Introduction

Oracle Life Sciences AI Data Platform is a cloud-based platform that allows healthcare organizations, research institutes, and life sciences companies to find insights using large-scale, de-identified patient data sets. LSAIDP is designed for providers, academic researchers, and pharmaceutical organizations to view Oracle Health Real-World Data using industry-standard methods and tools.

Oracle Life Sciences AI Data Platform (LSAIDP) gathers clinical data from the Oracle Learning Health Network, which includes patient records contributed by many healthcare institutions. The data is ingested, normalized, standardized, and de-identified using the Expert Determination methodology as defined in HIPAA Privacy Rule § 164.514(b)(1), and made available in both the Oracle Core Data Model and the Observational Medical Outcomes Partnership (OMOP) Common Data Model, with mapping to standard clinical ontologies such as ICD-10 and SNOMED.

Access Oracle Life Sciences AI Data Platform through your organization's dedicated Oracle Cloud Infrastructure (OCI) tenant. LSAIDP supports different workflows for data discovery, analysis, and visualization. Review the list of Oracle applications to see which guided path is the most appropriate for your workflow.

Oracle Health Real-World Data: Oracle Health Real-World Data (RWD) is de-identified health data made available to authorized LSAIDP users for approved research, discovery, feasibility, cohort, analytical, and reporting workflows. RWD is provided in both a core data model and the Observational Medical Outcomes Partnership (OMOP) Common Data Model, with mapping to standard clinical ontologies such as ICD-10 and SNOMED. RWD and reference schemas are read-only.

Oracle AI Data Platform Workbench: Oracle AI Data Platform Workbench (Workbench) is the recommended starting point for governed data discovery (RWD), workspace-based analysis, collaboration, and managed data preparation. Use Workbench first when your workflow requires Master Catalog context, workspace notebooks, compute clusters, managed or external tables, managed or external volumes. See Introduction to Oracle AI Data Platform for supporting product documentation.

The following areas are available in the Workbench workflow for governed data discovery:

Catalog and Data Assets: The Master Catalog contains authorized data assets, metadata, and access context. It can include standard catalogs and external catalogs that organize schemas, tables, views, and volumes according to your organization's configuration. When using Workbench, review catalog metadata before analysis to confirm that a data asset is appropriate for the intended research or operational use. Access to assets depends on customer configuration, OCI IAM access, and Workbench RBAC permissions. Where granted, permissions can cascade from the Master Catalog to child objects. See AIDP Data Management for supporting product documentation.
Workspaces: Workspaces organize notebooks, files, folders, workflows, and compute clusters. A workspace can provide access to authorized catalog assets, customer-provided data, and approved storage or compute resources. Data added directly to a workspace is available for workspace analysis, but it is not automatically added to the Master Catalog or made available outside the workspace.
Notebooks: Workbench notebooks are the primary analysis surface for SQL or Python workflows in LSAIDP. Depending on configuration, users can run SQL cells or run SQL in Python through Spark. Notebook cells can include Python, SQL, Markdown, and raw content. Notebooks auto-save while you work and can import .ipynb files. Notebook capabilities, kernels, and data access patterns depend on the workspace and compute cluster configuration. See AIDP Data Engineering for supporting product documentation.
Compute: Workbench notebooks and workflows use approved compute clusters. Users may need to attach an existing compute cluster, start approved compute, restart approved compute after configuration or dependency changes, or create compute if permitted before running SQL or Python analysis. Available compute cluster options depend on your organizational configuration, workspace settings, and permissions.

Oracle Autonomous Data Warehouse: Oracle Autonomous Data Warehouse (ADW) is the analytical persistence and direct SQL query layer for RWD. You can use Database Actions or other approved database tools for SQL workflows that require direct ADW access. ADW can also be configured as a destination for tabular customer-provided data or derived outputs.

Oracle Cloud Infrastructure Object Storage: Oracle Cloud Infrastructure Object Storage (Object Storage) can be used for approved customer-provided data (Bring Your Own Data or BYOD), curated files, exported outputs, and storage that backs external tables or external volumes where configured. Managed tables and managed volumes may use Workbench-managed storage, while external tables and external volumes point to approved external Object Storage locations. Object Storage can also be used as an OAC source for curated files or exported outputs where configured. See Object Storage overview for supporting product documentation.

Oracle Analytics Cloud: Oracle Analytics Cloud (OAC) is the cloud-based platform that Oracle uses to enable customers to conduct analytics and business intelligence, analyze data, build dashboards, and apply analytics without needing to maintain physical infrastructure such as servers. While Oracle Analytics Cloud can connect to large datasets, performance and report response time could lag unless you curate the dataset in advance. For example, Real-World Data contains more than 30 billion records and queries could take hours without first filtering.

Before using Oracle Analytics Cloud, Oracle recommends:

Prepare a cohort (or list) of patients in Database Actions, then gather the Real-World Data for those patients. That way you start with a more manageable dataset.
Curate aggregates. Prepare summarized datasets grouping by attributes of interest instead of data at the most granular level.

Note:

Oracle does not recommend connecting Oracle Analytics Cloud to the full Real-World Data dataset.

Data Science Service: Data Science Service (DSS) is a supported OCI notebook and machine learning path outside AIDP Workbench for organizations configured to use Data Science Service. Use DSS for existing or configured data science workflows that require OCI Data Science projects, JupyterLab notebook sessions, PySpark, package management, or approved ADW connectivity. Workbench remains the recommended starting point for governed LSAIDP discovery and analysis.

Customer-Provided Data and Derived Outputs: Users should bring customer-provided data into LSAIDP only through approved paths and should save derived outputs only to approved destinations. Customer-provided data and derived outputs may need to be created as managed tables, external tables, managed volumes, or external volumes before other users can discover or reuse them through the Master Catalog. Supported destinations can include workspace files and folders, managed tables, external tables, managed volumes, external volumes, Object Storage, ADW schemas, or other configured targets.