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

Analysis of "Hybrid Closed Loop Using a Do-It-Yourself Artificial Pancreas System in Adults With Type 1 Diabetes"

In an article in Journal of Diabetes Science and Technology, Nanayakkara and colleagues assessed the glycemic efficacy and safety of AndroidAPS, an open-source automated delivery (AID) system, in a crossover randomized controlled trial. Although the trial included only 20 participants during a relatively short 4-week intervention period, glycemic outcomes attained were similar to commercial AID systems and there were no safety concerns. Validation of open-source AID systems in studies such as this should help address clinician hesitancy regarding these systems, and affirms the role of patient-centered innovation and self-management in diabetes care.

[Position paper: Open-source technology in the treatment of people living with diabetes mellitus-an Austrian perspective : Technology Committee of the Austrian Diabetes Association]

People living with diabetes mellitus can be supported in the daily management by diabetes technology with automated insulin delivery (AID) systems to reduce the risk of hypoglycemia and improve glycemic control as well as the quality of life. Due to barriers in the availability of AID-systems, the use and development of open-source AID-systems have internationally increased. This technology provides a necessary alternative to commercially available products, especially when approved systems are inaccessible or insufficiently adapted to the specific needs of the users. Open-source technology is characterized by worldwide free availability of codes on the internet, is not officially approved and therefore the use is on the individual's own responsibility. In the clinical practice a lack of expertise with open-source AID technology and concerns about legal consequences, lead to conflict situations for health-care professionals (HCP), sometimes resulting in the refusal of care of people living with diabetes mellitus. This position paper provides an overview of the available evidence and practical guidance for HCP to minimize uncertainties and barriers. People living with diabetes mellitus must continue to be supported in education and diabetes management, independent of the chosen diabetes technology including open-source technology. Check-ups of the metabolic control, acute and chronic complications and screening for diabetes-related diseases are necessary and should be regularly carried out, regardless of the chosen AID-system and by a multidisciplinary team with appropriate expertise.

Application of machine learning in affordable and accessible insulin management for type 1 and 2 diabetes: A comprehensive review

Proper insulin management is vital for maintaining stable blood sugar levels and preventing complications associated with diabetes. However, the soaring costs of insulin present significant challenges to ensuring affordable management. This paper conducts a comprehensive review of current literature on the application of machine learning (ML) in insulin management for diabetes patients, particularly focusing on enhancing affordability and accessibility within the United States. The review encompasses various facets of insulin management, including dosage calculation and response, prediction of blood glucose and insulin sensitivity, initial insulin estimation, resistance prediction, treatment adherence, complications, hypoglycemia prediction, and lifestyle modifications. Additionally, the study identifies key limitations in the utilization of ML within the insulin management literature and suggests future research directions aimed at furthering accessible and affordable insulin treatments. These proposed directions include exploring insurance coverage, optimizing insulin type selection, assessing the impact of biosimilar insulin and market competition, considering mental health factors, evaluating insulin delivery options, addressing cost-related issues affecting insulin usage and adherence, and selecting appropriate patient cost-sharing programs. By examining the potential of ML in addressing insulin management affordability and accessibility, this work aims to envision improved and cost-effective insulin management practices. It not only highlights existing research gaps but also offers insights into future directions, guiding the development of innovative solutions that have the potential to revolutionize insulin management and benefit patients reliant on this life-saving treatment.

Objectives and outcomes of patient-driven innovations published in peer-reviewed journals: a qualitative analysis of publications included in a scoping review

OBJECTIVES

The aim of this study was to gain a deeper understanding of the objectives and outcomes of patient-driven innovations that have been published in the scientific literature, focusing on (A) the unmet needs that patient-driven innovations address and (B) the outcomes for patients and healthcare that have been reported.

METHODS

We performed an inductive qualitative content analysis of scientific publications that were included in a scoping review of patient-driven innovations, previously published by our research group. The review was limited to English language publications in peer-reviewed journals, published in the years 2008-2020.

RESULTS

In total, 83 publications covering 21 patient-driven innovations were included in the analysis. Most of the innovations were developed for use on an individual or community level without healthcare involvement. We created three categories of unmet needs that were addressed by these innovations: access to self-care support tools, open sharing of information and knowledge, and patient agency in self-care and healthcare decisions. Eighteen (22%) publications reported outcomes of patient-driven innovations. We created two categories of outcomes: impact on self-care, and impact on peer interaction and healthcare collaboration.

