Identify System Characteristics Research Articles

System-level variation in evidence-based multidisciplinary cancer care delivery.

11064 Background: Widespread health system integration of hospitals continues to reshape the landscape of U.S. healthcare delivery. While complex cancer care has become centralized to high-volume centers, care for common cancers happens across the spectrum of hospital sizes, with known variation in receipt of evidence-based practice (EBP) based on system affiliation. Strategies are needed to evaluate system performance in order to identify system characteristics and processes that contribute to high-quality cancer care. Methods: Using a novel data linkage between the National Cancer Database and AHRQ Compendium of U.S. Health Systems, adult patients treated for breast, colon, melanoma, and thyroid cancers between 2010 and 2020 at Commission on Cancer (CoC) hospitals were identified. Systems were categorized based on number of CoC hospitals (small, 2-3; medium, 4-9; large, 10+). Eight EBP measures were developed using national multidisciplinary guidelines, and measure-specific cohorts were extracted to calculate system-level EBP performance and within-system variation using mixed effects multivariable logistic regression models. Systems were then ranked across all measures for performance and variation, and average rankings were compared by system size. Results: 3,052,453 patients with breast (60.0%), colon (17.0%), melanoma (14.0%), and thyroid (9.0%) cancers were treated at 866 CoC hospitals in 368 health systems. The 8-measure analytic cohort had 104 systems, most of which were small systems (n=74, 71.2%). Large systems (n=9, 8.7%) had worse average performance rankings compared to medium or small systems, though this was not statistically significant (p=0.1) (Table). A significant increase in within-system variation (as measured by average ranking of variability across EBP measures) was seen with increasing system size (p<0.001). Conclusions: Significant variation in EBP performance for common cancers was noted based on health system size in this large, contemporary cohort, with greater within-system variation as system size increased. Future work will identify modifiable system characteristics associated with performance that could aid system leaders and policy makers to design, measure, and incentivize systems that employ effective cancer care delivery strategies. System rankings from 1 to 104 (best to worst), averaged over all measures; data presented as median (IQR). [Table: see text]

Low-Value Care at the Actionable Level of Individual Health Systems

Low-value health care remains prevalent in the US despite decades of work to measure and reduce such care. Efforts have been only modestly effective in part because the measurement of low-value care has largely been restricted to the national or regional level, limiting actionability. To measure and report low-value care use across and within individual health systems and identify system characteristics associated with higher use using Medicare administrative data. This retrospective cohort study of health system-attributed Medicare beneficiaries was conducted among 556 health systems in the Agency for Healthcare Research and Quality Compendium of US Health Systems and included system-attributed beneficiaries who were older than 65 years, continuously enrolled in Medicare Parts A and B for at least 12 months in 2016 or 2017, and eligible for specific low-value services. Statistical analysis was conducted from January 26 to July 15, 2021. Use of 41 individual low-value services and a composite measure of the 28 most common services among system-attributed beneficiaries, standardized to distance from the mean value. Measures were based on the Milliman MedInsight Health Waste Calculator and published claims-based definitions. Across 556 health systems serving a total of 11 637 763 beneficiaries, the mean (SD) use of each of the 41 low-value services ranged from 0% (0.01%) to 28% (4%) of eligible beneficiaries. The most common low-value services were preoperative laboratory testing (mean [SD] rate, 28% [4%] of eligible beneficiaries), prostate-specific antigen testing in men older than 70 years (mean [SD] rate, 27% [8%]), and use of antipsychotic medications in patients with dementia (mean [SD] rate, 24% [8%]). In multivariable analysis, the health system characteristics associated with higher use of low-value care were smaller proportion of primary care physicians (adjusted composite score, 0.15 [95% CI, 0.04-0.26] for systems with less than the median percentage of primary care physicians vs -0.16 [95% CI, -0.27 to -0.05] for those with more than the median percentage of primary care physicians; P < .001), no major teaching hospital (adjusted composite, 0.10 [95% CI, -0.01 to 0.20] without a teaching hospital vs -0.18 [95% CI, -0.34 to -0.02] with a teaching hospital; P = .01), larger proportion of non-White patients (adjusted composite, 0.15 [95% CI, -0.02 to 0.32] for systems with >20% of non-White beneficiaries vs -0.06 [95% CI, -0.16 to 0.03] for systems with ≤20% of non-White beneficiaries; P = .04), headquartered in the South or West (adjusted composite, 0.28 [95% CI, 0.14-0.43] for the South and 0.22 [95% CI, 0.02-0.42] for the West compared with -0.09 [95% CI, -0.26 to 0.08] for the Northeast and -0.44 [95% CI, -0.60 to -0.28] for the Midwest; P < .001), and serving areas with more health care spending (adjusted composite, 0.23 [95% CI, 0.11-0.35] for areas above the median level of spending vs -0.24 [95% CI, -0.36 to -0.12] for areas below the median level of spending; P < .001). The findings of this large cohort study suggest that system-level measurement and reporting of specific low-value services is feasible, enables cross-system comparisons, and reveals a broad range of low-value care use.

