Tuesday, January 17, 2017

Leveraging New Technologies for Better Diabetes Care


Kajal Bains                                                          David Trasoff, Ph.D.
Corporate Communications Associate            Director of Communications
Epinex Diagnostics, Incorporated                  
Epinex Diagnostics, Incorporated


Diabetes is widely considered to be the major health crisis facing the world today. It has become critical to address the diabetes epidemic on multiple fronts, and to take advantage of new technologies that are becoming available.

According to the International Diabetes Federation (IDF), 415 million people worldwide currently have diabetes and 642 million people are predicted to develop the disease by 2040. Diabetes is a chronic metabolic disorder in which the body does not produce or properly use insulin. Unmanaged diabetes leads to a progressive accumulation of complications and chronic conditions including heart disease, blindness, kidney failure, amputation of extremities due to circulation problems, and nerve disorders. There are three types of diabetes: Type 1, an autoimmune disease; Type 2, associated with lack of exercise, poor diet, obesity, and ageing; and gestational diabetes, which affects pregnant women. Approximately 90-95 percent of all people suffering from diabetes have Type 2 diabetes.

The standard protocol for diabetes monitoring and management has been a combination of multiple daily blood sugar tests and a twice-a-year test for HbA1c. Blood sugar testing is painful and expensive, and demands a high level of compliance to be useful. The required interval between HbA1c tests means that people with diabetes have to wait a full six months before they can get an updated analysis of their condition.

The crucial need to develop alternate diabetes monitoring systems has now been recognized by the highest level of the scientific and medical communities. The FDA recently sponsored a public workshop to discuss the future of diabetes management. It was geared specifically towards discussing measures of diabetes outcome beyond HbA1c, with the aim supporting the development of “novel therapies that directly address the needs” of diabetics.

The good news is that, over a decade of research has suggested that glycated albumin can be used as an effective monthly marker for diabetes management. Because albumin naturally replaces itself in the body every 30 days, true diabetic status is reflected in an accurate measurement of the damage to albumin caused by diabetes. Epinex is the only company that has developed and patented a monthly test for glycated albumin, the G1A Rapid Diabetes Monitoring Index Test. The goal is to provide this monthly test to healthcare professionals and consumers alike.

Another area of new technology with the potential to positively affect patient outcomes in diabetes care has been labeled “digital health.” Mobile device apps and online portals can expand the number of people who receive diabetes care and optimize how they prevent and treat their diabetes. This form of diabetes care can cater to millennials who are accustomed to using technology to address their needs and to underserved communities that cannot easily access doctor’s visits.

For instance, San Francisco-based digital health company Omada Health has initiated a program to help prevent Type 2 diabetes in low-income communities by improving how people eat and exercise. The company already provides an online program to help people with pre diabetes, but is now trying to spark lifestyle changes through a similar program designed specifically for people with pre diabetes in underserved communities. Since people in these communities cannot always afford year-round medical services, Omada hopes to implement technology they already use to help them regularly monitor their health.

Similarly, Epinex has pioneered “Am I Diabetic?” an app for mobile devices that provides information and tools about diabetes risk and management. We hope that our digital platform, in conjunction with the G1A test, will become a part of a new arsenal with the potential to revolutionize diabetes management to a more diverse population.

Kajal Bains is a fourth year Biological Sciences student at University of California, Irvine. She is the Corporate Communications Intern for Epinex Diagnostics, Inc. and has worked with Edwards Lifesciences in the past. After graduating this spring, she hopes to pursue a career in the biotechnology and medical device industries and pursue an MBA. She can be reached at kajal@epinex.com

David Trasoff, Ph.D., has degrees from the University of Rochester (B.A., Biology, Honors), Stanford University (M.A., Molecular Biology), and the University of California, Santa Barbara (Ph.D., Humanities). David has held teaching and research positions at several universities as well as operating businesses in graphic design, audio production, and event management.

Transforming Healthcare with Artificial Intelligence

By Al Naqvi
Executive Director
Society of Artificial Intelligence
for Medicine and Healthcare

American Institute of Artificial Intelligence

They were tiny, inconsequential, and dwarfed by the enormous giants that walked the earth during the Jurassic era. Waiting for their time to come, mammals fought hard and won the battle of survival, and then emerged to dominate the world for the next 65 million years. Like mammals, the artificial intelligence community worked diligently and determinedly through the ups and downs of the artificial intelligence field. Committed to doing something spectacular – and ignoring the financially rewarding rise of the unintelligent technologies –  they patiently persevered in their labs and research centers. And now their patience is paying off as their time has come. Welcome to the dawn of artificial intelligence! The coming decades will belong to this technology as it transforms our world and the greatest impact it will make is in the healthcare industry.

