The Use of Artificial Intelligence in Healthcare Accelerated During the Pandemic. It's Here to Stay. Artificial intelligence (AI) is transforming population health, making proactive care more feasible and processes more efficient than ever before.
By Ariel Shapira •
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Artificial 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 health" 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 public health, 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.
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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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All solutions are welcome
Hospitals and associated clinics have turned to AI solutions over the course of the pandemic to improve resource efficiency, strengthen diagnostics and manage patient volumes. This is particularly critical in preventative care, especially with orthopedic surgery. Orthopedic surgeries are expected to rise from 22.3 million in 2017 to 28.3 million in 2022 worldwide. When factoring in the scarcity of resources, that places pressure on surgeons, clinicians and radiologists.
Deep-learning-based technologies like Zebra Medical Vision ease the burden by providing radiologists medical imaging analytics for scans and automatically analyzing them for various clinical findings. Such findings can be passed onto doctors, who can take the reports into consideration when making a diagnosis.
Looking at the intersection of population-health management and healthcare-data analytics could be interesting, as each market is set to become s $40 billion market within a couple of years. If we're examining the genomic space alone, the tipping point is around the corner with an affordable cost of $600 for full genome sequencing today, on track for $100 sequencing in just a few years. As genomic data becomes financially plausible and the data generated from genomics doubles every year, expected to reach 20 exabytes by 2025, the 5,000 geneticists worldwide won't be able to process a significant fraction of it. Healthcare-data analytics in population health will be essential.
Precision medicine must rely on proper data processing and analysis. The AI models are already powerful enough — they just need the data to work with. Genetic-interpretation company Emedgene developed the concept of "cognitive genomic intelligence" — an inclusive, ever-growing platform that automatically produces insights from genomic data, reducing the time and cost of its interpretation, which traditionally requires hours of manual review and yields limited insights when solely relying on human intelligence.
H2O.AI is another solution that uses AI to analyze data throughout healthcare systems to mine, automate and predict processes. The ever-popular IBM Watson Health uses AI to provide value-based-care solutions for population-health management, directly benefiting providers, health plans, employers and pharmaceutical and biotech organizations.
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AI is raising the standards of population health, ultimately making it easier for doctors to make more informed decisions as they come up with optimized care regimens. The tech itself is widely considered an administrative luxury, which it may have been at first, but it has gone on to become a literal life-saver.
