3-step evolution of AI in the healthcare enterprise

Pioneering the functional application of artificial intelligence in the healthcare enterprise, MUUTAA seeks out the areas where sufficient data is at the disposition of our partners to deploy intelligence and reduce time spent on repetitive tasks or tasks with a high cognitive burden. The ultimate goal being to elevate care givers to perform higher value tasks that are clinically and patient oriented.
In a recent McKinsey report, three distinctive phases describe the expected evolution of AI in the healthcare enterprise. Supporting this transformative process, MUUTAA’s platform offers the environment and tools required to capitalize on the opportunities in the current phase and assist in the development of successful (data) strategies in the future.
Why do we continue to underline the importance of the evolution and the application of AI in the healthcare enterprise? The demand for healthcare services is going to change drastically in our lifetime considering the aging population, the increase in global population, precision medicine and workforce shortages. The gradual application of (AI) technologies will be a requirement for the system to prepare for the future.
From the aforementioned report:
Three phases of scaling AI in healthcare
We are in the very early days of our understanding of AI and its full potential in healthcare, in particular with regards to the impact of AI on personalisation. Nevertheless, interviewees and survey respondents conclude that over time we could expect to see three phases of scaling AI in healthcare, looking at solutions already available and the pipeline of ideas.
First, solutions are likely to address the low-hanging fruit of routine, repetitive and largely administrative tasks, which absorb significant time of doctors and nurses, optimising healthcare operations and increasing adoption. In this first phase, we would also include AI applications based on imaging, which are already in use in specialties such as radiology, pathology and ophthalmology.
In the second phase, we expect more AI solutions that support the shift from hospital-based to home-based care, such as remote monitoring, AI-powered alerting systems or virtual assistants, as patients take increasing ownership of their care. This phase could also include a broader use of NLP solutions in the hospital and home setting, and more use of AI in a broader number of specialties, such as oncology, cardiology or neurology, where advances are already being made. This will require AI to be embedded more extensively in clinical workflows, through the intensive engagement of professional bodies and providers. It will also require well designed and integrated solutions to use existing technologies effectively in new contexts. This scaling up of AI deployment would be fuelled by a combination of technological advancements (e.g., in deep learning, NLP, connectivity etc.) and cultural change and capability building within organisations.
In the third phase, we would expect to see more AI solutions in clinical practice based on evidence from clinical trials, with increasing focus on improved and scaled clinical decision-support (CDS) tools in a sector that has learned lessons from earlier attempts to introduce such tools into clinical practice and has adapted its mindset, culture and skills. Ultimately respondents would expect to see AI as an integral part of the healthcare value chain, from how we learn, to how we investigate and deliver care, to how we improve the health of populations. Important preconditions for AI to deliver its full potential in healthcare will be the integration of broader datasets across organisations, strong governance to continuously improve data quality, and greater confidence from organisations, practitioners and patients in both the AI solutions and the ability to manage the related risks.

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