CONCLUSIONS

The patient-driven innovations illustrated a diversity of innovative approaches to facilitate patients' and informal caregivers' daily lives, interactions with peers and collaborations with healthcare. As our findings indicate, patients and informal caregivers are central stakeholders in driving healthcare development and research forward to meet the needs that matter to patients and informal caregivers. However, only few studies reported on outcomes of patient-driven innovations. To support wider implementation, more evaluation studies are needed, as well as research into regulatory approval processes, dissemination and governance of patient-driven innovations.

Barriers to uptake of Open-Source automated insulin delivery Systems: Analysis of socioeconomic factors and perceived challenges of adults with type 1 diabetes from the OPEN survey

AIMS

Social and technical trends are empowering people with diabetes to co-create or self-develop medical devices and treatments to address their unmet healthcare needs, for example, open-source automated insulin delivery (AID) systems. This study aims to investigate the perceived barriers towards adoption and maintaining of open-source AID systems.

METHODS

This is a multinational study based on a cross-sectional, retrospective web-based survey of non-users of open-source AID. Participants (n = 129) with type 1 diabetes from 31 countries were recruited online to elicit their perceived barriers towards building and maintaining of an open-source AID system.

RESULTS

Sourcing the necessary components, lack of confidence in one's own technology knowledge and skills, perceived time and energy required to build a system, and fear of losing healthcare provider support appear to be major barriers towards the uptake of open-source AID.

CONCLUSIONS

This study identified a range of structural and individual-level barriers to uptake of open-source AID. Some of these individual-level barriers may be overcome over time through the peer support of the DIY online community as well as greater acceptance of open-source innovation among healthcare professionals. The findings have important implications for understanding the possible wider diffusion of open-source diabetes technology solutions in the future.

[Patients' Use of Digital Innovations in the Care Process: A Scoping Review]

BACKGROUND

Digital innovations in healthcare continue to be extensively researched and publicly discussed. The research perspective is often indication-specific or process-oriented and focuses on an application by health professionals in care settings. From the patient's perspective, there are additional digital innovations and opportunities for use that take place privately in addition to sectoral care services.

AIM

The aim of this scoping review was to map digital innovations currently available for patients and their possible applications in the care process by exploring the following question: Which digital innovations are currently available for patients in health care?

MATERIAL AND METHODS

A systematic literature search in four databases helped identify 44 international publications as relevant for our analysis. They were categorized and analyzed according to the types of digital innovations, their use by patients, and their location within the care process. In addition, the intentions whereby digital innovations can be applied were discussed.

RESULTS

We found that current research was focused on patient-applied digital innovations in the therapeutic field, and a broad application spectrum of interfaces for digital care was emerging. These included apps, smart devices, teleconsultation, patient portals, games, implants, robotics, intelligent information and communication systems, and ambient assisted living environments.

CONCLUSION

Many digitally supported health applications are designed to be used exclusively by patients themselves, or are performed in only partial interaction with providers. In this respect, the active participation and personal responsibility of patients in the treatment process could be strengthened with the help of digital innovations.

Mapping the Landscape of Do-it-Yourself Medicine

The practice of medicine is typically conceptualized as remaining within the boundaries of a hospital or clinic. However, in recent years, patients have been able to gain access to information about medical research as it is ongoing. As a result, there has been a rise in do-it-yourself (DIY) medicine, where individuals treat themselves for medical conditions outside of clinical settings, often mimicking experimental therapies that remain inaccessible to the wider public. For example, in DIY brain stimulation, individuals suffering from depression build at-home electrical headsets using nine-volt batteries, mimicking an experimental neuroscience technique used in scientific laboratories. In DIY fecal transplantation, those with intestinal disorders like C. Difficile and inflammatory bowel disease transplant stool from donors into themselves with the aid of blenders and enemas. In the open Artificial Pancreas System movement, diabetes patients hacked together an artificial pancreas system from their glucose monitors and insulin pumps, years before such a system was approved by the United States Food and Drug Administration (US FDA). To date, scholarship on DIY medicine has largely been relegated to specific medical domains (e.g., neurology, gastroenterology, infectious disease). In this paper, however, I recognize DIY medicine as a cross-cutting phenomenon that has emerged independently across medical domains but shares common features. I map the varieties of DIY medicine across these domains and suggest that four key factors lead to their creation, growth, and uptake. In doing so, this essay sheds light on an understudied area of biomedical citizen science that is likely to grow substantially in the coming decades.

