The 2013 DiabetesMine D-Data ExChange brochure - final

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About 30 data experts and entrepreneurs gathered on World Diabetes Day Nov. 14, 2013, at Stanford School of Medicine for the first-ever DiabetesMine D-Data ExChange event, sponsored by the California …

About 30 data experts and entrepreneurs gathered on World Diabetes Day Nov. 14, 2013, at Stanford School of Medicine for the first-ever DiabetesMine D-Data ExChange event, sponsored by the California HealthCare Foundation.

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  • 1. hosted by: thanks to: the DiabetesMine d-data exchange taking diabetes data to the next level Thursday, Nov. 14 (World Diabetes Day!), 1−5pm Clark Center, Stanford School of Medicine
  • 2. the DiabetesMine d-data exchange taking diabetes data to the next level Thursday, Nov. 14 (World Diabetes Day!), 1−5pm Clark Center, Stanford School of Medicine What the afternoon is made of... 1:00 Welcome from Amy Tenderich, Sara Krugman and Howard Look 1:15 Nate Heintzman, PhD Informatics & Technology for “Solving” Diabetes 1:25 Rita Sharma Unifying diabetes one device at a time 1:35 Ian Jorgensen Learning Through Feedback 1:45 John Costik Finding Our Way 1:55 Doug Kanter A Year of Diabetes Data 2:05 Jana Beck Diabetes Data Distributions 2:15 Howard Wolpert Re-Envisioning Meal Insulin Dosing 2:25 Brandon Arbiter Carb Counting in a Nutshell 2:35 Simon Carter Introducing ManageBGL - Diabetes 2:45 Snack and Networking Break 3:05 Anna McCollister-Slipp Galileo Cosmos Visual Data Analytics Platform 3:15 Karmel Allison Why I’m Not Working with Diabetes Device Data 3:25 Kyle McClain “A Story of Numbers” 3:35 Joe Cafazzo & Melanie Yeung The Need for Hospital Empathy  nd Device a Interoperability 3:45 Lane Desborough NightScout - Shared Situational Awareness  or f Spatially Distributed Family Members 3:55 Peter Nerothin The Million Mile Race - Leveraging Consumer Technologies to Achieve Public Health Outcomes 4:05 Discussion, Networking and Next Steps who is here? the DiabetesMine d-data exchange name: email: Allison Dunning Amy Tenderich Anna McCollister-Slipp Benjamin West Brandon Arbiter Bryan Mazlish D’Arcy Saum Doug Kanter Howard Wolpert, M.D. Howard Look Ian Jorgensen Jamie Bate Jana Beck John Costik Joseph Cafazzo Karmel Allison Kent Quirk Kyle Rose Kyle McClain Lane Desborough Melanie Yeung Nate Heintzman Nicolas Hery Peter Nerothin Rita Sharma Robert Cook Sara Krugman Scott Mark Sean Saint Simon Carter Steve McCanne Nina Serpiello Alix Gillet-Kirt allison@allisondunning.com amy@diabetesmine.com annaslipp@mac.com or annaslipp@me.com bewest@tidepool.org barbiter@gmail.com bmazlish@gmail.com darcysaum@gmail.com dougkanter@gmail.com howard.wolpert@joslin.harvard.edu howard@tidepool.org ian@linehq.com jamie@tidepool.org jana.eliz.beck@gmail.com jcostik@gmail.com joe.cafazzo@uhn.ca karmel@asweetlife.org kent@tidepool.org kyle.j.rose@gmail.com kyle.a.mcclain@gmail.com ldesboro@hotmail.com melanie.yeung@uhn.ca nheintzm@ucsd.edu hery.nicolas@gmail.com peter@insulindependence.org rita@glooko.com robertcook@gmail.com sara@linehq.com scott.mark@medtronic.com seansaint@gmail.com simon.carter@datamystic.com steve@mccanne.com nserpiello@gmail.com me@theeyeofalix.com
  • 3. Nate Heintzman, PhD Ian Jorgensen Informatics & Technology for “Solving” Diabetes A Tour of the Diabetes Data Ecosystem Learning Through Feedback The Diabetes Informatics & Analytics Lab (DIAL) at UC San Diego strives to answer the question, “What caused this glycemia, in this individual with diabetes, in this place, at this time?” Recognizing that blood glucose levels can be impacted by numerous factors including insulin, nutrition, physical activity, stress, genetics, environment, and more, the DIAL team employs diverse technologies and techniques to collect, integrate, analyze, and share multimodal data that hold the knowledge we need to collaboratively solve diabetes. This presentation will provide an overview of the DIAL team’s efforts to understand the complex relationships within the diabetes data ecosystem, at the level of the individual living with diabetes in the real world. FINDING OUR WAY We learn through play and feedback. We test something out, learn from it and do it differently the next time. In diabetes the feedback is delayed so it’s hard to learn. We’ve developed a metabolic simulator as a tool to explore, learn and teach about how our carbohydrates and insulin affect our glycemic control in the hope that using the data to simulate our ups and downs connects it to our daily lives and allows us to learn from it. John Costik Finding Our Way Rita Sharma Soon after my son Evan’s type 1 diabetes diagnosis in August 