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Using the PRISMA approach, we present the first systematic literature review of digital twin (DT) research in healthcare systems (HSs). This endeavor stems from the pressing need for a thorough analysis of this emerging yet fragmented research area, with the goal of consolidating knowledge to catalyze its growth. Our findings are structured around three research questions aimed at identifying: (i) current research trends, (ii) gaps, and (iii) realization challenges. Current trends indicate global interest and interdisciplinary collaborations to address complex HS challenges. However, existing research predominantly focuses on conceptualization; research on integration, verification, and implementation is nascent. Additionally, we document that a substantial body of papers mislabel their work, often disregarding modeling and twinning methods that are necessary elements of a DT. Furthermore, we provide a non-exhaustive classification of the literature based on two axes: the object (i.e., product or process) and the context (i.e., patient’s body, medical procedures, healthcare facilities, and public health). While this is a testament to the diversity of the field, it implies a specific pattern that could be reimagined. We also identify two gaps: (i) considering the human-in-the-loop nature of HSs with a focus on provider decision-making and (ii) implementation research. Lastly, we discuss two challenges for broad-scale implementation of DTs in HSs: improving virtual-to-physical connectivity and data-related issues. In conclusion, this study suggests that DT research could potentially help alleviate the acute shortcomings of HSs that are often manifested in the inability to concurrently improve the quality of care, provider wellbeing, and cost efficiency.

This article proposes the solver-aware system architecting framework for leveraging the combined strengths of experts, crowds and specialists to design innovative complex systems. Although system architecting theory has extensively explored the relationship between alternative architecture forms and performance under operational uncertainty, limited attention has been paid to differences due to who generates the solutions. The recent rise in alternative solving methods, from gig workers to crowdsourcing to novel contracting structures emphasises the need for deeper consideration of the link between architecting and solver-capability in the context of complex system innovation. We investigate these interactions through an abstract problem-solving simulation, representing alternative decompositions and solver archetypes of varying expertise, engaged through contractual structures that match their solving type. We find that the preferred architecture changes depending on which combinations of solvers are assigned. In addition, the best hybrid decomposition-solver combinations simultaneously improve performance and cost, while reducing expert reliance. To operationalise this new solver-aware framework, we induce two heuristics for decomposition-assignment pairs and demonstrate the scale of their value in the simulation. We also apply these two heuristics to reason about an example of a robotic manipulator design problem to demonstrate their relevance in realistic complex system settings.

The International Council on Systems Engineering (INCOSE) has initiated a Future of Systems Engineering (FuSE) program that includes a stream for advancing the theoretical foundations of the discipline of Systems Engineering (SE). A near‐term goal of FuSE is to assess the adequacy of current theoretical foundations of SE. The discipline of SE is converging toward model‐based practices (i.e., MBSE) that have not yet reached the maturity of model‐based practices in other engineering domains. For example, finite element analysis and computational fluid dynamics are grounded in mathematical theory, while, generally, MBSE is not. However, some attempts have been made to underpin MBSE with theoretical richness. This article presents a systematic literature study that surveyed state of the art on providing MBSE with mathematical foundations. Our protocol collected over 2000 publications that were reviewed for inclusion/exclusion, categorized, and analyzed. We provide insights to the type of mathematical theories used, domains of applications, and areas of SE to which the math was applied to, among other analysis. We also provide a synthesized discussion about the field moving forward, emphasizing positive trends along with the negatives and areas of concern. Overall, we found the field to be nascent. [ABSTRACT FROM AUTHOR]

ABSTRACT Decomposition is a critical enabler of complex system development, as it enables both task specialization and efficiency through parallel work. The process of decomposing involves partitioning system parameters into tightly coupled modules and managing any cross‐module coupling by designing passive interfaces or through active coordination. A rich literature has developed algorithms and tools to support this process. However, we contend that this view has placed too much emphasis on module selection, and not enough on the interaction with interface design. This perspective has significant implications for lifecycle costs and development time. To that end, this study explores how earlier consideration of interface design can create more valuable options to better navigate performance, cost, and schedule tradeoffs. Specifically, through an abstract simulation experiment, we demonstrate that (1) a sequential approach that first selects modules and then designs interfaces to support those modules, yields lower performance than an integrated approach that considers modules and supporting interfaces simultaneously; and (2) this result is even stronger when schedule and cost are considered as part of the evaluation. In other words, an integrated approach provides more options for project managers seeking to navigate the performance‐cost‐schedule tradeoff known as the golden triangle. These results emphasize the need for a decomposition aid that adopts a holistic view of the optimization problem, accounting for interface creation, intra‐organization collaboration, and valuing nonperformance measures of effectiveness. [ABSTRACT FROM AUTHOR]

