Workers compensation claims contain a special set of requirements when submitted in the EDI 837 format. This article describes these specific characteristics.
In a standard 837, the 2000B loop always contains subscriber information (the primary insured individual). Claim level information (2300 loop) is nested beneath the 2000B loop in this scenario. The 2000C (Patient) loop is present in the case in which the claim is related to a dependent of the subscriber. In these cases, the 2300 loop is nested under 2000C. In workers comp claims, a 2000B and 2000C loop always exist, and their purposes are a bit different. Information related to the employer goes into the 2000B loop, while the 2000C loop is used for the claimant (the injured worker). The concept of a dependent doesn’t exist in workers comp claims.
The SBR segment present in 2000B is a required…
As we strike out into 2018, the implementation of All-Payer Claims Databases (APCD) across states remains variable and dynamic. Massachusetts maintains a comprehensive implementation, aggregating data feeds from over 80 public and private payers. Massachusetts has leveraged their APCD to create a state-specific risk adjustment model to meet the ACA provision which balances funds from healthier populations to higher risk pools. Late in 2016, Minnesota concluded a feasibility study which determined their APCD could significantly improve risk adjustment vs. the federal model.
On the other hand, West Virginia and Tennessee have put APCD development on hold. California payers optionally submit claims and encounters to a public benefit corporation. Legal, fiscal and political concerns guarantee a fluid situation for insurers.
This blog post is focused on the technical obstacles that health plans face in states requiring APCD submission. Since these databases have phased in over the last decade through both voluntary and legislated…
The X12 HIPAA transaction set is used across the healthcare industry to transmit claim, enrollment and payment information. Given the importance and ubiquity of these EDI files, you might assume that translating them from ANSI to a relational database format would be well-supported with a range of options.
In practice, a task as common as parsing a claim or encounter and storing it in a database can quickly escalate into a significant problem.
One solution we’ve seen involves archiving a snapshot of the EDI file using filestream storage. This can satisfy some retention requirements, but provides little in terms of fine-grained tracking or analytic capabilities.
A more complete approach is to parse the X12 file into its discrete elements and store them in a relational database. The ideal solution captures the full extent of the EDI transactions while also applying a reasonable leveling of flattening to keep in the number of table joins under control.
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…
For reliability, accuracy and performance, both AI and machine learning heavily rely on large sets. Because the larger the pool of data, the better you can train the models. That’s why it’s critical for big data platforms to efficiently work with different data streams and systems, regardless of the structure of the data (or lack thereof), data velocity or volume.
However, that’s easier said than done.
Today every big data platform faces these systemic challenges:
Compute / Storage Overlap: Traditionally, compute and storage were never delineated. As data volumes grew, you had to invest in compute as well as storage.
Non-Uniform Access of Data: Over the years, too much dependency on business operations and applications have led companies to acquire, ingest and store data in different physical systems like file systems, databases and data warehouses (e.g. SQL Server or Oracle), big data systems (e.g….
Efficient data management keeps goods flowing smoothly in Denmark. Danske Fragtmaend, the country’s largest national transport and distribution firm, has been moving freight for more than a century. Today, Danske Fragtmaend delivers more than 40,000 consignments each day throughout Denmark, and businesses from small mom-and-pop operations to factories rely on its services.
The firm handles logistics in a central location, where 200 dispatchers keep an eye on the movement of thousands of trucks and their cargo. Both drivers and dispatchers need the latest information to operate efficiently, so they rely on a data platform based on SQL Server 2016. The storage system includes 160 terabytes of flash memory for fast I/O and high uptimes. Throughout the day, drivers continually scan transactions with PDAs and send shipping information including GPS coordinates to the data platform. Fast access to information is essential. Ulf…
When building applications with C# and SQL Server, it is often necessary to define codes in the database that correspond with enums in the application. However, it can be burdensome to maintain the enums manually, as codes are added, modified, and deleted in the database.
In this blog post, I’ll share a T4 template that I wrote which does this automatically. I looked online first, and did find a few different solutions for this, but none that worked for me as-is. So, I built this generic T4 template to do the job, and you can use it too.
Let’s say you’ve got a Color table and ErrorType table in the database, populated as follows:
Now you’d like enums in your C# application that correspond to these rows. The T4 template will generate them as follows:
Before showing the code that generates this, let’s point…
Upgrading your software can be daunting, Microsoft knows. The fast pace of business makes it easy to tell yourself, “I’ll do it later when I have time.” Microsoft gets it! But here are five key reasons to make time to upgrade to SQL Server 2016, which was named DBMS of the Year in 2016 by DBengines.com.
Seamless step-up without rewriting apps. Thanks to November’s SQL Server 2016 Service Pack 1 (SP1), SQL Server now has one programming surface across all editions. If you switch from Express to Standard, or Standard to Enterprise, you don’t have to rework code to take advantage of additional features. Time saved! In addition, the change brings access to innovative features across performance, security, and analytics not previously available in Express or Standard—a great reason to upgrade applications that run on those editions. The Enterprise edition of…
Introduction – Part 2
Part 1 of this post focused on the first category of how the Analysis Services Multidimensional (MD) duplicate attribute key error can arise. It reflects the perspective of an atomic attribute – an attribute having no attribute relationships other than with the dimension key attribute.
This post focuses on the second category of this error, which can arise when an attribute does have attribute relationships besides the (required) one with the dimension key attribute.
As is well known, creating attribute relationships is a best practice in Analysis Services MD for improving query performance. The most common reason attribute relationships are created is to support a natural hierarchy – so your data model has to have one for this to arise. The next most common reason is to support attribute properties, such as a sort order – i.e. when the…
Securing customer data while maintaining the highest levels of privacy have always been top priorities for Microsoft and the SQL organization. As a result, SQL Server, which also powers Azure SQL Database and Azure SQL Data Warehouse, continues to be one of the most secure Relational Database Management Systems (RDBMS) on the market.
At the RSA Conference last year, Microsoft talked about their commitment to security and privacy. Microsoft wants to share a few examples of industry-leading security features they shipped since then and update you on their plans to deliver the highest levels of security across the SQL Database product lineup.
Announcing the April general availability of Azure SQL Database Threat Detection for proactive monitoring and alerting of suspicious database activities and potential vulnerabilities.
Using machine learning, SQL Database Threat Detection continuously monitors and profiles application behavior, and detects suspicious database activities…