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The Health Care Benefits of Combining Wearables and AI

Reporter: Gail S. Thornton, M.A.

 

 

This article is excerpted from the Harvard Business Review, May 28, 2019

By Moni Miyashita, Michael Brady

In southeast England, patients discharged from a group of hospitals serving 500,000 people are being fitted with a Wi-Fi-enabled armband that remotely monitors vital signs such as respiratory rate, oxygen levels, pulse, blood pressure, and body temperature.

Under a National Health Service pilot program that now incorporates artificial intelligence to analyze all that patient data in real time, hospital readmission rates are down, and emergency room visits have been reduced. What’s more, the need for costly home visits has dropped by 22%. Longer term, adherence to treatment plans have increased to 96%, compared to the industry average of 50%.

The AI pilot is targeting what Harvard Business School Professor and Innosight co-founder Clay Christensen calls “non-consumption.”  These are opportunity areas where consumers have a job to be done that isn’t currently addressed by an affordable or convenient solution.

Before the U.K. pilot at the Dartford and Gravesham hospitals, for instance, home monitoring had involved dispatching hospital staffers to drive up to 90 minutes round-trip to check in with patients in their homes about once per week. But with algorithms now constantly searching for warning signs in the data and alerting both patients and professionals instantly, a new capability is born: providing healthcare before you knew you even need it.

The biggest promise of artificial intelligence — accurate predictions at near-zero marginal cost — has rightly generated substantial interest in applying AI to nearly every area of healthcare. But not every application of AI in healthcare is equally well-suited to benefit. Moreover, very few applications serve as an appropriate strategic response to the largest problems facing nearly every health system: decentralization and margin pressure.

Take for example, medical imaging AI tools — an area in which hospitals are projected to spend $2 billion annually within four years. Accurately diagnosing diseases from cancers to cataracts is a complex task, with difficult-to-quantify but typically major consequences. However, the task is currently typically part of larger workflows performed by extensively trained, highly specialized physicians who are among some of the world’s best minds. These doctors might need help at the margins, but this is a job already being done. Such factors make disease diagnosis an extraordinarily difficult area for AI to create transformative change. And so the application of AI in such settings  —  even if beneficial  to patient outcomes —  is unlikely to fundamentally improve the way healthcare is delivered or to substantially lower costs in the near-term.

However, leading organizations seeking to decentralize care can deploy AI to do things that have never been done before. For example: There’s a wide array of non-acute health decisions that consumers make daily. These decisions do not warrant the attention of a skilled clinician but ultimately play a large role in determining patient’s health — and ultimately the cost of healthcare.

According to the World Health Organization, 60% of related factors to individual health and quality of life are correlated to lifestyle choices, including taking prescriptions such as blood-pressure medications correctly, getting exercise, and reducing stress. Aided by AI-driven models, it is now possible to provide patients with interventions and reminders throughout this day-to-day process based on changes to the patient’s vital signs.

Home health monitoring itself isn’t new. Active programs and pilot studies are underway through leading institutions ranging from Partners Healthcare, United Healthcare, and the Johns Hopkins School of Medicine, with positive results. But those efforts have yet to harness AI to make better judgements and recommendations in real time. Because of the massive volumes of data involved, machine learning algorithms are particularly well suited to scaling that task for large populations. After all, large sets of data are what power AI by making those algorithms smarter.

By deploying AI, for instance, the NHS program is not only able to scale up in the U.K. but also internationally. Current Health, the venture-capital backed maker of the patient monitoring devices used in the program, recently received FDA clearance to pilot the system in the U.S. and is now testing it with New York’s Mount Sinai Hospital. It’s part of an effort to reduce patient readmissions, which costs U.S. hospitals about $40 billion annually.

The early success of such efforts drives home three lessons in using AI to address non-consumption in the new world of patient-centric healthcare:

1) Focus on impacting critical metrics – for example, reducing costly hospital readmission rates.

Start small to home in on the goal of making an impact on a key metric tied to both patient outcomes and financial sustainability. As in the U.K. pilot, this can be done through a program with select hospitals or provider locations. In another case Grady Hospital, the largest public hospital in Atlanta, points to $4M in saving from reduced readmission rates by 31% over two years thanks to the adoption of an AI tool which identifies ‘at-risk’ patients. The system alerts clinical teams to initiate special patient touch points and interventions.

2) Reduce risk by relying on new kinds of partners.

Don’t try to do everything alone. Instead, form alliances with partners that are aiming to tackle similar problems. Consider the Synaptic Healthcare Alliance, a collaborative pilot program between Aetna, Ascension, Humana, Optum, and others. The alliance is using Blockchain to create a giant dataset across various health care providers, with AI trials on the data getting underway. The aim is to streamline health care provider data management with the goal of reducing the cost of processing claims while also improving access to care. Going it alone can be risky due to data incompatibility issues alone. For instance, the M.D. Anderson Cancer Center had to write off millions in costs for a failed AI project due in part to incompatibility with its electronic health records system. By joining forces, Synaptic’s dataset will be in a standard format that makes records and results transportable.

3) Use AI to collaborate, not compete, with highly-trained professionals.

Clinicians are often looking to augment their knowledge and reasoning, and AI can help. Many medical AI applications do actually compete with doctors. In radiology, for instance, some algorithms have performed image-bases diagnosis as well as or better than human experts. Yet it’s unclear if patients and medical institutions will trust AI to automate that job entirely. A University of California at San Diego pilot in which AI successfully diagnosed childhood diseases more accurately than junior-level pediatricians still required senior doctors to personally review and sign off on the diagnosis. The real aim is always going to be to use AI to collaborate with clinicians seeking higher precision — not try to replace them.

MIT and MGH have developed a deep learning model which identifies patients likely to develop breast cancer in the future. Learning from data on 60,000 prior patients, the AI system allows physicians to personalize their approach to breast cancer screening, essentially creating a detailed risk profile for each patient.

