Healthcare analytics, AI solutions for biological big data, providing an AI platform for the biotech, life sciences, medical and pharmaceutical industries, as well as for related technological approaches, i.e., curation and text analysis with machine learning and other activities related to AI applications to these industries.
The Forum will focus on patient interactions across care settings, and the role technology and data can play in advancing knowledge discovery and care delivery. The agenda can be found here.
3.1.4 LIVE Day One – World Medical Innovation Forum ARTIFICIAL INTELLIGENCE, Boston, MA USA, Monday, April 8, 2019, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair
AI and DL for Stroke Patient management detection of acute intracranial haemorrhage from small dat sets
1 of every 10 death is a Stroke caused, 5.8 million people die of Stroke Stroke is a medical emergency, CT Scan
Spotting brain bleeding after
Deep Learning algorithms – explainable AI – human mimiking algorithm developed @MGH
Explainable AI – Multi-window mixing & multi-slice mixing is in PACS @MGH
commercial opportunity: Near stroke detection
@MGH Stroke with AI algorithms Patent IP @PartnerInnovation seeking funding for Stroke management
Laura Germine, PhD
Next generation of behavior assessment
in Psychiatry – neuropsychiatry
Problem of measurement of innovation with validity needed – Tools to measure and have outcomes
Unreasonable effectiveness of Good Data : Math achievement – visual-spatial attention
Looking for partners
Satrajit Ghosh, PhD
Mental health 1 in 4 adults 18% of adolescence 13% of children
first treatment effective only in 25% of cases
Brain structure and Function – using MR – observed behaviors – using Voice, speaking is a very complex activity
Talk intent emotions – window into the mind
Speech
Xudong Huang, PhD
Brain Drug Discovery – leveraging AI
Major depressive DIsorder ( MDD) – 16 million in US 210 Billion a year treatment burden
Alzheimer’s DIsease – 5.8 million AS in US – $290 in 2019 a year treatment burden
Potential druggable for MDD and AD
Tryptophan-Kynurenina pathway
Secreted Protein Acidic and Cysteine rich
AI-Powered Drug Discovery Platform – AtomNet
Preclinical drug discovery and development
Screened 10MIllion compounds – 48 inhibitors for tryptophan-catabolizing enzymes in
Tryptophan-Kynurenina pathway
Tina Kapur, PhD
AI to visualize needles in UltraSound-guided (US) liver biopsy – safer to patient and easier for the physicina
mass in liver suspected to be from a metastasis in the pancreas
AI to enable the MD to see the needle completely independent of the US technician
Benefits if available to all performers of liver biopsy
Patients: Benefit from location of tissue biopsy sampling
prostate needle in MRI
Button labelled Needle, MD turn on/of button
navigation systems not in use
95% proceedures done free hand
1 Million US guided liver biopsy/yr, growing @4%
manufacturing of US equipment to be interested to embed
Bharti Khurana, MD
Home is the most dangerous place for women killing of women hit by husband. ages 25 to 38 – fracture of bone IPV – Intimate Partner Violence – 1 in 4 women and 1 in 9 men IPV is preventable under reporting
Tybanny of the Urgent
clinical decision support to predict risk probability automate alerts 95% 50% 15% – Probability of IPV – insivible to visible
empower healthcare providers
reduce ER volume will reduce cost
Vesela Kovacheva, MD, PhD
Titrating drug infusions – Personalized for patient safety reduce med error
Titrating drug infusions – automation system from anestesia – function automonically
local anestatic for Cesearian section – BP drog when spinal administration of anestatic agent
calculate every minure – 20 minutes are critical from drug infusion
decision to administer vasopressors is taken evey minute on the bP
Rural areas one anestosiolog suverviser three OR at the same time
1.25 million C-section
75% develop low BP
complications in babies decreased BP – tachepnis in neonatal – NICU 100Million $ per year.
develop same algorithms for propofol in sedetion and insulin in ICU
other surgeries – knee, hip, spinal
Constance Lehman, Md, PhD
Breast Cancer Out of 2 Billion women 2million will be diagnosed with breast cancer
screening will prevent development
current tools of mamography – no single interpretation and shortage
memograph vs Future risk of BC development
Deep Learning model; Training model consequitive memograms Risk model developed – AI technology on memograpm 0.71 when other factors added
DIverse races – RAce blind AI model
AI model of diagnosis in one year after the memogram taken
breast density – imager certified, 6% are dense, 85% and every number in between
Expertise: MGH, MIT, Prior failure of CAD
Patents for commercialization beyond MGH
Lisa Nickerson, PhD
70,000 drug overdose, 50,000 opioids related
Death from prescription opioids is on the increase after 2013 – fentanyl – causing overdose
Mobile Health Applications – Monitoring motor fluctuation in Parkinson’s Disease (PD)
7 – 10Million WOrldwide, 1 Million in the US,
dopamine-producing neuron
main medication in early stage – Levodopa
Need an objective and continuous monitoring toool for tacking the symptoms’ dynamics
mHealth for monitoring PD – mimiking clinical evaluations mail limitations: Deendency on standardized motor tasks in sufficient time resolution in symptoms severity during ADLs
Lunch with Experts: Intensive sessions addressing cutting-edge artificial intelligence topics.
Applying AI to Save Lives During the Opioid Crisis
The U.S. is in the throes of a devastating epidemic of opioid addiction and overdose — some 130 people die nationally every day from opioids, says the National Institute on Drug Abuse. With a total economic cost of more than $78 billion a year, AI is being harnessed to develop new tools that can help alleviate this national crisis. This session will discuss AI-based strategies that academic and industry teams are leveraging to help clinical and public health officials better predict, identify, and treat opioid addiction, and also data privacy concerns.
