Artificial Intelligence
While RPA has proved its success for some administrative functions, other technologies are emerging as options to help address the worker shortage and reduce workload in clinical and operational areas.
As ambient technologies improve, additional use cases to leverage voice will emerge – that leaves us with the question of how patients and physicians are responding to voice-enabled tools in their healthcare encounters.
Areas of opportunity include data analytics for research, enhancing EHR capabilities and using AI algorithms to support patient care and operational efficiency, says Mike Restuccia.
Digital technologies are constantly changing. Hardware is getting faster and more efficient. Software can harness these hardware improvements to improve the functionality of technology. And so healthcare staff and citizens will hopefully have access to tools that allow tasks to be completed quicker, or more intuitively ...
Health is in the throes of some of the most significant changes as systems reel from a variety of rapidly changing environments. Dr Charles Alessi, Chief Clinical Officer at HIMSS, explores lessons learned, as we look cautiously to a better 2022.
While the 'robot' aspect of RPA gets most of the attention, successful implementation centers on the people and processes that will be impacted by the technology – and in a healthcare system where burnout is rampant, there are plenty of those.
A new weekly series looks beyond the pandemic and explores strategies for driving lasting, IT-enabled operational and business improvements across healthcare.
At Penn Medicine, integrated product teams – comprising data scientists, physicians and software engineers, among others – are helping improve AI and machine learning applications.
While traditionally deeply skeptical of artificial intelligence in clinical settings, in today's fast-changing care delivery landscape many physicians are thinking more proactively about how AI can improve quality and patient experience.
Health systems that refuse to see themselves as engineering houses risk falling behind in their ability to properly leverage artificial intelligence and machine learning.
