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elsa's proposal, introduction to github

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Github has a service called "gist", which makes it easy to copy-paste chunks of stuff onto the web. This makes it accessible for sharing and searching, so make sure you don't mind putting it out there, or hit the "create secret gist" button.

Putting your stuff on the web makes it easier to find in 6 months using google when you may have forgotten about it, and also makes it easy for others to propose changes, and iterate.

Sanofi proposal

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Affiliations

Parsons the New School for Design, JDRF Central Virginia Chapter

Concept Name

Intelligent Information Platform for Type 1 Diabetes Self Care

Concept Headline

Data design with patients in mind: integrating physiological and human factors for more intelligent care.

Concept Description

We aim to generate, in a year or less, a meaningful improvement in the software used to track data, identify patterns, and make better decisions in the complex arena of self-management via CSII. By uniting and leveraging existing knowledge, technology and interests, we can go far beyond the capabilities of existing applications, and add value and rigor to data management for patients and stakeholders. By bringing together expertise, tools, and resources at our disposal today, we can improve care outcomes before the release of developing technologies (e.g., the artificial pancreas) and inform the development of care technologies going forward.

Concept Maturity

Early Prototype

Evidence-Based Health Outcomes

Describe how your concept provides an evidence-based way to improve the outcomes and/or experience of people living with diabetes in the US. Be specific about the data-driven improvement to outcomes and/or experience

on evidence based outcomes

Type 1 diabetes management is quantified via core metrics such as hbA1C, estimated average blood glucose, standard deviation or variability of blood glucose values over time, and frequency of acute hypoglycemia and emergency events. On the most basic level, we propose to assess the effectiveness of our intervention based on historical versus user tester scores on these metrics. In addition, we will evalute our project on metrics of quality of life, as judged by factors such as amount of time spent on care, efficiency and effectiveness of tracking, analysis, and representation work, and reduction of tradeoffs between lifestyle flexibility, therapy effort, and health status. By developing a fundamentally data-focused system that patients will use to track, analyze, identify patterns, and develop useful personal strategies through iterative interaction with their data and collaborate with their physicians, we will observe changes in these concrete healthcare metrics as well as more subjective measures (relating to quality of life and and usage experience versus existing solutions). More adequate models of not only the physiological but also human (lifestyle, psychological, interface) factors in self management using diabetes technology will also enable us to quantitatively and qualitatively assess usability. 70 percent of patients currently do not download their data, and the majority of patients on insulin pumps have hbA1Cs of 8.0% or higher. By creating a tool that enables users to see their data integrated with contextual information and obtain actionable insight into their disease, we believe there is clear opportunity for measurable improvement.

Decision-making

Based on your target audience, how does your concept enable better decision-making? What types of decisions does your concept inform? Across the spectrum of type 1 and/or type 2 diabetes (lifestyle and environmental factors to diagnosis, treatment, maintenance, and beyond), when does your concept make the greatest impact and how?

decision making

The target audience for this project can be broken down to three categories of beneficiaries. Those for whom our project will produce tangible benefits include: first, type 1 diabetics, primarily those using insulin pumps; second, their physicians, with whom patients need to collaborate to achieve care optimization; and third, researchers and device companies involved in in silico and outpatient trials who can benefit from greater understanding of human factors in type 1 management via technology. Our team aims to bridge the gaps most often occurring in software projects aimed at addressing issues in diabetes, such as a. lack of lead user (i.e., patient and clinicial) knowledge, b. inability to design and refine for the human user, c. outdated concepts of the user as having unlimited time and/or rationality, and d. crossfunctional yet narrow views of the problem at hand. We are able to bridge and identify, translate, and coordinate knowledge and expertise between relevant stakeholders including patients, doctors, researchers, device makers, charities, and independent experts. Our knowledge management protocols are key to this process.

DATA SCIENCE

How did you arrive at the creation of your concept from a design and development perspective? What analysis methods (e.g., baseline knowledge models, evidence-based practice, predictive analysis) and data sets (e.g., your own, government, specific industry) did you utilize or generate to manifest your concept? Are you combining data sets to create new methods of analysis?

On Data Science

We arrived at our concept through a. deep dives into personal experiences and datasets; b. large scale micronarrative research involving intensive mining of social media conversations as well as in-depth interviews with diverse stakeholders including friends, family, physicians, researchers, trial participants, and funding organizations; and c. quantitative data available from sources such as T1d Exchange, key technology presentations and meetings, and literature. Additionally, we have consulted and learned from prototypes of data tracking and analysis applications, information visualization generated through statistical coding, information design, and user and task analysis. For our concept, we are using patient data sets of our network of prototype testers collected through their devices as well as via self-tracking tools we are including as part of the platform. Ultimately, we hope to proceed, in a stepwise fashion, from integrating device data and contextual lifestyle and health data in the first generation, to integrating data from other applications of the users’ choice, from bike computers, to fit bits, to nutrition-tracking apps, and finally, to prototype a system for portable information upload and mobile access to realtime data and self-experimentation.

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