The Two Goals

Simplifying and capturing the formidable complexities of healthcare and zooming in on what can dramatically improve and revolutionize it, we should focus on two fundamental goals: 1) Finding new cures (therapeutic and/or diagnostic), and 2) Applying known cures effectively, efficiently, and for all those who can benefit from them. And artificial intelligence is having an impact on both.

Finding New Cures

One of the most silent and often ignored problems of our times is our stagnation in finding new cures. Unlike climate change or jobs, this issue somehow doesn’t climb to the political consciousness, yet it is one of the most consequential problems of our times. The new drug pipeline appears to be as ailing as the diseases it is trying to heal. Despite a 10-fold increase in investment, the results are miserable (Coller and Califf, 2009). There are staggering failure rates of 97%, even before projects reach the preclinical stage (Sams-Dodd, 2013) and 90% after Phase I, are the industry standards (Biotechnology Innovation Organization (BIO) et al., 2016). The proverbial “Valley of Death” concept captures the disconnect between the upstream and downstream drug discovery process and the “valley” requires complex navigation (Rai et al., 2008). Whether failure is due to toxicity, or efficacy (Sams-Dodd, 2013), or due to cost as a function of time and risk (DiMasi et al., 2009), or other reasons like managerial or organizational issues (Buonansegna et al., 2014), the overriding concern is that the human civilization stands naked and hopeless without the prospect of new cures.

With the advent of artificial intelligence, we can expect to close the gap. Specifically, the solutions are coming in the following areas:

  1. More efficient and smarter basic science and preclinical models
  2. Smarter devices for preclinical (pattern recognition etc.) 
  3. Genomics and molecular medicine
  4. Forensic analysis of clinical trials data (what failed, why)
  5. Sharing of clinical information to help develop new therapeutic options
  6. Finding new patterns in existing clinical data 
  7. Enhanced predicative ability to determine toxicity, efficacy etc. at early stages of development
  8. Integrating various aspects of new drug development such as identified by Mullane et al. (Mullane et al., 2014)
Even cancer, which is not a single disease but potentially hundreds or even thousands of diseases, can be considered as a computational problem that can be solved by artificial intelligence (Tenenbaum and Shrager, 2011).

Making Existing Cures More Efficient and Effective, For All

Now enter the clinical side, where artificial intelligence is improving the current standards of care. Just because we have a cure doesn’t mean it is being applied efficiently and effectively for all those who need it. Artificial Intelligence is now transforming clinical healthcare by:

  1. Improving the diagnostic speed and accuracy by analyzing data and observing never-before-seen patterns. This includes not only enhancing the ability to save lives by improving the speed and accuracy of diagnostics (for example Sepsis, a major killer), but also by artificial intelligence systems learning the ability to read scans.
  2. Artificial Intelligence systems are being developed and tested for population health management, patient tracking, condition management, hospital workflow management, advanced analytics, and the list goes on and on.
  3. Social robots, care bots, and healthcare management bots are being developed to help in providing care, patient monitoring, and doing patient or hospital chores.
  4. The efficiency of hospitals is being increased by using artificial intelligence for claims management, coding and reimbursement.
  5. In the future, we can expect healthcare kiosks and freestanding autonomous clinics providing primary care.
  6. On the behavioral health side, we are observing a tsunami of new solutions providing various behavioral therapies and interventions. This area will greatly improve access and diagnostic consistency across behavioral health.
And this is only the beginning. As a civilization, we must challenge ourselves to conquer disease and suffering. Anyone who is, or has a family member, suffering from a disease like cancer knows that the speed and accuracy of finding cures and timely and effective interventions matter. With artificial intelligence, our hopes stand renewed.

The author will be presenting about the above developments at the 22nd Annual Medical Technologies: A Frost & Sullivan Executive MindXchange.



Al Naqvi is the Executive Director of  The Society of Artificial Intelligence in Medicine and Healthcare and the Chief Executive Officer of the American Institute of Artificial Intelligence. He is also Editor-in-Chief of the Artificial Intelligence AI post www.aipost.com. 

Formerly, he was the Chief Financial Officer of a major healthcare/hospital system and prior to that a consultant in the drug development industry with a special focus on molecular medicine and nuclear medicine. Prior to that, he was Vice President of a Fortune 500 company and a technology entrepreneur. His doctorate thesis is on Artificial Intelligence Governance and his Machine Learning training is from Stanford University. ________________________________________________________________


Biotechnology Innovation Organization (BIO) et al. (2016) Clinical Development Success Rates 2006-2015. (June), . [online]. Available from: https://www.bio.org/sites/default/files/Clinical Development Success Rates 2006-2015 - BIO, Biomedtracker, Amplion 2016.pdf.