Taken together, these three lessons paired with solutions targeted at non-consumption have the potential to provide a clear path to effectively harnessing a technology that has been subject to rampant over-promising. Longer term, we believe the one of the transformative benefits of AI will be deepening relationships between health providers and patients. The U.K. pilot, for instance, is resulting in more frequent proactive check-ins that never would have happened before. That’s good for both improving health as well as customer loyalty in the emerging consumer-centric healthcare marketplace.

Source:

https://hbr.org/2019/05/the-health-care-benefits-of-combining-wearables-and-ai

 

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These twelve artificial intelligence innovations are expected to start impacting clinical care by the end of the decade.

Reporter: Gail S. Thornton, M.A.

 

This article is excerpted from Health IT Analytics, April 11, 2019.

 By Jennifer Bresnick

April 11, 2019 – There’s no question that artificial intelligence is moving quickly in the healthcare industry.  Even just a few months ago, AI was still a dream for the next generation: something that would start to enter regular care delivery in a couple of decades – maybe ten or fifteen years for the most advanced health systems.

Even Partners HealthCare, the Boston-based giant on the very cutting edge of research and reform, set a ten-year timeframe for artificial intelligence during its 2018 World Medical Innovation Forum, identifying a dozen AI technologies that had the potential to revolutionize patient care within the decade.

But over the past twelve months, research has progressed so rapidly that Partners has blown up that timeline. 

Instead of viewing AI as something still lingering on the distant horizon, this year’s Disruptive Dozen panel was tasked with assessing which AI innovations will be ready to fundamentally alter the delivery of care by 2020 – now less than a year away.

Sixty members of the Partners faculty participated in nominating and narrowing down the tools they think will have an almost immediate benefit for patients and providers, explained Erica Shenoy, MD, PhD, an infectious disease specialist at Massachusetts General Hospital (MGH).

“These are innovations that have a strong potential to make significant advancement in the field, and they are also technologies that are pretty close to making it to market,” she said.

The results include everything from mental healthcare and clinical decision support to coding and communication, offering patients and their providers a more efficient, effective, and cost-conscious ecosystem for improving long-term outcomes.

In order from least to greatest potential impact, here are the twelve artificial intelligence innovations poised to become integral components of the next decade’s data-driven care delivery system.

NARROWING THE GAPS IN MENTAL HEALTHCARE

Nearly twenty percent of US patients struggle with a mental health disorder, yet treatment is often difficult to access and expensive to use regularly.  Reducing barriers to access for mental and behavioral healthcare, especially during the opioid abuse crisis, requires a new approach to connecting patients with services.

AI-driven applications and therapy programs will be a significant part of the answer.

“The promise and potential for digital behavioral solutions and apps is enormous to address the gaps in mental healthcare in the US and across the world,” said David Ahern, PhD, a clinical psychologist at Brigham & Women’s Hospital (BWH). 

Smartphone-based cognitive behavioral therapy and integrated group therapy are showing promise for treating conditions such as depression, eating disorders, and substance abuse.

While patients and providers need to be wary of commercially available applications that have not been rigorously validated and tested, more and more researchers are developing AI-based tools that have the backing of randomized clinical trials and are showing good results.

A panel of experts from Partners HealthCare presents the Disruptive Dozen at WMIF19.
A panel of experts from Partners HealthCare presents the Disruptive Dozen at WMIF19.

Source: Partners HealthCare

STREAMLINING WORKFLOWS WITH VOICE-FIRST TECHNOLOGY

Natural language processing is already a routine part of many behind-the-scenes clinical workflows, but voice-first tools are expected to make their way into the patient-provider encounter in a new way. 

Smart speakers in the clinic are prepping to relieve clinicians of their EHR burdens, capturing free-form conversations and translating the content into structured documentation.  Physicians and nurses will be able to collect and retrieve information more quickly while spending more time looking patients in the eye.

Patients may benefit from similar technologies at home as the consumer market for virtual assistants continues to grow.  With companies like Amazon achieving HIPAA compliance for their consumer-facing products, individuals may soon have more robust options for voice-first chronic disease management and patient engagement.

IDENTIFYING INDIVIDUALS AT HIGH RISK OF DOMESTIC VIOLENCE

Underreporting makes it difficult to know just how many people suffer from intimate partner violence (IPV), says Bharti Khurana, MD, an emergency radiologist at BWH.  But the symptoms are often hiding in plain sight for radiologists.

Using artificial intelligence to flag worrisome injury patterns or mismatches between patient-reported histories and the types of fractures present on x-rays can alert providers to when an exploratory conversation is called for.

“As a radiologist, I’m very excited because this will enable me to provide even more value to the patient instead of simply evaluating their injuries.  It’s a powerful tool for clinicians and social workers that will allow them to approach patients with confidence and with less worry about offending the patient or the spouse,” said Khurana.

REVOLUTIONIZING ACUTE STROKE CARE

Every second counts when a patient experiences a stroke.  In far-flung regions of the United States and in the developing world, access to skilled stroke care can take hours, drastically increasing the likelihood of significant long-term disability or death.

Artificial intelligence has the potential to close the gaps in access to high-quality imaging studies that can identify the type of stroke and the location of the clot or bleed.  Research teams are currently working on AI-driven tools that can automate the detection of stroke and support decision-making around the appropriate treatment for the individual’s needs.  

In rural or low-resource care settings, these algorithms can compensate for the lack of a specialist on-site and ensure that every stroke patient has the best possible chance of treatment and recovery.

AI revolutionizing stroke care

Source: Getty Images

REDUCING ADMINISTRATIVE BURDENS FOR PROVIDERS

The costs of healthcare administration are off the charts.  Recent data from the Center for American progress states that providers spend about $282 billion per year on insurance and medical billing, and the burdens are only going to keep getting bigger.

Medical coding and billing is a perfect use case for natural language processing and machine learning.  NLP is well-suited to translating free-text notes into standardized codes, which can move the task off the plates of physicians and reduce the time and effort spent on complying with convoluted regulations.

“The ultimate goal is to help reduce the complexity of the coding and billing process through automation, thereby reducing the number of mistakes – and, in turn, minimizing the need for such intense regulatory oversight,” Partners says.