"Democratizing" artificial intelligence in medicine and healthcare: Mapping the uses of an elusive term

Introduction: "Democratizing" artificial intelligence (AI) in medicine and healthcare is a vague term that encompasses various meanings, issues, and visions. This article maps the ways this term is used in discourses on AI in medicine and healthcare and uses this map for a normative reflection on how to direct AI in medicine and healthcare towards desirable futures. Methods: We searched peer-reviewed articles from Scopus, Google Scholar, and PubMed along with grey literature using search terms "democrat*", "artificial intelligence" and "machine learning". We approached both as documents and analyzed them qualitatively, asking: What is the object of democratization? What should be democratized, and why? Who is the demos who is said to benefit from democratization? And what kind of theories of democracy are (tacitly) tied to specific uses of the term? Results: We identified four clusters of visions of democratizing AI in healthcare and medicine: 1) democratizing medicine and healthcare through AI, 2) multiplying the producers and users of AI, 3) enabling access to and oversight of data, and 4) making AI an object of democratic governance. Discussion: The envisioned democratization in most visions mainly focuses on patients as consumers and relies on or limits itself to free market-solutions. Democratization in this context requires defining and envisioning a set of social goods, and deliberative processes and modes of participation to ensure that those affected by AI in healthcare have a say on its development and use.

Barriers to Uptake of Open-Source Automated Insulin Delivery Systems: Analysis of Socioeconomic Factors and Perceived Challenges of Caregivers of Children and Adolescents With Type 1 Diabetes From the OPEN Survey

Background

As a treatment option for people living with diabetes, automated insulin delivery (AID) systems are becoming increasingly popular. The #WeAreNotWaiting community plays a crucial role in the provision and distribution of open-source AID technology. However, while a large percentage of children were early adopters of open-source AID, there are regional differences in adoption, which has prompted an investigation into the barriers perceived by caregivers of children with diabetes to creating open-source systems.

Methods

This is a retrospective, cross-sectional and multinational study conducted with caregivers of children and adolescents with diabetes, distributed across the online #WeAreNotWaiting online peer-support groups. Participants—specifically caregivers of children not using AID—responded to a web-based questionnaire concerning their perceived barriers to building and maintaining an open-source AID system.

Results

56 caregivers of children with diabetes, who were not using open-source AID at the time of data collection responded to the questionnaire. Respondents indicated that their major perceived barriers to building an open-source AID system were their limited technical skills (50%), a lack of support by medical professionals (39%), and therefore the concern with not being able to maintain an AID system (43%). However, barriers relating to confidence in open-source technologies/unapproved products and fear of digital technology taking control of diabetes were not perceived as significant enough to prevent non-users from initiating the use of an open-source AID system.

Conclusions

The results of this study elucidate some of the perceived barriers to uptake of open-source AID experienced by caregivers of children with diabetes. Reducing these barriers may improve the uptake of open-source AID technology for children and adolescents with diabetes. With the continuous development and wider dissemination of educational resources and guidance—for both aspiring users and their healthcare professionals—the adoption of open-source AID systems could be improved.

'We're all on the same team'. Perspectives on the future of artificial pancreas systems by adults in Australia with type 1 diabetes using open-source technologies: A qualitative study

AIMS

An emerging group of people with type 1 diabetes are not waiting for commercial solutions, choosing to manage their condition with open-source artificial pancreas systems (APS). Our aim was to explore their perspectives on the future of APS.

METHODS

Semi-structured telephone interviews were conducted (in Australia, October 2018 to January 2019) with 23 adults with type 1 diabetes currently using open-source APS. Interviews were recorded, transcribed and analysed thematically.

RESULTS

Participants described five key features of open-source APS they value: compatibility, user-led design, customisability, ability to evolve faster and community-driven. They attributed the success of the open-source APS movement to benefits they derive from these features: choice, solutions that meet their needs, ownership, staying one step ahead and real-time support. They expressed hope that future commercial products and healthcare would benefit from their learnings and from collaboration with the open-source APS community.

CONCLUSIONS

Participants believed that there will always be a place for the open-source community. It will continue to build on and advance commercial products, respond to user needs, offering a higher degree of control and customisation than afforded by commercial products and generating optimism for the future. Participants desired that future commercial diabetes technologies would be inspired by the open-source community and developed collaboratively with people with diabetes.

Patient-driven innovations reported in peer-reviewed journals: a scoping review

BACKGROUND

Awareness of patients' innovative capabilities is increasing, but there is limited knowledge regarding the extent and nature of patient-driven innovations in the peer-reviewed literature.

OBJECTIVES

The objective of the review was to answer the question: what is the nature and extent of patient-driven innovations published in peer-reviewed scientific journals?

ELIGIBILITY CRITERIA

We used a broad definition of innovation to allow for a comprehensive review of different types of innovations and a narrow definition of 'patient driven' to focus on the role of patients and/or family caregivers. The search was limited to years 2008-2020.

