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BACKGROUND: Older Americans, a growing segment of the population, have an increasing need for surgical services, and they experience a disproportionate burden of postoperative complications compared to their younger counterparts. A preoperative comprehensive geriatric assessment (pCGA) is recommended to reduce risk and improve surgical care delivery for this population, which has been identified as vulnerable. The pCGA optimizes multiple chronic conditions and factors commonly overlooked in routine preoperative planning, including physical function, polypharmacy, nutrition, cognition, mental health, and social and environmental support. The pCGA has been shown to decrease postoperative morbidity, mortality, and length of stay in a variety of surgical specialties. Although national guidelines recommend the use of the pCGA, a paucity of strategic guidance for implementation limits its uptake to a few academic medical centers. By applying implementation science and human factors engineering methods, this study will provide the necessary evidence to optimize the implementation of the pCGA in a variety of health care settings. OBJECTIVE: The purpose of this paper is to describe the study protocol to design an adaptable, user-centered pCGA implementation package for use among older adults before major abdominal surgery. METHODS: This protocol uses systems engineering methods to develop, tailor, and pilot-test a user-centered pCGA implementation package, which can be adapted to community-based hospitals in preparation for a multisite implementation trial. The protocol is based upon the National Institutes of Health Stage Model for Behavioral Intervention Development and aligns with the goal to develop behavioral interventions with an eye to real-world implementation. In phase 1, we will use observation and interviews to map the pCGA process and identify system-based barriers and facilitators to its use among older adults undergoing major abdominal surgery. In phase 2, we will apply user-centered design methods, engaging health care providers, patients, and caregivers to co-design a pCGA implementation package. This package will be applicable to a diverse population of older patients undergoing major abdominal surgery at a large academic hospital and an affiliate community site. In phase 3, we will pilot-test and refine the pCGA implementation package in preparation for a future randomized controlled implementation-effectiveness trial. We anticipate that this study will take approximately 60 months (April 2023-March 2028). RESULTS: This study protocol will generate (1) a detailed process map of the pCGA; (2) an adaptable, user-centered pCGA implementation package ready for feasibility testing in a pilot trial; and (3) preliminary pilot data on the implementation and effectiveness of the package. We anticipate that these data will serve as the basis for future multisite hybrid implementation-effectiveness clinical trials of the pCGA in older adults undergoing major abdominal surgery. CONCLUSIONS: The expected results of this study will contribute to improving perioperative care processes for older adults before major abdominal surgery. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/59428.

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Nanobodies are single-domain antibody fragments that have garnered considerable use as diagnostic and therapeutic agents as well as research tools. However, obtaining pure VHHs, like many proteins, can be laborious and inconsistent. High level cytoplasmic expression in E. coli can be challenging due to improper folding and insoluble aggregation caused by reduction of the conserved disulfide bond. We report a systems engineering approach leveraging engineered strains of E. coli, in combination with a two-stage process and simplified downstream purification, enabling improved, robust, soluble cytoplasmic nanobody expression, as well as rapid cell autolysis and purification. This approach relies on the dynamic control over the reduction potential of the cytoplasm, incorporates lysis enzymes for purification, and can also integrate dynamic expression of protein folding catalysts. Collectively, the engineered system results in more robust growth and protein expression, enabling efficient scalable nanobody production, and purification from high throughput microtiter plates, to routine shake flask cultures and larger instrumented bioreactors. We expect this system will expedite VHH development.

