With Artificial Intelligence and modernisation at peak, nowadays the healthcare industry faces an overwhelming flood of data, including patient records, lab results, imaging, prescriptions, in fact, the handwritten notes by different specialists are being digitised.
Especially in healthcare, where data needs smart management and a secure, compliance-ready system to keep workflows seamless and accessible 24/7.
That’s where AWS HealthLake steps in.
For CTOs, CIOs, and leaders in healthcare organizations who are exploring a move to the cloud, understanding AWS HealthLake is essential.
This post breaks down what AWS HealthLake does, why it matters, and which healthcare businesses can benefit most. Plus, why working with the right AWS consulting service can make or break your success in adopting it.
What is AWS HealthLake?
AWS HealthLake is a HIPAA-compliant service from Amazon Web Services designed to store, transform, query, and analyze large volumes of healthcare data. Instead of keeping data siloed and unstructured, HealthLake standardizes it into the Fast Healthcare Interoperability Resources (FHIR) format.
In simpler terms, AWS Healthcare data lake takes scattered medical data in the form of notes, charts, lab reports, and organizes it so you can run Amazon HealthLake analytics, build machine learning models, and generate insights that can improve care.
- First, AWS HealthLake transforms raw healthcare data management using specialized ML models, NLPs and FHIR interoperability.
- Then it aligns it with Healthcare Industry Standards, and applies Ontology Mapping to extract meaning from unstructured data.
- This process creates structured, indexed, and normalized data ready for Search & Query.
- Finally, this organized data can be used in Amazon SageMaker, Amazon QuickSight, or third-party applications to uncover insights, spot trends, and make predictions.
Why the industry need AWS HealthLake?
Healthcare data interoperability is extremely messy. Even within the same hospital, patient data may be stored across dozens of disconnected systems. Different formats, outdated legacy software, handwritten notes scanned as PDFs, all of this makes it nearly impossible to get a complete view of the patient.
That too, when the country or even state changes, becomes an added hustle altogether.When data is standardized, searchable, and machine-readable, it is a lot better:
- Physicians can see a 360-degree view of a patient’s history instantly.
- Researchers can spot population health trends.
- Data scientists can train predictive models for disease risk.
- Administrative teams can reduce duplicated tests and paperwork.
That’s the promise of HealthLake. But it’s not plug-and-play. This is why it is highly important for you to consult with a reliable AWS cloud computing provider who has handled healthcare migrations and knows where the real-world friction points are.
What does AWS HealthLake actually do? A closer look
AWS HealthLake has four core functions that healthcare teams should know:
1. Data Ingestion & Transformation
It automatically ingests data from existing systems like Electronic Health Records (EHRs) system, lab systems, and billing systems, and transforms it into the FHIR format (well, because HealthLake understands only this format)
This means whether data arrives as text, JSON, HL7 v2, or CSV files, HealthLake brings everything into a common structure.
2. Natural Language Processing (NLP)
HealthLake uses built-in machine learning to extract medical information (conditions, medications, procedures) from unstructured text, like doctors' notes.
That’s how handwritten notes or scanned documents become usable, searchable data for AWS HealthLake systems.
3. Search & Query
Once the data is standardized, HealthLake makes it searchable through APIs and dashboards. CTOs & founders can also enable their teams to query millions of records to answer questions like-
How many diabetic patients haven’t had an A1C test in 12 months? Or which patients on medication X also show adverse lab results? And many more.
4. Analytics & Machine Learning
With clean data in the AWS cloud in healthcare, you can connect HealthLake to AWS analytics tools (Athena, QuickSight) or machine learning tools (SageMaker) to do deeper analysis or build predictive models.
It becomes highly useful for such healthcare industries that are trying to bring automation or Artificial intelligence into their AWS healthcare solutions.
Real-world examples of AWS HealthLake
AWS HealthLake isn’t just theory. Numerous renowned healthcare organizations have already adopted this. Here are two concrete examples of such organizations that adopted Amazon Health Lake:
Children’s Hospital of Philadelphia (CHOP) – Children’s Brain Tumor Network (CBTN)
The Children’s Brain Tumor Network (CBTN), coordinated by the Center for Data Driven Discovery in Biomedicine (D3b) at CHOP, is on a mission to uncover new treatments for childhood brain and spinal cord tumors.