Buonansegna, E. et al. (2014) Pharmaceutical new product development: why do clinical trials fail? R&D Management. [Online] 44 (2), 189–202. [online]. Available from: http://doi.wiley.com/10.1111/radm.12053.

Coller, B. S. & Califf, R. M. (2009) Traversing the valley of death: a guide to assessing prospects for translational success. Science translational medicine. [Online] 1 (10), 10cm9. [online]. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2879158&tool=pmcentrez&rendertype=abstract.

DiMasi, J. A. et al. (2009) Trends in Risks Associated With New Drug Development: Success Rates for Investigational Drugs. Clinical Pharmacology & Therapeutics. [Online] 87 (3), 272–277. [online]. Available from: http://dx.doi.org/10.1038/clpt.2009.295\npapers3://publication/doi/10.1038/clpt.2009.295.

Mullane, K. et al. (2014) Translational paradigms in pharmacology and drug discovery. Biochemical Pharmacology. [Online] 87 (1), 189–210. [online]. Available from: http://dx.doi.org/10.1016/j.bcp.2013.10.019.

Rai, A. K. et al. (2008) Pathways Across the Valley of Death: Novel Intellectual Property Strategies for Accelerated Drug Discovery. Yale Journal of Health Policy, Law, and Ethics. 81. [online]. Available from: http://www.worldcat.org/oclc/809548427.

Sams-Dodd, F. (2013) Is poor research the cause of the declining productivity of the pharmaceutical industry? An industry in need of a paradigm shift. Drug Discovery Today. [Online] 18 (5–6), 211–217. [online]. Available from: http://dx.doi.org/10.1016/j.drudis.2012.10.010.

Tenenbaum, J. M. & Shrager, J. (2011) Cancer: A Computational Disease that AI Can Cure. AI Magazine. [Online] 32 (2), 14–26. [online]. Available from: http://www.aaai.org/ojs/index.php/aimagazine/article/view/2345.

9 Healthcare Predictions For 2017

By Reenita Das

Partner and Senior Vice President
Transformational Health

Frost & Sullivan

Every year at Frost & Sullivan, the Transformational Health team brainstorms top predictions for the year ahead. 2017 will definitely continue to be a year of tumultuous uncertainty and turbulence. But amidst this uncertainty, we know for a fact that technology will continue to flourish and will have an unprecedented impact on healthcare in terms of building some of the foundation blocks towards a connected home and healthcare ecosystem.

The following are our nine top predictions for healthcare for 2017:

Strong Push Toward Price Control And Transparency Measures Around Drugs

Public and political pressure on the control of surging drug prices, globally, will compel health authorities to bring transparency measures around drugs pricing, especially for some of the diabetes and cholesterol medicines where more low-cost generic competition is gaining market acceptance.

Blockchain Becomes One Of The Most Important Technologies In The Healthcare Industry

With the potential to change how healthcare information is stored, shared, secured and paid for, blockchain technologies have immense potential to tackle some of the biggest challenges in healthcare information management. Companies like Gem Health are among the few companies currently advocating the use and benefits of such a platform.

Artificial Intelligence (AI) Transforms Medical Imaging Informatics

As more and more experts and healthcare professionals find the usability of these AI systems as decision support tools and not decision makers, uptake of AI-enabled clinical decision support tools is expected to increase in the coming years. More particularly during 2017, AI will play a big role in diagnostic imaging by complementing radiologists with advanced interpretation and imaging informatics supports.

Deployment Of More Sophisticated Outcomes-Based Compensation Care Models

To date, the majority of outcome-based compensation models are, in reality, performance modifiers built on top of legacy fee-for-service reimbursement schemes. In 2017, we will begin to see more fully formed schemes that focus on patient support across the care continuum. As such, healthcare providers are in dire need of the right technologies and tools to help them effectively deploy and coordinate patients, personnel and infrastructure.

Apple To Enter Clinical Healthcare

Healthcare has been a big focus for Apple in the past two years, and the company is committed to creating more clinical actionable products and services. Last August, Apple acquired medical records startup Gliimpse in order to broaden its presence in the personal healthcare information management market and complement existing solutions; these include HealthKit, CareKit and ResearchKit. This marks a tangible shift for Apple toward more clinically oriented solutions.

