- Artificial Intelligence Adds Pressure to Health IT Networks
- Artificial Intelligence to Strain Health IT Infrastructure
The analytics market is expanding rapidly across all verticals, including healthcare, because of the advancements being applied to the technology. The platforms are gaining more intelligent capabilities as deep learning tools becomes more affordable and accessible. By 2020, Gartner predicts that 40 percent of enterprises will have invested in advanced analytics tools.
Gartner added machine learning to its data science Magic Quadrant because of the technology’s ties to analytics. Although machine learning doesn’t include every aspect of artificial intelligence (AI) that can be applied to healthcare data science, it is the most practical place to start when introducing AI into health IT infrastructure.
The increased number of data sources is also a contributing factor to the growth of intelligent data science platforms.
Connected medical and IoT devices generate more data that can be used to gain more insight into individual patient health as well as contribute to population health. This calls for more scalable solutions that can take a hybrid cloud and on-premises approach to combining data.
Gartner emphasized the importance of cohesiveness between the platform and the entire analytics pipeline, from the initial access, to the data, to the eventual use of the data. The ability for tools to be interoperable with other IT infrastructure tools is critical to the data science platform’s accuracy and success.
“Many organizations are awaiting the arrival of new platforms that will enable them to extend their current infrastructure and work well with their other technology,” report authors explained. “Users must be aware of the rapid changes in this market and monitor how vendors and other organizations are responding to those changes.”
“They should regularly assess the state of the market and the ability of their current vendor to respond and adapt,” report authors continued. “In addition, they should consider extending their analytic capability to include descriptive, diagnostic, predictive and prescriptive capabilities in a cohesive manner.”
Entities need to be sure that the data science platform they deploy complements their existing IT infrastructure instead of competing with it. This is why assessing current IT infrastructure is such a critical first step. If the data science platform an organization selects doesn’t work with the current infrastructure, the organization will have to reassess its choice or spend money replacing other systems for the data science platform to work.
While choosing the correct data science platform is key for success, Gartner stated that this is not the only challenge facing the adoption and successful deployment of a data science platform.
“Data, people and process must also be addressed,” Gartner advised. “Data science and machine-learning approaches require increasingly accurate data to build models representative of the real world. Information management therefore has an important role to play by helping to ensure that models are based on sound inputs and practices.”
Integrating machine learning into data science platforms isn’t a quick fix for what data goes into the tool. Organizations still need to make sure that they monitor the data collected to be sure it accurately reflects their patients.
AI in healthcare still has maturing to do, but beginning to apply it to analytics is a good way to integrate AI technology into health IT infrastructure. As the technology continues to develop and learn, it will adapt to the needs of healthcare organizations.
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