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Context: Medical devices fall under the broad topic encompass everything from basic hardware to integrated software systems. The integration of software into hardware devices is not simple due to requirements of regional regulatory bodies. Therefore, medical businesses need to oversee not only the creation of devices but also the observance of guidelines and standards established by regulatory bodies. While plan-driven methodologies prevented software from evolving or changing, agile methodologies have inherent characteristics of insufficient planning and documentation. Objectives: The objective of our research is to propose a suitable process model for medical device development, keeping in mind the regulatory requirements. Methods: First, based on the detailed analysis of literature and McHughs proposed model, we suggested the Enhanced Agile V-Model (EAV), which combines plan-driven and agile approaches. Second, we mapped the proposed model to the MDEVSPICE framework to confirm that it adhered to the rules outlined in the standard IEC62304. Finally, the proposed model is evaluated through implication to case study of wave therapeutic medical device. Results: The support of both agile and waterfall approach in EAV model helps in accommodating new requirements in the medical devices and the proposed systems engineering approach helps in hardware and software integration. The mapping of the EAV model to the MDEVSPICE shows complete compliance. Moreover, the implication of the proposed model has been clearly shown statistically and successfully implemented in our case study. Further, device usability and efficiency metrics showed confidence of P < 0.05 and for device safety and efficiency, we conducted an experiment which shows significant improvement in selected parameters. Conclusion: The proposed model shows conformance to regulatory standards, and successfully implemented in development of wave therapeutic device. However, its applicability to more compact and straightforward medical products is unknown and can be determined by using this model to analyze the performance of other medical products.

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Sustainable mining practices is a concept that embeds the principles of sustainable development into the whole mine life-cycle, from exploration, extraction and processing through to mine closure. The optimization of coal mine planning and the developing a standardized design for its sustainable development is very challenging and requires more effort. The present research attempts to address the conditions of sustainability and necessary measures for sustainable development, thereby providing appropriate solutions for each stage of mining operation besides expressing the necessity of sustainable development integration at different stages of mining life cycle (MLC). The approach of systems engineering is essential to assist the sustainability goals which are integrated with the expected results. Hence a method depending more on systems engineering principles and optimization can be incorporated to attain better results. Several socio-environmental factors associated with sustainability depends on the geographic condition and few mining engineering considerations such as mine location, topography, coal seam characteristics and so on. These systems engineering approach can be further enhanced by incorporating tools like Geographic Information System (GIS), which provides more accuracy and precision of the geographic conditions of the site identified for the coal mining plan. In order to begin this way of approach towards the sustainability development and mining planning, the appropriate optimization parameters should be identified. The outcome of these optimization parameters can be also achieved by optimizing coal mining system models.

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BACKGROUND: Perhaps nowhere else in the healthcare system than in the intensive care unit environment are the challenges to create useful models with direct time-critical clinical applications more relevant and the obstacles to achieving those goals more massive. Machine learning-based artificial intelligence (AI) techniques to define states and predict future events are commonplace activities of modern life. However, their penetration into acute care medicine has been slow, stuttering and uneven. Major obstacles to widespread effective application of AI approaches to the real-time care of the critically ill patient exist and need to be addressed. MAIN BODY: Clinical decision support systems (CDSSs) in acute and critical care environments support clinicians, not replace them at the bedside. As will be discussed in this review, the reasons are many and include the immaturity of AI-based systems to have situational awareness, the fundamental bias in many large databases that do not reflect the target population of patient being treated making fairness an important issue to address and technical barriers to the timely access to valid data and its display in a fashion useful for clinical workflow. The inherent "black-box" nature of many predictive algorithms and CDSS makes trustworthiness and acceptance by the medical community difficult. Logistically, collating and curating in real-time multidimensional data streams of various sources needed to inform the algorithms and ultimately display relevant clinical decisions support format that adapt to individual patient responses and signatures represent the efferent limb of these systems and is often ignored during initial validation efforts. Similarly, legal and commercial barriers to the access to many existing clinical databases limit studies to address fairness and generalizability of predictive models and management tools. CONCLUSIONS: AI-based CDSS are evolving and are here to stay. It is our obligation to be good shepherds of their use and further development.

