Search Results for "achandler"

SNIP 3 835 Balancing

835 and 837 EDI transactions have transformed the adjudication cycle for providers and health plans over the last two decades, but challenges remain in reconciling payments with claims. Recently, we’ve broken down the requirements for SNIP 3 claim balancing. Today we’ll focus on the 835 Claim Payment/Remittance Advice. Health plans submit 835s to providers (or their intermediaries) to explain which claims are being paid, and any reductions to the submitted amount and the reasoning for the adjustment. This is an important function – a significant pain point experienced by providers is the reconciliation of their income against claims submitted.
Before this valuable information can be loaded in practice management software, the 835 should pass validation checks. Common issues affecting 835s are balancing errors between the header and detail payment amounts. Imbalanced 835s lower the quality of reporting and can lead to billing…

Enhanced QNXT Integration – EDI Transformation and Tracking

TriZetto’s QNXT is a widely adopted platform for claim processing and membership administration. QNXT relies on the Microsoft stack, particularly BizTalk, .Net and SQL Server, to process and store EDI messages.
These technologies give developers many tools for customizing and tracking HIPAA transactions, but the complexity of implementing business rules and lifecycle reporting on EDI data are constant concerns for health plan payers.
Tallan’s T-Connect EDI Management Platform is an optimized integration solution founded on three core design principles:

An accessible API. One of the most common challenges our partners face is implementing business logic on EDI. T-Connect loads all HIPAA transactions into a fully compliant hierarchical data structure that can be manipulated with familiar tools such as Visual Studio and .Net.
Full database persistence. Going from EDI to a relational database is a frequent business need, but capturing the full set of fields present in an 837 alone represents…

The Oracle Business Intelligence Stack

Yesterday, I attended a seminar covering the Oracle BI landscape. My aim was to come out of this session with a clearer idea of how Oracle products correspond to the Microsoft BI stack. My impression going into this seminar was that Oracle had many, many applications bundled under the BI umbrella. Nevertheless, I was surprised by the sheer number of options available. Practically every piece of the MS BI Stack has at least two parallel products on the Oracle side, in some cases many more. My second impression was that Oracle has done some very credible work to integrate the vast number of applications they’ve developed alongside their Siebel and Hyperion purchases.
Here’s how Oracle visualizes their BI offering:

Introduction to Analysis Services

This post is intended to introduce Analysis Services 2005/2008 foundational concepts.
SQL Server Analysis Services is a component included in the Microsoft SQL Server product, and its use is fully covered under the same license (which applies to Integration Services and Reporting Services as well). Like the database engine, SSAS has a range of features stratified by Express, Workgroup, Standard Edition and Enterprise Editions.
SSAS is an OLAP, multi-dimensional database. While a relational data warehouse can also be described as OLAP, products such SSAS, Cognos and Essbase have fundamental differences. These OLAP servers:
· Aggregate data from a variety of sources into a compressed format optimized for query response
· Emphasize end-user navigation with modeling capabilities such as hierarchies, or drill-paths
· Employ MDX as the standard query language for retrieving result sets
· Use XMLA as the standard communication mechanism between clients and servers
· Automate…

MS BI News Roundup

Two big pieces of news on the Microsoft BI front:

Performance Point Server is being dismantled. After the next service pack, MS will cease develop of the product as its own entity. The Monitoring & Analytics capabilities will be bundled into the enterprise version of SharePoint. The Planning application looks to be dead in the water, however.Although PPS was complicated and unwieldly, it had potential and an interesting vision. I don’t see the failure of any part of the MS BI stack as a positive thing. The official press release and blog reactions are here.
While Microsoft had previously described a major-minor release schedule as the model for SQL Server going forward, the next version sounds like an interesting departure from that. Code-named “Kilimanjaro”, the next SQL Server release has been described as minor and BI-focused.Two components described thus far are “Madison”…

Partitioned Fact Tables

Once tables grow into the millions of records, they become candidates for partitioning. Table partitioning offers many benefits, particularly in warehouse environments. Since data is split into smaller units of storage, backups can target filegroups with a higher rate of change. Systems with multiple CPUs see improved query performance as partitioned data leads to greater parallelism. Perhaps most significant is the ability to swap in huge amounts of data by partition switching, an operation that is practically instantaneous.
Tables can be partitioned horizontally or vertically. With vertical partitioning, columns are split out into separate physical tables. This post focuses on horizontally partitioned tables, which take advantage of new constructs Microsoft added in the 2005 release – partition functions and partition schemes. Table partitioning is an Enterprise Edition only feature in SQL Server 2005 and 2008.

A Warehousing Overview

Data warehousing is a big subject. This overview is intended to cover some of the most representative issues on a high level: the nature of OLAP systems, star schemas, facts and dimensions, and differing perspectives (Inmon vs. Kimball) on warehouse design.
OLTP vs. OLAP
OLTP systems are the operational databases supporting applications. They are highly normalized, and focused on CRUD operations.
OLAP databases are usually arranged in star schemas and are built for speed in retrieving aggregated data.

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