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Archive for the ‘Intelligent Information Systems’ Category

Digital Therapeutics: A threat or opportunity to pharmaceuticals


Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Digital Therapeutics (DTx) have been defined by the Digital Therapeutics Alliance (DTA) as “delivering evidence based therapeutic interventions to patients, that are driven by software to prevent, manage or treat a medical disorder or disease”. They might come in the form of a smart phone or computer tablet app, or some form of a cloud-based service connected to a wearable device. DTx tend to fall into three groups. Firstly, developers and mental health researchers have built digital solutions which typically provide a form of software delivered Cognitive-Behaviour Therapies (CBT) that help patients change behaviours and develop coping strategies around their condition. Secondly there are the group of Digital Therapeutics which target lifestyle issues, such as diet, exercise and stress, that are associated with chronic conditions, and work by offering personalized support for goal setting and target achievement. Lastly, DTx can be designed to work in combination with existing medication or treatments, helping patients manage their therapies and focus on ensuring the therapy delivers the best outcomes possible.

 

Pharmaceutical companies are clearly trying to understand what DTx will mean for them. They want to analyze whether it will be a threat or opportunity to their business. For a long time, they have been providing additional support services to patients who take relatively expensive drugs for chronic conditions. A nurse-led service might provide visits and telephone support to diabetics for example who self-inject insulin therapies. But DTx will help broaden the scope of support services because they can be delivered cost-effectively, and importantly have the ability to capture real-world evidence on patient outcomes. They will no-longer be reserved for the most expensive drugs or therapies but could apply to a whole range of common treatments to boost their efficacy. Faced with the arrival of Digital Therapeutics either replacing drugs, or playing an important role alongside therapies, pharmaceutical firms have three options. They can either ignore DTx and focus on developing drug therapies as they have done; they can partner with a growing number of DTx companies to develop software and services complimenting their drugs; or they can start to build their own Digital Therapeutics to work with their products.

 

Digital Therapeutics will have knock-on effects in health industries, which may be as great as the introduction of therapeutic apps and services themselves. Together with connected health monitoring devices, DTx will offer a near constant stream of data about an individuals’ behavior, real world context around factors affecting their treatment in their everyday lives and emotional and physiological data such as blood pressure and blood sugar levels. Analysis of the resulting data will help create support services tailored to each patient. But who stores and analyses this data is an important question. Strong data governance will be paramount to maintaining trust, and the highly regulated pharmaceutical industry may not be best-placed to handle individual patient data. Meanwhile, the health sector (payers and healthcare providers) is becoming more focused on patient outcomes, and payment for value not volume. The future will say whether pharmaceutical firms enhance the effectiveness of drugs with DTx, or in some cases replace drugs with DTx.

 

Digital Therapeutics have the potential to change what the pharmaceutical industry sells: rather than a drug it will sell a package of drugs and digital services. But they will also alter who the industry sells to. Pharmaceutical firms have traditionally marketed drugs to doctors, pharmacists and other health professionals, based on the efficacy of a specific product. Soon it could be paid on the outcome of a bundle of digital therapies, medicines and services with a closer connection to both providers and patients. Apart from a notable few, most pharmaceutical firms have taken a cautious approach towards Digital Therapeutics. Now, it is to be observed that how the pharmaceutical companies use DTx to their benefit as well as for the benefit of the general population.

 

References:

 

https://eloqua.eyeforpharma.com/LP=23674?utm_campaign=EFP%2007MAR19%20EFP%20Database&utm_medium=email&utm_source=Eloqua&elqTrackId=73e21ae550de49ccabbf65fce72faea0&elq=818d76a54d894491b031fa8d1cc8d05c&elqaid=43259&elqat=1&elqCampaignId=24564

 

https://www.s3connectedhealth.com/resources/white-papers/digital-therapeutics-pharmas-threat-or-opportunity/

 

http://www.pharmatimes.com/web_exclusives/digital_therapeutics_will_transform_pharma_and_healthcare_industries_in_2019._heres_how._1273671

 

https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/exploring-the-potential-of-digital-therapeutics

 

https://player.fm/series/digital-health-today-2404448/s9-081-scaling-digital-therapeutics-the-opportunities-and-challenges

 

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2019 Koch Institute Symposium – Machine Learning and Cancer, June 14, 2019, 8:00 AM-5:00 PM ET MIT Kresge Auditorium, 48 Massachusetts Ave, Cambridge, MA

Announcement

Aviva Lev-Ari, PhD, RN,

Founder and Director of LPBI Group will be in attendance covering the event in REAL TIME

@pharma_BI

@AVIVA1950

 

Machine Learning and Cancer

The 18th Annual Koch Institute Summer Symposium on June 14, 2019 at MIT’s Kresge Auditorium will focus on Machine Learning and Cancer.

Both fields are undergoing dramatic changes, and their integration holds great promise for cancer research, diagnostics, and therapeutics. Cancer treatment and research have advanced rapidly with an increasing reliance on data-driven decisions. The volume, complexity, and diversity of research and clinical data—from genomics and single-cell molecular and image-based profiles to histopathology, clinical imaging, and medical records—far surpasses the capacity of individual scientists and physicians. However, they offer a remarkable opportunity to new approaches for data science and machine learning to provide holistic and intelligible interpretations to trained experts and patients alike. These advances will make it possible to provide far better diagnostics, discover possible chemical pathways for de novo synthesis of therapeutic compounds, predict accurately the risk of individuals for development of specific cancers years before metastatic spread, and determine the combination of agents that will stimulate immune rejection of a tumor or selectively induce the death of all cells in a tumor.

