Wearable continuous glucose monitoring sensors: A revolution in diabetes treatment

Worldwide, the number of people affected by diabetes is rapidly increasing due to aging populations and sedentary lifestyles, with the prospect of exceeding 500 million cases in 2030, resulting in one of the most challenging socio-health emergencies of the third millennium. Daily management of diabetes by patients relies on the capability of correctly measuring glucose concentration levels in the blood by using suitable sensors. In recent years, glucose monitoring has been revolutionized by the development of Continuous Glucose Monitoring (CGM) sensors, wearable non/minimally-invasive devices that measure glucose concentration by exploiting different physical principles, e.g., glucose-oxidase, fluorescence, or skin dielectric properties, and provide real-time measurements every 1–5 min. CGM opened new challenges in different disciplines, e.g., medicine, physics, electronics, chemistry, ergonomics, data/signal processing, and software development to mention but a few. This paper first makes an overview of wearable CGM sensor technologies, covering both commercial devices and research prototypes. Then, the role of CGM in the actual evolution of decision support systems for diabetes therapy is discussed. Finally, the paper presents new possible horizons for wearable CGM sensor applications and perspectives in terms of big data analytics for personalized and proactive medicine

Data Sovereignty in Data Donation Cycles - Requirements and Enabling Technologies for the Data-driven Development of Health Applications

Personalized healthcare is expected to increase the efficiency and the effectiveness of health services using different kinds of algorithms on existing data. This approach is currently confronted with the lack of digital data and the desire for self-determined personal data handling. However, the issue of health data donation is on the political agenda of some governments. Within this work, a knowledge base will be created by reviewing existing approaches and technologies regarding this topic with the focus on chronic diseases. A list of requirements will be derived from which we conceptualize a data donation cycle to demonstrate the challenges and opportunities of health data sovereignty and its future possibilities concerning data-driven health application development. By linking the requirements to technological approaches, the baseline for future open ecosystems will be presented

Enhancing diabetes self-management through mobile phone application

Mary Adu adopted a systematic health behavioural framework and user engagement process to develop and explore the efficacy of a novel mobile-phone app for diabetes self-management. Reported benefits of the app provide empirical evidence of support for its multi-feature functionality and comprehensive interventional role in diabetes self-management education and support

Data science, analytics and artificial intelligence in e-health : trends, applications and challenges

Acknowledgments. This work has been partially supported by the Divina Pastora Seguros company.More than ever, healthcare systems can use data, predictive models, and intelligent algorithms to optimize their operations and the service they provide. This paper reviews the existing literature regarding the use of data science/analytics methods and artificial intelligence algorithms in healthcare. The paper also discusses how healthcare organizations can benefit from these tools to efficiently deal with a myriad of new possibilities and strategies. Examples of real applications are discussed to illustrate the potential of these methods. Finally, the paper highlights the main challenges regarding the use of these methods in healthcare, as well as some open research lines

Development of a Composite Health Index in Children with Cystic Fibrosis: A Pipeline for Data Processing, Machine Learning, and Model Implementation using Electronic Health Records

Cystic Fibrosis (CF) is a heterogeneous multi-faceted genetic condition that primarily affects the lungs and digestive system. For children and young people living with CF, timely management is necessary to prevent the establishment of severe disease. Modern data capture through electronic health records (EHR) have created an opportunity to use machine learning algorithms to classify subgroups of disease to understand health status and prognosis. The overall aim of this thesis was to develop a composite health index in children with CF. An iterative approach to unsupervised cluster analysis was developed to identify homogeneous clusters of children with CF in a pre-existing encounter-based CF database from Toronto Canada. An external validation of the model was carried out in a historical CF dataset from Great Ormond Street Hospital (GOSH) in London UK. The clusters were also re-created and validated using EHR data from GOSH when it first became accessible in 2021. The interpretability and sensitivity of the GOSH EHR model was explored. Lastly, a scoping review was carried out to investigate common barriers to implementation of prognostic machine learning algorithms in paediatric respiratory care. A cluster model was identified that detailed four clusters associated with time to future hospitalisation, pulmonary exacerbation, and lung function. The clusters were also associated with different disease related variables such as comorbidities, anthropometrics, microbiology infections, and treatment history. An app was developed to display individualised cluster assignment, which will be a useful way to interpret the cluster model clinically. The review of prognostic machine learning algorithms identified a lack of reproducibility and validations as the major limitation to model reporting that impair clinical translation. EHR systems facilitate point-of-care access of individualised data and integrated machine learning models. However, there is a gap in translation to clinical implementation of machine learning models. With appropriate regulatory frameworks the health index developed for children with CF could be implemented in CF care

Pharmacist collaborative practice and the development and implementation of team-based care in outpatient healthcare settings: A case study at El Rio Community Health Center

