Artificial intelligence (AI) is transforming population health, making proactive care more feasible and processes more efficient than ever before.

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SwissCognitive, AI, Artificial Intelligence, Bots, CDO, CIO, CI, Cognitive Computing, Deep Learning, IoT, Machine Learning, NLP, Robot, Virtual reality, learningArtificial intelligence (AI) has disrupted numerous industries and prompted the addition of the suffix “-tech” to many of them: insurtech, fintech, agritech. Healthcare, in particular, has flourished because of AI, even before the pandemic, as machine intelligence makes scanning large populations for diseases feasible and drives a proactive approach to healthcare  keeping people healthy instead of waiting for them to get sick.

As the name suggests, “population ” focuses on cohorts over individuals, but there is more to it than that. For researchers in healthcare, population health relies on keeping track of the incidence of diseases in a variety of groups of people. For example, they might compare Covid-19 outbreaks among individuals of different demographics who reside in a range of ZIP codes. It focuses on the prevention or early detection of disease in large populations through screening.

This is different from the more generalized , which examines the health condition of a whole population of individuals. Catering to public health calls for an analysis of pollutants in the air and water. Tending to population health requires the examination of disease incidence in groups according to criteria such as age, gender or location.

What is AI actually doing in healthcare?       

When it comes to AI in healthcare, it’s safe to say technology cannot replace human doctors’ informed judgment and experience treating members of the public — nor does anyone intend for it to do so. With respect to population health, which has become even more important since the pandemic, AI is needed more than ever to provide diagnosis and treatment statistics and other information to specialists and public-health researchers. 

Population-health management software typically integrates patient data across healthcare IT systems for analysis. The data is used to better predict and manage illnesses and diseases. The software is also used to facilitate care delivery across populations based on need. In some ways, it caters towards clusters of people, but it ultimately helps improve the quality of individualized patient care. After all, the analysis of population data leads to better prediction of individual-health risks and a more accurate big-picture representation of health trends within different communities. […] 

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