Venture Capital (VC) Healthcare Investment Will Have A Record Year

An ideal confluence of events is poised to make 2017 a banner year for VC investment in healthcare. Strains on healthcare spending, the global recession, tightening regulatory oversight and other factors have put a stranglehold VC dollar flow over the past five years, particularly for very early-stage companies in the healthcare industry. However, with the maturation of certain emerging technologies, policy changes to the FDA and access to cash, it is expected there will be a resurgence in funding for new healthcare technologies.

The Digital Health Toolkit Comes To Behavioral Health

Digital health coaching platforms and wellness programs with proven behavioral therapies will find their way as an efficient alternative to post-care settings and rehabilitation centers. Innovative online patient engagement platforms are capable of capturing tailored information on lifestyle and behavioral health. This is based on health risks data that have a white space opportunity to provide patient risk classification solutions to make precision medicine a holistic approach.

With a view to avoid future excessive treatment costs, payers will encourage healthier lifestyles among members; they are likely to provide them with wearables and incentives for attaining specific health goals as motivation. In the New Year, wellbeing programs will become a central, critical business imperative, necessary for optimizing not just the productivity and performance of employees, but also for managing the bottom line.

Point-Of-Care Diagnostic Devices Push Telehealth Beyond Video Conferencing

Consumers will play a greater role in driving the uptake of point of care testing. In vitro diagnostic device (IVD) manufacturers will invest in digital strategy. This is in order to make their business models patient-centric with consumer-friendly devices, embed remote connectivity features for real-time access to data, and simplifying sample collection process.

Consumer Will Be The New King in Healthcare Decision Making

The concept of consumerism has been making inroads into the healthcare industry and is advancing proportionally with the shifting industry focus from volume to value-based care delivery models. With this thriving consumer engagement movement, consumers are more receptive to information and as they want to actively participate in their healthcare treatment during, pre- and post-care. Technology is also playing a pivotal role in this paradigm shift with connected health products such as wearables, telehealth, artificial intelligence
and others.

This article was written with contributions from the Visionary Healthcare Program team and Venkat Rajan, Global Director for Frost & Sullivan’s Transformational Health Practice.

Time to Get Real: Quantifying Health Outcomes With Real Life Data


By Deborah Kilpatrick, Ph.D.             Murali Doraiswamy, M.D.
Chief Executive Officer                    Professor of Psychiatry and Medicine
Evidation Health                             Duke University Health System

Given the widespread adoption of mobile technologies and digital health apps by patients, we now have a view into the continuous patient journey like never before. We can now “quantify real life” of patients and measure health outcomes beyond traditional clinical trials, at scale. And in this digital era of medicine, we have more robust analytical tools that can sift through massive, complex datasets faster and more reliably. Whether it is in Type 2 diabetes or multiple sclerosis or heart failure, the ability to quantify outcomes from real life patient data is going to change the way we think about the volume-to-value transformation.

Therapeutics industry leaders can now address some direct drivers of the historical gap between trial efficacy and post-launch effectiveness with solutions that enable:

  1. Access to broader connected populations
  2. Collection of novel real life data from patients
  3. Quantification of real life outcomes

1. Access to Connected Populations

Clinical development strategies for drugs and devices include fundamentals ranging from recruiting eligible patients to capturing data at various points in time according to a pre-specified protocol. None of these steps go away in the digital era, but the tactics for getting them done are undergoing truly revolutionary change.[1],[2]

Digital technologies provide new channels for accessing target patient populations. In connecting with patients outside of traditional clinical settings, we are able to recruit patients for studies much faster, discover patterns across segments, and support patients in their everyday lives.[3]

Equally important, the benefits are not limited to the number of patients recruited or the improved efficiencies of the process. Digital technologies fundamentally expand the datasets we can use to quantify outcomes in the real world. That means we can more accurately correlate outcomes with patients’ daily lives and behaviors.

2. Collection of Novel Real Life Data

The most important expansion of our clinical development data universe is arguably our new ability to continuously and passively measure patient behaviors upon informed consent. For example, tracking sleep, physical activity, social media activity, and wireless sensor data all enhance the context available for analysis.[4]

In the near term, this new information can shed light on the efficacy-effectiveness gap between phase III trial results and what happens in post-launch settings. When combined with medical information including EHRs, claims data, and genomics, this new understanding of how patient behaviors drive health outcomes creates a direct path to precision medicine solutions.

