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population health


The Use of Artificial Intelligence in Healthcare Accelerated During the Pandemic. It's Here to Stay.

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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.


Clarify Health scores $115M in series C funding to grow AI-powered data analytics platform

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Enterprise analytics company Clarify Health has secured $115 million in series C funding to scale its self-service healthcare analytics cloud and business software. Clarify Health combines longitudinal data for more than 300 million "unique patient lives" from government and commercial claims, electronic health records (EHRs) and prescriptions, according to the company. These data can help healthcare professionals manage population health and commercialize pharmaceutical and biotechnology products. "By linking CMS claims data with commercial claims, EHR, prescription and socioeconomic data, our models are trained on large cohorts and a more complete picture of each patient's longitudinal healthcare journey," Clarify Health CEO Jean Drouin, M.D., told Fierce Healthcare. The San Francisco-based company was launched in 2015 and has raised $178 million to date, according to Crunchbase.


machine learning in public health

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This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Another prominent example in this regard came from DeepMind's publication of the possible protein structures associated with the COVID-19 virus (SARS-CoV-2) using their AlphaFold system. For example, our process of vetting results in the Global Burden of Disease Study [2] included the visual inspection of thousands of plots showing data together with model estimates. Our experience developing methods for computer certification of verbal autopsy has bolstered our belief that using an explainable approach, even with a reduction in accuracy, can be superior. Qualified practitioners are in short supply. There is increasing awareness that health … enhancing the ability to see and navigate in a procedure. Going beyond the conventional long-haul process, AI techniques are increasingly being applied to accelerate the fundamental processes of early-stage candidate selection and mechanism discovery. This could be the biggest impact of AI tools as it can potentially transform the quality of life for billions of people around the world. These technologies are also being used in the following ways: Preventing crime: AI and machine learning help authorities track and manage the huge amount of data generated by public surveillance devices, and analyze that data in real time for anomalies and threats.


Artificial Intelligence: Applications in Healthcare Delivery

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The rediscovery of the potential of artificial intelligence (AI) to improve healthcare delivery and patient outcomes has led to an increasing application of AI techniques such as deep learning, computer vision, natural language processing, and robotics in the healthcare domain. Many governments and health authorities have prioritized the application of AI in the delivery of healthcare. Also, technological giants and leading universities have established teams dedicated to the application of AI in medicine. These trends will mean an expanded role for AI in the provision of healthcare. Yet, there is an incomplete understanding of what AI is and its potential for use in healthcare.


UK to invest £2.6M in drone and satellite tech to deliver vital supplies

Daily Mail - Science & tech

The UK government is setting aside £2.6 million for new satellite and drone technology that could deliver essential supplies during the coronavirus lockdown. The UK Space Agency (UKSA) is funding new solutions to deliver equipment such as test kits, masks, gowns and goggles for frontline NHS staff. The joint initiative with the European Space Agency could lead to vital equipment soaring through British skies via drones to support the NHS in tackling COVID-19. Companies can submit their proposals, including ideas for deployment and a pilot phase, on the European Space Agency (ESA) website. The UK's space industry is also looking for ways to combat the spread of coronavirus and preventing future epidemics using satellites.


Defining the role of clinical AI in identifying and addressing patient risk and improving population health across communities - AIMed

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The role of artificial intelligence in healthcare continues to evolve as does the definition of what it is and is not. This state of flux has contributed to slower than desired adoption, unmet expectations, and gaps between deployment and value realization. If clinical artificial intelligence more specifically is to transform patient care, it must deliver insights that are unique, individualized, can be tied to community and align with existing workflows. Join Jvion, a market leader in clinical AI, along with leadership from Microsoft, for a one-hour webinar that will provide clarity and guidance to aid in addressing patient risk and improving population health across communities.


The Risk to Population Health Equity Posed by Automated Decision Systems: A Narrative Review

arXiv.org Artificial Intelligence

Artificial intelligence is already ubiquitous, and is increasingly being used to autonomously make ever more consequential decisions. However, there has been relatively little research into the consequences for equity of the use of narrow AI and automated decision systems in medicine and public health. A narrative review using a hermeneutic approach was undertaken to explore current and future uses of AI in medicine and public health, issues that have emerged, and longer-term implications for population health. Accounts in the literature reveal a tremendous expectation on AI to transform medical and public health practices, especially regarding precision medicine and precision public health. Automated decisions being made about disease detection, diagnosis, treatment, and health funding allocation have significant consequences for individual and population health and wellbeing. Meanwhile, it is evident that issues of bias, incontestability, and erosion of privacy have emerged in sensitive domains where narrow AI and automated decision systems are in common use. As the use of automated decision systems expands, it is probable that these same issues will manifest widely in medicine and public health applications. Bias, incontestability, and erosion of privacy are mechanisms by which existing social, economic and health disparities are perpetuated and amplified. The implication is that there is a significant risk that use of automated decision systems in health will exacerbate existing population health inequities. The industrial scale and rapidity with which automated decision systems can be applied to whole populations heightens the risk to population health equity. There is a need therefore to design and implement automated decision systems with care, monitor their impact over time, and develop capacities to respond to issues as they emerge.


Machine learning and medical education

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Artificial intelligence (AI) is poised to help deliver precision medicine and health.1,2 The clinical and biomedical research communities are increasingly embracing this modality to develop tools for diagnosis and prediction as well as to improve delivery and effectiveness of healthcare. New breakthroughs are being developed in an unprecedented fashion and the developed ones have obtained regulatory approval and found their way into routine medical practice.3,4,5 Yet, the medical school curriculum as well as the graduate medical education and other teaching programs within academic hospitals across the United States and around the world have not yet come to grips with educating students and trainees on this emerging technology. Several expert opinions have pointed to the benefits and limitations associated with the use of ML in medicine,1,2,6,7,8,9,10 but the aspect related to formally educating the younger generation of medical professionals has not been openly discussed.


3 questions to ask before investing in machine learning for pop health

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The goal of population health is to use data to identify those who will benefit from intervention sooner, typically in an effort to prevent unnecessary hospital admissions. Machine learning introduces the potential of moving population health away from one-size-fits-all risk scores and toward matching individuals to specific interventions. The combination of the two has enormous potential. However, many of the factors that will determine success or failure have nothing to do with technology and should be considered before investing in machine learning or population health. Population health software, with or without machine learning, only produces suggestions.


Machine Learning in population health: Creating conditions that ensure good health.

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Machine Learning (ML) in healthcare has an affinity for patient-centred care and individual-level predictions. Both individual health and population health are not divergent, but at the same time, both are not the same and may require different approaches. ML in public health applications receives far less attention. The skills available to public health organizations to transition towards an integrated data analytics is limited. Hence the latest advances in ML and artificial intelligence (AI) have made very little impact on public health analytics and decision making.