Some time ago, the IT architecture of a hospital would comprise on-premises servers, a large binder full of disaster recovery plans, and IT professionals trying to catch up all the time. That’s no longer true. Nowadays, cloud computing is the foundation of any modern health care system – be it patient portals or remote telehealth visits, advanced diagnostics using machine learning algorithms, and real-time patient monitoring.
The U.S. cloud computing in healthcare market is expected to grow beyond $90 billion by 2027, thanks to digital transformation projects, accelerated development of telehealth following the pandemic, and the increasing amount of data collected by healthcare providers. However, some obstacles still stand in the way, including security and compliance issues, as well as high costs.
Here’s how cloud computing works in the health care sector, where difficulties might arise, and what can be expected in the near future.
What Cloud Computing Means in a Healthcare Context
At its essence, cloud computing entails accessing the computational resources – storage, compute power, applications, and network access – through the Internet. The three major types of cloud computing that apply in the health care environment include the following:
- Infrastructure as a Service (IaaS) – In IaaS, cloud companies such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud offer computing infrastructure. IaaS is used in the health care setting to host applications, store images, and conduct analytics, without requiring the purchase of the underlying physical servers.
- Platform as a Service (PaaS) – developers develop healthcare applications in PaaS, which consists of computing platforms offered by cloud companies. This helps cut down development times for health apps significantly.
- Software as a Service (SaaS) – this entails using applications provided by cloud providers as a service. Epic offers its EHR cloud-based, while platforms such as Teladoc fall under the SaaS model.
Hybrid and multi-cloud approaches are commonly seen among the largest health systems, which combine private cloud infrastructures with public cloud options.
The Real Benefits of Cloud in Healthcare
1. Scalability Without Capital Expenditure
On-premises architecture necessitates provisioning for peak load, meaning that there will be periods when excess capacity goes unused. The cloud resolves this issue. With regards to the ongoing pandemic, those healthcare institutions that previously moved their infrastructure into the cloud successfully scaled up their telehealth capacities within days rather than weeks or even months.
2. Interoperability and Data Sharing
In the healthcare industry, there are several longstanding issues that continue to create difficulties for providers and patients alike. For example, patient information can be scattered across multiple hospitals, separate clinics of specialists, and pharmacy chains; none of these institutions exchange data with one another effectively and without any significant delays. In response to this challenge, cloud-based solutions utilizing FHIR standards have been devised.
3. Advanced Analytics and AI Enablement
Cloud technology provides the backbone on which all AI in healthcare operates. Machine learning training using imaging information, forecasting patients’ risk of decompensation, and identifying care gaps for patients with chronic conditions – all these tasks can be performed only using cloud computing power. Cloud providers enable hospitals and software companies specializing in digital health solutions to use their GPU clusters, pre-built ML pipelines, and dedicated AI tools for health care, such as Amazon Comprehend Medical or MedPaLM from Google Cloud.
4. Disaster Recovery and Business Continuity
In recent years, there have been many ransomware attacks against U.S. health systems. As a result, several cases ended with hospitals being compelled to switch to manual procedures involving pen and paper for several days or even weeks. Using cloud technologies in backups and disaster recovery ensures a fast recovery time objective. Data stored across regions minimizes downtime as a result of cyberattacks or natural disasters.
5. Cost Efficiency Over Time
The move from capital expenditure (purchase of servers) to operational expenditure (costs of using cloud computing services) makes cash flow management easier for health systems running on tight budgets. Although cloud computing is not always cheaper – especially if dealing with stable, large-scale operations – the cost of ownership during a period of five to ten years often makes cloud the better choice, considering the costs involved with server replacement, maintenance, and lost opportunities.
6. Support for Distributed and Remote Care
Following the pandemic, the delivery of healthcare has changed forever. Doctors now treat their patients from their homes; patients receive treatment remotely. Telemonitoring devices transmit patient data from all sorts of places – living rooms, senior homes, etc. The only way to make such a healthcare delivery process possible is through cloud computing.
The Challenges Healthcare Organizations Actually Face
The benefits are real, but so are the barriers. Organizations that go in without a clear-eyed view of the challenges tend to underestimate both the complexity and the timeline of cloud adoption.
HIPAA Compliance and Shared Responsibility
All leading cloud vendors provide HIPAA-compliant services and are ready to sign the Business Associate Agreement (BAA), but signing the BAA does not mean that you become HIPAA-compliant. Signing the agreement means that the vendor commits to processing your data under HIPAA compliance policies within its scope. You remain fully responsible for your configuration of services, data access, and security. Misconfigurations of the S3 buckets, overly open IAM policies, and unencrypted transfer processes have been repeatedly mentioned as sources of HIPAA noncompliance among cloud customers. These reasons are exclusively related to the customer.
Legacy System Integration
The vast majority of health care systems in the United States use EHR and other clinical applications developed over decades that were not intended for connection through cloud services initially. Integration processes require significant time and budget investments and involve risks associated with implementation processes. Phased strategies with well-developed rollback plans are recommended, although even in such cases, integration projects usually take more time and money than expected.
Data Sovereignty and Residency
Additionally, there are restrictions imposed by the federal government and some state governments, along with certain research firms, when it comes to keeping patient records on their servers. While multi-cloud and hybrid architectures can solve this issue, these types of architectures present certain architectural challenges. Data localization considerations due to the health data protection regulations passed at the state level should come into account in the development of cloud architectures.
Vendor Lock-In
Organizations in healthcare that design their cloud architecture relying on proprietary technologies, including the use of artificial intelligence algorithms, data structures, and database management systems, might find themselves not having enough room for negotiation as far as pricing goes. What is more, moving from one provider to another will become a problem.
