PODCAST: Healthcare Machine Learning Can Self Create / Improve Algorithms?

Machine Learning is a Subset of Artificial Intelligence (AI) Where Computer Software Can Create and Improve on Algorithms on Its Own.

Machine Learning for Population Health

PODCAST: 'Hacking of the American Mind' | The Leading ...

By Eric Bricker MD

Healthcare Machine Learning Company ClosedLoop.ai is One of the Best at Applying Machine Learning to Population Health Data.

ClosedLoop.ai is So Good, They Won the CMS AI Challenge … Beating Out 300 Other Organizations Including IBM, the Mayo Clinic and Deloitte.

The Promise of Machine Learning in Population Health is to Better Predict Which People Will Benefit From an Intervention Because They Are at Greater Risk of a Complication of a Disease or an ER Visit or a Hospitalization.

ClosedLoop.ai Beautifully Applied Their Machine Learning Abilities to Create a Pandemic Risk Model That Helped a New York City Health Insurance Plan Identify Which Members Would Be Most Likely to Have Severe Complications of COVID-19.

As a Result, the Insurance Company Helped These Individuals Have Groceries and Prescription Medication Delivered to Them So They Could Stay at Home and Avoid Exposure to COVID.

There You Have It!  A Practical, Real-World Example of Machine Learning in Population Health That Literally Saved Some People’s Lives.

Disclaimer: Dr. Bricker is the Chief Medical Officer of Virtual Care Company First Stop Health.

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MEDICAL: Artificial Intelligence in EHRs

ELECTRIC HEALTH RECORDS

By White Hat Anonymous

Epic Systems, the country’s leading e-health record company, says an algorithm it developed can accurately flag sepsis in patients 76% of the time. The life-threatening disease, which arises from infections, is a major concern for hospitals: One-third of patients who die in hospitals have sepsis, per the CDC. 

  • Generally, the earlier sepsis is diagnosed and treated, the better a patient’s chances of survival—and hundreds of hospitals use Epic Systems’s sepsis prediction model, The Verge reports. 

The problem: According to a study published this week in JAMA Internal Medicine, Epic Systems may have gotten the success rate wrong: The model is only correct 63% of the time—“substantially worse than the performance reported by its developer,” the researchers wrote. 

  • Part of the issue can be traced to the algorithm’s development, Stat News reports. It was trained to flag when doctors would submit bills for sepsis treatment—which doesn’t always line up with patients’ first signs of symptoms. 
  • “It’s essentially trying to predict what physicians are already doing,” Dr. Karandeep Singh, study author.

See the source image

When reached for comment, Epic Systems told us the researchers’ hypothetical scenario lacked “the required validation, analysis, and tuning that organizations need to do before deployment,” adding that the JAMA study’s findings differed from other research. 

CITE: https://healthcarefinancials.files.wordpress.com/2007/10/foreword-mata.pdf

ORDER: https://www.amazon.com/Dictionary-Health-Information-Technology-Security/dp/0826149952/ref=sr_1_5?ie=UTF8&s=books&qid=1254413315&sr=1-5

Bottom line: Algorithms can augment healthcare, but the life-or-death nature of their use requires serious due diligence.

ASSESSMENT: Your thoughts are appreciated

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PODCAST: Artificial Intelligence in Health Care Today

Transforming Health care ETHICALLY

By Stanford Online

[Drs. Serena Yeung and Matthew Lungreen]

Artificial intelligence has the potential to transform healthcare, driving innovations, efficiencies, and improvements in patient care.

WHITE-PAPER LINK: https://www.healthit.gov/sites/default/files/jsr-17-task-002_aiforhealthandhealthcare12122017.pdf

But, this powerful technology also comes with a unique set of ethical and safety challenges.

LINK: https://www.amazon.com/Dictionary-Health-Information-Technology-Security/dp/0826149952/ref=sr_1_5?ie=UTF8&s=books&qid=1254413315&sr=1-5

So, how can AI be integrated into healthcare in a way that maximizes its potential while also protecting patient safety and privacy? 

