CHARTSaaS RA IT overcomes roadblocks to predictive analytics by providers

“Using data to drive operational efficiencies in clinical systems is step one, according to Mr. Bartley [Andy Bartley, senior solutions architect at Intel Corp]. However, he believes predictive analytics can be used for much more — it can help hospitals and healthcare organizations operate smarter, control costs better, improve patient access and enhance outcomes … Mr. Bartley identified four common areas healthcare organizations should focus on to make the jump into predictive analytics.

1. Scalable infrastructure. The availability of data in healthcare is fairly new — major EHR adoption has happened only over the last six years, Mr. Bartley notes. Because of this, a lot of innovation is happening in the predictive analytics space and the pace of change is not likely to slow any time soon. Mr. Bartley advises hospitals to look for flexible, extensible analytics platforms that are able to support new types of frameworks and technologies as the industry evolves.

2. Executive sponsorship. However, choosing the right infrastructure is not the most difficult part of adopting predictive analytics. “In many ways, the technology problem is the easiest part to solve,” Mr. Bartley said. “The organization and political challenges end up being the major road blocks to adoption.” Executive sponsorship is what Mr. Bartley calls the “make-or-break factor” for long-term success with predictive analytics. Many organizations often begin projects in the IT department, but work to gain buy-in from the chief medical information officer to ensure balance between the clinical and technical views.

3. Change management. About one in four healthcare organizations struggle with clinical workflow integration, according to the Intel-Forrester study. Mr. Bartley believes this challenge boils down to two main issues: engagement and adaptability, or changing workflows in real-time. The best way to optimize change management is through communication, according to Mr. Bartley. This means ensuring there are strong feedback loops between clinical and data science teams. It also means managing expectations within the organization as a whole so staff doesn’t expect too much too early and lose motivation.

4. Use case selection. The best strategy to use with predictive analytics is to ‘balance quick wins and big bets,’ according to Mr. Bartley. This means finding quick 10-week projects with real results to demonstrate the utility of the technology, while simultaneously building momentum and working on longer-term projects that truly define the importance of the predictive analytics team in the organization. The key with use case selection is choosing cases where the hospital has sufficient data and to pick cases with few external factors that could limit the overall impact of the project.”

In the preceding excerpt from the article entitled “4 common roadblocks to adopting predictive analytics in healthcare organizations” by Emily Rappleye in the September 28, 2017 issue of Becker’s Health IT & CIO Review (, three of the four roadblocks cited can be overcome by the CHARTSaaS approach to app creation. The fourth, executive sponsorship, can be overcome by a full and fair discussion of cloud computing in general and the potential of Cloud Healthcare Appliance Real-Time Solution as a Service in particular.

Please validate the foregoing CHARTSaaS RA-related propositions to your own satisfaction by reading the white paper at, viewing the tutorials posted on YouTube ( and and also by reviewing the details of CHARTSaaS™ and the CHARTSaaS RA™ in these presentations:

Healthcare providers will benefit significantly from appreciating and then applying a CHARTSaaS RA-compliant IT solution. To do so will mitigate medical mistakes (currently the third leading cause of patient deaths. per Makaray and Daniel (; thereby minimizing patient adverse events and optimizing clinical case outcomes while maximizing the cost-effectiveness of care and treatment, and also accelerating the accrual and facilitating the application of medical knowledge.

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