NLP is already in relatively wide use for this task, and healthcare organizations are expected to continue adopting this strategy as a way to control costs and speed up their billing cycles.

UNLEASHING HEALTH DATA THROUGH INFORMATION EXCHANGE

AI will combine with another game-changing technology, known as FHIR, to unlock siloes of health data and support broader access to health information.

Patients, providers, and researchers will all benefit from a more fluid health information exchange environment, especially since artificial intelligence models are extremely data-hungry.

Stakeholders will need to pay close attention to maintaining the privacy and security of data as it moves across disparate systems, but the benefits have the potential to outweigh the risks.

“It completely depends on how everyone in the medical community advocates for, builds, and demands open interfaces and open business models,” said Samuel Aronson, Executive Director of IT at Partners Personalized Medicine.

“If we all row in the same direction, there’s a real possibility that we will see fundamental improvements to the healthcare system in 3 to 5 years.”

OFFERING NEW APPROACHES FOR EYE HEALTH AND DISEASE

Image-heavy disciplines have started to see early benefits from artificial intelligence since computers are particularly adept at analyzing patterns in pixels.  Ophthalmology is one area that could see major changes as AI algorithms become more accurate and more robust.

From glaucoma to diabetic retinopathy, millions of patients experience diseases that can lead to irreversible vision loss every year.  Employing AI for clinical decision support can extend access to eye health services in low-resource areas while giving human providers more accurate tools for catching diseases sooner.

REAL-TIME MONITORING OF BRAIN HEALTH

The brain is still the body’s most mysterious organ, but scientists and clinicians are making swift progress unlocking the secrets of cognitive function and neurological disease.  Artificial intelligence is accelerating discovery by helping providers interpret the incredibly complex data that the brain produces.

From predicting seizures by reading EEG tests to identifying the beginnings of dementia earlier than any human, artificial intelligence is allowing providers to access more detailed, continuous measurements – and helping patients improve their quality of life.

Seizures can happen in patients with other serious illnesses, such as kidney or liver failure, explained, Bandon Westover, MD, PhD, executive director of the Clinical Data Animation Center at MGH, but many providers simply don’t know about it.

“Right now, we mostly ignore the brain unless there’s a special need for suspicion,” he said.  “In a year’s time, we’ll be catching a lot more seizures and we’ll be doing it with algorithms that can monitor patients continuously and identify more ambiguous patterns of dysfunction that can damage the brain in a similar manner to seizures.”

AUTOMATING MALARIA DETECTION IN DEVELOPING REGIONS

Malaria is a daily threat for approximately half the world’s population.  Nearly half a million people died from the mosquito-borne disease in 2017, according to the World Health Organization, and the majority of the victims are children under the age of five.

Deep learning tools can automate the process of quantifying malaria parasites in blood samples, a challenging task for providers working without pathologist partners.  One such tool achieved 90 percent accuracy and specificity, putting it on par with pathology experts.

This type of software can be run on a smartphone hooked up to a camera on a microscope, dramatically expanding access to expert-level diagnosis and monitoring.

AI for diagnosing and detecting malaria

Source: Getty Images

AUGMENTING DIAGNOSTICS AND DECISION-MAKING

Artificial intelligence has made especially swift progress in diagnostic specialties, including pathology. AI will continue to speed down the road to maturity in this area, predicts Annette Kim, MD, PhD, associate professor of pathology at BWH and Harvard Medical School.

“Pathology is at the center of diagnosis, and diagnosis underpins a huge percentage of all patient care.  We’re integrating a huge amount of data that funnels through us to come to a diagnosis.  As the number of data points increases, it negatively impacts the time we have to synthesize the information,” she said.

AI can help automate routine, high-volume tasks, prioritize and triage cases to ensure patients are getting speedy access to the right care, and make sure that pathologists don’t miss key information hidden in the enormous volumes of clinical and test data they must comb through every day.

“This is where AI can have a huge impact on practice by allowing us to use our limited time in the most meaningful manner,” Kim stressed.

PREDICTING THE RISK OF SUICIDE AND SELF-HARM

Suicide is the tenth leading cause of death in the United States, claiming 45,000 lives in 2016.  Suicide rates are on the rise due to a number of complex socioeconomic and mental health factors, and identifying patients at the highest risk of self-harm is a difficult and imprecise science.

Natural language processing and other AI methodologies may help providers identify high-risk patients earlier and more reliably.  AI can comb through social media posts, electronic health record notes, and other free-text documents to flag words or concepts associated with the risk of harm.

Researchers also hope to develop AI-driven apps to provide support and therapy to individuals likely to harm themselves, especially teenagers who commit suicide at higher rates than other age groups.

Connecting patients with mental health resources before they reach a time of crisis could save thousands of lives every year.

REIMAGINING THE WORLD OF MEDICAL IMAGING

Radiology is already one of AI’s early beneficiaries, but providers are just at the beginning of what they will be able to accomplish in the next few years as machine learning explodes into the imaging realm.

AI is predicted to bring earlier detection, more accurate assessment of complex images, and less expensive testing for patients across a huge number of clinical areas.

But as leaders in the AI revolution, radiologists also have a significant responsibility to develop and deploy best practices in terms of trustworthiness, workflow, and data protection.

“We certainly feel the onus on the radiology community to make sure we do deliver and translate this into improved care,” said Alexandra Golby, MD, a neurosurgeon and radiologist at BWH and Harvard Medical School.

“Can radiology live up to the expectations?  There are certainly some challenges, including trust and understanding of what the algorithms are delivering.  But we desperately need it, and we want to equalize care across the world.”

Radiologists have been among the first to overcome their trepidation about the role of AI in a changing clinical world, and are eagerly embracing the possibilities of this transformative approach to augmenting human skills.”

“All of the imaging societies have opened their doors to the AI adventure,” Golby said.  “The community very anxious to learn, codevelop, and work with all of the industry partners to turn this technology into truly valuable tools. We’re very optimistic and very excited, and we look forward to learning more about how AI can improve care.”