They used AWS HealthLake to merge data from over 35 different studies and 5,000+ patients into a single, interoperable dataset. Here’s what this made possible:
Large-scale cohort identification: Researchers can quickly find patient groups that meet complex criteria, critical for speeding up research.
Unified “ground truth” data: Bringing together genomic, clinical, and research data in one place improves accuracy and reproducibility.
Faster discovery: Clean, structured data shortens the path from raw information to actionable insights.
As Adam Resnick, PhD, Scientific Director at CBTN, put it:
“Technology like AWS HealthLake holds significant potential for enabling the large-scale patient cohort identification necessary to help accelerate the pace of research.”
In other words, HealthLake isn’t just storing data; it’s also fueling new medical data security breakthroughs.
Cortica
Cortica delivers healthcare for children with autism and other brain conditions, combining neurology, behavioral therapy, and technology into a single care pathway.
Before AWS HealthLake, data like medical histories, behavioral assessments, and lab results lived in separate systems, slowing care decisions.
By adopting these HIPAA compliant AWS services, Cortica built a centralized platform that now:
Securely stores and organizes patient data, making it easier for clinical teams to track progress.
Uses Amazon SageMaker notebooks alongside HealthLake data to analyze each child’s development toward therapy goals.
Enables HIPAA-compliant data sharing with researchers, families, and care partners — speeding up autism research.
What once took months of manual integration now takes just weeks with HealthLake, transforming how clinicians see and act on patient data.
As Cortica shared:
“AWS HealthLake empowered us to create a centralized platform that gives our team deeper insight into each patient’s journey and progress.”
Who benefits most from AWS HealthLake?
Not every healthcare data management business needs HealthLake immediately. But AWS HealthLake benefits are particularly impactful for:
- Multi-location hospital networks
Hospitals often have different EHR systems at different sites, plus older legacy systems. HealthLake helps bring all that patient data together into a single, searchable format, so doctors and administrators can see a full patient history no matter where the care happened.
- Specialty clinics & research centers
These organizations often need to run studies on thousands of patients to find trends, predict risks, or test treatments. HealthLake makes this easier by turning unstructured notes and reports into structured data that’s ready for machine learning or advanced Amazon HealthLake analytics.
- Healthtech startups
Startups building AI & ML in healthcare, telemedicine apps, or patient dashboards need reliable, clean data as a foundation. HealthLake acts like a standardized data backbone, helping them avoid months of custom integrations and letting them focus on innovation.
- Payers & insurance providers
Insurance teams need clean, unified medical data security to spot billing errors, detect fraud, track treatment outcomes, and manage costs more effectively. With HealthLake, they can automate data analysis and get faster, more accurate insights.
In all these use cases of Amazon Health Lake, a successful rollout depends on getting the migration right. This is where hiring an AWS consultant pays off.
Read more: What is AWS Cloud Consulting? From Planning to Execution Explained
Why healthcare migrations aren’t like other migrations?
Owing to my reliable expertise in AWS adoption, I’ve seen firsthand why the “copy-paste” mindset doesn’t work here.
- Regulatory compliance (HIPAA, HITRUST): Fines and brand damage can follow any slip.
- Data privacy: Patient data can’t leave secure zones or be exposed in logs.
- Legacy systems: Many on-premises systems are old, undocumented, and hard to integrate.
- Data quality: Typos, incomplete fields, and inconsistent coding in clinical data.
Without specialist guidance, healthcare projects can stall or create unexpected security gaps. Therefore, it's better for you to get guidance from AWS cloud consultants with proven experience in healthcare, not just generic cloud experience.
How AWS HealthLake fits into your cloud strategy
Migrating to the cloud isn’t just about moving data. It's about what you can do once it’s there.
Step 1: Gather all your patient, clinical, and operational data in one place.
Step 2: Standardize it using HealthLake’s FHIR transformation.
Step 3: Make it searchable and ready for advanced analytics.
Step 4: Build dashboards, reports, and AI and machine learning in healthcare on top.
Hiring AWS consultants helps map this architecture to your exact business goals.
Key benefits of AWS HealthLake for healthcare businesses
Let’s keep it real. HealthLake isn’t just about “cloud for the sake of cloud.” Here’s what healthcare software development company tell us they care about and what HealthLake delivers:
- Unified, standardized data across systems.
