudy resources.
To find a common language between the setups, healthcare cloud adopters might have to invest in middleware or custom APIs, data standardization, or cloud integration platforms with pre-built connectors. We also recommend that healthcare organizations opt for phased migrations to ensure data continuity. This approach allows cloud and legacy systems to run in parallel during the transition period so that care teams have redundant access to critical systems, just in case.
PHI access control and audit trails
Unlike on-prem systems that excel at role-based access controls, cloud-based systems may default to broader access roles that don’t align with clinical workflows (e.g., leaving nurses with view-only access). Obtaining that level of access control in the cloud might involve more complex access models like attribute-based access control or policy-based access control, with both requiring custom logic and advanced IAM integrations.
Another crucial concern is related to logging every access to PHI, something that the cloud doesn’t have by design. To maintain detailed access logs across systems and comply with regulations, healthcare organizations might have to add centralized log aggregation platforms, compliance platforms, or custom monitoring tools to their cloud stack.
Latency in critical applications
Some life-critical applications, such as ICU monitoring dashboards, require real-time data and 99.999% uptime. Reliability of such caliber is out of reach for fully cloud-based environments due to the ever-present risk of internet disruptions and cloud service downtimes. That’s why hybrid cloud deployments are so popular in hospitals. They allow critical systems to remain on-premise or in private clouds, while less critical operations live in the public cloud.
Vendor lock-in and data portability
Once the medical data estate gets ported to a cloud provider’s ecosystem, migrating that data into a different location might become a problem. The thing is that cloud vendors often use proprietary data storage formats, APIs, or data models confined to their ecosystem only. In this case, replicating this environment at a different cloud provider equals extensive data transformation, revalidation, and replatforming efforts.
To prevent this scenario, medical professionals should select providers that explicitly support open data standards, such as FHIR and HL7, among others. Additionally, healthcare IT teams can design systems based on modular, vendor-agnostic components that don’t come from the vendor’s proprietary services. Middleware platforms, containerized workloads, and cloud-agnostic APIs can also create a buffer between the applications and the cloud infrastructure, providing higher control.
Cloud cost management
The concept of predictable, traceable, and efficient cloud costs doesn’t align well with the black box nature of cloud pricing. Opaque billing models, hectic data flows, and a host of other variables lead to healthcare innovators under-budgeting cloud expenses. Pair that with unoptimized storage, over-provisioned compute, and hidden egress fees, and the gap between expectation and actual spend grows even wider.
Partnering with an experienced cloud migration partner will help healthcare institutions get a tighter grip on their cloud management costs, right-size their storage and computing resources, and dodge costly compliance drifts.
Adopt cloud computing without the risk: our framework
Healthcare stakeholders can’t afford surprises — not in cost, not in uptime, and especially not in compliance. Here are some tried-and-true strategic tips from our cloud engineers that will help you minimize unexpected surprises:
Map what matters
To paint a clear picture of the security risks, compliance obligations, and dependencies, start by defining critical workloads:
- Identifying systems that handle PHI, clinical data, or sensitive operations.
- Mapping data flows across regions, services, and vendors.
- Classifying risks related to HIPAA, HITRUST, and other regulations.
- Addressing data security concerns and seeing which controls the organization already has in place.
Migrate in steps
We usually avoid all-at-once migration scenarios as they are not as manageable as phased ones. Instead, our cloud engineers start with less critical workloads and gradually move up to more complex ones, building security, compliance, and rollback plans at every step.
- Implementing data encryption at rest and in transit, using NIST-based standards.
- Preparing rollback plans and failure mechanisms for every phase.
- Employing applicable access control models (RBAC, ABAC, PBAC) and identity federation.
- Keeping comprehensive audit logs for all actions around sensitive data.
Test and validate before the grand finale
Before a full rollout, cloud engineers must ensure that every data safeguard is configured properly to prevent sensitive data exposure in the future. Additionally, preliminary validation checks serve as a safety net for regulatory compliance, allowing teams to double-check their adherence to compliance requirements.
- Checking encryption mechanisms and access controls.
- Running mock audits and penetration tests to uncover any residual compliance gaps.
- Performing system integration testing to ensure seamless cross-system interaction and data flows.
Optimize for visibility and control
Cloud migration is not a one-off deal. To keep the budget tight, compliance strong, and system secure, healthcare organizations require a post-migration cloud management strategy dedicated to governance and cost attribution.
- Implementing resource tagging to help healthcare organizations monitor costs by department, project, and application.
- Setting up real-time dashboards that keep an eye on cloud usage, budget thresholds, and unusual patterns in cloud spending.
- Configuring automated alerts for failed backups and misconfigured permissions for workloads with PHI.
- Performing regular audits of IAM roles, policies, and audit logs to reduce the risk of compliance drift.
Summary
Unlike on-premise infrastructure, the cloud technology enables healthcare providers to become more agile, data-driven, and flexible in their healthcare delivery. However, implementing cloud computing presents distinctive challenges to innovators, including data security, compliance, and reliable data recovery. If left unattended, those challenges can turn cloud transformation into exposure, rather than empowerment.
But with a structured migration strategy, governance, and tooling, cloud-based healthcare solutions can deliver tangible benefits, including better patient care, accelerated clinical workflows, and more. If you need an experienced tech partner for your cloud migration projects, Orangesoft has 14 years of expertise and a proven framework to guide your company through every stage of the cloud transformation lifecycle.