Source:

https://healthitanalytics.com/news/top-12-artificial-intelligence-innovations-disrupting-healthcare-by-2020

 


In-House Development of an intellectual property value calculator (IP-V-Cal) for Valuation of INTANGIBLE products: Intellectual property (IP) assets of Digital Printed Products (DPP) – Online Journal(s), e-Books and a Corpus of Real Time generated eProceedings of the Top Biotech Global Conferences

Author: Aviva Lev-Ari, PhD, RN

1. All the points I made, below in my e-mail 4AM on 6/28/2019, see below – to match Intangible Assets in Document #2, 4/19/2019 in Inbox of each FIT member.

(after reading Rick’s article we received a link to and after reading the link that I provided in this e-mail)

2. We Need Amnon to enter into an Excel spread sheet as Column #1 all the Contributing Factors to valuation in that 4AM e-mail

3. Columns #2,#3,#4 will be Gail, Amnon, Rick (Business Team)

4. Columns #5,#6 will be Dr. Williams, Prof. Feldman (our Board)

5. Columns #7,#8 #9,#10 will be Dr. Pearlman, Dr. Dror Nir, Dr. Saha, Dr. Irina (Scientists Team)

7. Each Column dedicated to each of out 10 alive and well Active FIT

Will be split into two columns

6. Column #11 will be Aviva’s 

8. First column of each of the 10 FIT members will be filled by each by a number between 50 to 100, representing the subjectively perceived contributing weight of the Factor mentioned in Column 1: list of factors contributing to Venture’s valuation

9. Second column of each of the 10 FIT members will list the member’s subjective perspective on Ranking the Factors in Column #1

10. To Column #1: each Business Team member (mentioned in 3, above), needs to contribute FIVE new factors taken from your discussion on Valuation add them with your initials to Column #1

11. Aviva will bring DATA from Article Scoring System already populating a database designed to quantify the 

11.1 Valuation of the Journal

11.2 Valuation of the BioMed e-Series: each book, each series, all 16 volumes

11.3 Author’s factor in pricing 11.1 and 11.2

 

12. Valuation of 70 eProceedings 

(60 by Aviva; 10 by Dr. Williams)

needs to be tied to a growth factor in LPBI Group’s INFLUENCE on Twitter

12.1 @pharma_BI

12.2 @AVIVA1950

12.3 Gail’s Twitter account

12.4 Dr. Williams’ Twitter account

12.5 Dr. Asha’s Twitter account

12.6 Dr. Irina’s Twitter account

Factors of INFLUENCE:

– Growth in #Followers on Twitter 

– Ratio #Tweets/#Likes

– Cumulative # of Followers’ Followers

13. LinkedIn

I discovered new features and I wish to conduct a Skype training session with narrow messaging to FIT members

Please contribute your thoughts, while 

 

– Amnon is building this Excel

– Irina will be designing DropBox for this Excel that TEN FIT members need to add a number 50 to 100, Ranking the relative importance of each factor for Venture Valuation

GREAT initiative by Business Team to focus on valuation, thank you.

Thank you all FIT members, get ready yo add your subjective numbers into the Valuation Excel.

Amnon, please share with me in a Skype session the draft of this Excel, before we deplore this instrument, place it in Dropbox and announce the window of one week when we collect 2×10 data points on each of the Factors in Column #1, 

Board members and Scientists: you are welcome to contribute Factors in Valuation of the Venture in Column #1, the longer this column, the greater the granularity. 

We can then focus only on factors that scored above the Mean or any cut off point we will agree upon.

Thank you all – it is exciting to get the entire Team, developing a custom tailor methodology for DIGITAL printed products, NO OFF THE SHELF MODEL WILL FIT US. 

Current valuation models that do NOT APPLY to our venture include the following:

“Book to market value” 

– we got Intangibles, Column #1

– LPBI Group’s Tangibles are royalties for books sold. 

(all data was reviewed by Dr. Williams, for Section #13 in Document #2, for the period, 4/2012 to 4/19/2019)

“VC investment dilution models” 

– we kept 100% of ownership 

– our shareholders are 12 FIT members: 

— >>>>>> 10 are active members 

– (Scientist Team: Formula in place) (Gail included)

– (Business Team: 10% of UPSIDE) (Gail included)

— >>>>>> Past commitments outstanding:

– to Dr. Larry and 

– to Adam Sonnenberg

– (Formula in place). 

– No UPSIDE, due to idle status since 2/6/2019, Exit period launch.

Thank you again.

Aviva Lev-Ari, PhD, RN

Editor-in-Chief, BioMed e-Series

http://PharmaceuticalIntelligence.com

Director & Founder

Leaders in Pharmaceutical Business Intelligence (LPBI) Group

On Jun 28, 2019, at 3:57 AM, Amnon Danzig <amnon.danzig@gmail.com> wrote:

This is a nice article that put in place Corporate Finance practices.

However, The Business Team currently is struggling in much more earlier stages of the valuation of the Group.

We have a long way to go before we are entering the valuation scene in numeric terms.

Aviva,

I must confess that in the last two weeks we (Rick, Gail and me) invested huge amount of work to disclose the real valuation of the Group.

It is a work-in-process.

Thank you for your patience.

