IIOT Healthcare

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August 2019
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Machine learning identifies preop risks linked to postop Medicare super-users

Editor's Note In this study of more than 1 million Medicare patients, 4.8% were super-users of healthcare and incurred 31.7% of Medicare expenditures after surgery. A machine learning approach identified the following as the most significant risk factors linked to super-utilization of healthcare in the year following surgery: hemiplegia/paraplegia weight…

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By: Judy Mathias
August 20, 2019
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AI, machine learning help unlock health data at Michigan Medicine

Editor's Note The University of Michigan’s health system has 34 artificial intelligence (AI) and machine learning research projects underway, 28 of which have principal investigators, the August 12 Health Data Management reports. Projects include analyzing electronic health records (EHRs), ECG monitor data, and analytics to predict acute hemodynamic instability and…

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By: Judy Mathias
August 14, 2019
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AI helps Aetna resolve claims overnight

Editor's Note Health insurer Aetna has created an artificial intelligence (AI) software to settle insurance claims−a development that could lead to automation of other processes and free up staff to focus on higher-level tasks, the July 25 CIO reports. Aetna estimates the software, which is able to resolve claims overnight…

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By: Judy Mathias
July 29, 2019
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Legal and ethical questions temper excitement about AI--Part 2

As part of a special series on artificial intelligence (AI), OR Manager is taking a deep dive into the many facets of this new technology and its impact on patient care. In this issue we continue our examination of the challenges related to AI, which began in last month’s issue…

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By: Cynthia Saver, MS, RN
July 24, 2019
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Consider all angles when choosing AI technology

This article concludes OR Manager’s special series on artificial intelligence (AI). Parts 1 and 2 (May 2019 and June 2019) introduced AI, defining the different types of technology and describing its many current and potential applications for surgery. The series also presented examples of AI (June and July 2019). We…

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By: Cynthia Saver, MS, RN
July 24, 2019
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Improving OR efficiency with machine learning prediction of case-time duration

Editor's Note In this study, researchers developed service- and surgeon-specific statistical models using linear regression and machine learning to predict case-time duration at a large academic medical center. Results showed: The machine-learning algorithm had the highest predictive capability. The surgeon-specific was superior to the service-specific model, with higher accuracies and…

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By: Judy Mathias
July 22, 2019
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Applications, limitations of AI for fracture detection, classification

Editor's Note Preliminary experience in the automated detection and classification of fractures using artificial intelligence (AI) shows promise, and AI may enhance processing and communicating probabilistic tasks in orthopedic surgery, this study finds. For fracture detection, researchers compared the human findings in 10 studies with AI findings. In two studies,…

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By: Judy Mathias
July 9, 2019
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What if healthcare AI is the next asbestos?

Editor's Note At a precision medicine conference in Boston on June 18, Harvard Law School professor Jonathan Zittrain likened the use of artificial intelligence (AI) in healthcare to asbestos, saying: “it’s all over the place, even though at no point did you explicitly install it, and it has possibly some…

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By: Judy Mathias
June 20, 2019
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Legal and ethical questions temper excitement about AI--Part 1

As part of a special series on artificial intelligence (AI), OR Manager is taking a deep dive into the many facets of this new technology and its impact on patient care. Part 1 and Part 2 of the introduction to AI (May 2019 and June 2019) defined types of AI…

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By: Cynthia Saver, MS, RN
June 18, 2019
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New technology tracks blood loss, reduces transfusions

Blood loss during labor and delivery (L&D) and surgical procedures can lead to serious complications that might be prevented with early detection; however, detection can be challenging. For example, clinicians have traditionally estimated blood loss visually—a subjective and often inaccurate process. Humans’ eyes simply aren’t good at making precise measurements,…

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By: Cynthia Saver, MS, RN
June 18, 2019
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