This study presents the results from rapid review of how model‐based systems engineering (MBSE) is utilized in healthcare systems (HSs). We conduct a review of the last twelve years and find that MBSE adoption in HSs is accelerating, with use of various MBSE languages and tools, as well as their integration with other simulation and modeling techniques. We find that similar to engineered systems, the most common MBSE language is systems modeling language (SysML), followed by unified modeling language (UML) and others. Additionally, we observe that MBSE methods are frequently used in conjunction with other analytical techniques, such as simulation and co‐simulations, to analyze and enhance various HS operations, or to assist with making tradeoffs between HS attributes such as quality and cost. Moreover, we provide a non‐exhaustive classification of current research based on two dimensions: healthcare applications and MBSE use cases. Notably, MBSE is being implemented generally with patient‐centric objectives in various HS domains, including IoT‐enabled smart healthcare, clinical medicine, medical device development, healthcare process enhancement, and healthcare facilities management. While the primary MBSE use case involves modeling different aspects of healthcare operations, there is a significant number of studies that pursue requirements engineering, systems analysis, integration, verification and validation, as well as risk analysis and management. Furthermore, we identify two promising research gaps. First, there is a need for the integration of MBSE with state‐of‐the‐art data‐driven analytical methodologies such as hybrid simulation and artificial intelligence techniques. Second, HSs could greatly benefit from representing the cognitive functions and processes of human decision‐makers in the loop, such as healthcare providers (e.g., doctors and nurses), who are instrumental in sustaining the HS performance and functionality. We contend that MBSE and other SE methods and techniques could improve HSs design, operations, and management; while fostering resilience and long‐term sustainability. [ABSTRACT FROM AUTHOR]

Abstract Background Distressing symptoms are common in advanced cancer. Medicinal cannabinoids are commonly prescribed for a variety of symptoms. There is little evidence to support their use for most indications in palliative care. This study aims to assess a 1:20 delta-9-tetrahydrocannabinol/cannabidiol (THC/CBD) cannabinoid preparation in the management of symptom distress in patients with advanced cancer undergoing palliative care. Methods and design One hundred and fifty participants will be recruited across multiple sites in Queensland, Australia. A teletrial model will facilitate the recruitment of patients outside of major metropolitan areas. The study is a pragmatic, multicenter, randomised, placebo-controlled, two-arm trial of escalating doses of an oral 1:20 THC/CBD medicinal cannabinoid preparation (10 mg THC:200 mg CBD/mL). It will compare the efficacy and safety outcomes of a titrated dose range of 2.5 mg THC/50mgCBD to 30 mg THC/600 mg CBD per day against a placebo. There is a 2-week patient-determined titration phase, to reach a dose that achieves symptom relief or intolerable side effects, with a further 2 weeks of assessment on the final dose. The primary objective is to assess the effect of escalating doses of a 1:20 THC/CBD medicinal cannabinoid preparation against placebo on change in total symptom distress score, with secondary objectives including establishing a patient-determined effective dose, the effect on sleep quality and overall quality of life. Some patients will be enrolled in a sub-study which will more rigorously evaluate the effect on sleep. Discussion MedCan-3 is a high-quality, adequately powered, placebo-controlled trial which will help demonstrate the utility of a THC:CBD 1:20 oral medicinal cannabis product in reducing total symptom distress in this population. Secondary outcomes may lead to new hypotheses regarding medicinal cannabis’ role in particular symptoms or in particular cancers. The sleep sub-study will test the feasibility of using actigraphy and the Insomnia Severity Index (ISI) in this cohort. This will be the first large-scale palliative care randomised clinical trial to utilise the teletrial model in Australia. If successful, this will have significant implications for trial access for rural and remote patients in Australia and internationally. Trial registration ANZCTR ACTRN12622000083796 . Protocol number 001/20. Registered on 21 January 2022. Recruitment started on 8 August 2022.

In an increasingly interconnected & cyber-physical world, complexity is often cited as the root cause of adverse project outcomes, including cost-overruns and schedule delays. This realization has prompted calls for better complexity management, which hinges on the ability to recognize and measure complexity early in the design process. However, while numerous complexity measures (CMs) have been promulgated, there is limited agreement about "how" complexity should be measured and what a good measure should entail. In this paper, we propose a framework for benchmarking CMs in terms of how well they are able to detect systematic variation along key aspects of complexity growth. Specifically, the literature is consistent in expecting that complexity growth is correlated with increases in size, number of interconnections, and randomness of the system architecture. Therefore, to neutrally compare six representative CMs, we synthetically create a set of system architectures that systematically vary across each dimension. We find that none of the measures are able to detect changes in all three dimensions simultaneously, though several are consistent in their response to one or two. We also find that there is a dichotomy in the literature regarding the archetype of systems that are considered as complex: CMs developed by researchers focused on physics-based (e.g., aircraft) tend to emphasize interconnectedness and structure whereas flow-based (e.g., the power grid) focus on size. Our findings emphasize the need for more careful validation across proposed measures. Our framework provides a path to enable shared progress towards the goal of better complexity management. [ABSTRACT FROM AUTHOR]