Sarah Wakeman, MD, Medical Director, Substance Use Disorder Initiative, MGH; Assistant Professor, Medicine, HMS
Scott Weiner, MD, Director, Brigham Comprehensive Opioid Response and Education (B-CORE) Program, BH; Assistant Professor, HMS
Community Hospitals: Key Component in Healthcare Transformation
Community hospitals are the largest sources of patient care in the U.S. As such, they represent a frontier in the transformation of health care. How are these organizations using AI and digital technologies to drive transformation? What are the distinctions from academic medical centers? This session will address these and other topics that impact community hospitals.
Moderator: Michael Jaff, DO, President, NWH, PHS, Professor of Medicine, HMS
Across the spectrum of patient care, the management of diabetes has been flooded with new technology and treatment options for both type 1 and type 2 diabetes – there is a range of new devices and software, including automatic insulin infusion systems, glucose sensors, AI-based algorithms and decision support tools, with an artificial pancreas on the horizon. This session will focus on these areas and clinical use cases that highlight the value of AI.
Moderator: Deborah Wexler, MD, Clinical Director, Diabetes Center, MGH; Associate Professor, HMS
Marie McDonnell, MD, Section Chief and Director, Diabetes Program, BH; Lecturer, HMS
Marie Schiller, VP, Connected Care and Insulins Product Development and Site Head, Cambridge Innovation Center, Eli Lilly
AI and Its Impact on the Future of Emergency Care
There are over 136 million Emergency Department visits annually in the U.S. providing 24/7 unscheduled treatment for problems from minor illness to life threatening traumatic injuries. Emergency department care teams provide high quality, safe care in an efficient fashion. In this session, we consider the future of AI in emergency care from the initial decision to seek emergency care, to diagnostic processes within the ED and final disposition decision.. From chat bots for patient triage, telehealth for patient visits to machine learning outcome prediction, we will consider how these novel technologies will impact emergency care delivery.
Moderator: Adam Landman, MD, VP and CIO, BH; Associate Professor of Emergency Medicine, HMS
Peter Chai, MD, Assistant Professor, Emergency Medicine, BH, HMS
Kohei Hasegawa, MD, Attending Physician, Emergency Medicine, MGH; Associate Professor, Emergency Medicine, HMS
Sean Kelly, MD, CMO, Imprivata; Assistant Professor, Emergency Medicine, HMS
Bijoy Sagar, VP, Chief Digital Technology Officer, Stryker
Mental Health, Smartphone Apps and the Promise of AI
Patients can face significant barriers when it comes to accessing high-quality, evidence-based treatment for mental illness. AI-enabled technologies, including smartphone-based tools, that may help close this treatment gap for patients worldwide. This session will focus on efforts to develop smartphone apps and other tools, including those designed to help predict patients’ moods and provide cognitive behavioral therapy.
Moderator: Sabine Wilhelm, PhD, Chief of Psychology; Director, OCD and Related Disorders Program, MGH; Professor, Psychology, HMS
David Silbersweig, MD, Chairman, Department of Psychiatry, BH; Stanley Cobb Professor of Psychiatry, HMS
Jeremy Sohn, VP, Global Head of Digital Business Development and Licensing , Novartis
From Startup to Impact (Pharma and Diagnostics)
This session will introduce you to five leading start-up companies who will each share their respective impact in the pharmaceutical and diagnostic realms in 10-minute pitches.
Moderator: James Brink, MD, Radiologist-in-Chief, MGH; Juan M. Taveras Professor of Radiology, HMS
Moderator: James Nicholls, Managing Director, Fitzroy Health
As the potential of AI comes into clearer view, many academic medical centers are taking notice and crafting institutional strategies for incorporating AI into clinical practice. But where are the most meaningful opportunities? What are the biggest challenges? And, importantly, will patient care be noticeably different — better, more available, and/or less costly?
Chief Data Science Officer, PHS; Vice Chairman, Radiology, MGH; Associate Professor, Radiology, HMS
scaling Machine learning focused areas high accuracy, training ground truth, today the humans establish it in the future with AI ground truth will be created by AI
how to handle and move the intelligence and discoveries across units
AI is a tool for conducting faster, more efficient clinical trials. Panelists will discuss how AI-enabled methods can further adaptive trial capabilities, trial design and trial management.
As providers embrace value-based approaches, the demands of clinical data collection, assessment, and information-sharing loom large. In this data-driven environment, clinicians must sift through ever-growing pools of information that can exceed the limits of human capability. An assortment of AI-based solutions is now emerging that may offer some relief. Panelists will discuss how these approaches are helping to support better, more personalized care, and the challenges faced by clinicians and managers for effective adoption.
Cardiovascular diseases remain the leading cause of death worldwide and an expense, making this area ripe for AI-enabled innovations. Teams are pursuing a range of AI-based tools in cardiovascular medicine: including AI-powered drug discovery and diagnostics to automated cardiac image analyses and AI-guided care delivery pathways. Panelists will discuss where AI is having a sizeable impact. The discussion will also include the perspectives of a patient who benefited from AI-enabled cardiovascular care.