2012, it became clear that we could do more for him. We could keep him healthier and safer. The tools to do so were right there in front of us, but they needed to be put together. We built a system that, like type 1, never sleeps. It has made Evan healthier, safer, and freer. For the rest of us? The same… and we’re just getting started. Giving our son the care he deserves Unifying Diabetes One Device at a Time Glooko aims to create a unified platform for diabetes management. This means that patients can download their diabetes data into their mobile phones. It means that health care professionals can view their patients’ BG numbers, medications, activity and food intake all in one view regardless of meter brand, and regardless of the patient’s location. It means that a child can download her meter at school using her iPhone and her mom can instantly see the results on her Android tablet. It means that health care professionals are motivated to download data for their patients because now it requires only one cable, an iPad and a self-serve Kiosk to download pretty much any kind of meter. Unification means different things to different people, but in the end, it means breaking the barriers to optimal diabetes management. Soon after Evan’s type 1 diabetes diagnosis in August 2012, it became clear that we could do more for him. We could keep him Doug Kanter A Year of Diabetes Data healthier and safer. The tools to do so were right there in front of us, but they needed to be put together. We built a system that, like Type 1, never sleeps. It has made Evan healthier, safer, and freer. For the rest of us? The same… and we’re just getting started. For all of 2012, I aggregated every piece of data related to my type 1 diabetes. This included medical readings from my CGM, insulin pump and blood glucose meter. I also kept track of every meal I ate, my location and all my exercise activity. The result was that 2012 was the healthiest year of my life.
  • 4. Jana Beck Simon Carter Diabetes Data Distributions Introducing ManageBGL Diabetes Self-Management Tool with Hypo Prediction One of the things I find most lacking – and most necessary – with present diabetes software is the tools for getting an overview of your data at various timescales. In order to understand your trajectory (improved control? an unfortunate backslide?), it’s necessary to have visualizations of diabetes data that can show you, in a single image, what your control looked like in a given time period, whether that’s a week, a month, or even a year. The ability to compare time periods is also crucial. Finally, good visualizations of blood glucose values arranged by day of the week and by time of day are essential for pattern recognition and troubleshooting. Howard Wolpert Re-Envisioning Meal Insulin Dosing Achieving optimal post-meal glucose control remains one of the most challenging aspects of diabetes management. The current carbohydrate counting-based approach is difficult to apply in practice. The shortcomings of this approach – which assumes that carbohydrate is the only dietary macronutrient that needs to be considered in mealtime insulin dose calculation – are further highlighted by our recently completed closed-loop research studies indicating that higher-fat meals require considerably more insulin coverage than lower-fat meals with identical carbohydrate content. Brandon Arbiter Carb Counting in a Nutshell Carb counting brought a paradigm shift to the treatment of type 1 diabetes. But now we know that matching an insulin dose to a meal is more complicated than just a carb count. What are the implications of insulin pumps, CGMs, big data, and mHealth on carb counting in real life? Tidepool introduces Nutshell, mealtime insulin decision-making for the next generation.meals with identical carbohydrate content. ManageBGL is a virtual insulin pump and CGMS for diabetics on insulin. For the first time ever, diabetics can predict hypos (low blood sugars) 3 hours before they occur, children at school can share their readings with parents at work or home, and receive coaching through dose changes. ManageBGL provides an insulin dose calculator for meals and correction doses, and keeps track of insulin on board to prevent overdosing. It includes high and low pattern detection, a food database, and an extensive reporting system. By replacing the calculation functions of an insulin pump, it saves users thousands of dollars both up front and on consumables each year. ManageBGL’s point reward system also helps teenagers stay on track, and parents can oversee without nagging. Its slick multi-patient interface allows multi-diabetic families or hospitals and clinics to easy switch between patients. You can use it