Introduction: The Healthcare Cloud Dilemma
Cloud infrastructure has transformed the face of modern healthcare. From Electronic Health Records (EHRs) and Picture Archiving & Communication Systems (PACS) to telemedicine platforms and AI-powered diagnostics, critical healthcare systems are now built on cloud foundations.
However, many healthcare IT leaders still find themselves reacting to unexpected cloud billing spikes, budget overruns, or last-minute cutbacks. The reason is a fundamental gap in forecasting and visibility.
This blog lays out how healthcare organizations can transition from firefighting to forecasting by predicting cloud costs, planning usage, and controlling spend to enable innovation and efficiency.
The Reality of Cloud Chaos in Healthcare: The Inevitable Surge in Adoption and Costs
The healthcare cloud computing market rose from US$46.1 billion in 2023 to US$53.8 billion in 2024 and is projected to grow at a 17.5% CAGR to US$120.6 billion by 2029.
And yet, healthcare providers face some unique challenges. Here’s how healthcare’s operational demands make cloud cost management uniquely difficult:
- Spiky Workloads: AI training, imaging analysis, and telehealth lead to unpredictable surges in compute and storage.
- Mission-Critical Uptime: With patient care on the line, systems must be available 24/7 – downtime isn’t an option.
- Stringent Compliance Requirements: Frameworks like HIPAA, HITRUST, and MeitY enforce high standards for data security and residency.
- Shadow IT Proliferation: Different departments may provision their own resources, creating fragmentation and a lack of centralized oversight.
Real-World Examples: Hidden Costs, Big Impacts
Many healthcare organizations underestimate how seemingly routine decisions can escalate into substantial cloud expenses.
For instance,
- Healthcare providers often experience multi-fold increase in their cloud bill after training large language models for diagnostic purposes without anticipating the compute demands.
- A staging environment used for EHR testing was left running post-deployment, resulting in tens of thousands of dollars in idle resource costs over just a few months.
- When cloud usage is spread across multiple departments without clear ownership, accountability becomes blurred – making it difficult to control or reduce spending effectively.
Recognizing Firefighting Symptoms
When healthcare IT lacks forecasting capabilities, a reactive posture takes over. Here are common signs:
- Unexpected Billing Surprises: Often caused by storage misconfigurations or auto-scaling gone unchecked.
- No Ownership of Resources: When no team takes charge, optimization is impossible.
- Inconsistent Tagging: Without standardized tags, allocating spend by department or function becomes guesswork.
- Ad-Hoc Workload Termination: To stay within budget, teams kill workloads reactively – risking patient care or system stability.
- Guess-Based Budgeting: Forecasts built on last year’s usage without accounting for new projects or seasonality.
- Compliance Risk: A sprawling, unmonitored cloud estate increases the chances of data breaches or audit failures.
These symptoms aren’t just operational inconveniences – they are warning signs that your cloud strategy may be undermining both care quality and financial health.
What a Forecasting Mindset Looks Like
Moving from firefighting to forecasting means embedding predictability and control into your cloud operations. Especially with multi-cloud environments becoming the trend, this requires both cultural and technical shifts:
- Predictive Modeling: Use historical data and known trends (e.g., flu season, respiratory illness surges, or heat waves) to model future cloud usage and costs.
- Cross-Functional Visibility: IT, finance, and business units need shared dashboards and KPIs to align on spend management.
- Comprehensive Tagging and Chargebacks: Every resource must be tagged by project, department, sensitivity level, and usage type – then reported back to its owner.
- Right-sized Infrastructure: Match infrastructure scale to workload needs, especially for heavy-demand systems like AI or PACS.
- Real-Time Dashboards: Monitor actual vs. projected spend, triggering alerts when drift occurs or anomalies emerge.
The 4-Phase Framework for Forecasting Healthcare Cloud Spend
Forecasting isn’t a one-off effort – it’s a repeatable process. Here’s a structured framework healthcare IT teams can adopt:
1. Baseline and Categorize
The first step toward accurate forecasting is to establish a clear understanding of current cloud usage. This involves conducting a comprehensive audit across all departments, business units, and compliance zones. By grouping workloads based on their functions – such as Electronic Health Records (EHR), Laboratory Information Management Systems (LIMS), analytics, or telehealth – healthcare IT teams can uncover usage trends and align cloud spend with clinical and operational priorities. This baseline becomes the reference point for all future planning and optimization.
2. Tag and Attribute
Once usage is mapped, the next priority is implementing a robust and consistent tagging strategy. Every cloud resource should carry clear metadata indicating which department it belongs to, the nature of the workload it supports, the level of sensitivity involved, and the associated project. This level of granularity not only improves visibility but also enables precise cost attribution. Equally critical is assigning ownership to each resource group, ensuring there is someone accountable for monitoring usage and driving optimization.
3. Model and Simulate
With clean, structured data in place, organizations can begin to layer forecasting capabilities onto their cloud operations. This includes building models based on historical usage patterns – such as recurring surges during flu seasons or AI-driven diagnostic peaks and simulating future scenarios. For example, what would be the cost impact of launching a new virtual care platform? What happens if AI-related compute needs to grow by 20%? These simulations empower leaders to make informed decisions and prepare budgets that reflect real-world possibilities.
4. Monitor and Course-Correct
Forecasting isn’t static – it’s a continuous cycle that requires real-time monitoring and iterative refinement. Organizations should implement automated budget alerts and systems that detect when actual spend deviates from projections. Monthly reviews between IT and finance teams help course-correct as new demands or changes emerge.
Leveraging AIOps and intelligent automation tools can further streamline this process, flagging idle resources or inefficiencies and suggesting right-sizing actions. As maturity increases, teams can communicate progress using visual models, such as a cloud maturity curve, to demonstrate evolution from reactive to predictive management.
Tangible Benefits of Cloud Forecasting
Organizations that adopt this approach report significant outcomes:
Conclusion: Predictability is the New Power
In healthcare, reacting too late isn’t just expensive – it can compromise patient care. As cloud becomes the engine behind critical operations, controlling its costs must move from art to science.
CtrlS enables healthcare IT teams to make this shift – from firefighting to forecasting – by combining compliance-grade resilience with enterprise-grade cost optimization.
Forecasting gives healthcare IT the clarity to collaborate, plan, and innovate with confidence. The path forward is clear: establish visibility, enforce accountability, and institutionalize forecasting as a discipline.
Ready to go from chaos to control? CtrlS Cloud Optimize is your trusted partner to get there.
Manzar Saiyed, Vice President - Service Delivery, CtrlS Datacenters
With over 15 years of rich experience in project and program management, Manzar has been instrumental in planning and executing mid to large size complex initiatives across different technologies and geographies. At CtrlS, he is responsible for solutioning and bidding for large system integration projects across emerging markets.
marketing@ctrls.in