Workforce Readiness
It is not simply an IT problem, but an organizational change management problem. The healthcare practitioners will need training for using cloud computing systems. The IT practitioners will need new skillsets that include cloud computing security, DevOps, and infrastructure-as-code. There is very little availability of cloud computing architects in the field of healthcare, and so it is fair to say that workforce readiness is one of the most challenging problems in this entire process.
Downtime and Connectivity Dependence
The cloud system itself might be very dependable, but at the same time, it is also vulnerable to problems. In the past, there have been several instances when clouds from AWS, Azure, and Google Cloud Platform (GCP) have experienced some kind of disruption. Those involved in the clinical setting need offline contingency plans.
Regulatory Landscape in 2026
The regulatory framework surrounding cloud computing for healthcare purposes has undergone many changes in the past two years.
In addition to the 21st Century Cures Act Interoperability provisions that drive the adoption of FHIR APIs and, as such, create a necessity for health systems and vendors of digital health solutions to implement cloud-native API layers, obstructing information access is now a violation of federal law.
The ONC’s Health IT Transparency Final Rule, finalized in 2024, requires adherence to new provisions for health data availability, algorithmic transparency, and decision-support software, all related to cloud computing for AI and analytics.
On top of that, the Federal Trade Commission is continuously monitoring the use of health data by various organizations and paying particular attention to applications used by individuals in a cloud environment, not covered by HIPAA. Therefore, privacy frameworks must be more extensive than HIPAA’s.
At the same time, individual states, such as Washington State, with the My Health MY Data Act enacted in 2024, create an intricate maze of regulations for cloud computing for healthcare.
Future Trends Shaping Healthcare Cloud Through 2028
Edge Computing Meets Clinical Care
Latency is one issue that will affect some clinical applications where time is critical; real-time surgical robotics, constant monitoring in the ICU, and diagnostic tests during emergencies cannot afford to have their results come after a trip to a faraway data center. Edge computing has been adopted in an attempt to deal with these limitations by providing more processing power near the point where data originates. In 2026 and beyond, hybrid architectures with edge nodes performing real-time processing and cloud providing long-term data storage and analytics are anticipated.
AI-as-a-Service in Clinical Workflows
Clinical systems have access to advanced AI technology through cloud services. These include ambient clinical documentation, predictive readmission models, diagnostic imaging, and prior authorization automation. With more developments in the field and further regulatory clearance of AI models, we expect them to become commonplace in the coming years. They have not reached such popularity because of latency issues, but rather because most hospitals lack AI specialists to develop them themselves.
Confidential Computing
Another issue with healthcare cloud computing is the concern that even encrypted data could be accessed while processing the data in RAM. Confidential computing refers to the use of TEEs based on hardware encryption that protects the data even when computing. This concept enables health organizations to use cloud environments for analyzing sensitive patient information (genomics data, behavioral health records) while protecting it from the cloud environment itself. Examples include AWS Nitro Enclaves, Azure Confidential Computing, and Google Confidential VMs.
Cloud-Native EHR Platforms
The cloud migration of Epic’s software solutions and Oracle Health’s Cerner solution to the cloud represents a paradigm shift in EHR technology deployment models. Cloud native EHRs ease health organizations’ infrastructure challenges and speed up innovation, but at the same time, they make health organizations entrust their crucial clinical system to the cloud environment of an external vendor, requiring thorough due diligence.
Sustainability and Green Cloud
There is increasing pressure on healthcare institutions to become environmentally responsible. Data centers are one of the greatest energy-consuming assets. Cloud providers, including AWS, Azure, and GCP, have committed themselves to renewable energy. Health organizations moving from older on-premise data centers to cloud-based computing can see notable reductions in their carbon footprint. It would be interesting to see sustainability become part of the criteria in choosing a cloud provider within the next five years.
FAQs
Is public cloud safe enough for PHI?
Indeed, but it depends on the configuration and governance. AWS, Azure, and Google Cloud support HIPAA-eligible solutions and enter into BAA agreements. The problem here lies not in cloud architecture but rather in configuration. A properly configured public cloud might be even more secure than an understaffed and ill-resourced on-premises data center.
What’s the difference between hybrid cloud and multi-cloud in healthcare?
The hybrid cloud involves both private cloud and public cloud services. Alternatively, multi-cloud utilizes several public cloud providers. Both approaches are employed at once by many health organizations, which run some applications in a private cloud while deploying others in different public clouds, i.e., AWS, Azure, and GCP.
How long does a hospital cloud migration typically take?
There is much variability depending on the size and complexity of an organization. A typical targeted workload migration may take 3-6 months. Full data center migration from an on-premises location may take from 2 to 5 years.
Do cloud providers guarantee HIPAA compliance?
Not really. While cloud vendors provide HIPAA-compliant infrastructure and enter into BAAs, it’s still a joint effort. The security policies regarding how you control access, encryption, log-in, and manage the data will depend on you.
What should healthcare IT leaders look for in a cloud provider?
Make sure HIPAA BAAs are available; there’s compliance documentation for healthcare; you have data residency; SLAs with penalty clauses in terms of uptime; and an ecosystem of partners. Healthcare-specific reference customers and an industry team are also signs that the vendor is aligned to meet your needs.
How is AI changing the ROI calculation for cloud in healthcare?
Absolutely. Even before AI, it had made sense based on the cost savings and scalability of cloud infrastructure alone. But thanks to AI capabilities like ambient documentation, predictive analytics, and decision support, there’s an additional benefit that becomes ever more compelling.
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