Host adaptation to novel pathogen introduction: Predicting conditions that promote evolutionary rescue.

Novel pathogen introduction can have drastic consequences for naive host populations, and outcomes can be difficult to predict. Evolutionary rescue (ER) provides a foundation for understanding whether hosts are driven to extinction or survive via adaptation. Currently, patterns of host population dynamics alongside evidence of adaptation are used to infer ER. However, the gap between established ER theory and complexity inherent in natural systems makes interpreting empirical patterns difficult because they can be confounded with ecological drivers of survival under current theory. To bridge this gap, we expand ER theory to include biological selective agents, such as pathogens. We find birth processes to be more important than previously theorised in determining ER potential. We employ a novel framework evaluating ER potential within natural systems and gain ability to identify system characteristics that make ER possible. Identifying these characteristics allows a shift from retrospective observation to a predictive mindset, and our findings suggest that ER occurrence may be more limited than previously thought. We use the plague system of Yersinia pestis infecting Cynomys ludovicianus (black-tailed prairie dogs) and Spermophilus beecheyi (California ground squirrels) as a case study.

Estimating health care delivery system value for each US state and testing key associations.

ObjectiveTo estimate health care systems' value in treating major illnesses for each US state and identify system characteristics associated with value.Data sourcesAnnual condition‐specific death and incidence estimates for each US state from the Global Burden Disease 2019 Study and annual health care spending per person for each state from the National Health Expenditure Accounts.Study designUsing non‐linear meta‐stochastic frontier analysis, mortality incidence ratios for 136 major treatable illnesses were regressed separately on per capita health care spending and key covariates such as age, obesity, smoking, and educational attainment. State‐ and year‐specific inefficiency estimates were extracted for each health condition and combined to create a single estimate of health care delivery system value for each US state for each year, 1991–2014. The association between changes in health care value and changes in 23 key health care system characteristics and state policies was measured.Data collection/extraction methodsNot applicable.Principal findingsUS state with relatively high spending per person or relatively poor health‐outcomes were shown to have low health care delivery system value. New Jersey, Maryland, Florida, Arizona, and New York attained the highest value scores in 2014 (81 [95% uncertainty interval 72‐88], 80 [72‐87], 80 [71‐86], 77 [69‐84], and 77 [66‐85], respectively), after controlling for health care spending, age, obesity, smoking, physical activity, race, and educational attainment. Greater market concentration of hospitals and of insurers were associated with worse health care value (p‐value ranging from <0.01 to 0.02). Higher hospital geographic density and use were also associated with worse health care value (p‐value ranging from 0.03 to 0.05). Enrollment in Medicare Advantage HMOs was associated with better value, as was more generous Medicaid income eligibility (p‐value 0.04 and 0.01).ConclusionsSubstantial variation in the value of health care exists across states. Key health system characteristics such as market concentration and provider density were associated with value.

Seeds of good anthropocenes: developing sustainability scenarios for Northern Europe

Scenario development helps people think about a broad variety of possible futures; however, the global environmental change community has thus far developed few positive scenarios for the future of the planet and humanity. Those that have been developed tend to focus on the role of a few common, large-scale external drivers, such as technology or environmental policy, even though pathways of positive change are often driven by surprising or bottom-up initiatives that most scenarios assume are unchanging. We describe an approach, pioneered in Southern Africa and tested here in a new context in Northern Europe, to developing scenarios using existing bottom-up transformative initiatives to examine plausible transitions towards positive, sustainable futures. By starting from existing, but marginal initiatives, as well as current trends, we were able to identify system characteristics that may play a key role in sustainability transitions (e.g., gender issues, inequity, governance, behavioral change) that are currently under-explored in global environmental scenarios. We suggest that this approach could be applied in other places to experiment further with the methodology and its potential applications, and to explore what transitions to desirables futures might be like in different places.