Amnon

Amnon Danzig

Business Strategy 

https://lnkd.in/e-zTVz4

Leaders in Pharmaceutical Business Intelligence (LPBI) Group, Israel

http://pharmaceuticalintelligence.com 

e-Mailamnon.danzig@gmail.com

(M) +972-54-6998405

 www.amnondanzig.com

SkypeID: Amnon.Danzig  LinkedIn Profile Twitter Profile

On Fri, Jun 28, 2019 at 10:30 AM Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu> wrote:

What is Aviva’s take on LPBI Group Valuation?

https://bothsidesofthetable.com/do-you-really-even-need-vc-72013e985fab

  • Advantages of LPBI Group:
  1. We had low barrier to entry and 
  2. We had/have Zero labor cost
  3. We are virtual, therefore, no overhead expenses 
  4. Run rate at WordPress.com Business Premium Annual Fee $200 and LinkedIn Annual Business Premium $1000
  5. Our 1st CARDINAL factor of production [The Team] is the DEAPTH and very diverse EXPERTISE residing in the Scientist Team and in the Business TEAM
  6. Leadership expressed by new timely challenge selection – Directions into new domains
  7. Team ability to swarm around new domains (new timely challenge selection), Examples: 
  • 2015-2016 – 3D BioPrinting – Book: Series E, Volume 4
  • Volume 4: Medical 3D BioPrinting – The Revolution in Medicine, Technologies for Patient-centered Medicine: From R&D in Biologics to New Medical Devices. On Amazon.com since since 12/30/2017

https://www.amazon.com/dp/B078QVDV2W

  • 2016-2017 – Drug Discovery – JV with SBH Sciences, Dr. Raphael Nir

https://pharmaceuticalintelligence.com/drugdiscovery-lpbi-group/

  • 2019-2020 – AI + ML in Medicine – Book: Series B, Volume 2

https://pharmaceuticalintelligence.com/biomed-e-books/genomics-orientations-for-personalized-medicine/volume-two-genomics-methodologies-ngs-bioinformatics-simulations-and-the-genome-ontology/

  1. Our 2nd CARDINAL factor of Leadership is the FIDELITY of a CORE team of Scientists
  2. Aviva’s ability to multitask on several levels – FIVE persons in just One woman (nee: 1950): 
  • LPBI Group’s DAILY activity on Twitter, LinkedIn, Facebook
  • IN PERSON at +60 Conferences – yielded a Corpus of eProceedings N=70 [10 by Dr. Williams 
  • Curator of new content – Journal is LIVE at 

1,639,029 eReaders 

Content

5,642

Posts

686

Categories

10,083

Tags

  • Book Editor – multiple domains of knowledge: Series A, B, D, E
  • Full functions of Editor-in-Chief: 16 Titles, content acquisition, eTOCs designer
  • Relations builder with multiple Ecosystems: Israel, US, Europe – stay tune

4/19/2019 

@pharma_Bi # Followers = 505 

6/28/2019

@pharma_Bi # Followers = 519 RatioTweets to Likes: 25,000/3,086

4/19/2019 

@AVIVA1950 # Followers = 359

6/28/2019

@AVIVA1950 # Followers = 439 RatioTweets to Likes: 11,000/5,615

How can you use all of the above for your Valuation Modeling???


Narrative Building for the Future of LPBI Group: List of Talking Points

 

Exchange between Gail and Aviva

 

On Tuesday, June 25, 2019, 11:43:27 AM EDT, Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu> wrote:

https://www.terarecon.com/blog/beyond-the-screen-episode-6-next-generation-ai-companies-providing-physicians-a-starting-point-in-ai?utm_campaign=AuntMinnie%20June%202019

HOW can we get  Kevin Landwher of terarecon.com to create a Podcast for LPBI Group IP Assets, including a section on our forthcoming Genomics, Volume 2 

https://pharmaceuticalintelligence.com/biomed-e-books/genomics-orientations-for-personalized-medicine/volume-two-genomics-methodologies-ngs-bioinformatics-simulations-and-the-genome-ontology/

In response to this question we are in discussion on POINTS #1,2,3,4

 

From: Gail Thornton <gailsthornton@yahoo.com>

Reply-To: Gail Thornton <gailsthornton@yahoo.com>

Date: Sunday, June 30, 2019 at 8:38 AM

To: Aviva Lev-Ari <aviva.lev-ari@comcast.net>

Cc: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>, Rick Mandahl <rmandahl@gmail.com>, Amnon Danzig <amnon.danzig@gmail.com>

Subject: Please AUDIT PODCAST —>>>>>>>> Beyond the Screen Episode 6: Next Generation AI Companies Providing Physicians a Starting Point in AI

Aviva:

These videos from terarecon.com typically focus on one topic (not many as you’ve described below). 

If there are too many topics proposed to this company, they will not be interested.

My recommendation is for you to finalize Genomics, volume 2, and let’s see the story we have about that specific topic.

Gali 

 

On Tuesday, June 25, 2019, 11:43:27 AM EDT, Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu> wrote:

https://www.terarecon.com/blog/beyond-the-screen-episode-6-next-generation-ai-companies-providing-physicians-a-starting-point-in-ai?utm_campaign=AuntMinnie%20June%202019

HOW can we get  Kevin Landwher of terarecon.com to create a Podcast for LPBI Group IP Assets, including a section on our forthcoming Genomics, Volume 2 

https://pharmaceuticalintelligence.com/biomed-e-books/genomics-orientations-for-personalized-medicine/volume-two-genomics-methodologies-ngs-bioinformatics-simulations-and-the-genome-ontology/

 

On Saturday, June 29, 2019, 03:56:08 PM EDT, Aviva Lev-Ari <aviva.lev-ari@comcast.net> wrote:

 

POINT #1 for VIDEO coverage – Focus on Genomics, Volume 2

After 7/15, Prof. Feldman will be back in the US, stating to work on Part 5 in Genomics, Volume 2. We will Skype to discuss what to include in 5.1, 5.2, 5.3, 5.4

On 7/15, I am submitting my work on creation of Parts 1,2,3,4,6

Dr. Williams and Dr. Saha are working already on Part 7&8.

Below you have abbreviated eTOCs.

Go to URL of the Book to see what I placed already inside this book.

