What I do
Hint: I’m more than a software developer
Software delivery is hard. There are many tiny details that need to be considered, technologies to master, and business cases to learn. Delivering working software is a huge challenge.
It’s also expensive. A small software development team (2 good developers and a project manager) can easily cost $250,000/year.
Finally, it’s risky. Studies have shown that most project are 6 to 12 months behind schedule and 50 to 100% over budget.
We try to deliver software:
- With high quality
- On budget
- On time
I present topics and give talks to help developers and Agile Managers hit those goals.
This site serves as an information resource for .NET Developers and Agile Managers to get a glimpse into how I work in my capacity as an SDLC auditor. This serves as a starting point for further conversation.
On the .NET Developer side, I focus on two topics:
- Staying on track
- Keeping the quality high
- Working with management
On the Agile Management side:
- Be efficient (or ruthless)
- Measure and adjust
There are also some fun articles on the .NET Micro Framework and embedded hardware programming. It’s a side hobby of mine and I love sharing what I’ve learned.
The Fee For Service model for medical payments is the predominant payment scheme in the United States. In the Fee for Service model, physicians are incentivized to provide more treatments regardless of effectiveness. There is no link between the recipient of services and payment for those services. The recipient of the medical services, the patient, does not directly participate in the fee transaction. They are incentivized to accept any health service which may provide some benefit.
The capitation-based model pays a fixed amount for each patient enrolled with the provider. This discourages the medical facility from providing unnecessary care. Providers will be paid the same amount regardless if the patient seeks care or not. Remuneration is based on the average healthcare utilization of the patient. Any individual patient may cost more (or less) than the expected utilization. However, under-utilization will balance out over-utilization. The ability to provide deep analysis of entire populations of medical records is critical to determining the average risk of an institutions users.
Historically, this analysis would take weeks and require many people reviewing millions of documents. Big Data, a mature set of technologies, provides a solution for this problem. Using modern data analysis techniques, coupled with Big Data systems, we are able to determine risk severity of millions of patients rapidly. We reduce the cost of data analysis while increasing the accuracy resulting in an objective measure of risk from subjective medical records.
This article was written for iQuartic. You can find the original post here.