COTY in Copenhagen – AI augment capability of EMTs dispatcher is prompted with questions to decide if this call is Heart arrest caving few minutes for EMT response
Administrator, Centers for Medicare and Medicaid Services
2020 20% of all expenses spent will be on Healthcare in the US
Gov’t was a barrier to innovations
initiative of cutting regulations
innovation – how we pay providers for value produced vs regulation that stay in the way
gov’t slow to respond: FDA approval and CMS access to treatment and reimbursement
Analysis of drug a patient takes, CMS – quality, medical record given to patient across all providers they use and be able to give to a new provides all historical data
Data privacy and security
Innovators in Colorado – health care cost need be lowered in a major way
3.4.3 The Regulatory challenge in adopting AI, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair
In the last couple of years we are witnessing a surge of AI applications in healthcare. It is clear now, that AI and its wide range of health-applications are about to revolutionize diseases’ pathways and the way the variety of stakeholders in this market interact.
Not surprisingly, the developing surge has waken the regulatory watchdogs who are now debating ways to manage the introduction of such applications to healthcare. Attributing measures to known regulatory checkboxes like safety, and efficacy is proving to be a complex exercise. How to align claims made by manufacturers, use cases, users’ expectations and public expectations is unclear. A recent demonstration of that is the so called “failure” of AI in social-network applications like FaceBook and Twitter in handling harmful materials.
‘Advancing AI in the NHS’ – is a report covering the challenges and opportunities of AI in the NHS. It is a modest contribution to the debate in such a timely and fast-moving field! I bring here the report’s preface and executive summary hoping that whoever is interested in reading the whole 50 pages of it will follow this link: f53ce9_e4e9c4de7f3c446fb1a089615492ba8c
Acknowledgements
We and Polygeia as a whole are grateful to Dr Dror Nir, Director, RadBee, whose insights
were valuable throughout the research, conceptualisation, and writing phases of this work; and to Dr Giorgio Quer, Senior Research Scientist, Scripps Research Institute; Dr Matt Willis, Oxford Internet Institute, University of Oxford; Professor Eric T. Meyer, Oxford Internet Institute, University of Oxford; Alexander Hitchcock, Senior Researcher, Reform; Windi Hari, Vice President Clinical, Quality & Regulatory, HeartFlow; Jon Holmes, co-founder and Chief Technology Officer, Vivosight; and Claudia Hartman, School of Anthropology & Museum Ethnography, University of Oxford for their advice and support.
Almost every day, as MP for Cambridge, I am told of new innovations and developments that show that we are on the cusp of a technological revolution across the sectors. This technology is capable of revolutionising the way we work; incredible innovations which could increase our accuracy, productivity and efficiency and improve our capacity for creativity and innovation.
But huge change, particularly through adoption of new technology, can be difficult to communicate to the public, and if we do not make sure that we explain carefully the real benefits of such technologies we easily risk a backlash. Despite good intentions, the care.data programme failed to win public trust, with widespread worries that the appropriate safeguards weren’t in place, and a failure to properly explain potential benefits to patients. It is vital that the checks and balances we put in place are robust enough to sooth public anxiety, and prevent problems which could lead to steps back, rather than forwards.
Previous attempts to introduce digital innovation into the NHS also teach us that cross-disciplinary and cross-sector collaboration is essential. Realising this technological revolution in healthcare will require industry, academia and the NHS to work together and share their expertise to ensure that technical innovations are developed and adopted in ways that prioritise patient health, rather than innovation for its own sake. Alongside this, we must make sure that the NHS workforce whose practice will be altered by AI are on side. Consultation and education are key, and this report details well the skills that will be vital to NHS adoption of AI. Technology is only as good as those who use it, and for this, we must listen to the medical and healthcare professionals who will rightly know best the concerns both of patients and their colleagues. The new Centre for Data Ethics and Innovation, the ICO and the National Data Guardian will be key in working alongside the NHS to create both a regulatory framework and the communications which win society’s trust. With this, and with real leadership from the sector and from politicians, focused on the rights and concerns of individuals, AI can be advanced in the NHS to help keep us all healthy.
Daniel Zeichner
MP for Cambridge
Chair, All-Party Parliamentary Group on Data Analytics
Executive summary
Artificial intelligence (AI) has the potential to transform how the NHS delivers care. From enabling patients to self-care and manage long-term conditions, to advancing triage, diagnostics, treatment, research, and resource management, AI can improve patient outcomes and increase efficiency. Achieving this potential, however, requires addressing a number of ethical, social, legal, and technical challenges. This report describes these challenges within the context of healthcare and offers directions forward.
Data governance
AI-assisted healthcare will demand better collection and sharing of health data between NHS, industry and academic stakeholders. This requires a data governance system that ensures ethical management of health data and enables its use for the improvement of healthcare delivery. Data sharing must be supported by patients. The recently launched NHS data opt-out programme is an important starting point, and will require monitoring to ensure that it has the transparency and clarity to avoid exploiting the public’s lack of awareness and understanding. Data sharing must also be streamlined and mutually beneficial. Current NHS data sharing practices are disjointed and difficult to negotiate from both industry and NHS perspectives. This issue is complicated by the increasing integration of ’traditional’ health data with that from commercial apps and wearables. Finding approaches to valuate data, and considering how patients, the NHS and its partners can benefit from data sharing is key to developing a data sharing framework. Finally, data sharing should be underpinned by digital infrastructure that enables cybersecurity and accountability.
Digital infrastructure
Developing and deploying AI-assisted healthcare requires high quantity and quality digital data. This demands effective digitisation of the NHS, especially within secondary care, involving not only the transformation of paper-based records into digital data, but also improvement of quality assurance practices and increased data linkage. Beyond data digitisation, broader IT infrastructure also needs upgrading, including the use of innovations such as wearable technology and interoperability between NHS sectors and institutions. This would not only increase data availability for AI development, but also provide patients with seamless healthcare delivery, putting the NHS at the vanguard of healthcare innovation.