SOURCES OF EVIDENCE

Four electronic databases (Medline (Ovid), Web of Science Core Collection, PsycINFO (Ovid) and Cinahl (Ebsco)) were searched in December 2020 for publications describing patient-driven innovations and complemented with snowball strategies.

CHARTING METHODS

Data from the included articles were extracted and categorised inductively.

RESULTS

A total of 96 articles on 20 patient-driven innovations were included. The number of publications increased over time, with 69% of the articles published between 2016 and 2020. Author affiliations were exclusively in high income countries with 56% of first authors in North America and 36% in European countries. Among the 20 innovations reported, 'Do-It-Yourself Artificial Pancreas System' and the online health network 'PatientsLikeMe', were the subject of half of the articles.

CONCLUSIONS

Peer-reviewed publications on patient-driven innovations are increasing and we see an important opportunity for researchers and clinicians to support patient innovators' research while being mindful of taking over the work of the innovators themselves.

The Cybersecurity and the Care Robots: A Viewpoint on the Open Problems and the Perspectives

Care robots represent an opportunity for the health domain. The use of these robots has important implications. They can be used in surgery, rehabilitation, assistance, therapy, and other medical fields. Therefore, care robots (CR)s, have both important physical and psychological implications during their use. Furthermore, these devices, meet important data in clinical applications. These data must be protected. Therefore, cybersecurity (CS) has become a crucial characteristic that concerns all the involved actors. The study investigated the collocation of CRs in the context of CS studies in the health domain. Problems and peculiarities of these devices, with reference to the CS, were faced, investigating in different scientific databases. Highlights, ranging also from ethics implications up to the regulatory legal framework (ensuring safety and cybersecurity) have been reported. Models and cyber-attacks applicable on the CRs have been identified.

Change Management and Digital Innovations in Hospitals of Five European Countries

The objective of the paper is to evaluate the quality of systemic change management (CHM) and readiness for change in five Central European countries. The secondary goal is to identify trends and upcoming changes in the field of digital innovations in healthcare. The results show that all compared countries (regardless of their historical context) deal with similar CHM challenges with a rather similar degree of success. A questionnaire distributed to hospitals clearly showed that there is still considerable room for improvement in terms of the use of specific CHM tools. A review focused on digital innovations based on the PRISMA statement showed that there are five main directions, namely, data collection and integration, telemedicine, artificial intelligence, electronic medical records, and M-Health. In the hospital environment, there are considerable reservations in applying change management principles, as well as the absence of a systemic approach. The main factors that must be monitored for a successful and sustainable CHM include a clearly defined and widely communicated vision, early engagement of all stakeholders, precisely set rules, adaptation to the local context and culture, provision of a technical base, and a step-by-step implementation with strong feedback.

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

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.

How Adults with Type 1 Diabetes Are Navigating the Challenges of Open-Source Artificial Pancreas Systems: A Qualitative Study

Objective: An emerging group of people with type 1 diabetes are building and using their own artificial pancreas systems (APS). Currently, these "user-led," open-source systems are not endorsed by regulatory bodies. People face multiple challenges when building and using open-source APS (e.g., lacking required technical knowledge, difficulties sourcing hardware). We explored the experiences of adults with type 1 diabetes using open-source APS to understand how they are navigating these challenges. Research Design and Methods: We conducted semistructured telephone interviews (October 2018 to January 2019) with Australian adults about their experiences using an open-source APS. Interviews were recorded, transcribed, and analyzed thematically. Results: Participants included 23 adults with type 1 diabetes, aged 25-64 years, 10 (43%) women, with 1-34 months of experience of open-source APS. Participants used four key strategies to navigate challenges: (1) peer support, (2) self-sufficiency, (3) risk management, and (4) trade-offs. Participants relied on peer support and self-sufficiency to fill perceived gaps in professional and industry support. They felt that using an open-source solution was no riskier than previous management and demonstrated a conscious weighing-up of risks. Overall, participants felt that the experienced benefits for their physical health and quality of life outweighed the challenges and potential risks. Conclusions: Adults with type 1 diabetes are applying several interweaving psychosocial and practical strategies to navigate the challenges of building and using open-source APS. The findings highlight the importance of health professionals' showing support and understanding for those choosing to use an open-source APS.

