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The COVID-19 pandemic has negatively affected individuals with chronic conditions, such as type 2 diabetes mellitus (T2DM). The full extent of the impact however remains unknown, mainly due to the limited research availability. This study examines the pandemic's impact on T2DM diagnosis and management in the United States. The methods include a literature review and an online survey of American healthcare professionals regarding their experiences of T2DM during the pandemic. Findings indicated significant reductions in healthcare utilisation among T2DM patients and a decline in the quality of care for this population. These reductions may have been attributed to fewer HbA1C tests being performed and emergency department visits, with a high proportion of individuals experiencing uncontrolled diabetes and receiving treatment intensification, especially among racial/ethnic minority groups, rural populations, and those with comorbidities. Effective strategies are needed to support T2DM regular follow-up and self-management, tailored to patient needs and culturally appropriated. Technologies like telemedicine can help address these needs, potentially reducing healthcare costs and improving clinical outcomes and quality of life for people with T2DM.

This comprehensive review investigates the transformative potential of sensor-driven digital twin technology in enhancing healthcare delivery within smart environments. We explore the integration of smart environments with sensor technologies, digital health capabilities, and location-based services, focusing on their impacts on healthcare objectives and outcomes. This work analyzes the foundational technologies, encompassing the Internet of Things (IoT), Internet of Medical Things (IoMT), machine learning (ML), and artificial intelligence (AI), that underpin the functionalities within smart environments. We also examine the unique characteristics of smart homes and smart hospitals, highlighting their potential to revolutionize healthcare delivery through remote patient monitoring, telemedicine, and real-time data sharing. The review presents a novel solution framework leveraging sensor-driven digital twins to address both healthcare needs and user requirements. This framework incorporates wearable health devices, AI-driven health analytics, and a proof-of-concept digital twin application. Furthermore, we explore the role of location-based services (LBS) in smart environments, emphasizing their potential to enhance personalized healthcare interventions and emergency response capabilities. By analyzing the technical advancements in sensor technologies and digital twin applications, this review contributes valuable insights to the evolving landscape of smart environments for healthcare. We identify the opportunities and challenges associated with this emerging field and highlight the need for further research to fully realize its potential to improve healthcare delivery and patient well-being.

Healthcare must deliver high quality, high value, patient-centric care while improving access and costs even as aging and active populations increase demand for services like knee arthroplasty. Machine learning and artificial intelligence (ML/AI) using past clinical data primarily replicates existing cause-to-effect actions. This is insufficient to forecast outcomes, costs, resource utilization and complications when radical process re-engineering like COVID- inspired telemedicine occurs. To predict episodes of care for innovative arthroplasty patient journeys, a sophisticated integrated knowledge network must model optimal novel care pathways. We focus on the first step of the patient journey: shared surgical decision making. Patient engagement is critical to successful outcomes, yet existing methods cannot model impact of specific decision variables like interactive clinician/caregiver/patient participation in pre- and post-operative rehabilitation, and other factors like comorbidities. We demonstrate coupling of simulation and AI/ML for augmented intelligence musculoskeletal virtual care decisions for knee arthroplasty. This novel coupled-solution integrates critical data and information with tacit clinician knowledge., (©2022 AMIA - All rights reserved.)