The symposium will address these issues through three sessions:

  • Machine Learning in Cancer Research: the Need and the Opportunity
  • Machine Learning to Decipher Cellular and Molecular Mechanisms in Cancer
  • Machine Learning into the Clinic

Sessions will be followed by a panel discussion of broadly informed experts moderated by MIT President Emerita Susan Hockfield.

Introductory remarks will be given by symposium co-chairs and Koch Institute faculty members Regina Barzilay, Aviv Regev and Phillip Sharp.

 

Keynote Speakers | Machine Learning in Cancer Research: the Need and the Opportunity

James P. Allison, PhD

MD Anderson Cancer Center

Regina Barzilay, PhD

MIT Computer Science and Artificial Intelligence Lab, Koch Institute for Integrative Cancer Research at MIT

Aviv Regev, PhD

Broad Institute, Koch Institute for Integrative Cancer Research at MIT

 

Session Speakers

Michael R. Angelo, MD, PhD

Stanford Unviersity

Andrew Beck

PathAI

Stephen H. Friend, MD, PhD

Sage Bionetworks

Tommi Jaakkola, PhD

MIT Computer Science and Artificial Intelligence Lab

Dana Pe’er, PhD

Memorial Sloan Kettering Cancer Center

Peter Sorger, PhD

Harvard Medical School

Olga Troyanskaya, PhD

Princeton University

Brian Wolpin, MD

Dana-Farber Cancer Institute

 

Panel Discussion | Big Data, Computation and the Future of Health Care

James (Jay) Bradner, MD

Novartis

Clifford A. Hudis, MD

American Society of Clinical Oncology

Constance D. Lehman, MD, PhD

Massachusetts General Hospital

Norman (Ned) Sharpless, MD

National Cancer Institute

 

Moderator: Susan Hockfield, PhD

Koch Institute for Integrative Cancer Research at MIT

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SOURCE

From: 2019 Koch Institute Symposium <ki-events@mit.edu>

Reply-To: <ki-events@mit.edu>

Date: Tuesday, March 12, 2019 at 11:30 AM

To: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>

Subject: Invitation to the 2019 Koch Institute Symposium – Machine Learning and Cancer

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TALK ANNOUNCEMENT from Boston Chapter of ASA

 

Reporter: Aviva Lev-Ari, PhD, RN

 

From: Tom Lane [mailto:Tom.Lane@mathworks.com]
Sent: Tuesday, January 15, 2019 12:59 PM
To: Tom Lane
Subject: [BCASA] INFORMS talk “Impact of Bots on Opinions in Social Networks” Wed Feb 6 at MITRE in Bedford

 

Upcoming event from INFORMS, kindly shared with BCASA members:

 

Please join us for the first INFORMS BC talk of 2019 on the Impact of Bots on Opinions in Social Networks by Professor Tauhid Zaman 

·         1.  Please join us for the first INFORMS BC talk of 2019 on the Impact of Bots on Opinions in Social Networks by Professor Tauhid Zaman

Daniel Rice

 

RSVP to lservi@mitre.org by Monday, February 4th, 2018

MITRE is asking that if you plan to attend please RSVP by sending an email to lservi@mitre.org indicating your 1) name, 2) email, (3) company or university, (4) whether you are a US citizen and, if not, country of citizenship.


Date: Wednesday Feb 6, 2019, at 6:30 PM

Location:
The MITRE Corporation
M Building, 202 Burlington Road
Bedford, MA

Title: The Impact of Bots on Opinions in Social Networks

Speaker: Tauhid Zaman

Abstract:
We present an analysis of the impact of automated accounts, or bots, on opinions in a social network. We model the opinions using a variant of the famous DeGroot model, which connects opinions with network structure. We find a nontrivial correlation between opinions based on this network model and based on the content of tweets of Twitter users discussing the 2016 U.S. presidential election between Hillary Clinton and Donald Trump, providing evidence supporting the validity of the model. We then utilize the network model to predict what the opinions would have been if the network did not contain any bots which may be trying to manipulate opinions. Using a bot detection algorithm, we identify bot accounts which comprise less than 1% of the network. By analyzing the bot posts, we find that there are twice as many bots supporting Donald Trump as there are supporting Hillary Clinton.  We remove the bots from the network and recalculate the opinions using the network model. We find that the bots produce a significant shift in the opinions, with the Clinton bots producing almost twice as large a change as the Trump bots, despite being fewer in number. Analysis of the bot behavior reveals that the large shift is due to the fact that the bots post one hundred times more frequently than humans.  The asymmetry in the opinion shift is due to the fact that the Clinton bots post 50% more frequently than the Trump bots.  Our results suggest a small number of highly active bots in a social network can have a disproportionate impact on opinions.

Bio: Tauhid is an Associate Professor of Operations Management at the MIT Sloan School of Management. He received his BS, MEng, and PhD degrees in electrical engineering and computer science from MIT.  His research focuses on solving operational problems involving social network data using probabilistic models, network algorithms, and modern statistical methods.  Some of the topics he studies in the social networks space include predicting the popularity of content, finding online extremists, and geo-locating users.  His broader interests cover data driven approaches to investing in startup companies, non-traditional choice modeling, algorithmic sports betting, and biometric data.  His work has been featured in the Wall Street Journal, Wired, Mashable, the LA Times, and Time Magazine.

 

SOURCE

From: “Tom Lane (ASA)” <Tom.Lane@mathworks.com>

Date: Tuesday, February 5, 2019 at 9:41 AM

To: “Tom Lane (ASA)” <Tom.Lane@mathworks.com>

Subject: [BCASA] INFORMS talk “Impact of Bots on Opinions in Social Networks”Wed Feb 6 at MITRE in Bedford

 

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Graph Database Market Update 2019 – Cambridge Semantics’ AnzoGraph Graph Database product Awarded Highest Rating

Reporter: Aviva Lev-Ari, PhD, RN

 

Cambridge Semantics, the leading provider of big data management and enterprise analytics software, announced that its AnzoGraph Graph Database product was rated highest in analytic processing capabilities in the latest Graph Database Market Update 2019 by Bloor Research.

