Let’s say the Finance Department of a clothing retailer has some great reports that let them see all the sales across the United States; so great, in fact, that they want to share them with all Regional Managers so they can communicate about the hot spots in their region. The problem is the Regional Managers aren’t permitted to see data outside their region, and giving them access to these reports would allow them to filter to any region they wanted. We could create separate Datasets and reports filtered to the region for the manager that is given access to them, but that would be time-consuming, and a nightmare to maintain. Luckily, Power BI provides the ability to implement Row-Level Security (RLS).
So, what is RLS? Simply put, it controls a user’s access to each individual row of the Dataset. In…
The Microsoft Office Store contains a growing library of custom Power BI visualizations developed by Microsoft and the community. While Power BI offers built-in visualizations, custom visuals can be downloaded for free and are used to enhance the way you display your data within reports and dashboards. Tallan has now taken its Power BI expertise to the next level by contributing our very own custom visual. Introducing the ‘Calendar by Tallan’ Power BI Custom Visual!
When associating dates with data, the first real-world visual that comes to mind is a standard 12-month calendar. While other custom calendar visuals exist in the Office store, the offerings did not portray the dates in this familiar manner or display the range of data desired. Tallan’s Calendar visual enables you to view the aggregation of data across a range of dates in a standard calendar…
Ensuring that 837 EDI transactions meet validity checks is critical to improving auto-adjudication and encounter submission acceptance rates. SNIP Type 3 describes the rules for balancing header and detail levels of the Claim, Premium Payment and Remittance Advice transaction sets. Previously, our blog covered the logic required to balance 835 transactions. Now we’ll look at the steps necessary to balance claims with service lines, including Coordination of Benefits loops in multiple payer scenarios.
Claims and encounters may be represented by a variety of X12 transaction types: 837 Professional, Institutional and Dental, as well as their corresponding post-adjudicated variants (298, 299, 300), intended for submission to All-Payer Claims Databases. The following logic applies to all versions of the 837 equally, with a few caveats noted below.
Rule 1 – Balancing Claim Charge Amounts
The first claim balancing rule is straightforward: given the parent-child relationship of 2300 claim loops to their 2400 service…
The Electronic Data Interchange (EDI) consists of a file in a specific format that represents data exchanged in a transaction from supply chain to healthcare. EDI 835 Claim Payment transaction provides payments information in reference to claims in EDI 837 Healthcare Claim format. The details include transactions such as charges, deductible, copay, payers, payee, etc. The information is stored a hierarchical structure. The standard of EDI format is well defined and the complexity can be very overwhelming. Additionally, we do not want this high degree of detail slowing our processing time.
One of the problems that enterprise systems face with EDI is file size. A single EDI 835 may contain multiple claim records and the quantity of claims in a single file can make it very difficult to process the file. Systems are often bogged down when dealing with a very…
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…
The Customer Data and Analytics (CDnA) team at Microsoft enables strategic data insights that shape the entire organization, from high-level leadership policies to small product decisions. At its core, CDnA creates and monitors indicators, known as “Power Metrics,” for some of the key divisions and businesses in Microsoft. CDnA delivers Power BI dashboards to several teams, including the Windows and Devices Group (WDG), Office, Bing, Cortana and Microsoft CEO Satya Nadella’s Senior Leadership Team (SLT). The SLT leverages the Power BI dashboards to monitor progress on strategic initiatives at the company.
Both CDnA and the Power BI product have mutually benefited from a close relationship since the Power BI public preview in 2015. In fact, the early and ongoing input from this internal Microsoft team and its users has helped make Power BI the “enterprise ready”, robust, and feature-rich platform it…
Seattle Reign players, coaches and staff celebrate a win over the Portland Thorns.
Sports ignite peoples’ passion like nothing else. Standout athletes become legends through their performance on the field. But what sets them apart often comes down to decisions made off the field, such as diet, mental and physical preparation and the frequency or intensity of practice. The difference between a win or a loss can be decided by an extra five minutes of wind sprints, levels of hydration or getting to bed 30 minutes earlier the night before.
At Microsoft, they believe sports provide a tremendous opportunity to use technology to transform how individual athletes and teams push the limits and gain the edge they’re looking for in the most challenging of circumstances. Imagine making clutch decisions that are based on insight, rather than gut. And what if coaches could…
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….