Standards
The recent advances in AI and the surrounding hype has meant that the development of AI-assisted healthcare remains haphazard across the industry, with quality being difficult to determine or varying widely. Without adequate product validation, including in
real-world settings, there is a risk of unexpected or unintended performance, such as sociodemographic biases or errors arising from inappropriate human-AI interaction. There is a need to develop standardised ways to probe training data, to agree upon clinically-relevant performance benchmarks, and to design approaches to enable and evaluate algorithm interpretability for productive human-AI interaction. In all of these areas, standardised does not necessarily mean one-size-fits-all. These issues require addressing the specifics of AI within a healthcare context, with consideration of users’ expertise, their environment, and products’ intended use. This calls for a fundamentally interdisciplinary approach, including experts in AI, medicine, ethics, cognitive science, usability design, and ethnography.
Regulations
Despite the recognition of AI-assisted healthcare products as medical devices, current regulatory efforts by the UK Medicines and Healthcare Products Regulatory Agency and the European Commission have yet to be accompanied by detailed guidelines which address questions concerning AI product classification, validation, and monitoring. This is compounded by the uncertainty surrounding Brexit and the UK’s future relationship with the European Medicines Agency. The absence of regulatory clarity risks compromising patient safety and stalling the development of AI-assisted healthcare. Close working partnerships involving regulators, industry members, healthcare institutions, and independent AI-related bodies (for example, as part of regulatory sandboxes) will be needed to enable innovation while ensuring patient safety.
The workforce
AI will be a tool for the healthcare workforce. Harnessing its utility to improve care requires an expanded workforce with the digital skills necessary for both developing AI capability and for working productively with the technology as it becomes commonplace.
Developing capability for AI will involve finding ways to increase the number of clinician-informaticians who can lead the development, procurement and adoption of AI technology while ensuring that innovation remains tied to the human aspect of healthcare delivery. More broadly, healthcare professionals will need to complement their socio-emotional and cognitive skills with training to appropriately interpret information provided by AI products and communicate it effectively to co-workers and patients.
Although much effort has gone into predicting how many jobs will be affected by AI-driven automation, understanding the impact on the healthcare workforce will require examining how jobs will change, not simply how many will change.
Legal liability
AI-assisted healthcare has implications for the legal liability framework: who should be held responsible in the case of a medical error involving AI? Addressing the question of liability will involve understanding how healthcare professionals’ duty of care will be impacted by use of the technology. This is tied to the lack of training standards for healthcare professionals to safely and effectively work with AI, and to the challenges of algorithm interpretability, with ”black-box” systems forcing healthcare professionals to blindly trust or distrust their output. More broadly, it will be important to examine the legal liability of healthcare professionals, NHS trusts and industry partners, raising questions
Recommendations
TheNHS,theCentreforDataEthicsandInnovation,andindustryandacademicpartnersshould conduct a review to understand the obstacles that the NHS and external organisations face around data sharing. They should also develop health data valuation protocols which consider the perspectives of patients, the NHS, commercial organisations, and academia. This work should inform the development of a data sharing framework.
TheNationalDataGuardianandtheDepartmentofHealthshould monitor the NHS data opt-out programme and its approach to transparency and communication, evaluating how the public understands commercial and non-commercial data use and the handling of data at different levels of anonymisation.
TheNHS,patientadvocacygroups,andcommercialorganisationsshould expand public engagement strategies around data governance, including discussions about the value of health data for improving healthcare; public and private sector interactions in the development of AI-assisted healthcare; and the NHS’s strategies around data anonymisation, accountability, and commercial partnerships. Findings from this work should inform the development of a data sharing framework.
TheNHSDigitalSecurityOperationsCentreshould ensure that all NHS organisations comply with cybersecurity standards, including having up-to-date technology.
NHSDigital,theCentreforDataEthicsandInnovation,andtheAlanTuringInstituteshould develop technological approaches to data privacy, auditing, and accountability that could be implemented in the NHS. This should include learning from Global Digital Exemplar trusts in the UK and from international examples such as Estonia.
TheNHSshould continue to increase the quantity, quality, and diversity of digital health data across trusts. It should consider targeted projects, in partnership with professional medical bodies, that quality-assure and curate datasets for more deployment-ready AI technology. It should also continue to develop its broader IT infrastructure, focusing on interoperability between sectors, institutions, and technologies, and including the end users as central stakeholders.
TheAlanTuringInstitute,theAdaLovelaceInstitute,andacademicandindustrypartnersinmedicineandAIshould develop ethical frameworks and technological approaches for the validation of training data in the healthcare sector, including methods to minimise performance biases and validate continuously-learning algorithms.
TheAlanTuringInstitute,theAdaLovelaceInstitute,andacademicandindustrypartnersinmedicineandAIshould develop standardised approaches for evaluating product performance in the healthcare sector, with consideration for existing human performance standards and products’ intended use.
TheAlanTuringInstitute,theAdaLovelaceInstitute,andacademicandindustrypartnersinmedicineandAIshould develop methods of enabling and evaluating algorithm interpretability in the healthcare sector. This work should involve experts in AI, medicine, ethics, usability design, cognitive science, and ethnography, among others.
DevelopersofAIproductsandNHSCommissionersshould ensure that usability design remains a top priority in their respective development and procurement of AI-assisted healthcare products.
TheMedicinesandHealthcareProductsRegulatoryAgencyshould establish a digital health unit with expertise in AI and digital products that will work together with manufacturers, healthcare bodies, notified bodies, AI-related organisations, and international forums to advance clear regulatory approaches and guidelines around AI product classification, validation, and monitoring. This should address issues including training data and biases, performance evaluation, algorithm interpretability, and usability.
