Welcome!

Machine Learning Authors: Pat Romanski, Yeshim Deniz, Liz McMillan, Elizabeth White, Zakia Bouachraoui

Related Topics: Agile Computing, Java IoT, Microservices Expo, Machine Learning , Government Cloud, @DXWorldExpo

Agile Computing: Article

Has healthcare.gov Claimed “Mission Accomplished” Too Soon?

Mission Accomplished? Doesn’t look like it for those who want to find their health insurance

Over the weekend those in charge of Healthcare.gov have claimed "Mission Accomplished," that the site is now performing optimally, and will be able to handle 50,000 concurrent users. The healthcare.gov team also stated that if there happens to be a problem they have software in place to help get to the root cause. Any affected user will get a number in line and receive an email when they can return to the site.

However, when we tried to access the site today with our Ajax Edition to see what changes have been made, we received the following error message. In fact as of this report there have been new indications that thousands of people are getting the error message. The question we have is "is this Mission really Accomplished?"

Mission Accomplished? Doesn't look like it for those who want to find their health insurance

It is only about the end user experience
The Healthcare.gov team claimed victory based on some performance metrics that only offer a limited server-side diagnostic view of problems. This means that they are using internal metrics to claim victory that problems are behind them, that error rates are down to 1%, and that response times are under a second. The problem is that none of these metrics have anything to do with the end user. None of the issues that lead to customer frustration are really addressed. Application experts know, back-end server-side performance does not equal end-user satisfaction. Fastcompany.com recently wrote an article about how it is only about the end user when it comes to managing performance. If the performance of the end user is poor it does not matter what is done to fix the errors. People will still be frustrated with the site and will continue to complain. One study suggests that only 10% of users who have a problem will report it.

Let's look at the end-user performance for healthcare.gov over time from across the country for the past 30 days, from November 1 to December 2.

User Experience on November 1

User Experience on December 2: Not a whole lot has changed

As you can see, While the site has improved somewhat since we started monitoring Healthcare.gov, it's certainly not fixed.

Click here to read the full analysis and insight on what readers can take-away from the missteps involved in rolling out Healthcare.gov.

More Stories By Stephen Wilson

Stephen Wilson is a 15 year IT professional that currently holds the Subject Matter Expert role for Compuware APM within the Field Technology Sales organization. His role puts him in front of customers and their challenges on a daily basis. His background includes both development and operations. This kind of insight into the challenges that both developers face as well as those faced by the operational team allows him to be seen as a trusted advisor to his customers. His unique perspective into client needs and goals give creditability to the need for performance not just at one level but across the entire lifecycle.

Comments (0)

Share your thoughts on this story.

Add your comment
You must be signed in to add a comment. Sign-in | Register

In accordance with our Comment Policy, we encourage comments that are on topic, relevant and to-the-point. We will remove comments that include profanity, personal attacks, racial slurs, threats of violence, or other inappropriate material that violates our Terms and Conditions, and will block users who make repeated violations. We ask all readers to expect diversity of opinion and to treat one another with dignity and respect.


CloudEXPO Stories
With more than 30 Kubernetes solutions in the marketplace, it's tempting to think Kubernetes and the vendor ecosystem has solved the problem of operationalizing containers at scale or of automatically managing the elasticity of the underlying infrastructure that these solutions need to be truly scalable. Far from it. There are at least six major pain points that companies experience when they try to deploy and run Kubernetes in their complex environments. In this presentation, the speaker will detail these pain points and explain how cloud can address them.
The deluge of IoT sensor data collected from connected devices and the powerful AI required to make that data actionable are giving rise to a hybrid ecosystem in which cloud, on-prem and edge processes become interweaved. Attendees will learn how emerging composable infrastructure solutions deliver the adaptive architecture needed to manage this new data reality. Machine learning algorithms can better anticipate data storms and automate resources to support surges, including fully scalable GPU-centric compute for the most data-intensive applications. Hyperconverged systems already in place can be revitalized with vendor-agnostic, PCIe-deployed, disaggregated approach to composable, maximizing the value of previous investments.
When building large, cloud-based applications that operate at a high scale, it's important to maintain a high availability and resilience to failures. In order to do that, you must be tolerant of failures, even in light of failures in other areas of your application. "Fly two mistakes high" is an old adage in the radio control airplane hobby. It means, fly high enough so that if you make a mistake, you can continue flying with room to still make mistakes. In his session at 18th Cloud Expo, Lee Atchison, Principal Cloud Architect and Advocate at New Relic, discussed how this same philosophy can be applied to highly scaled applications, and can dramatically increase your resilience to failure.
Machine learning has taken residence at our cities' cores and now we can finally have "smart cities." Cities are a collection of buildings made to provide the structure and safety necessary for people to function, create and survive. Buildings are a pool of ever-changing performance data from large automated systems such as heating and cooling to the people that live and work within them. Through machine learning, buildings can optimize performance, reduce costs, and improve occupant comfort by sharing information within the building and with outside city infrastructure via real time shared cloud capabilities.
As Cybric's Chief Technology Officer, Mike D. Kail is responsible for the strategic vision and technical direction of the platform. Prior to founding Cybric, Mike was Yahoo's CIO and SVP of Infrastructure, where he led the IT and Data Center functions for the company. He has more than 24 years of IT Operations experience with a focus on highly-scalable architectures.