Background: The United States is experiencing a primary care physician shortage that will grow in the next decade as demand for primary care services is projected to increase. The growth in physician, Nurse Practitioner, and Physician Assistant supply alone will not be adequate to meet the demand for primary care services by 2020. Creating pharmacist-inclusive collaborative care teams for outpatient clinical care can help alleviate this health care delivery shortage. Methods: A qualitative mixed-methods case study was conducted in Tucson, Arizona to determine the supports and structures behind the Pharmacy-Based Diabetes Management Program (PBDMP) at El Rio Community Health Center. Using key informant interviews from El Rio, other outpatient clinical pharmacy programs (OCPPs), and the Tucson Accountable Care Organization, coupled with Lean Management brainstorming group sessions, the study elicited information about how the experience of El Rio with the PBDMP can inform nationwide development and implementation guidelines for other OCPPs. Results: The PBDMP at El Rio provides a blueprint for other programs interested in creating an OCPP. Key contributing factors to program success within El Rio and the other OCPPs interviewed included a focus on six key practices. Challenges inhibiting success were pharmacist provider status and reimbursement of clinical services provided. Translation: Three public health practice products were developed as a framework to provide future OCPPs interested in implementing a pharmacist-inclusive practice model: 1) implementation guidelines, 2) a self-assessment outpatient clinical pharmacy program worksheet for clinics looking to create or expand an OCPP, and 3) a student management decision case study. Conclusion: This study demonstrates the value of considering all potential members of a care team for diabetes care management. The decision by a clinic to create an OCPP should be based on team-based approaches to patient-centered chronic disease care management. Clinics looking to participate in a CDTM model OCPP need to identify if organizational transformation is needed for program buy-in and consider relational coordination between clinical roles as a major component of the coordinated work needed for a successful OCPP

Artificial Intelligence in Medicine and Healthcare: applications, availability and societal impact

This report reviews and classifies the current and near-future applications of Artificial Intelligence (AI) in Medicine and Healthcare according to their ethical and societal impact and the availability level of the various technological implementations. It provides conceptual foundations for well-informed policy-oriented work, research, and forward-looking activities that address the opportunities and challenges created in the field of AI in Medicine and Healthcare. This report is aimed for policy developers, but it also makes contributions that are of interest for researchers studying the impact and the future of AI on Healthcare, for scientific and technological stakeholders in this field and for the general public. This report is based on an analysis of the state of the art of research and technology, including software, personal monitoring devices, genetic tests and editing tools, personalized digital models, online platforms, augmented reality devices, and surgical and companion robotics. From this analysis, it is presented the concept of “extended personalized medicine”, and it is explored the public perception of medical AI systems, and how they show, simultaneously, extraordinary opportunities and drawbacks. In addition, this report addresses the transformation of the roles of doctors and patients in an age of ubiquitous information and identifies three main paradigms in AI-supported Medicine: “fake-based”, “patient-generated”, and “scientifically tailored” views. This Report presents: - An updated overview of the many aspects related to the social impact of Artificial Intelligence and its applications in Medicine and Health. A new ‘Technology Availability Scale’ is defined to evaluate and compare their current status. - Recent examples of the growing social concerns and debates in the general press, social media and other web-bases sources. - A ‘Visual Overview of AI and AI-mediated technologies in Medicine and Healthcare’, in which two figures show, respectively, a (newly proposed) classification according to their ethical and social impact, and the most relevant ethical and social aspects considered for such classification. Some key questions, controversies, significant, and conflicting issues are outlined for each aspect. - A ‘Structured Overview’, with a sorted list of technologies and their implementations, including perspectives, conflicting views and potential pitfalls, and a corresponding, extensive list of references. - A conclusive set of policy challenges, namely the need of informed citizens, key aspects (of AI and AI-mediated technologies in Medicine and Healthcare) to evaluate, and some recommendations towards a European leadership in this sector. - We finally relate our study with an update on the use of AI technologies to fight the SARS-CoV-2 virus and COVID-19 pandemic disease.JRC.A.5-Scientific Developmen

Artificial Intelligence in Medicine and Healthcare: applications, availability and societal impact

Comisión Europea. Joint Research Centre. Serie: JRC Science for Police ReportThis report reviews and classifies the current and near-future applications of Artificial Intelligence (AI) in Medicine and Healthcare according to their ethical and societal impact and the availability level of the various technological implementations. It provides conceptual foundations for well-informed policy-oriented work, research, and forward-looking activities that address the opportunities and challenges created in the field of AI in Medicine and Healthcare. This report is aimed for policy developers, but it also makes contributions that are of interest for researchers studying the impact and the future of AI on Healthcare, for scientific and technological stakeholders in this field and for the general public.This report is based on an analysis of the state of the art of research and technology, including software, personal monitoring devices, genetic tests and editing tools, personalized digital models, online platforms, augmented reality devices, and surgical and companion robotics. From this analysis, it is presented the concept of “extended personalized medicine”, and it is explored the public perception of medical AI systems, and how they show, simultaneously, extraordinary opportunities and drawbacks. In addition, this report addresses the transformation of the roles of doctors and patients in an age of ubiquitous information and identifies three main paradigms in AI-supported Medicine: “fake-based”, “patient-generated”, and “scientifically tailored” views.This Report presents:- An updated overview of the many aspects related to the social impact of Artificial Intelligence and its applications in Medicine and Health. A new ‘Technology Availability Scale’ is defined to evaluate and compare their current status.- Recent examples of the growing social concerns and debates in the general press, social media and other web-bases sources.- A ‘Visual Overview of AI and AI-mediated technologies in Medicine and Healthcare’, in which two figures show, respeComisión Europea. Joint Research Centr