 

Appended and attached is the press release for your reference.

 

Regards

Linda Sekhar
+949-872- 8631

Global Results Communications | GRC

 

                                                 

 

 

 

Cambridge Semantics Awarded Highest Rating for Analytic Processing Environments in the Graph Database Market Update 2019 by Bloor Research

 

AnzoGraph Graph Database Named #1 in Several Categories of Research

 

Boston—January 23, 2019— Cambridge Semantics, the leading provider of big data management and enterprise analytics software, announced that its AnzoGraph Graph Database product was rated highest in analytic processing capabilities in the latest Graph Database Market Update 2019 by Bloor Research.

 

The Bloor Graph Database Market Update 2019 compares property graph with RDF databases, native versus non-native implementations, single versus multi-model databases and operational versus analytic solutions. It discusses the latest trends in this market, along with an assessment of the leading vendors in the market and captures any market shift in the last two years. Relative to shifts in the graph database market, Bloor noted that Cambridge Semantics unbundled AnzoGraph from its Anzo Data Lake offering, and that Amazon and SAP also entered the graph database market.

 

Within the report, Cambridge Semantics was ranked number one in several categories including:

       Integration- Analytic environments

       Language- Analytic environments

       Features- Analytic environments

 

“There are relatively few vendors in the graph database market that have the performance to focus specifically on analytics. Cambridge Semantics not only does this with AnzoGraph, but does so without requiring (very expensive) hardware acceleration or a proprietary language,” said Philip Howard, research director at Bloor Research.

 

‘’It is an honor to be named a leading vendor for analytics in the Graph Database market 2019,” said Alok Prasad, president of Cambridge Semantics.  “We have witnessed a surging interest in graph databases to conduct interactive analytics to get better insights. Last year, we were very excited to spin-out AnzoGraph, our graph analytics database for hyper-fast performance at Big Data scale. We are seeing very strong interest in this new offering.’’

 

AnzoGraph is a massively parallel distributed native graph analytics database built to interactively analyze trillions of relationships at record speed.  The database provides BI-style analytics, graph algorithms and inferencing with open W3C standards based graph technology and labelled property graphs.  The underlying technology is a third-generation data analytics engine built by the engineers who built Netezza and the technology behind Amazon Redshift. AnzoGraph has been in production at large enterprise customers as a part of Anzo and is now available behind the firewall or in the cloud on AWS, Google Cloud Platform, Microsoft Azure and all cloud environments supporting Docker.

 

About Bloor Research

Bloor Research is a global, independent research and analyst house, focused on the idea that Evolution is Essential to business success and ultimately survival. For nearly 30 years, we have enabled businesses to understand the potential offered by technology and choose the optimal solutions for their needs.

 

About Cambridge Semantics

Cambridge Semantics Inc., The Smart Data Company®, is a big data management and enterprise analytics software company that offers a universal semantic layer to connect and bring meaning to all enterprise data. The company offers two award winning products: Anzo for Enterprise Knowledge Graphs and integrated analytics and AnzoGraph, a graph analytics database.

 

Cambridge Semantics is based in Boston, Massachusetts.

For more information visit www.cambridgesemantics.com or follow us on Facebook, LinkedIn and Twitter: @CamSemantics.

###

 

Media Contact:

Lora Wilson/Valerie Christopherson

Global Results Communications for Cambridge Semantics

cambridge@globalresultspr.com

+1 (949) 608-0276

 

SOURCE

From: Linda Sekhar <LSekhar@globalresultspr.com>

Date: Wednesday, January 23, 2019 at 11:28 AM

Subject: Press release: Cambridge Semantics Awarded Highest Rating for Analytic Processing Environments in the Graph Database Market Update 2019 by Bloor Research

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Role of Informatics in Precision Medicine: Notes from Boston Healthcare Webinar: Can It Drive the Next Cost Efficiencies in Oncology Care?

Reporter: Stephen J. Williams, Ph.D.

 

Boston Healthcare sponsored a Webinar recently entitled ” Role of Informatics in Precision Medicine: Implications for Innovators”.  The webinar focused on the different informatic needs along the Oncology Care value chain from drug discovery through clinicians, C-suite executives and payers. The presentation, by Joseph Ferrara and Mark Girardi, discussed the specific informatics needs and deficiencies experienced by all players in oncology care and how innovators in this space could create value. The final part of the webinar discussed artificial intelligence and the role in cancer informatics.

 

Below is the mp4 video and audio for this webinar.  Notes on each of the slides with a few representative slides are also given below:

Please click below for the mp4 of the webinar:

 

 


  • worldwide oncology related care to increase by 40% in 2020
  • big movement to participatory care: moving decision making to the patient. Need for information
  • cost components focused on clinical action
  • use informatics before clinical stage might add value to cost chain

 

 

 

 

Key unmet needs from perspectives of different players in oncology care where informatics may help in decision making

 

 

 

  1.   Needs of Clinicians

– informatic needs for clinical enrollment

– informatic needs for obtaining drug access/newer therapies

2.  Needs of C-suite/health system executives

– informatic needs to help focus of quality of care

– informatic needs to determine health outcomes/metrics

3.  Needs of Payers

– informatic needs to determine quality metrics and managing costs

– informatics needs to form guidelines

– informatics needs to determine if biomarkers are used consistently and properly

– population level data analytics

 

 

 

 

 

 

 

 

 

 

 

 

What are the kind of value innovations that tech entrepreneurs need to create in this space? Two areas/problems need to be solved.