In this session faculty from the Stanford AI in Healthcare specialization discuss the challenges and opportunities involved in bringing AI into the clinic, safely and ethically, as well as its impact on the doctor-patient relationship.

They also outline a framework for analyzing the utility of machine learning models in healthcare and will describe how the US healthcare system impacts strategies for acquiring data to power machine learning algorithms.

ASSESSMENT: Your thoughts are appreciated.

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PODCAST: Healthcare Artificial Intelligence, Machine Learning and Natural Language Processing

PODCAST ON UnitedHeathcare Group Annual Report

Dr. Eric R. Bricker, Internist in Dallas, TX | US News Doctors

By Eric Bricker MD

An Annual Report from UnitedHealth Group Says United is Going Drive Growth by Using AI and Machine Learning to 1) Help High Risk Patients, 2) Assist Patients with Multiple Chronic Diseases, 3) Partner with Providers and 4) Be More Patient-Centric.

Some More Concrete Examples of How AI and Machine Learning Can Be Used in Healthcare and Health Insurance Are:

1) Better Underwriting of Risk

2) More Highly Focused Prior Authorization

3) Cherry-Pick the Individual Health Insurance Market

However, the Execution of AI’s and Machine Learning’s Finding Requires Human Behavior Modification–an Almost Impossible Task for Any Insurance Carrier to Accomplish Because of Their Low Credibility with Patients, Doctors and Nurses. Without Credibility and Trust, All the AI and Machine Learning in the World Will NOT Change People’s Behavior.

PODCAST LINK: https://www.youtube.com/watch?v=c9knoA30sD4

Disclaimer: Dr. Bricker is the founder of Texas Family Insurance – an independent insurance agent that sells Oscar Health.

ASSESSMENT: Your comments are appreciated.

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Could ARTIFICIAL INTELLIGENCE Help Make Health Care More Fair?

Helping us believe what patients say

A new study shows how training deep-learning models on patient outcomes could help reveal gaps in existing medical knowledge.

By Karen Hao

In the last few years, research has shown that deep learning can match expert-level performance in medical imaging tasks like early cancer detection and eye disease diagnosis. But there’s also cause for caution.

Other research has shown that deep learning has a tendency to perpetuate discrimination. With a health-care system already riddled with disparities, sloppy applications of deep learning could make that worse.

LINK: https://www.technologyreview.com/2021/01/22/1016577/ai-fairer-healthcare-patient-outcomes/?truid=349b552221c994e2540a304649746d7c&utm_source=weekend_reads&utm_medium=email&utm_campaign=weekend_reads.unpaid.engagement&utm_term=emtech-next-2021&utm_content=05.01.all&mc_cid=3ae91e4c2b&mc_eid=72aee829ad

LIABILITY: And, what about malpractice liability?

LINK: https://qz.com/1905712/when-ai-in-healthcare-goes-wrong-who-is-responsible-2/

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Risk Management, Liability Insurance, and Asset Protection Strategies for Doctors and Advisors: Best Practices from Leading Consultants and Certified Medical Planners™

ORDER TEXTBOOK: https://www.routledge.com/Risk-Management-Liability-Insurance-and-Asset-Protection-Strategies-for/Marcinko-Hetico/p/book/9781498725989

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Artificial Intelligence in Medicine!

AI in Medicine – Who is Responsible?

[By staff reporters]

https://qz.com/1905712/when-ai-in-healthcare-goes-wrong-who-is-responsible-2/

 

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Update on ARTIFICIAL INTELLIGENCE [A.I.]

Future Fate: YES -or- NO?

[By staff reporters]

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A.I. Adoption and Perceptions in Healthcare

By http://www.MCOL.com

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Assessment: Your thoughts are appreciated.