TheMedicinesandHealthcareProductsRegulatoryAgency,theCentreforDataEthicsandInnovation,andindustrypartnersshould evaluate regulatory approaches, such as regulatory sandboxing, that can foster innovation in AI-assisted healthcare, ensure patient safety, and inform on-going regulatory development.
TheNHSshould expand innovation acceleration programmes that bridge healthcare and industry partners, with a focus on increasing validation of AI products in real-world contexts and informing the development of a regulatory framework.
TheMedicinesandHealthcareProductsRegulatoryAgencyandotherGovernmentbodiesshould arrange a post-Brexit agreement ensuring that UK regulations of medical devices, including AI-assisted healthcare, are aligned as closely as possible to the European framework and that the UK can continue to help shape Europe-wide regulations around this technology.
TheGeneralMedicalCouncil,theMedicalRoyalColleges,HealthEducationEngland,andAI-relatedbodiesshould partner with industry and academia on comprehensive examinations of the healthcare sector to assess which, when, and howjobs will be impacted by AI, including analyses of the current strengths, limitations, and workflows of healthcare professionals and broader NHS staff. They should also examine how AI-driven workforce changes will impact patient outcomes.
TheFederationofInformaticsProfessionalsandtheFacultyofClinicalInformaticsshould continue to lead and expand standards for health informatics competencies, integrating the relevant aspects of AI into their training, accreditation, and professional development programmes for clinician-informaticians and related professions.
HealthEducationEnglandshould expand training programmes to advance digital and AI-related skills among healthcare professionals. Competency standards for working with AI should be identified for each role and established in accordance with professional registration bodies such as the General Medical Council. Training programmes should ensure that ”un-automatable” socio-emotional and cognitive skills remain an important focus.
TheNHSDigitalAcademyshould expand recruitment and training efforts to increase the number of Chief Clinical Information Officers across the NHS, and ensure that the latest AI ethics, standards, and innovations are embedded in their training programme.
Legalexperts,ethicists,AI-relatedbodies,professionalmedicalbodies,andindustryshould review the implications of AI-assisted healthcare for legal liability. This includes understanding how healthcare professionals’ duty of care will be affected, the role of workforce training and product validation standards, and the potential role of NHS Indemnity and no-fault compensation systems.
AI-relatedbodiessuchastheAdaLovelaceInstitute,patientadvocacygroupsandotherhealthcarestakeholdersshould lead a public engagement and dialogue strategy to understand the public’s views on liability for AI-assisted healthcare.
Digital Therapeutics: A Threat or Opportunity to Pharmaceuticals
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
3.3.7 Digital Therapeutics: A Threat or Opportunity to Pharmaceuticals, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair
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.
3.1.1 World Medical Innovation Forum, Partners Innovations, ARTIFICIAL INTELLIGENCE | APRIL 8–10, 2019 | Westin, BOSTON, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair
Lunch with Top Leading Experts: Intensive sessions addressing cutting-edge artificial intelligence topics.
Applying AI to Save Lives During the Opioid Crisis
The U.S. is in the throes of a devastating epidemic of opioid addiction and overdose — some 130 people die in this country every day from opioids, says the National Institute on Drug Abuse. With a total economic cost of more than $78 billion a year, academic and industry organizations are harnessing AI to develop new tools that can help alleviate this national crisis. This session will discuss some of the AI-based strategies that academic and industry teams are leveraging to help clinical and public health officials better predict, identify, and treat opioid addiction, as well as some of the concerns around data privacy.
Sarah Wakeman, MD, Medical Director, Substance Use Disorder Initiative, MGH; Assistant Professor, Medicine, HMS
Scott Weiner, MD, Director, Brigham Comprehensive Opioid Response and Education (B-CORE) Program, BWH; Assistant Professor, HMS
Community Hospitals: Key Component in Healthcare Transformation
Community hospitals are the largest sources of patient care in the U.S. As such, they represent a critical frontier in the transformation of health care. How are these organizations using AI and digital technologies to drive transformation? What are the key distinctions from academic medical centers? This session will address these and other critical topics that impact community hospitals and their essential, though often overlooked, role in health care.
Moderator: Michael Jaff, DO, President, NWH, PHS, Professor of Medicine, HMS
Across the full spectrum of patient care, the management of diabetes has been flooded with new technology and treatment options for both type 1 and type 2 diabetes – there is a range of new devices and software, including automatic insulin infusion systems, glucose sensors, AI-based algorithms and decision support tools, with artificial pancreas on the horizon. This session will focus on these areas as well as clinical use cases that highlight the value of AI.
Moderator: Deborah Wexler, MD, Clinical Director, Diabetes Center, MGH; Associate Professor, HMS
Marie McDonnell, MD, Section Chief and Director, Diabetes Program, BWH; Lecturer, HMS
David Silbersweig, MD, Chairman, Department of Psychiatry, BWH; Stanley Cobb Professor of Psychiatry, HMS
From Startup to Impact (Pharma and Diagnostics)
With all the hype surrounding AI, this session will focus on what really matters. Impact! Who is really moving the needle in life sciences today? This session will introduce you to five leading companies who will share their client stories over lunch.
Moderator: James Brink, MD, Radiologist-in-Chief, MGH; Juan M. Taveras Professor of Radiology, HMS
In 1984 Isaac Asimov was asked to predict what life in 2019 would be like. Using the same aperture, we as what will health care look like 35 years from now? What capabilities will clinicians have that they now struggle with? And what will be the biggest challenges? Current trends suggest that we will see some significant gains in the areas of cancer immunotherapy, gene therapy for devastating rare diseases, and treatments for common neuropsychiatric conditions, including schizophrenia and depression. Panelists will draw on their visionary perspective and will reflect on what to expect and why.