Dr. Williams and Prof. Feldman will compose 

Preface

Introduction to Volume 2

Volume Summary

Epilogue

Based on these four parts and the eTOCs you will have ample content for the video, which may start with the epitome of our book creation: Genomics Volume 2 (you interview the three Editors why it is Epitome)

POINT #2 or #3 or #4  for VIDEOs to Focus on coverage for Marketing LPBI Group

by DESCRIPTION of what was accomplished

 

  • Venture history/background
  • Venture milestones: all posts in the Journal with the Title
  • “We celebrate …..
  • 5-6 Titles like that, I may add two more
  • Site Statistics
  • Book articles cumulative views (Article Scoring System: Data Extract)
  • section on BioMed e-Series
  • section on List of Conference covered in Real Time
  • FIT Team input to Venture Valuation: top 5 or top 10 Factors in consensus 
  • the 3D graphs on Opportunity Maps: Gail, Rick, Amnon, Aviva – each explains their own outcome
  • section on Pipeline

Video on What is the Ideal Solution for the FUTURE of LPBI Group

  • Interviews with All FIT Members

For POINT #1:

To build the narrative for a VIDEO dedication to Genomics, Volume Two and Marketing campaign as a NEW BOOK on NGS, the Narrative will use content extracts to built a CASE for

Why GENOMICS Volume 2 – is the Epitome of all BioMed e-Series???????

 

forthcoming Genomics, Volume 2 

https://pharmaceuticalintelligence.com/biomed-e-books/genomics-orientations-for-personalized-medicine/volume-two-genomics-methodologies-ngs-bioinformatics-simulations-and-the-genome-ontology/

 

Aviva completed Parts 1,2,3,4,6, 

[5 is by Prof. Feldman] 

[7,8 are by Scientists on FIT]:

Latest in Genomics Methodologies for Therapeutics:

Gene Editing, NGS & BioInformatics,

Simulations and the Genome Ontology

 

2019

Volume Two

Prof. Marcus W. Feldman, PhD, Editor

Prof. Stephen J. Williams, PhD, Editor

And

Aviva Lev-Ari, PhD, RN, Editor 

https://pharmaceuticalintelligence.com/biomed-e-books/genomics-orientations-for-personalized-medicine/volume-two-genomics-methodologies-ngs-bioinformatics-simulations-and-the-genome-ontology/

Abbreviated eTOCs

Part 1: NGS

1.1 The Science

1.2 Technologies and Methodologies

1.3 Clinical Aspects

1.4 Business and Legal

 

Part 2: CRISPR for Gene Editing and DNA Repair

2.1 The Science

2.2 Technologies and Methodologies

2.3 Clinical Aspects

2.4 Business and Legal

 

Part 3: AI in Medicine

3.1 The Science

3.2 Technologies and Methodologies

3.3 Clinical Aspects

3.4 Business and Legal

3.5 Latest in Machine Learning (ML) Algorithms harnessed for Medical Diagnosis: Pattern Recognition & Prediction of Disease Onset

 

Part 4: Single Cell Genomics

4.1 The Science

4.2 Technologies and Methodologies

4.3 Clinical Aspects

4.4 Business and Legal

 

Part 5: Evolution Biology Genomics Modeling @Feldman Lab, Stanford University – Written and Curated by Prof. Marc Feldman

5.1

5.2

5.3

5.4

 

Part 6: Simulation Modeling in Genomics

6.1   Mutation Analysis – Gene Encoding

6.2   Mitochondrial Variations

6.3   Variant Analysis

6.4   Variant Detection in Hereditary Cancer Genes

6.5   Immuno-Informatics

6.6   RNA Sequencing

6.7   Complex Insertions and Deletions

6.8   Evolutionary Biology

6.9   Simulation Programs

6.10  A comparison of tools for the simulation of genomic next-generation sequencing data

 

Part 7: Applications of Genomics: Genotypes, Phenotypes and Complex Diseases

7.1 Genome-wide associations with complex diseases (GWAS)

7.2 Non-coding DNA and phenotypes—including diseases like cancer

7.3 Epigenomic associations with phenotypes including cancer

7.4 Rare variants and diseases

7.5 Population-level genomics and the meaning of group differences

7.6 Targeting drugs for complex diseases

 

Part 8: Epigenomics and Genomic Regulation

8.1  Genomic controls on epigenomics

8.2  The ENCODE project and gene regulation

8.3  Small interfering RNAs and gene expression

8.4  Epigenomics in cancer

8.5  Environmental epigenomics


AI System Used to Detect Lung Cancer

Reporter: Irina Robu, PhD

Lung cancer is characterized by uncontrolled cell growth in tissues of the lung. The growth spreads beyond the lung by metastasis into nearby tissues. The most common symptoms are coughing (including coughing up blood), weight loss, shortness of breath, and chest pains. The two main types of lung cancer are small-cell lung carcinoma(SCLC) and non-small-cell lung carcinoma (NSCLC). Lung cancer may be seen on chest radiographs and computed tomography(CT) scans. However, computers seem to be as good or better than regular doctors at detecting tiny lung cancers on CT scans according to scientists from Google.

The AI designed by Google was able to interpret images using the same skills as humans to read microscope slides, X-rays, M.R.I.s and other medical scans by feeding huge amounts of data from medical imaging into the systems. It seems that the researchers were able to train computers to recognize patterns linked to a specific condition.
In a new Google study, the scientists applied artificial intelligence to CT scans used to screen people for lung cancer. Current studies have shown that screening can reduce the risk of dying from lung cancer and can also identify spots that might later become malignant.

The researchers created a neural network with multiple layers of processing and trained the AI by giving it many CT scans from patients whose diagnoses were known. This allows radiologists to sort patients into risk groups and decide whether biopsies are needed or follow up to keep track of the suspected regions. Even though the technology seems promising, but it can have pitfalls such as missing tumors, mistaken benign spots for malignancies and push patients into risky procedures.

Yet, the ability to process vast amounts of data may make it imaginable for artificial intelligence to recognize subtle patterns that humans simply cannot see. It is well understood that the systems should be studied extensively before using them for general public use. The lung-screening neural network is not ready for the clinic yet.

SOURCE

A.I. Took Test To Detect Lung Cancer And Smashed It

 

 


Analysis of Utilizing LPBI Group’s Scientific Curation Platform as an Educational Tool: New Paradigm for Student Engagement

Author: Stephen J. Williams, Ph.D.