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BACKGROUND: Clinical decision support (CDS) tools that incorporate machine learning-derived content have the potential to transform clinical care by augmenting clinicians' expertise. To realize this potential, such tools must be designed to fit the dynamic work systems of the clinicians who use them. We propose the use of academic detailing-personal visits to clinicians by an expert in a specific health IT tool-as a method for both ensuring the correct understanding of that tool and its evidence base and identifying factors influencing the tool's implementation. OBJECTIVE: This study aimed to assess academic detailing as a method for simultaneously ensuring the correct understanding of an emergency department-based CDS tool to prevent future falls and identifying factors impacting clinicians' use of the tool through an analysis of the resultant qualitative data. METHODS: Previously, our team designed a CDS tool to identify patients aged 65 years and older who are at the highest risk of future falls and prompt an interruptive alert to clinicians, suggesting the patient be referred to a mobility and falls clinic for an evidence-based preventative intervention. We conducted 10-minute academic detailing interviews (n=16) with resident emergency medicine physicians and advanced practice providers who had encountered our CDS tool in practice. We conducted an inductive, team-based content analysis to identify factors that influenced clinicians' use of the CDS tool. RESULTS: The following categories of factors that impacted clinicians' use of the CDS were identified: (1) aspects of the CDS tool's design (2) clinicians' understanding (or misunderstanding) of the CDS or referral process, (3) the busy nature of the emergency department environment, (4) clinicians' perceptions of the patient and their associated fall risk, and (5) the opacity of the referral process. Additionally, clinician education was done to address any misconceptions about the CDS tool or referral process, for example, demonstrating how simple it is to place a referral via the CDS and clarifying which clinic the referral goes to. CONCLUSIONS: Our study demonstrates the use of academic detailing for supporting the implementation of health information technologies, allowing us to identify factors that impacted clinicians' use of the CDS while concurrently educating clinicians to ensure the correct understanding of the CDS tool and intervention. Thus, academic detailing can inform both real-time adjustments of a tool's implementation, for example, refinement of the language used to introduce the tool, and larger scale redesign of the CDS tool to better fit the dynamic work environment of clinicians.

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Within the past decade, the aerospace engineering industry has evolved beyond the constraints of using single, large, custom satellites. Due to the increased reliability and robustness of commercial, off-the-shelf printed circuit board components, missions have instead transitioned towards deploying swarms of smaller satellites. Such an approach significantly decreases the mission cost by reducing custom engineering and deployment expenses. Nanosatellites can be quickly developed with a more modular design at lower risk. The Alpha mission at the Cornell University Space Systems Studio is fabricated in this manner. However, for the purpose of development, the initial proof of concept included a two-satellite system. The manuscript will discuss system engineering approaches used to model and mature the design of the pilot satellite. The two systems that will be primarily focused on are the attitude control system of the carrier nanosatellite and the radio frequency communications on the excreted femto-satellites. Milestones achieved include ChipSat to ChipSat communication, ChipSat to ground station communication, packet creation, error correction, appending a preamble, and filtering the signal. Other achievements include controller traceability/verification and validation, software rigidity tests, hardware endurance testing, Kane damper, and inertial measurement unit tuning. These developments matured the technological readiness level (TRL) of systems in preparation for satellite deployment.

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Background: During the COVID-19 pandemic, there was an increasing need to expand diagnostic testing in hospitals. At Keio University Hospital (KUH), clinical staff were concerned that the demand for PCR testing might exceed the capacity of the Clinical Laboratory. In response, basic researchers at Keio University School of Medicine (KUSM) set out to build a new, collaborative, PCR testing system. To be authorized to perform such diagnostic PCR testing, KUSM registered its core laboratory as an external clinical laboratory (ECL). Methods: In the pandemic, there was a pressure to build the PCR system quickly. Speed required discussions that developed a shared understanding of the unprecedented, new KUH/KUSM PCR system. To design, construct, and archive the new PCR testing system, we used a systems engineering (SE) approach. This included diagram visualization of functional flows and application of the Unified Architecture Framework (UAF), both of which are often used in system building. We considered daily demand for PCR testing at KUH and KUSM, and daily COVID-19 infections in Japan. Results: We operated the collaborative PCR testing system from August 2020 to June 2022. Given public health insurance reimbursement policies, KUH focused on individuals with suspicious symptoms, while the ECL at KUSM screened samples from asymptomatic individuals. KUSM performed about half as many tests as KUH. Interviewing KUH staff revealed that diagrams helped promote a better understanding of the KUH/KUSM PCR testing system. Conclusion: When designing temporary systems that may be repurposed in the future, we suggest using an SE approach with diagrams and UAF perspectives. This approach will enable stakeholders to understand what is being proposed to be built, and facilitate achieving an informed consensus on the proposed system. We suggest that SE approaches should be widely used in projects that involve building and operating complex, collaborative systems, and documenting the process.

