Hiking is quite the tradition here at Deerwalk. We walk every weekend. A hiking coordinator is assigned, interested participants invited, and some of the most exotic destinations in Nepal explored. We then publish reports, pictures, and videos of our journeys at EverestUncensored.org, a comprehensive guide to Hiking in Nepal.
Deerwalk is pleased to announce the 1.4 release of Makalu, our analytics platform.
The Makalu Dashboard now contains top 10 graphs for Benefit Categories, Diagnosis Groups, Procedure Groups and Place of Service with a slider effect displaying the top 10 reports for respective graphs. The header contains the application details and processing information for the Client.
In the request form, the clinical user enters demographic information related to the request for an authorization such as level of urgency, diagnosis, date of patient’s admission/start date of services, anticipated discharge/end date, place of service, planned procedure and information about the providers. The authorizations have “Attach Documents” functionality allowing the nurses to attach documents provided by providers, facilities, members, and others related to the authorization request.
This is part 1 of a two part article on how Utilization Management works in Everest. Part 1 covers introduction to the module.
The role of coordinated care is emerging as a major component of US healthcare delivery models, not only as a means of improving quality of care but also for controlling healthcare costs. Our Everest product is designed to support all aspects of the care coordination process, of which Utilization Management (UM) is a key strategy.
Deerwalk is pleased to announce the initial release of our member portal, Yala. This new product offers your members leading edge content, tools, and interactive learning opportunities to help them make informed choices for maintaining good health. Whether it’s identifying health risks or tracking progress toward health goals, the member portal makes it easy for individuals to actively participate in their own healthcare. Yala is private label ready for our clients.
[Authored by Sawan Vaidya and Minesh Maharjan.]
Groovy is a succinct yet powerful programming language. We had our first brush with it a few years back (Read the old post here.) when it was the new kid on the block. We liked it then, and decided that further investigation was in order before we consider using it for our own products. We decided to pick some moderately complex projects to test Groovy’s maturity and capabilities. The projects would need to go through the most common programming scenarios at Deerwalk and also be fun! We picked the classical Maze project mostly because we wanted to test Groovy’s savvy with Collections, which are central to Deerwalk’s needs.
Identifying inpatient claims in healthcare data is centerpiece to most Healthcare data analysis. Inpatient claims is one of the more expensive claims over all claim groups. Therefore, identification of Inpatient claims in claims data is vital. Unfortunately, there is no standard algorithm out there to identify Inpatient admission claims. They are rarely alluded to as such in the data in a direct manner.
The Diagnosis Groupers developed by Deerwalk was bundled in the initial release of its product, Makalu, in January 2012. These Diagnosis Groupers provide support for the reporting categories and quality metrics that our clients frequently need. Moreover, since many of our clients use “home-grown” codes, being able to assign these non-standard diagnosis codes to standardized groupers allows them to easily incorporate these codes into reports and analytics.
We are pleased to announce the release of version 1.3 of our healthcare data analytics platform, Makalu.
We are pleased to announce the release of version 1.3 of our healthcare data analytics platform, Makalu. The platform, which utilizes big data technologies such as Hadoop and Elastic Search at its core, is fully private label ready to our customers. The platform’s most recent enhancements make it more ready than ever for use by Accountable Care Organizations (ACO) and Patient-centered Medical Homes.
A good knowledge of data structures always helps in designing good systems, whether we are working with relational databases or NOSQL databases. However, this knowledge is much more important when working with NOSQL systems. One important fact to remember is that while a lot of optimization for SQL based solutions is during query time, NOSQL solutions (especially Hadoop/HBase) are design time optimized. You design an optimized schema and the queries are almost straight forward.