 

 

Use of LBPI Platform for Educational Purposes

Goal:  to offer supplemental information for student lessons in an upper level Biology course on Cell Signaling and Cell Motility with emphasis on disease etiology including cancer, neurological disease, and cardiovascular disease.

Course:  Temple University Department of Biology course Cell Signaling and Motility Spring semester 2019. Forty five students enrolled.

Methodology:  Each weekly lesson was presented to students as a PowerPoint presentation.  After each lesson the powerpoint presentation was originally meant to be disseminated to each class-registered student on the students Canvas account.  Canvas is a cloud based Learning Management Software developed by educational technology company Salt Lake City, Utah company Infrastructure, Inc.  According to rough figures, Canvas® charges a setup fee and at least $30 per user (for a university the size of Temple University: 55,000 students at $30 each = 1.6 million a semester for user fees only).

As a result of a technical issue with uploading the first week lesson on this system, I had informed the class that, as an alternative means, class presentation notes and lectures will be posted on the site www.pharmaceuticalintelligence.com as a separate post and searchable on all search engines including Google, Twitter, Yahoo, Bing, Facebook etc. In addition, I had informed the students that supplemental information, from curated posts and articles from our site, would be added to the class lecture post as supplemental information they could use for further reading on the material as well as helpful information and reference for class projects.

The posted material was tagged with #TUBiol3373 (university abbreviation, department, course number) and disseminated to various social media platforms using our system.  This allowed the students to enter #TUBiol3373 in any search engine to easily find their lecture notes and supplemental information.

This gave students access to lectures on a mobile platform which was easily discoverable due to our ability to do search engine optimization. (#TUBiol3373 was among the first search results on most popular search engines).

From a technical standpoint,  the ease at which posts of this nature can be made as well as the ease of including links to full articles as references as well as media has been noted.  Although students seem to navigate the Canvas software with ease, they had noticed many professors have issues or problems with using this software, especially with navigating the software for their needs.   LBPI’s platform is an easily updated, accessible, and extensive knowledge system which can alleviate many of these technical issues and provide the added value of incorporating media based instructional material as well as downloadable file and allow the instructor ability to expound on the presented material with commentary.  In addition due to the social nature of the platform, feedback can be attained by use of curated site statistics and commentary sections as well as online surveys.

 

Results

After the first week, all 45 students used LBPI platform to access these lecture notes with 17 out of 45 continuing to refer to the site during every week (week 1-4) to the class notes.  This was evident from our site statistics as well as number of downloads of the material.  The students had used the #TUBIol3373 and were directed to the site mainly from search engines Google and Yahoo.  In addition, students had also clicked on the links corresponding to supplemental information which I had included, from articles on our site.  In addition, because of the ability to incorporate media on our site, additional information including instructional videos and interviews were included in lecture posts, and this material was easily updated on the instructor’s side.

Adoption of the additional material from our site was outstanding, as many students had verbally said that the additional material was very useful in their studies.  This was also evidenced by site statistics owing to the secondary clicks made from the class lecture post going to additional articles, some not even included as links on the original post.

In addition, and  more important, students had incorporated many of the information from the additional site articles posted and referenced in their class group projects.  At end of semester a survey was emailed to each student  to assess the usefulness of such a teaching strategy. Results of the polling are shown below.

Results from polling of students of #TUBiol3373 “Cell Signaling & Motility” Class

Do you find using a web based platform such as a site like this an easier communication platform for posting lecture notes/added information than a platform like Canvas®? (5 votes)

Answer Votes Percent  
Yes 2 40%  
Somewhat but could use some improvement 2 40%  
No 1 20%  
Did not use web site 0 0%  

 

Do you find using an open access, curated information platform like this site more useful than using multiple sources to find useful extra study/presentation materials? (6 votes)

Answer Votes Percent  
Yes 5 83%  
No 1 17%  

 

Did you use the search engine on the site (located on the top right of the home page) to find extra information on topics for your presentations/study material? (5 votes)

Answer Votes Percent  
Yes 4 67%  
No 1 17%  
Did not use web site 1 17%  

 

Were you able to easily find the supplemental information for each lecture on search engines like Google/Yahoo/Bing/Twitter using the hashtag #TUBiol3373? (6 votes)

Answer Votes Percent  
Yes I was able to find the site easily 4 67%  
No 1 17%  
Did not use a search engine to find site, went directly to site 1 17%  
Encountered some difficulty 0 0%  
Did not use the site for supplemental or class information 0 0%  

 

How did you find the supplemental material included on this site above the Powerpoint presented material for each of the lectures? (7 votes)

Answer Votes Percent  
Very Useful 4 57%  
Did not use supplemental information 2 29%  
Somewhat Useful 1 14%  
Not Useful 0 0%  

How many times did you use the information on this site (https://www.pharmaceuticalintelligence.com) for class/test/project preparation? (7 votes)

Answer Votes Percent  
Frequently 3 43%  
Sparingly 2 29%  
Occasionally 1 14%  
Never 1 14%  

 

 

 

 

 

 

 

Views of #TUBiol3373 lessons/posts on www.pharmaceuticalintelligence.com                    

 

Lesson/Title Total # views # views 1st day # views 2nd day % views day 1 and 2 % views  after 1st 2 days
Lesson 1 AND 2 Cell Signaling & Motility: Lessons, Curations and Articles of reference as supplemental information: #TUBiol3373 60 27 15 93% 45%
Lesson 3 Cell Signaling And Motility: G Proteins, Signal Transduction: Curations and Articles of reference as supplemental information: #TUBiol3373 56 12 11 51% 93%
Lesson 4 Cell Signaling And Motility: G Proteins, Signal Transduction: Curations and Articles of reference as supplemental information: #TUBiol3373 37 17 6 48% 31%
Lesson 5 Cell Signaling And Motility: Cytoskeleton & Actin: Curations and Articles of reference as supplemental information: #TUBiol3373 13 6 2 17% 15%
Lesson 8 Cell Signaling and Motility: Lesson and Supplemental Information on Cell Junctions and ECM: #TUBiol3373 16 8 2 22% 13%
Lesson 9 Cell Signaling: Curations and Articles of reference as supplemental information for lecture section on WNTs: #TUBioll3373 20 10 3 28% 15%
Curation of selected topics and articles on Role of G-Protein Coupled Receptors in Chronic Disease as supplemental information for #TUBiol3373 19 11 2 28% 13%
Lesson 10 on Cancer, Oncogenes, and Aberrant Cell Signal Termination in Disease for #TUBiol3373 21 10 2 26% 20%
Totals 247 69 46 31% 62%
           