Decomposition is a dominant design strategy because it enables complex problems to be broken up into loosely coupled modules that are easier to manage and can be designed in parallel. However, contrary to widely held expectations, we show that complexity can increase substantially when natural system modules are fully decoupled from one another to support parallel design. Drawing on detailed empirical evidence from a NASA space robotics field experiment, we explain how new information is introduced into the design space through three complexity addition mechanisms of the decomposition process: interface creation, functional allocation, and second-order effects. These findings have important implications for how modules are selected early in the design process and how future decomposition approaches should be developed. Although it is well known that complex systems are rarely fully decomposable and that the decoupling process necessitates additional design work, the literature is predominantly focused on reordering, clustering, and/or grouping-based approaches to define module boundaries within a fixed system representation. Consequently, these approaches are unable to account for the (often significant) new information that is added to the design space through the decomposition process. We contend that the observed mechanisms of complexity growth need to be better accounted for during the module selection process in order to avoid unexpected downstream costs. With this work, we lay a foundation for valuing these complexity-induced impacts to performance, schedule, and cost, earlier in the decomposition process. [ABSTRACT FROM AUTHOR]

Abstract Introduction Following 2016 legislation permitting limited access to cannabis for research and medicinal purposes, the number of randomized clinical trials (RCTs) investigating the effectiveness of medicinal cannabis (MC) on symptom burden relief in cancer contexts has increased in Australia. This study aimed to understand the perceptions, hopes and concerns of people with advanced cancer regarding the future availability and regulation of MC in Australia. Methods This qualitative study draws on semistructured interviews conducted between February 2019 and October 2020 in Brisbane, Australia, as part of an MC RCT substudy. Interviews were undertaken on 48 patients with advanced cancer in palliative care eligible to participate in an MC trial (n = 26 participated in an RCT; n = 2 participated in a pilot study; n = 20 declined). Interviews included a discussion of patients' decision‐making regarding trial participation, concerns about MC and perceptions of future availability, including cost. Transcribed interviews were analysed inductively and abductively, informed by constructivist thematic analysis conventions. Results Overall, participants supported making MC legally accessible as a prescription‐only medication. Fear of financial toxicity, however, compromised this pathway. Steep posttrial costs of accessing MC prompted several people to decline trial participation, and others to predict—if found effective—that many would either access MC through alternative pathways or reduce their prescribed dosage to enable affordable access. Conclusions These findings suggest that—despite a relatively robust universal healthcare system—Australians are potentially vulnerable to and fearful of financial toxicity. Prevalent in the United States, financial toxicity occurs when disadvantaged cancer patients access necessary but expensive medications with lasting consequences: bankruptcy, ongoing anxiety and cancer worry. Interview transcripts indicate that financial fears—and the systems sustaining them—may pose a threat to RCT completion and to equitable access to legal MC. Such findings support calls for embedding qualitative substudies and community partnerships within RCTs, while also suggesting the importance of subsidisation to overcoming injustices. Patient or Public Contribution A patient advisory committee informed RCT design. This qualitative substudy foregrounds patients' decision‐making, perceptions and experiences.

Objectives: Tinnitus is hearing a sound without any external acoustic stimulus. There are some clues of hypertension can cause tinnitus in different ways. The aim of the study was to evaluate the relationship between tinnitus and masked hypertension including echocardiographic parameters and severity of tinnitus. Methods: This study included 88 patients with tinnitus of at least 3 months duration and 85 age and gender-matched control subjects. Tinnitus severity index was used to classify the patients with tinnitus. After a complete medical history, all subjects underwent routine laboratory examination, office blood pressure measurement, hearing tests and ambulatory blood pressure monitoring. Masked hypertension is defined as normal office blood pressure measurement and high ambulatory blood pressure level. Results: Baseline characteristics in patients and controls were similar. Prevalence of masked hypertension was significantly higher in patients with tinnitus than controls (18.2% vs 3.5%, p = 0.002). Office diastolic BP (76 ± 8.1 vs. 72.74 ± 8.68, p = 0.01), ambulatory 24-H diastolic BP (70.2 ± 9.6 vs. 66.9 ± 6.1, p = 0.07) and ambulatory daytime diastolic BP (73.7 ± 9.5 vs. 71.1 ± 6.2, p = 0.03) was significantly higher in patients with tinnitus than control group. Tinnitus severity index in patients without masked hypertension was 0 and tinnitus severity index in patients with masked hypertension were 2 (1–5). Conclusion: This study demonstrated that masked hypertension must be kept in mind if there is a complaint of tinnitus without any other obvious reason.