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Levan is a fructan polymer with many industrial applications such as the formulation of hydrogels, drug delivery, and wound healing, among others. To this end, metabolic systems engineering is a valuable method to improve the yield of a specific metabolite in a wide range of bacterial and eukaryotic organisms. In this study, we report a systems biology approach integrating genomics data for the Bacillus subtilis model, wherein the metabolic pathway for levan biosynthesis is unpacked. We analyzed a revised genome-scale enzyme-constrained metabolic model (ecGEM) and performed simulations to increase levan biopolymer production capacity in B. subtilis. We used the model ec_iYO844_lvn to (1) identify the essential genes and bottlenecks in levan production, and (2) specifically design an engineered B. subtilis strain capable of producing higher levan yields. The FBA and FVA analysis showed the maximal growth rate of the organism up to 0.624 hr-1 at 20 mmol gDw-1 hr-1 of sucrose intake. Gene knockout analyses were performed to identify gene knockout targets to increase the levan flux in B. subtilis. Importantly, we found that the pgk and ctaD genes are the two target genes for the knockout. The perturbation of these two genes has flux gains for levan production reactions with 1.3- and 1.4-fold the relative flux span in the mutant strains, respectively, compared to the wild type. In all, this work identifies the bottlenecks in the production of levan and possible ways to overcome them. Our results provide deeper insights on the bacterium's physiology and new avenues for strain engineering.

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BACKGROUND: There are intense discussions about the ethical and societal implications of biomedical engineering, but little data to suggest how scientists think about the ethics of their work. The aim of this study is to describe how scientists frame the ethics of their research, with a focus on the field of molecular systems engineering. METHODS: Semi-structured qualitative interviews were conducted during 2021-2022, as part of a larger study. This analysis includes a broad question about how participants view ethics as related to their work, with follow up probes about the topics they consider most important. Interviews were transcribed, inductively coded by two researchers to consensus, and analyzed thematically. RESULTS: Twenty-four scientists participated in the study. Interviewees hold positions as professors, principal investigators, and senior staff researchers in universities or research institutes in the United States and Europe. Among those scientists who reported reflecting on ethical considerations in their work, many equated ethics with research ethics topics (e.g., safety, replicability), or with regulation and guidelines. Participants expressed the view that ethical issues are primarily relevant for clinical trials of bioengineered products, or for those working with animal or human subjects. Scientists described their research as "too early" or "not examining anything living" with regard to ethical reflection. Finally, many felt that ethics is seen as territory for experts and therefore beyond scientists' competencies. CONCLUSIONS: Molecular systems engineering scientists currently focus on regulatory aspects as the framework for their ethical analyses. They describe using a framework to define when life arises, as a means to determine when further ethical engagement is warranted. Further research is needed to investigate how scientists relate to the ethics of their scientific work, and build consensus around concepts of life, autonomous behavior, and physiological relevance of bioengineered systems.

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PURPOSE: To investigate the feasibility and accuracy of radiologists categorizing the method of detection (MOD) when performing image-guided breast biopsies. METHODS: This retrospective, observational study was conducted across a health care enterprise that provides breast imaging services at 18 imaging sites across four US states. Radiologists used standardized templates to categorize the MOD, defined as the first test, sign, or symptom that triggered the subsequent workup and recommendation for biopsy. All image-guided breast biopsies since the implementation of the MOD-inclusive standardized template-from October 31, 2017 to July 6, 2023-were extracted. A random sample of biopsy reports was manually reviewed to evaluate the accuracy of MOD categorization. RESULTS: A total of 29,999 biopsies were analyzed. MOD was reported in 29,423 biopsies (98.1%) at a sustained rate that improved over time. The 10 MOD categories in this study included the following: 15,184 mammograms (51.6%); 4,561 MRIs (15.5%); 3,473 ultrasounds (11.8%); 2,382 self-examinations (8.1%); 2,073 tomosynthesis studies (7.0%); 432 clinical examinations (1.5%); 421 molecular breast imaging studies (1.4%); 357 other studies (1.2%); 338 contrast-enhanced digital mammograms (1.1%); and 202 PET studies (0.7%). Original assignments of the MOD agreed with author assignments in 87% of manually reviewed biopsies (n = 100, 95% confidence interval: [80.4%, 93.6%]). CONCLUSIONS: This study demonstrates that US radiologists can consistently and accurately categorize the MOD over an extended time across a health care enterprise.

