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The Data Explosion: Achieving Interoperability in Healthcare Imaging

Explore the challenges of managing the expanding data landscape in healthcare imaging and the critical aspects of achieving interoperability. From coping with data growth to standardizing algorithms, discover the right strategies for converting data into knowledge and ensuring compatibility across platforms.

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The Data Explosion: Achieving Interoperability in Healthcare Imaging

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  1. The Data ExplosionHow can we achieve interoperability?James WilliamsSiemens Corporate ResearchPrinceton, NJ

  2. ? What are the right questions? • Size: data is growing, how do we cope? • Repeatability: how do we standardize and normalize image generation? • Availability: how do we get what we want, where we want, in time to make a difference? • Processing: how do we make algorithms comparable and portable? • Validation: what are the right mechanisms for validation? • Context: how can we convert data into knowledge?

  3. The challengeof size • Data sizes grow relentlessly • Spatial resolution: CT • Temporal Resolution: US • Multiple modalities, PET/CT… • Follow-up studies • Network bandwidth as the bottleneck: • Faster networks or… • Move processing near the data! • When we can, compress: What is diagnostic quality? Where do we need it & when?

  4. The challengeof repeatability • Sources of variation in acquisition • Hardware • Calibration • Protocol • Reconstruction • Automation of MR scan-rescan protocol: AutoAlign & Phoenix

  5. Goalsof availability • The imaging workspace available anywhere • reading room • office • home • All data relevant to the patient in one click • All data relevant to the protocol available and searchable (teaching cases etc..) • Some steps in the right direction: web clients, hanging protocols

  6. Towards the compatibility andcomparability of processing • Visualization, detection, segmentation, registration measured with respect to reference standards (VRD) • Cross platform executables • Plug-in processing, DICOM WG23 • ITK, a reference implementation? • Open the platforms, but we must assure: • Safety • Privacy • Stability

  7. QSR The challengeof algorithm validation • Create reference databases as open challenges • Should part of the database be hidden to avoid over-training? • Database must grow and change as acquisition evolves • Fast-tracking through the FDA • Validation & liability as a 3rd party business? CE

  8. Data in context • Beyond images: patient record, lab data, genomic, proteomic data • The universal medical record: technology is not the problem. Individual privacy, ethics and economics are the key drivers. • The anonymous database of everything. The benefit is clear, how do we cover the cost? • Making knowledge from data. The structure has to support exploration for anecdote, and testing of hypothesis.

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