Connie Moser, Chief Operating Officer, Verge Health
Chief Digital Strategy Officer Roundtable
With the advent of healthcare AI-enabled technologies, this session brings together several chief digital health officers from a range of organizations. The discussion will address key tradeoffs in sequencing technology across academic medical centers; what technologies are being prioritized; and how consumer expectations are impacting the future delivery model of healthcare.
Richard Zane, MD, Chief Innovation Officer, UCHealth; Professor and Chair,Department of Emergency Medicine, University of Colorado School of Medicine
Innovation Fellows: A New Model of Collaboration
The Innovation Fellows Program provides short-term, experiential career development opportunities for future leaders in health care focused on accelerating collaborative innovation between science and industry. It facilitates personnel exchanges between Harvard Medical School staff from Partners’ hospitals and participating biopharmaceutical, device, venture capital, digital health, payor and consulting firms. A successful example of open innovation, Fellows and Hosts learn from each other as they collaborate on projects ranging from clinical development to digital health & artificial intelligence to new care delivery models and industry disruption. Come listen to the experience and insights of our panelists, including Fellows, Industry Partners and hospital leadership, and learn how this new model of collaboration can deliver value and lead to broader relationships between industry and academia.
Last Mile: Fully Implementing AI in Healthcare
Moderator: Keith Dreyer, DO, PhD, Chief Data Science Officer, PHS; Vice Chairman, Radiology, MGH; Associate Professor, Radiology, HMS
Katherine Andriole, PhD, Director of Research Strategy and Operations, MGH & BWH CCDS; Associate Professor, Radiology, HMS
Samuel Aronson, Executive Director, IT, Personalized Medicine, PHS
Sandhya Rao, MD, Senior Medical Director, Population Health, PHS; Assistant Professor, Medicine, HMS
Standards and Regulation: The Emerging AI Framework
From Startup to Impact (Provider Solutions)
With all the hype surrounding AI, this session will focus on what really matters. Impact! Who is really moving the needle for healthcare providers today? This session will introduce you to five leading companies who will share their client stories over lunch.
Twelve clinical AI teams culled through the Innovation Discovery Grant program present their work illustrating how AI can be used to improve patient health and healthcare delivery. This session is designed for investors, entrepreneurs, investigators, and others who are interested in commercializing AI opportunities that are currently in development with support from the Innovation Office.
The culture of innovation throughout Partners HealthCare naturally fosters robust discussions about new “disruptive” technologies and which ones will have the biggest impact on health care. The Disruptive Dozen was created to identify and rank the technologies that Partners faculty feel will break through over the next decade to significantly improve health care. This year, the Disruptive Dozen focuses on relevant advances and opportunities in artificial intelligence (AI).
R&D for Artificial Intelligence Tools & Applications: Google’s Research Efforts in 2018
Reporter: Aviva Lev-Ari, PhD, RN
3.2.1 R&D for Artificial Intelligence Tools & Applications: Google’s Research Efforts in 2018, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair
Posted by Jeff Dean, Senior Fellow and Google AI Lead, on behalf of the entire Google Research Community 2018 was an exciting year for Google’s research teams, with our work advancing technology in many ways, including fundamental computer science research results and publications, the application of our research to emerging areas new to Google (such as healthcare and robotics), open source software contributions and strong collaborations with Google product teams, all aimed at providing useful tools and services. Below, we highlight just some of our efforts from 2018, and we look forward to what will come in the new year. For a more comprehensive look, please see our publications in 2018.
Role of Informatics in Precision Medicine: Notes from Boston Healthcare Webinar: Can It Drive the Next Cost Efficiencies in Oncology Care? Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)
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
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:
Can Blockchain Technology and Artificial Intelligence Cure What Ails Biomedical Research and Healthcare, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)
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:
Recent achievements in next-generation artificial intelligence
Basic concepts of highly distributed storage systems (HDSS) as a preferred method for medical data storage
Open source blockchain Exonium and its application for healthcare marketplace
A blockchain-based platform allowing patients to have control of their data and manage access
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:
Prediction of drug properties(2, 3) and toxicities(4)
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:
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, Oncotarget9, 5665-5690.
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 pharmaceutics13, 2524-2530.
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 research16, 1401-1409.
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 sciences133, 70-78.
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, Aging8, 1021-1033.
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 reports7, 45938.
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 pharmaceutics14, 3098-3104.
Ordonez, F. J., and Roggen, D. (2016) Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition, Sensors (Basel)16.
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.”
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.”
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.
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:
Live Coverage: MedCity Converge 2018 Philadelphia: AI in Cancer and Keynote Address
Reporter: Stephen J. Williams, PhD
3.3.4 Live Coverage: MedCity Converge 2018 Philadelphia: AI in Cancer and Keynote Address, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair
8:30 AM -9:15
Practical Applications of AI in Cancer
We are far from machine learning dictating clinical decision making, but AI has important niche applications in oncology. Hear from a panel of innovative startups and established life science players about how machine learning and AI can transform different aspects in healthcare, be it in patient recruitment, data analysis, drug discovery or care delivery.
Ayan: working at IBM and Thompon Rueters with structured datasets and having gone through his own cancer battle, he is now working in healthcare AI which has an unstructured dataset(s)
Carla: collecting medical images over the world, mainly tumor and calculating tumor volumetrics
Tufia: drug resistant breast cancer clinician but interested in AI and healthcareIT at Mayo
John: taking large scale datasets but a machine learning skeptic
moderator: how has imaging evolved?