from an iPhone/iPod/iPad, Android SmartPhone/tablet, PC or Mac. Galileo Cosmos™ Anna McCollister−Slipp Galileo Cosmos Visual Data Analytics Platform EXPLORE YOUR DATA IN A BOLD NEW WAY Our platform has many potential uses, but my vision and passion in creating the company and designing the platform are two-fold: 1) Democratize the process of conducting complex health data research, making it easier and more feasible for more people from an array of disciplines to access, mine and analyze data, and 2) Catalyze the development of new methods for identifying and validating new outcomes measures in diabetes and other diseases, enabling us to develop outcomes measures that are more reflective of our current (and future) understanding of the disease development, treatment and management process. (for example: to rapidly mine and assess the use of patient-generated data as a measure for drug/treatment efficacy and compare outcomes among a variety of cohorts; or to incorporate current understanding and facilitate exploration of the newer science of metabolomics as a marker/influencer of disease occurrence and progression, etc.).
  • 5. Karmel Allison Lane Desborough Why I’m Not Working with Diabetes Device Data NightScout - Shared Situational Awareness for Spatially Distributed Family Members Three and a half years ago, I decided I was frustrated with the state of diabetes care and data analysis, and certainly we could do better. But in the end I decided there was a bigger problem to solve: we need to first prevent and then cure diabetes. And, as a software engineer, I found I had skills I could contribute to this fight. This is a brief history of how and why I became a bioinformaticist, what I am doing now to better understand the immunology of diabetes, and why I encourage you to join the fight. Kyle McClain Introducing NightScout, a spare-time project to remotely monitor my son’s continuous glucose values so we can reduce the cognitive and emotional burden of living with diabetes -- especially overnight. I hope to demonstrate that the biological driving force of protecting your child from harm provides the motivation; that powerful open-source tools, low-cost hosting platforms, interoperability standards, and free javascript frameworks provide the ability; and the start of the school year provides the trigger to what amounts to ePatient hacking / jugaad innovation / “social technology development,” a powerful and exciting force to help reduce the burden for families living with type 1 diabetes. “A Story of Numbers” Diabetes is a numbers game, we all know this. But for the average diabetic making use of those numbers has been elusive. The good news is we live in an age where technology gives us the opportunity to wield these numbers to our advantage. That is the purpose of Gludi – to explore how to make these numbers work for us and how that information can be shared among a group of people who can encourage us as we play the game of numbers. Joseph Cafazzo & Melanie Yeung The Need for Hospital Empathy and Device Interoperability THE NEE EMPATHY IN DESIGN :
 D CHA The incumbents in the design of medical technology sufferFOR Cafazzo andINMelanieETES MEDTECH from NGE DIAB Yeun a technology Joseph g heritage rooted in the hospital setting. Their focus remains the targeting of care providers as the main decision makers. We will discuss how change in the medtech industry requires new entrants with a focus on the new consumerism in healthcare - with the patient at the center. This change cannot be achieved without an information ecosystem that fosters innovation. Currently, the majority of diabetes devices are using manufacturer-specific protocols that require specific applications, establishing a collaboration, or reverse engineering efforts to extract the data. Standardizing data communication sets a common language for everyone to understand and enables a multitude of opportunities such as interoperability between devices, connectivity to applications, and other novel innovations. Centre for Global eHealth Innovation, Toronto Genera l Hospital Peter Nerothin The Million Mile Race - Leveraging Consumer Technologies to Achieve Public Health Outcomes Peter will invite the audience to consider the impact of a virtual race event for patients living with diabetes. Under this model, participants from around the world will upload one million miles of physical activity, using mobile consumer technologies (fitness apps and wearable devices) to report walking, running and cycling workouts. IRB-approved clinical studies will run in parallel with the event, demonstrating increased QOL and a reduction in A1C within subsets of the participant population.
  • 6. hosted by: thanks to: