Do-It-Yourself Automated Insulin Delivery: A Leading Example of the Democratization of Medicine

Burnside, Mercedes; Crocket, Hamish; Michael, Mayo; Pickering, John W.; Tappe, Adrian; Bock, Martin de

doi:10.1177/1932296819890623

Cited by 28 publications

(22 citation statements)

References 22 publications

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“…In challenging traditional top-down hierarchies in medicine, open-source AID presents a patient-focused initiative that serves to empower people with diabetes through personalized technology. Because of the availability of current technology and individualized innovations, open-source AID has previously been described as having the potential to democratize health care, revolutionizing treatment and the way people with diabetes as well as other stakeholders such as care teams, researchers, and device manufacturers view chronic conditions such as diabetes [38]. The importance of peer support in the context of open-source AID use has recently been highlighted elsewhere, and a sense of community underpinning the development and diffusion of open-source AID has been emphasized by individual users [39].…”

Section: Do-it-yourself Is Not Do-it-alone: the Impact Of Peer Supportmentioning

confidence: 99%

Why #WeAreNotWaiting - Motivations and Self-Reported Outcomes Among Users of Open-Source Automated Insulin Delivery Systems: A Multinational Survey

et al. 2020

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Background: Automated insulin delivery (AID) systems have been shown to be safe and effective in reducing hyperglycemia and hypoglycemia but are not universally available, accessible, or affordable. Therefore, user-driven open-source AID systems are becoming increasingly popular.Objective: This study aims to investigate the motivations for which people with diabetes (types 1, 2, and other) or their caregivers decide to build and use a personalized open-source AID. Methods:A cross-sectional web-based survey was conducted to assess personal motivations and associated self-reported clinical outcomes.Results: Of 897 participants from 35 countries, 80.5% (722) were adults with diabetes and 19.5% (175) were caregivers of children with diabetes. Primary motivations to commence open-source AID included improving glycemic outcomes (476/509 adults, 93.5%, and 95/100 caregivers, 95%), reducing acute (443/508 adults, 87.2%, and 96/100 caregivers, 96%) and long-term (421/505 adults, 83.3%, and 91/100 caregivers, 91%) complication risk, interacting less frequently with diabetes technology (413/509 adults, 81.1%; 86/100 caregivers, 86%), improving their or child's sleep quality (364/508 adults, 71.6%, and 80/100 caregivers, 80%), increasing their or child's life expectancy (381/507 adults, 75.1%, and 84/100 caregivers, 84%), lack of commercially available AID systems (359/507 adults, 70.8%, and 79/99 caregivers, 80%), and unachieved therapy goals with

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Section: Do-it-yourself Is Not Do-it-alone: the Impact Of Peer Supportmentioning

confidence: 99%

Why #WeAreNotWaiting - Motivations and Self-Reported Outcomes Among Users of Open-Source Automated Insulin Delivery Systems: A Multinational Survey

et al. 2020

Self Cite

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“…This includes the appropriation of tools, observation of variables, and interpretation (Lupton, 2019), in a flexible system of epistemological enquiry (Ruckenstein and Pantzar, 2017). From such perspectives, self-tracking represents a changing palette of "situated objectivity", aligning with initiatives for the democratization of science (Burnside et al, 2020). Personal research guided by self-tracking can be understood as a "missing link" within recent movements and paradigms such as "DIY science" (Ferretti, 2019) or citizen science (Hecker et al, 2018).…”

Section: From Critique To Knowledge Value Perspectives In Social Studies Of Sciencementioning

confidence: 99%

Shared motivations, goals and values in the practice of personal science - A community perspective on self-tracking for empirical knowledge

Hidalgo¹,

Ball²,

Opoix³

et al. 2021

Preprint

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Some individuals do not limit their self-tracking efforts to passively collecting and observing gathered data about themselves, but rather develop it into forms of self-research and self-experimentation, also called “personal science”. This type of N-of-1 research is relevant to the fields of personal informatics, patient-led research and social studies of science, but as a knowledge generation practice is still poorly understood. To fill this gap, we conducted 22 semi-structured interviews to investigate the intrinsic and extrinsic motivations of individuals engaging in personal science activities, as well as shared goals and values present in self-research communities. Our analysis is based on a conceptual framework that integrates previous approaches in self-research, as well as in connection with citizen science, the scientific ethos and cooperation in peer production. We identify how self-researchers seek to go beyond personal metrics about their health and wellbeing regarding data provided by wearables, are engaged over time by individual involvement in technology and scientific-related activity, and collaborate following similar goals and values when learning and sharing empirical knowledge with peers. In this sense, personal science can be understood as an example of a more participatory and inclusive scientific culture driven by self-reflection, critical thinking and openness.

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“…Intelligent computing and machine learning have gained prominence for their roles in patient-centered healthcare and the democratization of medicine over the last 20 years. 1–3 Machine learning classifiers have been shown to outperform clinical judgment at the population level by reducing screening burdens and access inequities for common illnesses and conditions. 4–6 As with any data, analytical techniques brought to them can contain a range of biases, from sample bias to measurement bias to representation bias and historical bias.…”

Section: Introductionmentioning

confidence: 99%

Bias and fairness assessment of a natural language processing opioid misuse classifier: detection and mitigation of electronic health record data disadvantages across racial subgroups

Thompson

Sharma

Bhalla

et al. 2021

Journal of the American Medical Informatics Association

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Objectives To assess fairness and bias of a previously validated machine learning opioid misuse classifier. Materials & Methods Two experiments were conducted with the classifier’s original (n = 1000) and external validation (n = 53 974) datasets from 2 health systems. Bias was assessed via testing for differences in type II error rates across racial/ethnic subgroups (Black, Hispanic/Latinx, White, Other) using bootstrapped 95% confidence intervals. A local surrogate model was estimated to interpret the classifier’s predictions by race and averaged globally from the datasets. Subgroup analyses and post-hoc recalibrations were conducted to attempt to mitigate biased metrics. Results We identified bias in the false negative rate (FNR = 0.32) of the Black subgroup compared to the FNR (0.17) of the White subgroup. Top features included “heroin” and “substance abuse” across subgroups. Post-hoc recalibrations eliminated bias in FNR with minimal changes in other subgroup error metrics. The Black FNR subgroup had higher risk scores for readmission and mortality than the White FNR subgroup, and a higher mortality risk score than the Black true positive subgroup (P < .05). Discussion The Black FNR subgroup had the greatest severity of disease and risk for poor outcomes. Similar features were present between subgroups for predicting opioid misuse, but inequities were present. Post-hoc mitigation techniques mitigated bias in type II error rate without creating substantial type I error rates. From model design through deployment, bias and data disadvantages should be systematically addressed. Conclusion Standardized, transparent bias assessments are needed to improve trustworthiness in clinical machine learning models.

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Do-It-Yourself Automated Insulin Delivery: A Leading Example of the Democratization of Medicine

Cited by 28 publications

References 22 publications

Why #WeAreNotWaiting - Motivations and Self-Reported Outcomes Among Users of Open-Source Automated Insulin Delivery Systems: A Multinational Survey

Why #WeAreNotWaiting - Motivations and Self-Reported Outcomes Among Users of Open-Source Automated Insulin Delivery Systems: A Multinational Survey

Shared motivations, goals and values in the practice of personal science - A community perspective on self-tracking for empirical knowledge

Bias and fairness assessment of a natural language processing opioid misuse classifier: detection and mitigation of electronic health record data disadvantages across racial subgroups

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