3. Quantification of Real Life Outcomes

Gathering novel data from more people, more efficiently, is only helpful if it leads to scientifically valid conclusions that prove outcomes. We have always known that patient behaviors directly influence symptomology and disease progression/regression in many therapeutic areas, but quantifying the impact has traditionally been an elusive goal. That has changed.[5]

Therapeutic areas that are benefiting most from this new approach are those where patient behaviors outside the clinic walls disproportionately impact health outcomes. In our experience to date, the use cases for quantifying how real life patient behaviors drive health outcomes are now quite broad and accessible. For example:

  • Segmenting populations by behaviors: identifying super-responders and non-responders to a medication adherence mobile app in cardiometabolic disease
  • Evaluating "Services around the Pill": optimizing digital health interventions to impact vaccination patterns and reduce infection rates during flu season
  • Characterizing real life quality of life improvement: measuring data reflecting productivity and daily symptom improvement in depression and anxiety
  • Identifying new digital biomarkers for disease status based on quantified behaviors: establishing links between daily activity patterns and flares in multiple sclerosis
Pharma Company Puts It All Together to Quantify Impact

Connecting with patient populations, collecting continuous behavioral data, and quantifying health outcomes on large behavior datasets all benefit from digital tools -- but the greatest benefit is realized when the three tactics are combined.

For example, a top global pharma company worked with Evidation Health with the goal of improving patient adherence to medication and lifestyle modification in the diabetes market. The project started by connecting with 300,000 patients and segmenting that population into clusters. The platform collected real life behavioral data across well over 6 months of observation and linked relevant behavioral activity (diet, sleep, etc.) to medical adherence.

Ultimately, this enabled a top global pharma company to quantify health outcomes and leverage behavior-driven insights for dynamic intervention targeting.

We might not have imagined a decade ago that we’d be here so quickly. But suddenly we find ourselves able to quantify health outcomes like never before, in settings we never imagined, in populations we might not have ever reached—in their real lives. This is indeed an idea whose time has come.

To learn more about how healthcare companies are using Evidation Health’s Real Life Study Solution to quantify health outcomes in the digital era of medicine, follow @evidation.

PMD is a scientific advisor to Evidation Health and has served as an advisor to leading businesses, advocacy groups and government agencies.  

[1] Rosa C, Campbell AN, Miele GM, Brunner M, Winstanley EL. Using e-technologies in clinical trials. Contemp Clin Trials. 2015;45(Pt A):41-54.

[2] Juusola JL, Quisel TR, Foschini L, Ladapo JA. The Impact of an Online Crowdsourcing Diagnostic Tool on Health Care Utilization: A Case Study Using a Novel Approach to Retrospective Claims Analysis. J Med Internet Res. 2016;18(6):e127.

[3] Kumar S, Oley L, Juusola JL. Efficiency of Virtual Recruitment Methods for Broad and Specific Study Populations. 38th Annual North American Meeting of the Society for Medical Decision Making, October 23 - 26, 2016.

[4] Pourzanjani A, Quisel TR, Foschini L. Adherent Use of Digital Health Trackers Is Associated with Weight Loss. PLoS ONE 11(4): e0152504.

[5] Rock Health. The Emerging Influence of Digital Biomarkers. 2016. Available at https://rockhealth.com/reports/the-emerging-influence-of-digital-biomarkers-on-healthcare/.

Deborah Kilpatrick is the Chief Executive Officer of Evidation Health. Prior to this role, she served as the Chief Commercial Officer of genomic diagnostics company CardioDx. Earlier in her career, Deborah held multiple leadership roles at Guidant Corporation, including Research Fellow, Director of R&D, and Director of New Ventures in the Vascular Intervention Division. She serves on the Georgia Tech Advisory Board and is a Fellow of the American Institute of Medical and Biological Engineering. Deborah is a co-founder of the MedtechVision Conference, now held annually in Silicon Valley and has received many awards including 100 Women of Influence in Silicon Valley. She holds BS, MS and PhD degrees in mechanical engineering with a bioengineering focus from Georgia Tech.

Murali Doraiswamy is a scientific advisor to Evidation Health and has served as an advisor to leading businesses, advocacy groups and government agencies.  Doraiswamy is a leading physician scientist in the areas of brain health and personalized medicine at Duke Medicine where he is a Professor in the Division of Translational Neuroscience and Director of the Neurocognitive Disorders Program in Psychiatry. He also serves as a member of the Duke Institute for Brain Sciences and the Duke Center for Personalized Medicine. Doraiswamy has served as an advisor to leading government agencies, advocacy groups and businesses, and received many awards including a special Congressional recognition.  He is the coauthor of a popular book, The Alzheimer's Action Plan.