Carla: ten times images but not ten times radiologists so stressed field needs help with image analysis; they have seen measuring lung tumor volumetrics as a therapeutic diagnostic has worked
moderator: how has AI affected patient recruitment?
Tufia: majority of patients are receiving great care but AI can offer profiles and determine which patients can benefit from tertiary care;
John: 1980 paper on no free lunch theorem; great enthusiasm about optimization algortihisms fell short in application; can extract great information from e.g. images
moderator: how is AI for healthcare delivery working at mayo?
Tufia: for every hour with patient two hours of data mining. for care delivery hope to use the systems to leverage the cognitive systems to do the data mining
John: problem with irreproducible research which makes a poor dataset: also these care packages are based on population data not personalized datasets; challenges to AI is moving correlation to causation
Carla: algorithisms from on healthcare network is not good enough, Google tried and it failed
John: curation very important; good annotation is needed; needed to go in and develop, with curators, a systematic way to curate medial records; need standardization and reproducibility; applications in radiometrics can be different based on different data collection machines; developed a machine learning model site where investigators can compare models on a hub; also need to communicate with patients on healthcare information and quality information
Ayan: Australia and Canada has done the most concerning AI and lifescience, healthcare space; AI in most cases is cognitive learning: really two types of companies 1) the Microsofts, Googles, and 2) the startups that may be more pure AI
Final Notes: We are at a point where collecting massive amounts of healthcare related data is simple, rapid, and shareable. However challenges exist in quality of datasets, proper curation and annotation, need for collaboration across all healthcare stakeholders including patients, and dissemination of useful and accurate information
9:15 AM–9:45 AM
Opening Keynote: Dr. Joshua Brody, Medical Oncologist, Mount Sinai Health System
The Promise and Hype of Immunotherapy
Immunotherapy is revolutionizing oncology care across various types of cancers, but it is also necessary to sort the hype from the reality. In his keynote, Dr. Brody will delve into the history of this new therapy mode and how it has transformed the treatment of lymphoma and other diseases. He will address the hype surrounding it, why so many still don’t respond to the treatment regimen and chart the way forward—one that can lead to more elegant immunotherapy combination paths and better outcomes for patients.
hodgkin’s lymphoma best responder to PD1 therapy (nivolumab) but hepatic adverse effects
CAR-T (chimeric BCR and TCR); a long process which includes apheresis, selection CD3/CD28 cells; viral transfection of the chimeric; purification
complete remissions of B cell lymphomas (NCI trial) and long term remissions past 18 months
side effects like cytokine release (has been controlled); encephalopathy (he uses a hand writing test to see progression of adverse effect)
Vaccines
teaching the immune cells as PD1 inhibition exhausting T cells so a vaccine boost could be an adjuvant to PD1 or checkpoint therapy
using Flt3L primed in-situ vaccine (using a Toll like receptor agonist can recruit the dendritic cells to the tumor and then activation of T cell response); therefore vaccine does not need to be produced ex vivo; months after the vaccine the tumor still in remission
versus rituximab, which can target many healthy B cells this in-situ vaccine strategy is very specific for the tumorigenic B cells
HoWEVER they did see resistant tumor cells which did not overexpress PD-L1 but they did discover a novel checkpoint (cannot be disclosed at this point)
Please follow on Twitter using the following #hashtags and @pharma_BI
#MCConverge
#AI
#cancertreatment
#immunotherapy
#healthIT
#innovation
#precisionmedicine
#healthcaremodels
#personalizedmedicine
#healthcaredata
And at the following handles:
@pharma_BI
@medcitynews
Please see related articles on Live Coverage of Previous Meetings on this Open Access Journal
Vyasa Analytics Demos Deep Learning Software for Life Sciences at Bio-IT World 2018 – Vyasa’s booth (#632), Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)
Vyasa Analytics Demos Deep Learning Software for Life Sciences at Bio-IT World 2018 – Vyasa’s booth (#632)
Reporter: Aviva Lev-Ari, PhD, RN
BOSTON – May 10, 2018 – Vyasa Analytics, a provider of deep learning software and analytics for life sciences and healthcare organizations, today announces three pre-built deep learning analytics modules for its Cortex software at Bio-IT World Conference & Expo. Cortex enables the secure, scalable application of deep learning-based artificial intelligence (AI) analytics to enterprise data, identifying patterns, relationships and concepts across disparate data sources.
The new Neural Concept Recognition, Image Analytics and ChemVector analytics modules in Cortex enable life sciences organizations to quickly and easily apply deep learning analytics to large data streams of text, images and chemical structures. Like all deep learning analytical modules in Cortex’s library, these new modules allow users to ask complex questions of their data and use the answers to gain critical insights.
“Life sciences and healthcare organizations are using AI tools to advance research and development and deliver better patient care. Deep learning algorithms provide a set of powerful approaches that help us apply analytics more effectively and comprehensively across large scale data sources,” said Dr. Christopher Bouton, founder and CEO of Vyasa. “The idea of AI has been around for decades, but we are now experiencing a perfect storm of GPU-based computing power, deep learning algorithm advances and highly scalable data sources that enables paradigm-shifting machine learning and analytics capabilities.”
Vyasa will be demoing three deep learning analytics modules for Cortex at Bio-IT World 2018 in Boston from May 15 to 17, including:
Neural Concept Recognition. This module can be trained on text concepts (e.g. drugs, diseases, pathways, conditions, side effects, genes) in structured and unstructured data. Users can ask Cortex complex questions across large scale data sets, and discover unexpected relationships between concept types. Concept recognition analytics is applicable to a wide range of use cases from competitive intelligence, to drug repurposing and EHR analytics.