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OBJECTIVES: To enhance the Business Process Management (BPM)+ Healthcare language portfolio by incorporating knowledge types not previously covered and to improve the overall effectiveness and expressiveness of the suite to improve Clinical Knowledge Interoperability. METHODS: We used the BPM+ Health and Object Management Group (OMG) standards development methodology to develop new languages, following a gap analysis between existing BPM+ Health languages and clinical practice guideline knowledge types. Proposal requests were developed based on these requirements, and submission teams were formed to respond to them. The resulting proposals were submitted to OMG for ratification. RESULTS: The BPM+ Health family of languages, which initially consisted of the Business Process Model and Notation, Decision Model and Notation, and Case Model and Notation, was expanded by adding 5 new language standards through the OMG. These include Pedigree and Provenance Model and Notation for expressing epistemic knowledge, Knowledge Package Model and Notation for supporting packaging knowledge, Shared Data Model and Notation for expressing ontic knowledge, Party Model and Notation for representing entities and organizations, and Specification Common Elements, a language providing a standard abstract and reusable library that underpins the 4 new languages. DISCUSSION AND CONCLUSION: In this effort, we adopted a strategy of separation of concerns to promote a portfolio of domain-agnostic, independent, but integrated domain-specific languages for authoring medical knowledge. This strategy is a practical and effective approach to expressing complex medical knowledge. These new domain-specific languages offer various knowledge-type options for clinical knowledge authors to choose from without potentially adding unnecessary overhead or complexity.

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Over the last decade, bioengineering has seen a sustained growth in scientific publications, patents, and clinical trials. As the field attempts to bridge the gap between discovery and clinical application, a broader societal dialogue is needed to build public trust and address potential ethical, societal, and regulatory challenges. In this essay, we discuss societal aspects linked to the clinical use of biomedical engineering approaches and technologies, with a specific focus on molecular systems engineering. Drawing on data from interviews with 24 scientists, we identified four key aspects for fostering societal support for translational efforts in this domain: (1) effective science communication and internal awareness; (2) open societal dialogue; (3) fair and equitable access to new technologies; and (4) adequate science and technology policies. We conclude that molecular systems engineering would benefit from anticipating future challenges with the view of building a robust bond of trust with lay publics, regulators, and society at large.

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Brain-Computer Interface (BCI) and Neurofeedback (NF) both rely on the technology to capture brain activity. However, the literature lacks a clear distinction between the two, with some scholars categorizing NF as a special case of BCI while others view BCI as a natural extension of NF, or classify them as fundamentally different entities. This ambiguity hinders the flow of information and expertise among scholars and can cause confusion. To address this issue, we conducted a study comparing BCI and NF from two perspectives: the background and context within which BCI and NF developed, and their system design. We utilized Functional Flow Block Diagram (FFBD) as a system modelling approach to visualize inputs, functions, and outputs to compare BCI and NF at a conceptual level. Our analysis revealed that while NF is a subset of the biofeedback method that requires data from the brain to be extracted and processed, the device performing these tasks is a BCI system by definition. Therefore, we conclude that NF should be considered a specific application of BCI technology. By clarifying the relationship between BCI and NF, we hope to facilitate better communication and collaboration among scholars in these fields.