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

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 available therapy options (348/509 adults, 68.4%, and 69/100 caregivers, 69%). Improving their own sleep quality was an almost universal motivator for caregivers (94/100, 94%). Significant improvements, independent of age and gender, were observed in self-reported glycated hemoglobin (HbA_1c), 7.14% (SD 1.13%; 54.5 mmol/mol, SD 12.4) to 6.24% (SD 0.64%; 44.7 mmol/mol, SD 7.0; P<.001), and time in range (62.96%, SD 16.18%, to 80.34%, SD 9.41%; P<.001).

Conclusions

These results highlight the unmet needs of people with diabetes, provide new insights into the evolving phenomenon of open-source AID technology, and indicate improved clinical outcomes. This study may inform health care professionals and policy makers about the opportunities provided by open-source AID systems.

International Registered Report Identifier (IRRID)

RR2-10.2196/15368

Efficacy, safety, and user experience of DIY or open-source artificial pancreas systems: a systematic review

The do-it-yourself artificial pancreas system (DIYAPS) is a patient-driven initiative with the potential to revolutionise diabetes management, automating insulin delivery with existing pumps and CGM combined with open-source algorithms. Given the considerable interest in this topic within the diabetes community, we have conducted a systematic review of DIYAPS efficacy, safety, and user experience. Following recognised procedures and reporting standards, we identified 10 eligible publications of 730 participants within the peer-reviewed literature. Overall, studies reported improvements in time in range, HbA1c (glycated haemoglobin), reduced hypoglycaemia, and improved quality of life with DIYAPS use. While results were positive, the identified studies were small, and the majority were observational and at high risk of bias. Further research including well-designed randomised trials comparing DIYAPS with appropriate comparators is recommended.

Second-Generation Digital Health Platforms: Placing the Patient at the Center and Focusing on Clinical Outcomes

Artificial intelligence (AI) digital health systems have drawn much attention over the last decade. However, their implementation into medical practice occurs at a much slower pace than expected. This paper reviews some of the achievements of first-generation AI systems, and the barriers facing their implementation into medical practice. The development of second-generation AI systems is discussed with a focus on overcoming some of these obstacles. Second-generation systems are aimed at focusing on a single subject and on improving patients' clinical outcomes. A personalized closed-loop system designed to improve end-organ function and the patient's response to chronic therapies is presented. The system introduces a platform which implements a personalized therapeutic regimen and introduces quantifiable individualized-variability patterns into its algorithm. The platform is designed to achieve a clinically meaningful endpoint by ensuring that chronic therapies will have sustainable effect while overcoming compensatory mechanisms associated with disease progression and drug resistance. Second-generation systems are expected to assist patients and providers in adopting and implementing of these systems into everyday care.

Versorgung von Kindern und Jugendlichen mit Typ-1-Diabetes: Lösungen für technische und psychosoziale Herausforderungen

Diabetes mellitus Typ 1 ist die häufigste endokrinologische Erkrankung bei Kindern und Jugendlichen unter 15 Jahren. Eine Heilungsperspektive bezüglich der Autoimmunreaktion gegen die insulinbildenden Betazellen ist weiterhin nicht in Sicht. Dennoch konnte durch technische Innovationsschübe bei Glukosesensoren, Insulinpumpen und Steuerungsalgorithmen innerhalb der letzten Jahre die Stoffwechselkontrolle optimiert werden. Diese Entwicklungen führen zusammen mit individuellen Diabetesschulungen und psychosozialer Unterstützung zu einer deutlichen Verbesserung der Versorgung.

In diesem Übersichtsartikel wird die aktuelle Versorgungssituation von Kindern und Jugendlichen mit Typ-1-Diabetes sowie ihren Eltern dargestellt. In Deutschland ist die multidisziplinäre, spezialisierte Versorgung durch Teams aus Kinder- und Jugenddiabetolog*innen, Diabetesberater*innen, Sozialarbeiter*innen und Kinder- und Jugendpsychotherapeut*innen seit vielen Jahren etabliert und führt zu einer im internationalen Vergleich sehr guten Versorgungsqualität. Fokussiert werden die Diabetesschulung mit dem Schwerpunkt, das Selbstmanagement optimal zu unterstützen, die psychosoziale Begleitung und Intervention sowie die Inklusion in Schulen und Kindertagesstätten. Wir gehen außerdem auf neue soziale Entwicklungen der Diabetes-Online-Community ein. Ein aktuelles Beispiel ist die patientenbetriebene Bewegung „Do-It-Yourself Artificial Pancreas System“ (DIY-APS), die als Open-Source-Projekt mittlerweile Innovationsgeber auch für Medizinproduktehersteller ist. Zum Schluss beleuchten wir die damit verbundenen Chancen, aber auch die Verschiebung der klassischen Arzt-Patienten-Rollen.