BUSINESS, FINANCE, INVESTING & INSURANCE TEXTS FOR DOCTORS:

1 – https://lnkd.in/ebWtzGg

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3 – https://lnkd.in/ewJPTJs

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It’s time to put humans at the center of A.I.

More on Artificial Intelligence
By MIT Technology Rerview

“To make more helpful and useful machines, we’ve got to bring back the contextual understanding,” says Fei-Fei Li, chief scientist of Google Cloud, in an interview with MIT Technology Review.

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Read why she wants to inject some humanity into AI

https://www.technologyreview.com/s/609060/put-humans-at-the-center-of-ai/?utm_source=MIT+Technology+Review&utm_campaign=f21f8e4086-The_Download&utm_medium=email&utm_term=0_997ed6f472-f21f8e4086-154253973

Conclusion

Your thoughts and comments on this ME-P are appreciated. Feel free to review our top-left column, and top-right sidebar materials, links, urls and related websites, too. Then, subscribe to the ME-P. It is fast, free and secure.

Speaker: If you need a moderator or speaker for an upcoming event, Dr. David E. Marcinko; MBA – Publisher-in-Chief of the Medical Executive-Post – is available for seminar or speaking engagements.

Contact: MarcinkoAdvisors@msn.com

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SoftBank’s big plan for a smarter internet is brilliant

Enter the Singularity of A.I.

[By Vitaliy Katsenelson CFA]

Masayoshi Son doesn’t do anything small nor does he do things in a simple way.

SoftBank’s big plan for a smarter internet is brilliant

Conclusion

Your thoughts and comments on this ME-P are appreciated. Feel free to review our top-left column, and top-right sidebar materials, links, urls and related websites, too. Then, subscribe to the ME-P. It is fast, free and secure.

Speaker: If you need a moderator or speaker for an upcoming event, Dr. David E. Marcinko; MBA – Publisher-in-Chief of the Medical Executive-Post – is available for seminar or speaking engagements. Contact: MarcinkoAdvisors@msn.com

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Risk Management, Liability Insurance, and Asset Protection Strategies for Doctors and Advisors: Best Practices from Leading Consultants and Certified Medical Planners™    8Comprehensive Financial Planning Strategies for Doctors and Advisors: Best Practices from Leading Consultants and Certified Medical Planners™

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Machines Playing Doctor?

The Machines Are Getting Ready to Play Doctor

Bert Mesko

[By Bertalan Meskó, MD PhD]

A team of researchers at Stanford University, led by Andrew Ng, a prominent AI researcher and an adjunct professor there, has shown that a machine-learning model can identify heart arrhythmias from an electrocardiogram (ECG) better than an expert.

Read more

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Conclusion

Your thoughts and comments on this ME-P are appreciated. Feel free to review our top-left column, and top-right sidebar materials, links, URLs and related websites, too. Then, subscribe to the ME-P. It is fast, free and secure.

Speaker: If you need a moderator or speaker for an upcoming event, Dr. David E. Marcinko; MBA – Publisher-in-Chief of the Medical Executive-Post – is available for seminar or speaking engagements. Contact: MarcinkoAdvisors@msn.com

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Chance of leading Pharma companies producing a successful app has decreased from 2.0% (2014) to 0.5% (2016)

Pharma Report Findings

By David Ireland Berlin

[Research2Guidance ]

Leading Pharma companies continue to struggle to gain significant reach (downloads) within their target groups. While companies have cumulatively over doubled their number of active apps available on Apple App and Google Play stores (2014 – Q1 2017), most have added to the growing tail of under performers.

Why is it that Pharma companies continue to struggle in mhealth?

Have they showed other signs of improvement within their app portfolios, or through 3rd party digital eco-systems?

Only 0.5% of all apps published by the leading 12 Pharma companies have managed to achieve annual downloads of over 100K; one of the major findings from the recently published, 2nd edition Pharma App Benchmarking 2017 report by Research2Guidance.