  • innovations in data depth and breadth
  • need to aggregate information to inform intervention

Different players in value chains have different data needs

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Data Depth: Cumulative Understanding of disease

Data Depth: Cumulative number of oncology transactions

  • technology innovators rely on LEGACY businesses (those that already have technology) and these LEGACY businesses either have data breath or data depth BUT NOT BOTH; (IS THIS WHERE THE GREATEST VALUE CAN BE INNOVATED?)
  • NEED to provide ACTIONABLE as well as PHENOTYPIC/GENOTYPIC DATA
  • data depth more important in clinical setting as it drives solutions and cost effective interventions.  For example Foundation Medicine, who supplies genotypic/phenotypic data for patient samples supplies high data depth
  • technologies are moving to data support
  • evidence will need to be tied to umbrella value propositions
  • Informatic solutions will have to prove outcome benefit

 

 

 

 

 

How will Machine Learning be involved in the healthcare value chain?

  • increased emphasis on real time datasets – CONSTANT UPDATES NEED TO OCCUR. THIS IS NOT HAPPENING BUT VALUED BY MANY PLAYERS IN THIS SPACE
  • Interoperability of DATABASES Important!  Many Players in this space don’t understand the complexities integrating these datasets

Other Articles on this topic of healthcare informatics, value based oncology, and healthcare IT on this OPEN ACCESS JOURNAL include:

Centers for Medicare & Medicaid Services announced that the federal healthcare program will cover the costs of cancer gene tests that have been approved by the Food and Drug Administration

Broad Institute launches Merkin Institute for Transformative Technologies in Healthcare

HealthCare focused AI Startups from the 100 Companies Leading the Way in A.I. Globally

Paradoxical Findings in HealthCare Delivery and Outcomes: Economics in MEDICINE – Original Research by Anupam “Bapu” Jena, the Ruth L. Newhouse Associate Professor of Health Care Policy at HMS

Google & Digital Healthcare Technology

Can Blockchain Technology and Artificial Intelligence Cure What Ails Biomedical Research and Healthcare

The Future of Precision Cancer Medicine, Inaugural Symposium, MIT Center for Precision Cancer Medicine, December 13, 2018, 8AM-6PM, 50 Memorial Drive, Cambridge, MA

Live Conference Coverage @Medcity Converge 2018 Philadelphia: Oncology Value Based Care and Patient Management

2016 BioIT World: Track 5 – April 5 – 7, 2016 Bioinformatics Computational Resources and Tools to Turn Big Data into Smart Data

The Need for an Informatics Solution in Translational Medicine

 

 

 

 

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Can Blockchain Technology and Artificial Intelligence Cure What Ails Biomedical Research and Healthcare

Curator: Stephen J. Williams, Ph.D.

Updated 12/18/2018

In the efforts to reduce healthcare costs, provide increased accessibility of service for patients, and drive biomedical innovations, many healthcare and biotechnology professionals have looked to advances in digital technology to determine the utility of IT to drive and extract greater value from healthcare industry.  Two areas of recent interest have focused how best to use blockchain and artificial intelligence technologies to drive greater efficiencies in our healthcare and biotechnology industries.

More importantly, with the substantial increase in ‘omic data generated both in research as well as in the clinical setting, it has become imperative to develop ways to securely store and disseminate the massive amounts of ‘omic data to various relevant parties (researchers or clinicians), in an efficient manner yet to protect personal privacy and adhere to international regulations.  This is where blockchain technologies may play an important role.

A recent Oncotarget paper by Mamoshina et al. (1) discussed the possibility that next-generation artificial intelligence and blockchain technologies could synergize to accelerate biomedical research and enable patients new tools to control and profit from their personal healthcare data, and assist patients with their healthcare monitoring needs. According to the abstract:

The authors introduce new concepts to appraise and evaluate personal records, including the combination-, time- and relationship value of the data.  They also present a roadmap for a blockchain-enabled decentralized personal health data ecosystem to enable novel approaches for drug discovery, biomarker development, and preventative healthcare.  In this system, blockchain and deep learning technologies would provide the secure and transparent distribution of personal data in a healthcare marketplace, and would also be useful to resolve challenges faced by the regulators and return control over personal data including medical records to the individual.

The review discusses:

  1. Recent achievements in next-generation artificial intelligence
  2. Basic concepts of highly distributed storage systems (HDSS) as a preferred method for medical data storage
  3. Open source blockchain Exonium and its application for healthcare marketplace
  4. A blockchain-based platform allowing patients to have control of their data and manage access
  5. How advances in deep learning can improve data quality, especially in an era of big data

Advances in Artificial Intelligence

  • Integrative analysis of the vast amount of health-associated data from a multitude of large scale global projects has proven to be highly problematic (REF 27), as high quality biomedical data is highly complex and of a heterogeneous nature, which necessitates special preprocessing and analysis.
  • Increased computing processing power and algorithm advances have led to significant advances in machine learning, especially machine learning involving Deep Neural Networks (DNNs), which are able to capture high-level dependencies in healthcare data. Some examples of the uses of DNNs are:
  1. Prediction of drug properties(2, 3) and toxicities(4)
  2. Biomarker development (5)
  3. Cancer diagnosis (6)
  4. First FDA approved system based on deep learning Arterys Cardio DL
  • Other promising systems of deep learning include:
    • Generative Adversarial Networks (https://arxiv.org/abs/1406.2661): requires good datasets for extensive training but has been used to determine tumor growth inhibition capabilities of various molecules (7)
    • Recurrent neural Networks (RNN): Originally made for sequence analysis, RNN has proved useful in analyzing text and time-series data, and thus would be very useful for electronic record analysis. Has also been useful in predicting blood glucose levels of Type I diabetic patients using data obtained from continuous glucose monitoring devices (8)
    • Transfer Learning: focused on translating information learned on one domain or larger dataset to another, smaller domain. Meant to reduce the dependence on large training datasets that RNN, GAN, and DNN require.  Biomedical imaging datasets are an example of use of transfer learning.
    • One and Zero-Shot Learning: retains ability to work with restricted datasets like transfer learning. One shot learning aimed to recognize new data points based on a few examples from the training set while zero-shot learning aims to recognize new object without seeing the examples of those instances within the training set.

