1 Getting Started with Oracle Health Sciences Translational Research Center

This section contains the following topics:

Overview
Architecture
Regulatory Compliance

1.1 Overview

Oracle Health Sciences Translational Research Center (TRC) suite comprises of Oracle Health Sciences Cohort Explorer (CE) 3.0 and Oracle Health Sciences Omics Data Bank (ODB) 3.0. TRC enables storing, integrating, controlling, and providing means to analyze clinical and omics data required to support the complete biomarker lifecycle. This includes the data acquisition, discovery, and research as well as clinical use of patient and specimen information.

1.1.1 What You Can Do Using Oracle Health Sciences Translational Research Center

Translational Research Center v3.0 features the following:

Cohort-driven built-in reports:
- Demographic statistics reports including age, gender, ethnicity
- Clinical statistics reports
- Genomic reports including mutations, copy number variation, drill-into single and dual channel gene expression
- Structural Variation histograms based on occurrence frequency in genes and (or) gene pairs
Web infrastructure to support collaboration
- UIs and workflows to enable sharing cohort queries and lists
- User group creation and maintenance through custom-built UIs
Usability enhancements
- Autocomplete and type-in enabled for all searchable concepts
- Improvements to layouts and interface flexibility
- Workflow improvements to simplify user experience
- New icons, images and so on to improve the look and feel
More than twofold increase in the Cohort Data Model schema
- New subject study tables and unidirectional link between subject and patient
- New attributes have been added across multiple concept areas including Observation, Clinical Encounter, Patient and Subject Family, Allergy and so on Histories, Familial Relationship
- Personally Identifiable attributes such as First Name, Last Name, Contact Information are now supported in the schema with obfuscation governed by individual customer's requirements
RNA-sequence based querying for cohorts
Genome Viewers for Variants and Copy Number Variation
Drill in hierarchy viewer for Diagnosis and Anatomical Site
Job Scheduling
New Clinical concepts for search: Encounters, Observation types, Sources of data, Coding systems, Familial History and so on
Gene Set generation through cut-and-paste or import from files
Multiple-reference support in querying including multiple reference of DNA, genes, proteins, pathways, mutation impact, variant annotations and so on

1.2 Architecture

ODB consists of the following three tiers:

Database tier that includes tables and views (to simplify creating certain patterns of queries), indexes, sequences, and PL/SQL packages. PL/SQL packages are of two types:
- Utility: For example, supporting integration between Cohort Data Mart (CDM) and ODB.
- Data Movement: Processes data loaded into staging tables.
Client tier comprising of the following functions:
- Java loaders packaged in jar file for loading of reference data.
- Shell or batch files facilitating execution of Java loaders.
- Shell or batch files (one per file type) that facilitate executing PL/SQL based loaders.
Middle tier consists of a set of ADF-based UIs deployed into WebLogic Server.

CDM consists of the following two tiers:

Database tier that includes tables, views (to simplify creation of certain patterns of queries), indexes, sequences and PL/SQL packages
Middle tier consist of set of ADF-based User Interfaces deployed into WebLogic Server. It also contains ETL that brings data from Oracle Healthcare Data Warehouse Foundation (HDWF) into CDM.

Figure 1-1 illustrates the overall data flow in TRC.

Figure 1-1 Data Flow in TRC

Description of "Figure 1-1 Data Flow in TRC"

The TRC suite of products enables the following functions:

querying for patient count based on a combination of genomics and clinical attributes. The clinical attributes are available through querying clinical data model, namely CDM as part of OHSCE. The omics attributes are available through the same interface yet the data will come from Omics Data Bank model, namely ODB
listing the actual patients that correspond to the obtained count and their clinical profiles
looking at the timeline view of the clinical history of such patients
comprehensive reporting on the cohort including cohorts demographics, clinical, genomic features and further drilling into the details on the statistics
looking at the individual patient's clinical and genomic data together. Also, integration of such APIs as Visquick enables display of omics data in a user-friendly way for bioinformaticians
exporting selected ODB omics data for patient cohort into standard file formats such as VCF, SEG, RES, which can then be loaded into genome viewers that support such data formats
downloading genomic files to the desktop through links on the application. This is available only for selected user groups
searching for genes and variants using a simple search interface that lets the end user perform live queries to find any genomic results that are present in Omics Data Bank model.
loading data into the Omics Data Bank model from several public reference sources using provided autoloader scripts
loading your own result data into the ODB using provided autoloader scripts

1.3 Regulatory Compliance

Cohort Explorer is developed with HIPAA regulations in mind. The software enables the customer to easily implement obfuscation rules to protect any patient identifiable medical information. Cohort Explorer development also follows the software development guidelines and requirements for FDA 21 CFR Part 11 compliant software.

1.3.1 Tracking Data

The origin of any data stored in CDM must be traceable to its source, and all transformations applied to the data must be accessible. Data sourced from HDWF is traced by the following criteria:

ETL Load: When data was loaded from HDWF into CDM.
ODI Interfaces: The version of ODI interface used to transform the data from the HDWF to CDM, and when the data was executed.
Configuration Seed Data: There are two tables which contain seed data. Based on these tables, data is loaded in CDM and another configuration table, which is automatically seeded during ETL load.

Data in Oracle HDWF also keeps audit trails of all modifications.

1.3.2 Managing ETL Versions

You can use a third-party versioning tool or the in-built functionality of ODI versioning to manage ETL versions. Currently, all ODI objects are in the default version.

1.3.3 Security

Data within the data warehouse is secure from updates by unauthorized personnel and can only be updated through controlled execution of ETL mappings. You can define custom standards in ODI to modify and execute ETL routines.

1.3.4 Disclaimer Regarding Third Party Data or Software

1.3.4.1 Public Domain Data

Oracle makes no express or implied warranty, including but not limited to warranties regarding the accuracy, completeness, merchantability, or fitness for a particular purpose, with respect to third party data loaded into this application or the results of any functions of the application using such data. It may be used for information purposes only, and no medical, clinical or other health related decisions may be based upon such results. You are solely responsible for your use of the third party data, including your right to use the data for your purposes.

1.3.4.2 Software

Visquick 1.0.2

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