HealthcareMetadata

Unleash Targeted Insights through Advanced Analytics As hospitals and health systems continue to improve the patient experience, it can be more challenging to identify specific initiatives to drive improvement. Press Ganey’s advanced analytics help health care leaders spark new initiatives by viewing opportunities through a new lens. Sophisticated techniques, such as segmentation, text analysis, multi-dimensional modeling and cluster analysis, evaluate multiple dimensions to uncover relationships, patterns and new insights to reveal targeted opportunities to improve

The Press Ganey Improvement Portal (patent pending) provides organizations with insights into the voice of their patients across the continuum of care through easy-to-use online dashboards and reports.

Set targets and view comparative performance based on Press Ganey's proprietary database – the largest real-time HCAHPS and patient satisfaction database in the industry.

The challenge is to identify individuals for whom intervention can make a difference and find intervention opportunities at the right time: before adverse health outcomes escalate medical costs.

Maximizing Care Management Savings through Advanced Total Population Targeting

PREDICTIVE MODELING FOR ACCURATE POPULATION TARGETING • Advanced Methodology, Focused Models) • Targeted Interventions) • Testing and Validation)

guess the probability of an outcome given a set amount of input data

Population health management (PHM)

racle SQL Developer Data Modeler provides a model driven approach for database design and generation, implemented by integrated set of models – Logical, Data types, Dimensional, Relational, Data Flow diagrams and Physical models for supported Oracle Databases, Microsoft SQL Server 2000 and 2005

Cube Views metadata

Dimensional Models   Dedicated full featured Dimensional model – star and snowflake schema easily can be built and expressed on detailed and compact diagrams   Dimensions – support for merging (level can belongs to more than one dimension), shared, fact and role playing dimensions   Hierarchies – value based hierarchies (parent-child), and regular and ragged level based hierarchies   Measures – fully, semi and none additive; different aggregation functions on different dimensions ; fact dimension; calculated measures   Query wizard allows Select statements to be generated from the dimensional model   Support for Oracle OLAP. This includes specifics like cube partitioning, sparse dimensions, and compressed measures   Built-in wizards help to define all required object types and view definitions that enable SQL access to dimensional data in Oracle AW (using the OLAP_TABLE interface)   Bidirectional integration with Oracle physical model - Dimensional model can be created using SQL dimension definitions in physical model or the definitions can be created from the dimensional model

Hyperion adds complementary products to Oracle's business intelligence offerings including a leading enterprise planning solution, world-class financial close and reporting products, and a powerful multi-source OLAP server

MDX stands for 'Multi-Dimensional Expressions' and is the standard language defined by Microsoft to query OLAP servers.

Effectively managing data means focusing on operational capture, decision support-oriented consumption, and data governance.

Cameo Data Modeler plugin provides data-related modeling for MagicDraw. It includes entity-relationship, database and XML schema modeling features.

In computing, a data warehouse or enterprise data warehouse (DW, DWH, or EDW) is a database used for reporting and data analysis. It is a central repository of data which is created by integrating data from one or more disparate sources. Data warehouses store current as well as historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons.

Data warehouses can be subdivided into data marts. Data marts store subsets of data from a warehouse.

Multidimensional Expressions (MDX) is a query language for OLAP databases, much like SQL is a query language for relational databases. It is also a calculation language, with syntax similar to spreadsheet formulas.

The specification was quickly followed by commercial release of Microsoft OLAP Services 7.0 in 1998 and later by Microsoft Analysis Services.

While it was not an open standard, but rather a Microsoft-owned specification, it was adopted by the wide range of OLAP vendors. This included both vendors on the server side such as Applix, icCube, MicroStrategy, NCR, Oracle Corporation, SAS, SAP, Teradata, Symphony Teleca, and vendors on the client side such as Panorama Software, PowerOLAP, XLCubed, Proclarity, AppSource, Jaspersoft, Cognos, Business Objects, Brio Technology, Crystal Reports, Microsoft Excel, and Microsoft Reporting Services.

The usual interface to manipulate an OLAP cube is a matrix interface like Pivot tables

MOLAP stores this data in an optimized multi-dimensional array storage, rather than in a relational database

The problem of deciding which aggregations (views) to calculate is known as the view selection problem. View selection can be constrained by the total size of the selected set of aggregations, the time to update them from changes in the base data, or both. The objective of view selection is typically to minimize the average time to answer OLAP queries, although some studies also minimize the update time

Otherwise, if the number of books per publisher is unbounded, this data model would lead to mutable, growing arrays, as in the following example:

Embedding¶ To de-normalize data, store two related pieces of data in a single document. Operations within a document are less expensive for the server than operations that involve multiple documents. In general, use embedded data models when: you have “contains” relationships between entities. See Model Embedded One-to-One Relationships Between Documents. you have one-to-many relationships where the “many” objects always appear with or are viewed in the context of their parent documents. See Model Embedded One-to-Many Relationships Between Documents. Embedding provides the following benefits: generally better performance for read operations. the ability to request and retrieve related data in a single database operation.

Referencing¶ To normalize data, store references between two documents to indicate a relationship between the data represented in each document. In general, use normalized data models: when embedding would result in duplication of data but would not provide sufficient read performance advantages to outweigh the implications of the duplication. to represent more complex many-to-many relationships. to model large hierarchical data sets. See Model Tree Structures in MongoDB. Referencing provides more flexibility than embedding; however, to resolve the references, client-side applications must issue follow-up queries. In other words, using references requires more roundtrips to the server.

we promise that your client application which uses the database is a separate work. To facilitate this, the mongodb.org supported drivers (the part you link with your application) are released under Apache license, which is copyleft free.

MongoDB Database Server and Tools¶ Free Software Foundation’s GNU AGPL v3.0. Commercial licenses are also available from 10gen, including free evaluation licenses. Drivers¶ mongodb.org supported drivers: Apache License v2.0. Third parties have created drivers too; licenses will vary there.

To say this another way: if you modify the core database source code, the goal is that you have to contribute those modifications back to the community.

How to Buy Contact sales to learn more about MongoDB Commercial Subscriptions, and how to buy. MongoDB Subscriptions Basic Standard Enterprise Edition MongoDB MongoDB MongoDB Enterprise SLA 4 Hours 1 Hour 30 Minutes Support 9am – 9pm local, M – F 24 x 7 x 365 24 x 7 x 365 License AGPL Commercial Commercial Emergency Patches Not Included Included Included Price $2,500 / Server / Year $5,000 / Server / Year $7,500 / Server / Year

Commercial License. Enterprises with restrictions around the AGPL license or those interested in OEM applications may prefer a standard commercial license, which is included in the MongoDB Standard and Enterprise Subscriptions.

MongoDB Enterprise is tested and certified on Red Hat