Highly Distributed Storage Systems (HDSS)

The explosion in data generation has necessitated the development of better systems for data storage and handling. HDSS systems need to be reliable, accessible, scalable, and affordable.  This involves storing data in different nodes and the data stored in these nodes are replicated which makes access rapid. However data consistency and affordability are big challenges.

Blockchain is a distributed database used to maintain a growing list of records, in which records are divided into blocks, locked together by a crytosecurity algorithm(s) to maintain consistency of data.  Each record in the block contains a timestamp and a link to the previous block in the chain.  Blockchain is a distributed ledger of blocks meaning it is owned and shared and accessible to everyone.  This allows a verifiable, secure, and consistent history of a record of events.

Data Privacy and Regulatory Issues

The establishment of the Health Insurance Portability and Accountability Act (HIPAA) in 1996 has provided much needed regulatory guidance and framework for clinicians and all concerned parties within the healthcare and health data chain.  The HIPAA act has already provided much needed guidance for the latest technologies impacting healthcare, most notably the use of social media and mobile communications (discussed in this article  Can Mobile Health Apps Improve Oral-Chemotherapy Adherence? The Benefit of Gamification.).  The advent of blockchain technology in healthcare offers its own unique challenges however HIPAA offers a basis for developing a regulatory framework in this regard.  The special standards regarding electronic data transfer are explained in HIPAA’s Privacy Rule, which regulates how certain entities (covered entities) use and disclose individual identifiable health information (Protected Health Information PHI), and protects the transfer of such information over any medium or electronic data format. However, some of the benefits of blockchain which may revolutionize the healthcare system may be in direct contradiction with HIPAA rules as outlined below:

Issues of Privacy Specific In Use of Blockchain to Distribute Health Data

  • Blockchain was designed as a distributed database, maintained by multiple independent parties, and decentralized
  • Linkage timestamping; although useful in time dependent data, proof that third parties have not been in the process would have to be established including accountability measures
  • Blockchain uses a consensus algorithm even though end users may have their own privacy key
  • Applied cryptography measures and routines are used to decentralize authentication (publicly available)
  • Blockchain users are divided into three main categories: 1) maintainers of blockchain infrastructure, 2) external auditors who store a replica of the blockchain 3) end users or clients and may have access to a relatively small portion of a blockchain but their software may use cryptographic proofs to verify authenticity of data.

 

YouTube video on How #Blockchain Will Transform Healthcare in 25 Years (please click below)

 

 

In Big Data for Better Outcomes, BigData@Heart, DO->IT, EHDN, the EU data Consortia, and yes, even concepts like pay for performance, Richard Bergström has had a hand in their creation. The former Director General of EFPIA, and now the head of health both at SICPA and their joint venture blockchain company Guardtime, Richard is always ahead of the curve. In fact, he’s usually the one who makes the curve in the first place.

 

 

 

Please click on the following link for a podcast on Big Data, Blockchain and Pharma/Healthcare by Richard Bergström:

References

  1. Mamoshina, P., Ojomoko, L., Yanovich, Y., Ostrovski, A., Botezatu, A., Prikhodko, P., Izumchenko, E., Aliper, A., Romantsov, K., Zhebrak, A., Ogu, I. O., and Zhavoronkov, A. (2018) Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare, Oncotarget 9, 5665-5690.
  2. Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., and Zhavoronkov, A. (2016) Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data, Molecular pharmaceutics 13, 2524-2530.
  3. Wen, M., Zhang, Z., Niu, S., Sha, H., Yang, R., Yun, Y., and Lu, H. (2017) Deep-Learning-Based Drug-Target Interaction Prediction, Journal of proteome research 16, 1401-1409.
  4. Gao, M., Igata, H., Takeuchi, A., Sato, K., and Ikegaya, Y. (2017) Machine learning-based prediction of adverse drug effects: An example of seizure-inducing compounds, Journal of pharmacological sciences 133, 70-78.
  5. Putin, E., Mamoshina, P., Aliper, A., Korzinkin, M., Moskalev, A., Kolosov, A., Ostrovskiy, A., Cantor, C., Vijg, J., and Zhavoronkov, A. (2016) Deep biomarkers of human aging: Application of deep neural networks to biomarker development, Aging 8, 1021-1033.
  6. Vandenberghe, M. E., Scott, M. L., Scorer, P. W., Soderberg, M., Balcerzak, D., and Barker, C. (2017) Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer, Scientific reports 7, 45938.
  7. Kadurin, A., Nikolenko, S., Khrabrov, K., Aliper, A., and Zhavoronkov, A. (2017) druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico, Molecular pharmaceutics 14, 3098-3104.
  8. Ordonez, F. J., and Roggen, D. (2016) Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition, Sensors (Basel) 16.

Articles from clinicalinformaticsnews.com

Healthcare Organizations Form Synaptic Health Alliance, Explore Blockchain’s Impact On Data Quality

From http://www.clinicalinformaticsnews.com/2018/12/05/healthcare-organizations-form-synaptic-health-alliance-explore-blockchains-impact-on-data-quality.aspx

By Benjamin Ross

December 5, 2018 | The boom of blockchain and distributed ledger technologies have inspired healthcare organizations to test the capabilities of their data. Quest Diagnostics, in partnership with Humana, MultiPlan, and UnitedHealth Group’s Optum and UnitedHealthcare, have launched a pilot program that applies blockchain technology to improve data quality and reduce administrative costs associated with changes to healthcare provider demographic data.