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Increases in the magnitudes and frequencies of climate-related extreme events are redistributing risk across coastal systems, including their environmental, economic, and social components. Consequently, stakeholders (SHs) are faced with long-term challenges and complex information when managing assets, services, and uses of the coast. In this context, SH engagement is a key step for risk management and in the preparation of resilience plans to respond and adapt to climate change. This paper develops a participatory method to identify and prioritize a set of risk measures, combining multi-criteria analysis with sensitivity analysis. The process involved local and regional authorities of the Veneto region testing the method, including national, regional, and local government, catchment officers, research organizations, natural parks managers and Non-Governmental Organizations (NGOs). SHs identified and ranked a range of adaptation measures to increase climate resilience, with a focus on coastal risk in the Venice lagoon. Results demonstrate that the sensitivity analysis provides useful information on how different sectors of expertise can influence the ranking of the identified risk management measures, highlighting the value of investigating the preferences or priorities of different SH groups within the definition of adaptation plans.

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BACKGROUND: Medication errors, including dispensing errors, represent a substantial worldwide health risk with significant implications in terms of morbidity, mortality, and financial costs. Although pharmacists use methods like barcode scanning and double-checking for dispensing verification, these measures exhibit limitations. The application of artificial intelligence (AI) in pharmacy verification emerges as a potential solution, offering precision, rapid data analysis, and the ability to recognize medications through computer vision. For AI to be embraced, it must be designed with the end user in mind, fostering trust, clear communication, and seamless collaboration between AI and pharmacists. OBJECTIVE: This study aimed to gather pharmacists' feedback in a focus group setting to help inform the initial design of the user interface and iterative designs of the AI prototype. METHODS: A multidisciplinary research team engaged pharmacists in a 3-stage process to develop a human-centered AI system for medication dispensing verification. To design the AI model, we used a Bayesian neural network that predicts the dispensed pills' National Drug Code (NDC). Discussion scripts regarding how to design the system and feedback in focus groups were collected through audio recordings and professionally transcribed, followed by a content analysis guided by the Systems Engineering Initiative for Patient Safety and Human-Machine Teaming theoretical frameworks. RESULTS: A total of 8 pharmacists participated in 3 rounds of focus groups to identify current challenges in medication dispensing verification, brainstorm solutions, and provide feedback on our AI prototype. Participants considered several teaming scenarios, generally favoring a hybrid teaming model where the AI assists in the verification process and a pharmacist intervenes based on medication risk level and the AI's confidence level. Pharmacists highlighted the need for improving the interpretability of AI systems, such as adding stepwise checkmarks, probability scores, and details about drugs the AI model frequently confuses with the target drug. Pharmacists emphasized the need for simplicity and accessibility. They favored displaying only essential information to prevent overwhelming users with excessive data. Specific design features, such as juxtaposing pill images with their packaging for quick comparisons, were requested. Pharmacists preferred accept, reject, or unsure options. The final prototype interface included (1) checkmarks to compare pill characteristics between the AI-predicted NDC and the prescription's expected NDC, (2) a histogram showing predicted probabilities for the AI-identified NDC, (3) an image of an AI-provided "confused" pill, and (4) an NDC match status (ie, match, unmatched, or unsure). CONCLUSIONS: In partnership with pharmacists, we developed a human-centered AI prototype designed to enhance AI interpretability and foster trust. This initiative emphasized human-machine collaboration and positioned AI as an augmentative tool rather than a replacement. This study highlights the process of designing a human-centered AI for dispensing verification, emphasizing its interpretability, confidence visualization, and collaborative human-machine teaming styles.

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In medical settings, interprofessional education (IPE) plays an important role by bringing students from multiple disciplines together to learn how to collaborate effectively and coordinate safe patient care. Yet developing effective IPE is complex, considering that stakeholders from different schools and programs are involved, each with varying curriculum requirements and interests. Given its critical importance and inherent complexity, innovative approaches to address these challenges are needed to effectively develop and sustain effective IPE programs. Systems engineering (SE) combines a lifecycle perspective with established interdisciplinary processes to develop and sustain large complex systems. The need for SE approaches to manage healthcare complexity has been recognized, but the application of SE to IPE programs has been limited. We believe that there is a significant opportunity for IPE programs to benefit from the application of SE. The common themes running through SE and IPE led us to ask if SE can be used to address IPE complexity and achieve desired IPE outcomes. We believe that SE could facilitate further development and sustainability of a recently developed healthcare curriculum. We also propose to use SE to accelerate and manage future IPE curriculum development, while better understanding the states of vital IPE-related components. We discuss a framework that considers transitions of key IPE elements. We believe that use of interdisciplinary SE processes and holistic perspectives and methods such as system thinking will improve the management of system challenges while addressing IPE's inherent complexity and leading to better patient outcomes and more effective interprofessional collaboration.

