AI News, Partners Connected Health to Develop AI Tool to Predict Risk of ... artificial intelligence

Hitachi develops AI tech to predict hospital readmissions | ITWeb

Hitachi, in collaboration with Partners Connected Health (PCH), has developed artificial intelligence (AI) technology which can predict the risk of hospital readmissions for patients with heart failure within 30 days.

It notes the technology is an example of explainable AI, a new term currently defined as enabling machines to explain their decisions and actions to human users, and enabling them to understand, appropriately trust and effectively manage AI tools, while maintaining a high level of prediction accuracy.

As part of a study, the Partners Connected Health Innovation team says it simulated the readmission prediction programme among heart failure patients participating in the Partners Connected Cardiac Care Program (CCCP), a remote monitoring and education programme designed to improve the management of heart failure patients at risk for hospitalisation.

The analysis showed the prediction algorithm achieved a high accuracy of approximately AUC 0.71, and can significantly reduce the number of patient readmissions, says PCH.AUC, area under the curve, is a measure of prediction model performance with an ideal value range from 0 to 1.

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Top 12 Ways Artificial Intelligence Will Impact Healthcare

From chronic diseases and cancer to radiology and risk assessment, there are nearly endless opportunities to leverage technology to deploy more precise, efficient, and impactful interventions at exactly the right moment in a patient’s care.

As payment structures evolve, patients demand more from their providers, and the volume of available data continues to increase at a staggering rate, artificial intelligence is poised to be the engine that drives improvements across the care continuum.

Learning algorithms can become more precise and accurate as they interact with training data, allowing humans to gain unprecedented insights into diagnostics, care processes, treatment variability, and patient outcomes.

At the 2018 World Medical Innovation Forum (WMIF) on artificial intelligence presented by Partners Healthcare, a leading researchers and clinical faculty members showcased the twelve technologies and areas of the healthcare industry that are most likely to see a major impact from artificial intelligence within the next decade.

With the help of experts from across the Partners Healthcare system, including faculty from Harvard Medical School (HMS), moderators Keith Dreyer, DO, PhD, Chief Data Science Officer at Partners and Katherine Andriole, PhD, Director of Research Strategy and Operations at Massachusetts General Hospital (MGH), counted down the top 12 ways artificial intelligence will revolutionize the delivery and science of healthcare.

Using computers to communicate is not a new idea by any means, but creating direct interfaces between technology and the human mind without the need for keyboards, mice, and monitors is a cutting-edge area of research that has significant applications for some patients.

“By using a BCI and artificial intelligence, we can decode the neural activates associated with the intended movement of one’s hand, and we should be able to allow that person to communicate the same way as many people in this room have communicated at least five times over the course of the morning using a ubiquitous communication technology like a tablet computer or phone.”

Brain-computer interfaces could drastically improve quality of life for patients with ALS, strokes, or locked-in syndrome, as well as the 500,000 people worldwide who experience spinal cord injuries every year.

“If we want the imaging to give us information that we presently get from tissue samples, then we’re going to have to be able to achieve very close registration so that the ground truth for any given pixel is known.”

Succeeding in this quest may allow clinicians to develop a more accurate understanding of how tumors behave as a whole instead of basing treatment decisions on the properties of a small segment of the malignancy.

However, algorithm developers must be careful to account for the fact that disparate ethnic groups or residents of different regions may have unique physiologies and environmental factors that will influence the presentation of disease.

EHRs have played an instrumental role in the healthcare industry’s journey towards digitalization, but the switch has brought myriad problems associated with cognitive overload, endless documentation, and user burnout.

For the hospitals sitting on mountains of EHR data and not using them to the fullest potential, to industry that’s not creating smarter, faster clinical trial design, and for EHRs that are creating these data not to use them…that would be a failure.”

“We’re now getting to the point where we can do a better job of assessing whether a cancer is going to progress rapidly or slowly and how that might change how patients will be treated based on an algorithm rather than clinical staging or the histopathologic grade,”

Using artificial intelligence to enhance the ability to identify deterioration, suggest thatsepsisis taking hold, or sense the development of complications can significantly improve outcomes and may reduce costs related to hospital-acquired condition penalties.

“When we’re talking about integrating disparate data from across the healthcare system, integrating it, and generating an alert that would alert an ICU doctor to intervene early on –

Machine learning algorithms and their ability to synthesize highly complex datasets may be able to illuminate new options for targeting therapies to an individual’s unique genetic makeup.

So whether we need to integrate data within one institution or across multiple institutions is going to be a key factor in terms of augmenting the patient population to drive the modeling process.”

Data quality and integrity issues, plus a mishmash of data formats, structured and unstructured inputs, and incomplete records have made it very difficult to understand exactly how to engage in meaningful risk stratification, predictive analytics, and clinical decision support.

EHR analytics have produced many successful risk scoring and stratification tools, especially when researchers employ deep learning techniques to identify novel connections between seemingly unrelated datasets.

However, patients tend to trust their physicians more than they might trust a big company like Facebook, he added, which may help to ease any discomfort with contributing data to large-scale research initiatives.

Continuing the theme of harnessing the power of portable devices, experts believe that images taken from smartphones and other consumer-grade sources will be an important supplement to clinical quality imaging –

Using smartphones to collect images of eyes, skin lesions, wounds, infections, medications, or other subjects may be able to help underserved areas cope with a shortage of specialists while reducing the time-to-diagnosis for certain complaints.

Artificial intelligence will provide much of the bedrock for that evolution by powering predictive analytics and clinical decision support tools that clue providers in to problems long before they might otherwise recognize the need to act.

“But if you have an AI algorithm and lots and lots of data from many patients, it’s easier to match up what you’re seeing to long term patterns and maybe detect subtle improvements that would impact your decisions around care.”

By powering a new generation of tools and systems that make clinicians more aware of nuances, more efficient when delivering care, and more likely to get ahead of developing problems, AI will usher in a new era of clinical quality and exciting breakthroughs in patient care.

BUSINESS INSIGHTS : The latest news, analysis, and trends about protection and care

About Allianz Partners Dedicated to bringing global protection and care, Allianz Partners is the B2B2C leader in assistance and insurance solutions in the following areas of expertise: assistance, international health &

This global family of over 19,000 employees is present in 78 countries, speaks 70 languages and handles 54 million cases per year, protecting customers and employees on all continents.

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