The collective body, called Synaptic Health Alliance, explores how blockchain can keep only the most current healthcare provider information available in health plan provider directories. The alliance plans to share their progress in the first half of 2019.

Providing consumers looking for care with accurate information when they need it is essential to a high-functioning overall healthcare system, Jason O’Meara, Senior Director of Architecture at Quest Diagnostics, told Clinical Informatics News in an email interview.

“We were intentional about calling ourselves an alliance as it speaks to the shared interest in improving health care through better, collaborative use of an innovative technology,” O’Meara wrote. “Our large collective dataset and national footprints enable us to prove the value of data sharing across company lines, which has been limited in healthcare to date.”

O’Meara said Quest Diagnostics has been investing time and resources the past year or two in understanding blockchain, its ability to drive purpose within the healthcare industry, and how to leverage it for business value.

“Many health care and life science organizations have cast an eye toward blockchain’s potential to inform their digital strategies,” O’Meara said. “We recognize it takes time to learn how to leverage a new technology. We started exploring the technology in early 2017, but we quickly recognized the technology’s value is in its application to business to business use cases: to help transparently share information, automate mutually-beneficial processes and audit interactions.”

Quest began discussing the potential for an alliance with the four other companies a year ago, O’Meara said. Each company shared traits that would allow them to prove the value of data sharing across company lines.

“While we have different perspectives, each member has deep expertise in healthcare technology, a collaborative culture, and desire to continuously improve the patient/customer experience,” said O’Meara. “We also recognize the value of technology in driving efficiencies and quality.”

Following its initial launch in April, Synaptic Health Alliance is deploying a multi-company, multi-site, permissioned blockchain. According to a whitepaper published by Synaptic Health, the choice to use a permissioned blockchain rather than an anonymous one is crucial to the alliance’s success.

“This is a more effective approach, consistent with enterprise blockchains,” an alliance representative wrote. “Each Alliance member has the flexibility to deploy its nodes based on its enterprise requirements. Some members have elected to deploy their nodes within their own data centers, while others are using secured public cloud services such as AWS and Azure. This level of flexibility is key to growing the Alliance blockchain network.”

As the pilot moves forward, O’Meara says the Alliance plans to open ability to other organizations. Earlier this week Aetna and Ascension announced they joined the project.

“I am personally excited by the amount of cross-company collaboration facilitated by this project,” O’Meara says. “We have already learned so much from each other and are using that knowledge to really move the needle on improving healthcare.”

 

US Health And Human Services Looks To Blockchain To Manage Unstructured Data

http://www.clinicalinformaticsnews.com/2018/11/29/us-health-and-human-services-looks-to-blockchain-to-manage-unstructured-data.aspx

By Benjamin Ross

November 29, 2018 | The US Department of Health and Human Services (HHS) is making waves in the blockchain space. The agency’s Division of Acquisition (DA) has developed a new system, called Accelerate, which gives acquisition teams detailed information on pricing, terms, and conditions across HHS in real-time. The department’s Associate Deputy Assistant Secretary for Acquisition, Jose Arrieta, gave a presentation and live demo of the blockchain-enabled system at the Distributed: Health event earlier this month in Nashville, Tennessee.

Accelerate is still in the prototype phase, Arrieta said, with hopes that the new system will be deployed at the end of the fiscal year.

HHS spends around $25 billion a year in contracts, Arrieta said. That’s 100,000 contracts a year with over one million pages of unstructured data managed through 45 different systems. Arrieta and his team wanted to modernize the system.

“But if you’re going to change the way a workforce of 20,000 people do business, you have to think your way through how you’re going to do that,” said Arrieta. “We didn’t disrupt the existing systems: we cannibalized them.”

The cannibalization process resulted in Accelerate. According to Arrieta, the system functions by creating a record of data rather than storing it, leveraging machine learning, artificial intelligence (AI), and robotic process automation (RPA), all through blockchain data.

“We’re using that data record as a mechanism to redesign the way we deliver services through micro-services strategies,” Arrieta said. “Why is that important? Because if you have a single application or data use that interfaces with 55 other applications in your business network, it becomes very expensive to make changes to one of the 55 applications.”

Accelerate distributes the data to the workforce, making it available to them one business process at a time.

“We’re building those business processes without disrupting the existing systems,” said Arrieta, and that’s key. “We’re not shutting off those systems. We’re using human-centered design sessions to rebuild value exchange off of that data.”

The first application for the system, Arrieta said, can be compared to department stores price-matching their online competitors.

It takes the HHS close to a month to collect the amalgamation of data from existing system, whether that be terms and conditions that drive certain price points, or software licenses.

“The micro-service we built actually analyzes that data, and provides that information to you within one second,” said Arrieta. “This is distributed to the workforce, to the 5,000 people that do the contracting, to the 15,000 people that actually run the programs at [HHS].”

This simple micro-service is replicated on every node related to HHS’s internal workforce. If somebody wants to change the algorithm to fit their needs, they can do that in a distributed manner.

Arrieta hopes to use Accelerate to save researchers money at the point of purchase. The program uses blockchain to simplify the process of acquisition.

“How many of you work with the federal government?” Arrieta asked the audience. “Do you get sick of reentering the same information over and over again? Every single business opportunity you apply for, you have to resubmit your financial information. You constantly have to check for validation and verification, constantly have to resubmit capabilities.”

Wouldn’t it be better to have historical notes available for each transaction? said Arrieta. This would allow clinical researchers to be able to focus on “the things they’re really good at,” instead of red tape.