Background: Smartphone technology represents an opportunity to deliver practical solutions for people affected by cancer at a scale that was previously unimaginable, such as information, appointment monitoring, and improved access to cancer support services. This study aimed to determine whether a smartphone application (app) reduced the unmet needs among people newly diagnosed with cancer., Methods: A single blind, multisite randomized controlled trial to determine the impact of an app-based, 4-month intervention. Newly diagnosed cancer patients were approached at three health service treatment clinics., Results: Eighty-two people were randomized (intervention; n = 43 and control; n = 39), average age was 59.5 years (SD: 12.9); 71% female; 67% married or in a de facto relationship. At baseline, there were no differences in participants' characteristics between the groups. No significant effects, in reducing unmet needs, were demonstrated at the end of intervention (4-month) or 12-month follow-up. Overall, 94% used the app in weeks 1-4, which decreased to 41% in weeks 13-16. Mean app use time per participant: Cancer Information, 6.9 (SD: 18.9) minutes; Appointment Schedule, 5.1 (SD: 9.6) minutes; Cancer Services 1.5 minutes (SD: 6.8); Hospital Navigation, 1.4 (SD: 2.8) minutes., Conclusions: Despite consumer involvement in the design of this smartphone technology, the app did not reduce unmet needs. This may have been due to the study being underpowered. To contribute to a meaningful understanding and improved implementation of smartphone technology to support people affected by cancer, practical considerations, such as recruitment issues and access to, and confidence with, apps, need to be considered. Australian New Zealand Clinical Trials Registration (ACTRN) Trial Registration: 12616001251415; WEF 7/9/2016., (© 2019 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Objectives: To present the breadth and depth of Consumer Health Informatics (CHI) initiatives and in particular, identify areas to further enhance such solutions with the incorporation of advances in artificial intelligence (AI)., Methods: A literature review was conducted. Inclusion criteria focused on barriers and facilitators as well as key milestones for CHI over the last 10 years. In addition, significant international initiatives in regions and /or countries and opportunities to incorporate the advances of AI also made up the inclusion criteria. Exclusion criteria included factors that made reach/range of solutions to individuals or groups of individuals less than 50 people. Journals in Medline were accessed as well as the grey literature., Results: 200 papers were identified but this was narrowed to 25 due to repetitions and similarities of the types of solutions presented. From this, it was possible to identify key barriers and facilitators including: socio-political factors, financial factors, infrastructure, as well as levels of health literacy., Conclusions: The relatively nascent domain of CHI has developed and is growing. Full benefits will only be realized when more precision solutions that incorporate AI advances will be designed. The example of RxPredict is provided as a case vignette to illustrate., Competing Interests: Disclosure The authors report no conflicts of interest in this work., (Georg Thieme Verlag KG Stuttgart.)

A variety of acute and chronic diseases require rehabilitation at home after treatment. Outpatient rehabilitation is crucial for the quality of the medical outcome but is mainly performed without medical supervision. Non-Compliance can lead to severe health risks and readmission to the hospital. While the patient is closely monitored in the hospital, methods and technologies to identify risks at home have to be developed. We analyze state-of-the-art monitoring systems and technologies and show possibilities to transfer these technologies into rehabilitation monitoring. For this purpose, we analyze sensor technology from the field of Quantified Self and Smart Homes. The available sensor data from this consumer grade technology is summarized to give an overview of the possibilities for medical data analytics. Subsequently, we show a conceptual roadmap to transfer data analytics methods to sensor based rehabilitation risk management.

Kenya ranks among the twenty-two countries that collectively contribute about 80% of the world's Tuberculosis cases; with a 50-200 fold increased risk of tuberculosis in HIV infected persons versus non-HIV hosts. Contemporaneously, there is an increase in mobile penetration and its use to support healthcare throughout Africa. Many are skeptical that such m-health solutions are unsustainable and not scalable. We seek to design a scalable, pervasive m-health solution for Tuberculosis care to become a use case for sustainable and scalable health IT in limited resource settings. We combine agile design principles and user-centered design to develop the architecture needed for this initiative. Furthermore, the architecture runs on multiple devices integrated to deliver functionality critical for successful Health IT implementation in limited resource settings. It is anticipated that once fully implemented, the proposed m-health solution will facilitate superior monitoring and management of Tuberculosis and thereby reduce the alarming statistic regarding this disease in this region.

In today's context of escalating costs, managed care, regulations such as the Health Insurance Portability and Accountability Act (HIPAA) and a technology savvy patient, the healthcare industry can no longer be complacent regarding embracing technologies to enable better, more effective and efficient practice management. In such an environment, many healthcare organisations are turning to m-commerce or wireless solutions. These solutions, in particular the mobile electronic patient record, have many advantages over their wired counterparts, including significant cost advantages, higher levels of physician acceptance, more functionalities as well as enabling easy accessibility to healthcare in remote geographic regions, however, they also bring with them challenges of their own. One such major challenge is security. To date, few models exist that help establish an appropriate framework, in the context of wireless in healthcare, in which to understand and evaluate all the security issues let alone facilitate the development of systematic and robust solutions. Our paper addresses this need by outlining an appropriate mobile trust model for such a scenario in healthcare organisations.