97% of Healthcare Data Goes Unused. GenAI Is Changing That.
97% of Healthcare Data Goes Unused. GenAI Is Changing That.
How generative AI is helping healthcare providers turn siloed, unstructured patient data into something clinicians can actually use.
Every patient walking through your doors carries thousands of data points collected across their health journey. Clinical notes, medication histories, imaging results, lab work, referral letters, discharge summaries. The information exists. The problem is that most of it is trapped.
Hospitals generate roughly 50 petabytes of healthcare data annually. According to research from the World Economic Forum, 97% of that data goes unused because it sits in unstructured formats scattered across siloed systems that do not communicate with each other.
For clinicians, this means spending more time searching for information than using it. For patients, it often results in fragmented care where providers do not have the full picture.
Generative AI is beginning to change that. And the opportunity for healthcare providers across Australia, New Zealand, and beyond is significant.
The Documentation Burden Is Real
The numbers tell a familiar story for anyone working in healthcare. Clinicians spend roughly twice as much time on administrative tasks as they do with patients. Fifty-six percent of healthcare providers say excessive documentation contributes to burnout. And two in three patients report a negative visit experience due to a perceived lack of empathy from their provider.
That lack of empathy is not because clinicians do not care. It is because they are buried in documentation, switching between systems, and manually piecing together a patient’s history before they can even start a conversation.
This is the problem generative AI is uniquely positioned to address. Not by replacing clinicians, but by giving them back the time and context they need to focus on what matters most: the patient in front of them.
Where GenAI Fits in Healthcare
Generative AI use cases in healthcare span a wide range, from medical research and clinical efficiency through to operations, patient experience, and digital health.
Some of the most impactful areas we are seeing include clinical efficiency. Longitudinal patient records can give clinicians a complete picture across visits, providers, and care settings. AI-assisted medical image interpretation can support faster and more consistent clinical decision-making.
Operational efficiency is another major opportunity. Generative AI can help automate referral letters, clinical coding, and prior authorisation documentation. Intelligent document processing can also reduce the manual handling of unstructured records.
Patient experience can also improve significantly. Discharge instructions and treatment plans can be personalised based on a patient’s history. Predictive models can help identify patients who may require earlier intervention.
In digital health, generative AI can power patient concierge experiences and enable remote care management at scale.
Across all of these use cases, the common thread is data. Specifically, the ability to take fragmented, unstructured patient data and make it accessible, searchable, and useful in real time.
The Data Challenge in Healthcare
Before any of these opportunities become practical, there is a foundational challenge to address. Patient data today is siloed across multiple systems, largely unstructured, difficult to analyse, and often constrained by limited technical resources.
Patients accumulate data from hospitals, GPs, specialists, pharmacies, laboratories, and increasingly wearable devices. Yet this data rarely lives in a single location. Even when it does, it is often stored in formats that are difficult for systems to interpret or integrate.
This is why getting the data layer right is so critical. Cloud-native health data services that normalise patient records into interoperable standards such as FHIR provide a foundation healthcare organisations can build on. Once the data is clean, structured, and accessible, AI use cases become practical rather than theoretical.
Patient Data Summarisation: A Practical Starting Point
One of the most practical and high-impact GenAI use cases in healthcare is patient data summarisation.
The concept is simple. Clinical notes, medication histories, allergy information, appointment records, and other structured and unstructured data are analysed using a foundation model to generate a clear, comprehensive summary. A clinician can review this summary in seconds rather than spending several minutes manually searching through records.
Cloud-native health data services and foundation models work together to make this possible. Patient data from EHR systems and third-party sources is normalised into standard formats such as FHIR, stored securely, and then made available to AI models that can summarise records, answer questions, and generate documentation.
In practice, this allows clinicians to ask natural language questions such as “summarise the clinical notes for this patient” or “list all current medications with dosages” and receive a contextualised response grounded in the patient’s actual records.
Beyond summarisation, the same architecture can support additional use cases such as generating follow-up checklists, drafting discharge summaries, recommending relevant clinical guidelines, and building longitudinal patient records that provide providers, patients, and payers with a single source of truth.
The Architecture Behind It
The technical pattern underpinning patient data summarisation is Retrieval Augmented Generation, or RAG.
Rather than relying solely on what a foundation model learned during training, RAG retrieves relevant patient-specific data from a secure knowledge base and provides that context to the model at query time.
This is particularly important in healthcare for two reasons. First, it ensures AI responses are grounded in the patient’s actual records rather than general medical knowledge. Second, it keeps sensitive patient data within the organisation’s secure infrastructure rather than sending it to a general-purpose external model.
In a typical implementation, patient data is stored in a FHIR-compliant data store. Embeddings are generated to enable similarity search across records. A foundation model, often accessed through services such as Amazon Bedrock, generates summaries and answers using the retrieved context. Orchestration frameworks such as LangChain manage document chunking, retrieval, and prompt handling.
For larger clinical documents, a chunking approach breaks the text into smaller sections. Each section is summarised individually before producing a consolidated summary. This allows long patient histories to be processed accurately without exceeding model context limits.
The Challenges Are Real Too
It is important to acknowledge the challenges healthcare organisations face when implementing GenAI.
Cost and implementation time remain real considerations. Training, optimising, and deploying specialised AI systems in healthcare requires investment and technical expertise. This is where working with a partner experienced in cloud architecture and health data integration can make a meaningful difference.
Trust in AI-generated outputs is another factor. Clinicians need confidence that AI-generated summaries are accurate and grounded in patient records. The RAG architecture helps address this by ensuring the model references the patient’s actual data rather than generating unsupported information.
Security, privacy, and compliance also remain critical. Healthcare data is governed by strict regulatory requirements. In New Zealand, the Privacy Act and Health Information Privacy Code set clear boundaries. In Australia, the Australian Privacy Principles apply. Any GenAI implementation must be designed with these obligations embedded from the start.
What This Means for Healthcare Providers in ANZ
The opportunity with GenAI in healthcare is substantial, but it must be approached with the same rigour and care the sector demands.
That starts with strong data foundations. Patient data must be clean, accessible, well-governed, and stored in interoperable formats. From there, organisations can introduce AI capabilities that address specific clinical or operational challenges, supported by strong security, observability, and compliance controls.
The organisations that will benefit most from GenAI are not the ones chasing the most exciting use case. They are the ones that get the foundations right and build thoughtfully from there.
From great frustration comes great innovation. In healthcare, the frustration of siloed and unusable data has been building for decades. The tools to address it now exist. The question is whether your foundations are ready.
We've Done This Before. Let's Talk.
At Easycoder, healthcare technology is one of our core domains. We work with EHR providers, practice management platforms, and health technology companies supporting thousands of providers worldwide. From cloud architecture and data integration through to GenAI implementation, we help healthcare organisations build solutions that are secure, compliant, and designed to scale.
Whether you are looking to modernise your health data infrastructure, explore patient data summarisation, or understand how GenAI fits into your clinical workflows, our team has the hands-on experience to help.
We would love to show you what is possible.