“If we had the top cancer researcher in the world, would you really want her spending her time learning about federal regulations as to how to spend money, or do you want her trying to solve cancer?” Arrieta said. “What we’re doing is providing that data to the individual in a distributed manner so they can read the information of historical purchases that support activity, and they can focus on the objectives and risks they see as it relates to their programming and their objectives.”

Blockchain also creates transparency among researchers, Arrieta said, which says creates an “uncomfortable reality” in the fact that they have to make a decision regarding data, fundamentally changing value exchange.

“The beauty of our business model is internal investment,” Arrieta said. For instance, the HHS could take all the sepsis data that exists in their system, put it into a distributed ledger, and share it with an external source.

“Maybe that could fuel partnership,” Arrieta said. “I can make data available to researchers in the field in real-time so they can actually test their hypothesis, test their intuition, and test their imagination as it relates to solving real-world problems.”

 

Shivom is creating a genomic data hub to elongate human life with AI

From VentureBeat.com
Blockchain-based genomic data hub platform Shivom recently reached its $35 million hard cap within 15 seconds of opening its main token sale. Shivom received funding from a number of crypto VC funds, including Collinstar, Lateral, and Ironside.

The goal is to create the world’s largest store of genomic data while offering an open web marketplace for patients, data donors, and providers — such as pharmaceutical companies, research organizations, governments, patient-support groups, and insurance companies.

“Disrupting the whole of the health care system as we know it has to be the most exciting use of such large DNA datasets,” Shivom CEO Henry Ines told me. “We’ll be able to stratify patients for better clinical trials, which will help to advance research in precision medicine. This means we will have the ability to make a specific drug for a specific patient based on their DNA markers. And what with the cost of DNA sequencing getting cheaper by the minute, we’ll also be able to sequence individuals sooner, so young children or even newborn babies could be sequenced from birth and treated right away.”

While there are many solutions examining DNA data to explain heritage, intellectual capabilities, health, and fitness, the potential of genomic data has largely yet to be unlocked. A few companies hold the monopoly on genomic data and make sizeable profits from selling it to third parties, usually without sharing the earnings with the data donor. Donors are also not informed if and when their information is shared, nor do they have any guarantee that their data is secure from hackers.

Shivom wants to change that by creating a decentralized platform that will break these monopolies, democratizing the processes of sharing and utilizing the data.

“Overall, large DNA datasets will have the potential to aid in the understanding, prevention, diagnosis, and treatment of every disease known to mankind, and could create a future where no diseases exist, or those that do can be cured very easily and quickly,” Ines said. “Imagine that, a world where people do not get sick or are already aware of what future diseases they could fall prey to and so can easily prevent them.”

Shivom’s use of blockchain technology and smart contracts ensures that all genomic data shared on the platform will remain anonymous and secure, while its OmiX token incentivizes users to share their data for monetary gain.

Rise in Population Genomics: Local Government in India Will Use Blockchain to Secure Genetic Data

Blockchain will secure the DNA database for 50 million citizens in the eighth-largest state in India. The government of Andhra Pradesh signed a Memorandum of Understanding with a German genomics and precision medicine start-up, Shivom, which announced to start the pilot project soon. The move falls in line with a trend for governments turning to population genomics, and at the same time securing the sensitive data through blockchain.

Andhra Pradesh, DNA, and blockchain

Storing sensitive genetic information safely and securely is a big challenge. Shivom builds a genomic data-hub powered by blockchain technology. It aims to connect researchers with DNA data donors thus facilitating medical research and the healthcare industry.

With regards to Andhra Pradesh, the start-up will first launch a trial to determine the viability of their technology for moving from a proactive to a preventive approach in medicine, and towards precision health. “Our partnership with Shivom explores the possibilities of providing an efficient way of diagnostic services to patients of Andhra Pradesh by maintaining the privacy of the individual data through blockchain technologies,” said J A Chowdary, IT Advisor to Chief Minister, Government of Andhra Pradesh.

Other Articles in this Open Access Journal on Digital Health include:

Can Mobile Health Apps Improve Oral-Chemotherapy Adherence? The Benefit of Gamification.

Medical Applications and FDA regulation of Sensor-enabled Mobile Devices: Apple and the Digital Health Devices Market

 

How Social Media, Mobile Are Playing a Bigger Part in Healthcare

 

E-Medical Records Get A Mobile, Open-Sourced Overhaul By White House Health Design Challenge Winners

 

Medcity Converge 2018 Philadelphia: Live Coverage @pharma_BI

 

Digital Health Breakthrough Business Models, June 5, 2018 @BIOConvention, Boston, BCEC

 

 

 

 

 

 

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Live Conference Coverage Medcity Converge 2018 Philadelphia: Clinical Trials and Mega Health Mergers

Reporter: Stephen J. Williams, PhD

1:30 – 2:15 PM Clinical Trials 2.0

The randomized, controlled clinical trial is the gold standard, but it may be time for a new model. How can patient networks and new technology be leveraged to boost clinical trial recruitment and manage clinical trials more efficiently?

Moderator: John Reites, Chief Product Officer, Thread @johnreites
Speakers:
Andrew Chapman M.D., Chief of Cancer Services , Sidney Kimmel Cancer Center, Thomas Jefferson University Hospital
Michelle Longmire, M.D., Founder, Medable @LongmireMD
Sameek Roychowdhury MD, PhD, Medical Oncologist and Researcher, Ohio State University Comprehensive Cancer Center @OSUCCC_James

 

Michele: Medable is creating a digital surrogate biomarker for short term end result for cardiology clinical trials as well as creating a virtual site clinical trial design (independent of geography)

Sameek:  OSU is developing RNASeq tests for oncogenic fusions that are actionable

John: ability to use various technologies to conduct telehealth and tele-trials.  So why are we talking about Clinical Trials 2.0?

