Health Care Providers & Services


DeepSOFA: A Continuous Acuity Score for Critically Ill Patients using Clinically Interpretable Deep Learning

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Traditional methods for assessing illness severity and predicting in-hospital mortality among critically ill patients require time-consuming, error-prone calculations using static variable thresholds. These methods do not capitalize on the emerging availability of streaming electronic health record data or capture time-sensitive individual physiological patterns, a critical task in the intensive care unit. We propose a novel acuity score framework (DeepSOFA) that leverages temporal measurements and interpretable deep learning models to assess illness severity at any point during an ICU stay. We compare DeepSOFA with SOFA (Sequential Organ Failure Assessment) baseline models using the same model inputs and find that at any point during an ICU admission, DeepSOFA yields significantly more accurate predictions of in-hospital mortality. A DeepSOFA model developed in a public database and validated in a single institutional cohort had a mean AUC for the entire ICU stay of 0.90 (95% CI 0.90–0.91)


Japan's health care sector still a magnet for Filipinos

The Japan Times

MANILA – Job opportunities in Japan's health industry continue to attract Filipinos a decade since it started accepting candidate nurses and caregivers under a bilateral economic agreement. Earlier this month, a new group of Filipino health workers who aspire to work as nurses and caregivers here began preparatory training in the Japanese language and culture in two centers in Manila. The 341 applicants comprise the 12th batch of candidate nurses and caregivers under the Japan-Philippines Economic Partnership Agreement forged in 2008. Japan accepted the first batch of Filipino health workers in 2009. And I think I will broaden my experience and learn more there.


The Troubling Tale of Artificial Intelligence and Medical Ethics, or perhaps not? -- AI Daily - Artificial Intelligence News

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The impact of artificial intelligence in the healthcare sector is undeniably potent, as we have seen and explored over the last few months, AI has the potential to truly revolutionize and every factor of modern healthcare. From diagnostic purposes such as we see at Moorfield's NHS Trust dealing with complex optical coherence tomography scans, or OCT scans for short, to the systems built by giants such as Philips Healthcare and Cerner who manage the daily management of the hospital. However, most clinicians agree that artificial intelligence has an assistive role to play in healthcare, far from a leading role, and as such these systems will help clinicians to assign priorities to patients, spot the easily missed features in specimens of medical imaging and generally speed up the process of a patient's journey through healthcare; although it must be mentioned that AI does what humans can do faster and more accurately in terms of assessing X-rays, MRIs and so forth, as you will have seen vividly boldly titled amongst most major technology news platforms, but these claims are in need of long term assessment with far more diversified patient sets that would be more typical of the overcrowded NHS wards that bring people from all walks of life for the common goal of an unconditionally excellent healthcare. As, with consent, data sets grow through the hospital's daily running as doctors request medical imaging modalities throughout the day, constantly improving the apt of the systems in place; as the data grows, so does the performance of the system.


Demographics and disruption demands new skills in Canada's health-care sector The Star

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Canada is on the verge of a silver tsunami, and our health-care sector isn't ready. The rapid aging of our population through the 2020s is about to strain our hospitals, clinics and long-term care facilities, and technology will bridge only part of the gap. As much hope and hype as we see in robotic caregivers, virtual physicians and wearable sensors, we'll need more humans in health care, and more human skills than ever. The past decade has shown how reliant health care is on skilled labour; it's been one of the fastest-growing sectors for employment, and shows no signs of letting up. A new report from RBC estimates only 17 per cent of health-care jobs are at significant risk of automation, compared with 34 per cent in the overall economy.


The computer will see you now: six examples of AI in healthcare

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As an industry defined by the relationship between patient and carer, at first glance it may seem incongruous to nudge healthcare towards a robotic future. In fact, artificial intelligence (AI) has the potential to completely reshape the health industry, offering greater support to human capabilities and allowing healthcare organizations to deliver higher-quality services more efficiently. AI is a broad term for computer systems that can "think" and act like humans. They can sense their environment, absorb information, learn from past experience, make decisions and take action. AI has transformative power for two reasons: the explosive growth in data, coupled with huge computational advances and processing speeds.


Google canceled the online publication of more than 100,000 X-rays after privacy concerns

Daily Mail - Science & tech

In 2017, Google was two days away from posting 112,000 chest X-rays taken of more than 30,000 patients on public servers before last-minute privacy concerns put a stop to the project. The X-rays were part of a program conducted with the National Institutes of Health to see if Google's machine learning tools could be used to better identify disease markers using visual information. The X-rays were collected at a government research hospital in Bethesda, Maryland where a large number of clinical research studies were being conducted. According to a new report in The Washington Post, based on emails obtained through a Freedom of Information Act request, Google and NIH began collaborating on the project in the summer of 2017 and were hoping to reveal findings from the project at an artificial intelligence conference in Hawaii on July 21. The X-rays were analyzed by Google's TensorFlow, an open source machine learning software that was developed by the Google Brain Team in 2015.


Healthcare - Open Source Leader in AI and ML

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The healthcare industry is evolving rapidly with large volumes of data and increasing challenges in cost and patient outcomes. Early adopters of AI in the healthcare space are reaping the benefits in terms of patient care and adding to their bottom line results, and everyone is taking notice. These companies are using AI for a number of scenarios including managing claims, detecting fraud, improving clinical workflows, and predicting hospital acquired infections. H2O.ai, the open source and automation leader in AI, is empowering leading healthcare companies to deliver AI solutions that are changing the industry.


Healthcare leaders debunk 3 myths about machine learning: Though machine learning has the power to dramatically change healthcare, physicians should not fear being overrun by robots, according to the Harvard Business Review.

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Though machine learning has the power to dramatically change healthcare, physicians should not fear being overrun by robots, according to the Harvard Business Review. Although machine learning can prevent patients from getting sick and diagnose a patient, the software can't provide care and treatment to patients. Additionally, machine learning lacks the human element in healthcare. While having data can be powerful, not all data is sufficient and necessary. Healthcare organizations must collect the right data and fully understand it.


Risks and remedies for artificial intelligence in health care

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Artificial intelligence (AI) is rapidly entering health care and serving major roles, from automating drudgery and routine tasks in medical practice to managing patients and medical resources. As developers create AI systems to take on these tasks, several risks and challenges emerge, including the risk of injuries to patients from AI system errors, the risk to patient privacy of data acquisition and AI inference, and more. Potential solutions are complex but involve investment in infrastructure for high-quality, representative data; collaborative oversight by both the Food and Drug Administration and other health-care actors; and changes to medical education that will prepare providers for shifting roles in an evolving system. The flashiest use of medical AI is to do things that human providers--even excellent ones--cannot yet do. For instance, Google Health has developed a program that can predict the onset of acute kidney injury up to two days before the injury occurs; compare that to current medical practice, where the injury often isn't noticed until after it happens.2


AI adoption and investments growing among health industry leaders

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A new survey, by Optum (an information and technology-enabled health services company) shows that that the application of artificial intelligence in healthcare is changing. AI is not only being widely adopted and invested in but it is also expected to drive an increased demand for AI talent. This matches a similar trend reported by KPMG. The survey, titled "OptumIQ Annual Survey on AI in Health Care", was conducted of 500 health care executives. This revealed that there was nearly an 88 percent increase in healthcare organizations that have a strategy in place and have implemented artificial intelligence, with these organizations having put a strategy in place in either 2018 or 2019.