Life Sciences R&D Specialized Image Analytics. Deep learning enables novel, powerful forms of image analytics, capable of being trained to detect patterns and objects in large scale image data sources.With just a few clicks in Cortex, the user can connect large streams of image data and apply analytics to those sources. Vyasa has finely-tuned this analysis for life sciences images, and it is ideal for cell assay screening, drug manufacturing and post-market screening for counterfeit packaging and tablets.
ChemVector de novo Compound Design. This proprietary Cortex module applies deep learning to chemical structures. Users can drag and drop one or more SDF files containing SMILES strings into Cortex, and Cortex can identify and generate novel compounds that optimize critical variables such as log-p, molecular weight and synthetic viability. ChemVector can be used with a range of other chemistry-specific analytical modules also available in Cortex.
Dr. Bouton, Vyasa’s founder and CEO, received his BA in Neuroscience (Magna Cum Laude) from Amherst College in 1996 and his Ph.D. in Molecular Neurobiology from Johns Hopkins University in 2001. Previously Dr. Bouton was the CEO of Entagen a software company founded in 2008 that provided innovative Big Data products including Extera and TripleMap. Entagen’s technologies won numerous awards including the “Innovative Technology of the Year Award for Big Data” from the Massachusetts Technology Leadership Council in 2012 and Entagen was recognized as a Gartner “Cool Vendor” in the Life Sciences in 2013. Entagen was acquired by Thomson Reuters in 2013. Dr. Bouton is an author on over a dozen scientific papers and book chapters and his work has been covered in a number of industry news articles.
Visit Vyasa and demo Cortex at booth #632, and watch the explainer video at www.vyasa.com.
About Vyasa Analytics
Vyasa Analytics provides deep learning software and analytics for life sciences and healthcare organizations. Cortex is Vyasa’s secure, highly scalable software platform for collaborative knowledge discovery and data analytics. Using Vyasa’s proprietary Neural Concept Recognition technology, Cortex identifies trends and patterns across disparate data sources, empowering project teams to gain insights and drive better decision making. Learn more at www.vyasa.com.
Linguamatics announces the official launch of its AI self-service text-mining solution for researchers.
Reporter: Aviva Lev-Ari, PhD, RN
3.4.9 Linguamatics announces the official launch of its AI self-service text-mining solution for researchers, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair
iScite provides end-users with direct access to powerful AI-driven insights from text
Boston — May 9, 2018 — Linguamatics, the leading NLP-based text analytics provider for biomedical applications, today announced the launch of Linguamatics iScite, a breakthrough innovation in scientific search that puts the precision and power of Linguamatics artificial intelligence (AI) technology directly into the hands of scientists, researchers and other knowledge workers. iScite offers a modern, easy-to-use scientific search engine that provides intuitive access to AI-powered searches across key biomedical data sources and delivers insightful answers to search questions.
iScite is designed as a next-generation search experience that empowers non-technical users to conduct their own NLP-based scientific searches to extract data insights. Rather than rely on time- and/or resource-crunched technical experts to create and perform searches, iScite enables users to quickly and independently find precise answers to their high-value questions.
“Traditional search methods are often time-consuming, expensive and ineffective, and the results are imprecise and difficult to sift through,” said Jane Reed, head of life science strategy for Linguamatics. “With iScite, users can take advantage of the power of NLP without the traditional complexities. Our patent-pending Answer-Routing Engine interprets users’ search terms and guides them to the best possible answers to their questions. Searches are seamless across multiple content sources, and users are quickly pointed to the exact content relevant to their search without having to laboriously read through every word of the source documents.”
iScite uses Linguamatics’ award-winning technology stack to handle the nuances of language and the variety of ways people express the same information, ensuring searches are comprehensive and accurate. Using advanced NLP relationship and pattern matching, iScite rapidly guides users directly to the relevant insights extracted from cloud-hosted scientific content. Results are presented in a structured, semantically-meaningful way, with options for dynamic filtering and faceting, and multiple collaboration features to allow easier sharing of insights with co-workers and key stakeholders. Behind the scenes Linguamatics uses a powerful blend of NLP and machine learning-based methods to achieve the best precision and recall.
“By empowering end-user scientists and clinicians with an easy-to-use search engine, we are speeding their access to the right knowledge for decision-making to advance the discovery, development and delivery of therapeutics,” said Linguamatics Executive Chairman John Brimacombe. “iScite has the potential to revolutionize the search process for the biomedical industry by providing everyone with rapid access to the knowledge they need, while freeing data scientists and informaticians to focus on the most challenging, in-depth search projects. iScite is a breakthrough in scientific research, filling an industry demand for a self-service alternative that delivers deep insights in a single search.”
Linguamatics will demonstrate iScite at Bio-IT World 2018 in Boston May 15-17. Visit us at booth #549, or go to our website, http://www.linguamatics.com/iscite, for more information.
About Linguamatics Linguamaticstransforms unstructured big data into big insights to advance human health and wellbeing. A world leader in deploying innovative text analytics for high-value knowledge discovery and decision support, Linguamatics’ solutions are used by top commercial, academic and government organizations, including 18 of the top 20 global pharmaceutical companies, the US Food and Drug Administration (FDA) and leading US healthcare organizations.
Linguamatics Media contact:
Michelle Ronan Noteboom, Sr. Account Director
Amendola Communications
+ 1 512.426.2870 mnoteboom@acmarketingpr.com
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