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.
[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.
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.
Human Computer Interaction (HCI), is a field of study which aims to facilitate the interaction of users, whether experts or novices, with computers. It improves user experience by identifying factors that helps reduce the learning curve for new users and also provides provisions such as keyboard shortcuts and other navigational aids for trained users.
In a typical health care model, there is an insurance company (“payer”) in between a hospital (“provider”) and an employer (“sponsor”). The payer is a healthcare organization that pays claims and administers insurance or benefit product. The sponsor buys the health benefit product from the payer for its employees. That means the employees become members and eligible to go to hospital when they need care. The providers are paid by the payer for the service provided to their members.
Deerwalk’s Makalu product is an innovative reporting and analytics platform for healthcare management. It’s flexible and configurable architecture makes it an ideal candidate for integrating with other platforms and services to create a more robust analysis for customers. It was clear in the initial vision of Makalu that comprehensive risk profiling would be a key value for decision making by Third Party administrators, health plans, brokers, consultants, physician groups and Accountable Care Organizations (ACOs).
Here at Deerwalk I am constantly asked the question, “What is the best way to work with Deerwalk?” The answer to this question always comes back with the less than satisfying answer of “It depends.” However unsatisfying as this is for the folks asking the question it is the truth.
Hbase is counterintuitive for any one with experience of row-based traditional Relational Database System. The major difference between Hbase or any of the other Google Bigtable clones with RDBMS, is that unlike RDBMS which mainly focuses on the question how the data is going to be stored, they instead focuses on how to access the data. That is RDBMS model mainly deals with identifying the entities and their relationship thus mandating the normalization of data for ACID assurance.