 

Note: for calculation of %views on days 1 and 2 of posting lesson and supplemental material on the journal; %views day1 and 2 = (#views day 1 + #views day 2)*100/45 {45 students in class}

For calculation of %views past day 1 and 2 = (total # views – day1 views – day2 views) * 100/45

For calculation in total column last two columns were divided by # of students (45) and # of posts (8)

 

Overall class engagement was positive with 31% of students interacting with the site during the course on the first two days after posting lessons while 61% of students interacted with the site during the rest of the duration of the course.  The higher number of students interacting with the site after the first two days after lecture and posting may be due to a higher number of students using the posted material for study for the test and using material for presentation purposes.

Engagement with the site for the first two days post lecture ranged from 93% engagement to 22% engagement.  As the class neared the first exam engagement with the site was high however engagement was lower near the end of the class period potentially due to the last exam was a group project and not a written exam.  Students appeared to engage highly with the site to get material for study for the written exam however there still was significant engagement by students for purposes of preparation for oral group projects.  Possibly engagement with the site post 2 days for the later lectures could be higher if a written exam was also given towards the end of the class as well.  This type of analysis allows the professor to understand the level of class engagement week by week.

The results of post-class polling confirm some of the conclusions on engagement.  After the final grades were given out all 45 students received an email with a link to the poll.  Of the 45 students emailed, there were 20 views of the poll with 5-7 answers per question.  Interestingly, most answers were positive on the site and the use of curated material for learning and a source of research project material.   It was very easy finding the posts using the #classname and most students used Google to find the material, which was at the top of Google search results.  Not many students used Twitter or other search engines.  Some went directly to the site.  A majority (71%) found the material useful or somewhat useful for their class presentations and researching topics.


New Targeted Cancer Therapy may be ‘Possible Hope’ for Some Pancreatic Cancer Patients

Reporter: Irina Robu, PhD

 

UPDATED on 7/18/2019

BREAKTHROUGH PANCREATIC CANCER TREATMENT PHASE III TRIAL OPENS IN ISRAEL

Hope is that successful trials will allow Rafael Pharmaceuticals will receive expedited FDA approval by late 2020.

BY MAAYAN JAFFE-HOFFMAN  JULY 18, 2019 18:30

“What it does is feeds misinformation to these regulatory elements, making them feel that there is too much carbon flow through both of these complexes, causing them to be inhibited,” Pardee said. “It simultaneously inhibits both complexes so tumor cells that are primarily driven by glucose cannot utilize glucose in the TCA cycle. Tumor cells that are primarily driven by glutamine usage cannot use glutamine-derived carbons in the TCA cycle. And, importantly, tumors cannot switch from one source to the other in the presence of CPI-613,” he explained.

He said that hitting two complexes simultaneously has many advantages. One is that the carbon source the tumor is primarily dependent on does not matter; another is that evolved resistance for both complexes simultaneously is very unlikely to happen.

Pardee said CPI-613’s key differentiators are that it is highly selective on the uptake and target level in cancer cells, which leads to less toxicity to healthy cells. This allows for patients to receive extended treatment courses and for the drug to be used in combination with other drugs.

CPI-613 is being administered in this clinical trial with a chemotherapy combination of fluorouracil, leucovorin, irinotecan, and oxaliplatin, called FOLFIRINOX.

SOURCE

https://www.jpost.com/HEALTH-SCIENCE/Breakthrough-pancreatic-cancer-treatment-phase-III-trial-opens-in-Israel-596059

 

New Targeted Cancer Therapy may be ‘Possible Hope’ for Some Pancreatic Cancer Patients

Pancreatic cancer is the 12th maximum common cancer and the fourth leading cause of cancer death. The cancer is often difficult to diagnose as there is no cost-effective ways to screen for the illness. For over 52% of people who are diagnosed after the cancer has spread and with a 5-year survival rate.

Scientists at Sheba Medical Center in Israel developed a targeted cancer therapy drug together with AstraZeneca and Merck which can offer a possible new solution for patients with a specific kind of pancreatic cancer by delaying the progression of the disease. To evaluate the safety and test the efficacy of a new drug treatment regimen based on Lynparza tablets. The tablets are a pharmacological inhibitor of the enzyme poly (ADP-ribose) polymerase which inhibit the enzyme. They were developed for a number of indications, but most prominently for the treatment of cancer, as numerous forms of cancer are more dependent for their development on the enzyme than regular cells are. This makes poly (ADP-ribose) polymerase an attractive target for cancer therapy.

Their study included 154 patients who were randomly assigned to get the tablets at a dose of 300 mg twice a day with metastatic pancreatic cancer who carried the genetic mutation called BRCA 1 and BRCA 2. BRCA1 and BRCA2 are human genes that produce proteins accountable for repairing damaged DNA and play a substantial role in preserving the genetic stability of cells. Once either of these genes is mutated, DNA damage can’t be repaired properly and cells become unstable. As a result, cells are more likely to develop additional genetic alterations that can lead to cancer.

Patients with these mutations make up six to seven percent of the metastatic pancreatic cancer patients. The trial using the using the medicine Lynparza offers possible hope for those who suffer from metastatic pancreatic cancer and have a BRCA mutation and slows down the disease progression. According to the researchers this is the first Phase 3 biomarker that is positive in pancreatic cancer and the drug gives incredible hope for patients with the advanced stage of the cancer.

SOURCE
https://www.timesofisrael.com/israeli-researchers-find-potential-hope-for-some-pancreatic-cancer-patients/