- Faster clinical research by making data searchable.
- Cost savings by replacing patchwork integrations with a single data lake.
- Easier compliance reporting through structured data.
- Foundation for AI tools that predict disease risk or personalize treatment.
These aren’t hypothetical; they're advantages of HealthLake we’ve seen in real projects when teams hire AWS consultants who know the healthcare space in detail.
AWS HealthLake Pricing Overview
AWS HealthLake operates on a pay-as-you-go model with no upfront costs. You’re billed based on the following structure:
Note: Though you can also calculate your AWS HealthLake cost, including your custom requirements.
Quick comparison table
| Feature | Standard Tier | Advanced Tier |
|---|---|---|
| Data Store Uptime | $0.27/hour (includes first 10 GB & 3,500 queries/hour) | $0.27/hour (includes first 10 GB & 3,500 queries/hour) |
| Additional Indexed Storage (beyond 10 GB) | $0.25/GB-month | $0.37/GB-month |
| Extra Queries (beyond 3,500/hour) | $0.015 per 10,000 queries | $0.048 per 10,000 queries |
| FHIR Data Export / Transformation | $0.19/GB transferred | $0.19/GB transferred |
For more details, refer to Amazon’s HealthLake pricing.
What to ask before adopting AWS HealthLake?
Before you green-light a migration, ask yourself (and your vendor):
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What are our most critical use cases? Reporting? Research? AI? Compliance?
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Where is our data now? What systems, formats, and how clean is it?
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Who will own post-migration governance? Someone has to maintain data quality and access controls.
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What integrations do we need? With EHRs, billing systems, or existing analytics tools.
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Do we have in-house skills, or do we need to hire AWS consultants? Specialized expertise often makes or breaks these projects.
These questions guide realistic timelines and budgets.
AWS HealthLake adoption challenges & fixes
From experience, here are 3 main hurdles that slow down AWS HealthLake projects and what you can do to avoid them from the beginning:
Data fragmentation Plan phased migrations and start with a department or use case before scaling.
Inconsistent data quality Do a data profiling and cleansing project first.
Stakeholder buy-in Show quick wins (like dashboards) early to keep teams engaged.
AWS HealthLake Post migration: Things to consier
The journey doesn’t stop once your data is in HealthLake.
- Build AI models (using SageMaker) to predict patient risk.
- Create dashboards for clinicians and executives.
- Connect with patient engagement apps and telehealth tools.
- Continuously update and expand the data lake as your systems evolve.
Your data becomes an active part of daily care, not just an archive.
Conclusion
AWS HealthLake can transform how your organization uses data, but it’s not automatically a fit for every team.
It shines for healthcare organizations who are ready to:
- Centralize data from multiple systems.
- Build analytics and AI on top of standardized data.
- Improve research, compliance, and patient care.
If that sounds like your organization’s roadmap, the next step is clear: hire AWS computing services with AWS healthcare certification, having an understanding of the industry and cloud migrations. Done right, AWS HealthLake isn’t just a database; it can become the backbone of a smarter, data-driven healthcare strategy for your complete healthcare systems.
FAQs
HealthLake is FHIR-aware and includes medical NLP, indexing, and query capabilities specific to healthcare, these things you'd need to manually build on a standard S3 + Athena setup.
No, HealthLake automatically transforms various formats (CSV, HL7 v2, JSON) into FHIR internally. However, using FHIR from the source simplifies integration & reduces errors.
Yes, but not out-of-the-box. You ’ll need to build connectors (via HL7 interfaces or FHIR APIs) or use middleware like Mirth Connect. Custom mapping & AWS consulting service is often required for legacy or proprietary formats.
Depends on your data volume and complexity. A proof of concept can take 4–6 weeks, while full integrations (including EHR + analytics) can span 3–6 months with proper planning.
You’ll need internal or outsourced DevOps, data engineers, and compliance leads to manage data flows, access control, updates, and model retraining. HealthLake isn’t fully hands-off.
Major challenges with AWS HealthLake adoption include learning curve with FHIR and AWS, upfront data cleaning needs, unpredictable costs for heavy query use, and limited hybrid on‑prem support especially when integrating legacy systems.