The report builds on figures identified from the previous edition, released in 2014. It also explores the leading 3rd party digital innovation strategies, their components, and their successfulness in terms of benefits (ROI, reach etc.). While companies have on average increased their app portfolio sizes from 65 to 153, average per app annual downloads remain low at just 3.3K.

Here are three of the main reasons why:

1. Companies are competing against a growing number of mhealth competitors. According to the 2016 mhealth Developer Economics Report, there were 290K mhealth apps listed on major app stores, an increase of +174K since 2014. Annual mhealth app download growth rates are also decreasing, adding to the growing pressure.

2. Pharma app portfolios have a narrower target audience than their mhealth competitors. App portfolios of Pharma companies differ from the average mhealth app portfolio in the greater degree to which they supply apps for HCP use cases. When comparing the app categorization differences between Pharma and mhealth, Pharma tends to target a greater share of patients over healthy individuals. This in turn decreases their potential target audience, making it harder to generate downloads.

3. Apps, on average, still fall behind their mhealth competitors in terms of product quality. Pharma app portfolios are not achieving the reach and retention experienced by the average mhealth provider. Inconsistencies of design elements and cross-referencing between apps are still far too common, and continue to let Pharma app quality down.

With the hype of, for example, Artificial Intelligence (AI), Augmented Reality (AR), Virtual Reality (VR), connected devices and sensors mounting throughout the digital health industry, consumers are simply demanding more usability from their mhealth products and services.

Life-Cycles

Traditional Pharma product production life-cycles are simply incompatible when dealing with their digital offerings, given the rate of technological change. However, there are a few instances whereby Pharma companies have achieved some success with their internal app publishing.

Johnson&Johnson for example have published three of the five most downloaded Pharma apps for 2016; J&J Official 7 Minute Workout and onetouch Reveal. All three achieved over 200K downloads for 2016. The success of their leading apps has improved their portfolios overall performance in comparison to 2014.

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All leading Pharma companies have grown their internal app portfolio size since 2014, and all but two (Abbott and Sanofi) have increased their reach.

However, thanks to the growing app portfolio size of Bayer and Novartis, and the high downloads of J&J, half of the leading Pharma companies have portfolios that fall below the average-lines. Previously, this was the case for five companies.

Taking J&J as an example, the success of a mere two or three apps can result in Pharma app market leadership.

Take Sanofi as another example. With the hype of Sanofi’s previous most downloaded app coming to an end (GoMeals), their internal app portfolio has experienced a significant decrease in annual downloads in comparison to 2014. Their portfolio has now fallen to 4th place in terms of reach behind J&J, Bayer and GSK. Novartis, Sanofi and Bayer have made the most significant strides in their app publishing activities, but are not seeing the ROI in the way of market penetration.

The newly released Pharma report goes into more company level detail on supply and demand from a platform, category, use-case and user-retention perspective. The report also analyses the 3rd party digital eco-system activities and strategies of these companies. Some appear to be relying more heavily on their 3rd party channels to source digital innovation than others, and their achievements to date give insight into which channels create the most potential for overall benefit to the Pharma company. These benefits can range from, for example, brand image improvement, digital innovation adoption, and new product distribution channels.

Assessment

Find out more about the app portfolios and digital strategies of leading Pharma companies by downloading your copy of the report today. Click here to find out more about what’s inside.                                                                                                          

Conclusion

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Q&A with Futurist Martine Rothblatt

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[By MIT Technology Review]

Whether machines and artificial intelligence will ever become conscious is an eternally debated topic, but Martine Rothblatt thinks it shouldn’t be.

We talked with Rothblatt about why she thinks denying that machines will become conscious is similar to denying evolution, and why society must prepare to grant rights to the virtual beings of the future.

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brain+function

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Q&A with Futurist Martine Rothblatt

Conclusion

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Speaker: If you need a moderator or speaker for an upcoming event, Dr. David E. Marcinko; MBA – Publisher-in-Chief of the Medical Executive-Post – is available for seminar or speaking engagements. Contact: MarcinkoAdvisors@msn.com

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