Andrew: We are not meeting many patients needs.  The provider also have a workload that prevents from the efficient running of a clinical trial.

Michele:  Personalized medicine: what is the framework how we conduct clinical trials in this new paradigm?

Sameek: How do we find those rare patients outside of a health network?  A fragmented health system is hurting patient recruitment efforts.

Wout: The Christmas Tree paradigm: collecting data points based on previous studies may lead to unnecessary criteria for patient recruitment

Sameek:  OSU has a cancer network (Orion) that has 95% success rate of recruitment.  Over Orion network sequencing performed at $10,000 per patient, cost reimbursed through network.  Network helps pharma companies find patients and patients to find drugs

Wout: reaching out to different stakeholders

John: what he sees in 2.0 is use of tech.  They took 12 clinic business but they integrated these sites and was able to benefit patient experience… this helped in recruitment into trials.  Now after a patient is recruited, how 2.0 model works?

Sameek:  since we work with pharma companies, what if we bring in patients from all over the US.  how do we continue to take care of them?

Andrew: utilizing a technology is critically important for tele-health to work and for tele-clinical trials to work

Michele:  the utilization of tele-health by patients is rather low.

Wout:  We are looking for insights into the data.  So we are concentrated on collecting the data and not decision trees.

John: What is a barrier to driving Clinical Trial 2.0?

Andrew: The complexity is a barrier to the patient.  Need to show the simplicity of this.  Need to match trials within a system.

Saleem: Data sharing incentives might not be there or the value not recognized by all players.  And it is hard to figure out how to share the data in the most efficient way.

Wout: Key issue when think locally and act globally but healthcare is the inverse of this as there are so many stakeholders but that adoption by all stakeholders take time

Michele: accessibility of healthcare data by patients is revolutionary.  The medical training in US does not train doctors in communicating a value of a trial

John: we are in a value-driven economy.  You have to give alot to get something in this economy. Final comments?

Saleem: we need fundamental research on the validity of clinical trials 2.0.

Wout:  Use tools to mine manually but don’t do everything manually, not underlying tasks

Andrew: Show value to patient

2:20-3:00 PM CONVERGEnce on Steroids: Why Comcast and Independence Blue Cross?

This year has seen a great deal of convergence in health care.  One of the most innovative collaborations announced was that of Cable and Media giant Comcast Corporation and health plan Independence Blue Cross.  This fireside chat will explore what the joint venture is all about, the backstory of how this unlikely partnership came to be, and what it might mean for our industry.

sponsored by Independence Blue Cross @IBX 

Moderator: Tom Olenzak, Managing Director Strategic Innovation Portfolio, Independence Blue Cross @IBX
Speakers:
Marc Siry, VP, Strategic Development, Comcast
Michael Vennera, SVP, Chief Information Officer, Independence Blue Cross

Comcast and Independence Blue Cross Blue Shield are teaming together to form an independent health firm to bring various players in healthcare onto a platform to give people a clear path to manage their healthcare.  Its not just about a payer and information system but an ecosystem within Philadelphia and over the nation.

Michael:  About 2015 at a health innovation conference they came together to produce a demo on how they envision the future of healthcare.

Marc: When we think of a customer we think of the household. So we thought about aggregating services to people in health.  How do people interact with their healthcare system?

What are the risks for bringing this vision to reality?

Michael: Key to experience is how to connect consumer to caregiver.

How do we aggregate the data, and present it in a way to consumer where it is actionable?

How do we help the patient to know where to go next?

Marc: Concept of ubiquity, not just the app, nor asking the provider to ask patient to download the app and use it but use our platform to expand it over all forms of media. They did a study with an insurer with metabolic syndrome and people’s viewing habits.  So when you can combine the expertise of IBX and the scale of a Comcast platform you can provide great amount of usable data.

Michael: Analytics will be a prime importance of the venture.

Tom:  We look at lots of companies that try to pitch technologies but they dont understand healthcare is a human problem not a tech problem.  What have you learned?

Marc: Adoption rate of new tech by doctors is very low as they are very busy.  Understanding the clinicians workflow is important and how to not disrupt their workflow was humbling for us.

Michael:  The speed at which big tech companies can integrate and innovate new technologies is very rapid, something we did not understand.  We want to get this off the ground locally but want to take this solution national and globally.

Marc:  We are not in competition with local startups but we are looking to work with them to build scale and operability so startups need to show how they can scale up.  This joint venture is designed to look at these ideas.  However this will take a while before we open up the ecosystem until we can see how they would add value. There are also challenges with small companies working with large organizations.

 

Please follow on Twitter using the following #hashtags and @pharma_BI

#MCConverge

#cancertreatment

#healthIT

#innovation

#precisionmedicine

#healthcaremodels

#personalizedmedicine

#healthcaredata

And at the following handles:

@pharma_BI

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Please see related articles on Live Coverage of Previous Meetings on this Open Access Journal

LIVE – Real Time – 16th Annual Cancer Research Symposium, Koch Institute, Friday, June 16, 9AM – 5PM, Kresge Auditorium, MIT

Real Time Coverage and eProceedings of Presentations on 11/16 – 11/17, 2016, The 12th Annual Personalized Medicine Conference, HARVARD MEDICAL SCHOOL, Joseph B. Martin Conference Center, 77 Avenue Louis Pasteur, Boston

Tweets Impression Analytics, Re-Tweets, Tweets and Likes by @AVIVA1950 and @pharma_BI for 2018 BioIT, Boston, 5/15 – 5/17, 2018

BIO 2018! June 4-7, 2018 at Boston Convention & Exhibition Center

https://pharmaceuticalintelligence.com/press-coverage/

 

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