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BACKGROUND: It is challenging to identify and understand the specific mechanisms through which an implementation strategy affects implementation outcomes, as implementation happens in the context of complex, multi-level systems. These systems and the mechanisms within each level have their own dynamic environments that change frequently. For instance, sequencing may matter in that a mechanism may only be activated indirectly by a strategy through another mechanism. The dosage or strength of a mechanism may vary over time or across different health care system levels. To elucidate the mechanisms relevant to successful implementation amidst this complexity, systems analysis methods are needed to model and manage complexity. METHODS: The fields of systems engineering and systems science offer methods-which we refer to as systems analysis methods-to help explain the interdependent relationships between and within systems, as well as dynamic changes to systems over time. When applied to studying implementation mechanisms, systems analysis methods can help (i) better identify and manage unknown conditions that may or may not activate mechanisms (both expected mechanisms targeted by a strategy and unexpected mechanisms that the methods help detect) and (ii) flexibly guide strategy adaptations to address contextual influences that emerge after the strategy is selected and used. RESULTS: In this paper, we delineate a structured approach to applying systems analysis methods for examining implementation mechanisms. The approach includes explicit steps for selecting, tailoring, and evaluating an implementation strategy regarding the mechanisms that the strategy is initially hypothesized to activate, as well as additional mechanisms that are identified through the steps. We illustrate the approach using a case example. We then discuss the strengths and limitations of this approach, as well as when these steps might be most appropriate, and suggest work to further the contributions of systems analysis methods to implementation mechanisms research. CONCLUSIONS: Our approach to applying systems analysis methods can encourage more mechanisms research efforts to consider these methods and in turn fuel both (i) rigorous comparisons of these methods to alternative mechanisms research approaches and (ii) an active discourse across the field to better delineate when these methods are appropriate for advancing mechanisms-related knowledge.

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Studies in human medicine indicate that between 22,000 and 400,000 people die every year as a direct result of medical errors. In veterinary medicine, 42% of human-caused incidents caused harm to the patient, including 5% resulting in death. In a university veterinary teaching hospital, there were 5.3 errors/1,000 patient visits, and 4 of these resulted in death. Veterinary medicine falls far behind other safety-critical industries in adopting a culture of patient safety. Organizations should respond in a just and effective way when errors occur. Psychological safety for team members to identify and speak up about areas of concern must be created and the results of improvements made based on these concerns shared within the professional group. If veterinary medicine is going to embrace patient safety culture, it needs to be included in the curriculum. Accrediting and licensing bodies need to require the teaching and application of principles of patient safety culture. Faculty must be trained to deliver patient safety-oriented care. Experts in human systems engineering should be brought in to educate veterinarians on how the systems we work in impact patient outcomes. If we are going to fulfill the promise of the Veterinarian's Oath, we must embrace patient safety culture and all the difficult changes it requires of our professional culture.

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Large irradiation surface solar simulator often has the problem of low irradiation uniformity. Therefore, a method for designing a large irradiation surface solar simulator with high irradiation uniformity is proposed. According to the law of conservation of energy and the edge-ray principle of non-imaging optics, the free-form surface concentrator is designed and optimized by using the simulated annealing algorithm based on Bessel curve to improve the incident beam uniformity of the integrator. The optical integrator and projection system are also designed and optimized to eliminate aberrations, improve light efficiency, and enlarge the irradiation area. The design is verified using LightTools software and achieves an effective irradiation size of Φ1200 mm with an irradiance of a solar constant and an irradiation uniformity of less than 2.0%. This study provides accurate and reliable solar irradiation for laboratory calibration and performance testing of spacecraft payloads.