Helicopter Simulator Performance Prediction Using the Random Forest Method.

INTRODUCTION: Different aspects of the aviation system, such as pilot's fitness, supervision, and working conditions, interact to produce or protect against flight safety hazards. Machine learning methods such as Random Forests may help identify system characteristics with the potential to affect flight safety from the large number of candidate predictors that results when multiple system levels are considered simultaneously.METHODS: There were 54 pilot-related and occupational candidate predictors of simulator flight performance in 2 malfunction scenarios completed by 51 male European helicopter emergency medical services pilots derived from pilots' self-report questionnaires and aeromedical examination records. In a cross-sectional explorative analysis, the Random Forest method was used to screen for informative predictors. Predictors scoring above the critical threshold for the conditional permutation variable importance (VI) statistic were selected.RESULTS: In five predictors, the VI statistic averaged across 2000 Random Forest runs exceeded the selection threshold: higher perceived rewards (VI = 0.0691) and predictability (VI = 0.0501) at work were associated with higher performance scores, and higher physiological dysregulation (VI = 0.0495) and alanine aminotransferase (VI = 0.0224) with lower scores. Performance also differed between the simulators at the two training sites (VI = 0.0298).DISCUSSION: Random Forests may usefully complement previously applied methods for the identification of human factors safety hazards. The identified performance predictors suggest further areas with potential for safety improvements.Bauer H, Nowak D, Herbig B. Helicopter simulator performance prediction using the Random Forest method. Aerosp Med Hum Perform. 2018; 89(11):967-975.

Company innovation system: an exploration based on examples from Arcelik and Vestel

We conceptualize the company as an innovation system. The systems approach to innovation has received limited attention at the company level. While it is widely accepted for nations, sectors, regions and technologies, and while some company-level building blocks have been proposed, the dominant approach at company level is to regard innovation as a process. A company innovation system consists of interacting components with the purpose to produce innovation. Those components can be actors, resources or institutional characteristics. The components can be configured and reconfigured for different purposes. An innovation process is therefore one of the many possible configurations within a system. A systems approach emphasizes part-whole interactions and evolutionary dynamics. We explore the concept of company innovation system using case examples of two Turkish electronics manufacturers: Arcelik, with its Telve Turkish coffee machine, and Vestel with its 3D Smart TV. We find that using the company innovation system approach, we can map innovation systems at the company level, we can indicate the relationships between the components of the system and we can identify system characteristics such as open versus closed, archetypical configurations, where new combinations come from, coordination mechanisms and exploration versus exploitation. As such, we can address questions about the company’s innovation management that are difficult to address by using a process approach. We conclude that the company innovation system approach provides additional and complementary insights to the existing company-level innovation approaches.

Viscosity and Morphology Modification of Length Sorted Single-Walled Carbon Nanotubes in PIB Matrices

This work evaluates the effectiveness of nanoscale particulates in producing non-Einstein-like responses in polymer matrices, to reduce their negative effects in low shear rate processing. This is of value to material processing applications which encompass extrusion, flow into cold mold, and generalized processing of nanocomposites. Through control and understanding of the structure processing relationships entailed through nanoscale additive materials, we begin to manage dispersion characteristics for more reliable and defect-free product development. In pursuit of identifying system characteristics that produce non-Einstein-like responses we isolate and characterize homogenous fractions of single-walled carbon nanotubes (SWNTs) with singular lengths. This enables the definition of a well-defined nanoscale particulate phase, within the polymer matrices. The effect of nanotube length and weight fraction on the polyisobutylene (PIB) matrices was evaluated with thermal and rheological testing. Our findings show that the viscosity of the produced nanocomposite systems has a length dependence and does not demonstrate the expected monotonous increases in the viscosity with an increase in weight fraction of nanotube additive within the matrix, demonstrating a non-Einstein-like viscosity response. Furthermore, we demonstrate length dependent crystallization in the studied systems, as an intermediate length nanotube initiates crystallization of polyisobutylene (PIB) affecting viscosity and mechanical properties.

공기 변화량 분포를 이용한 효율적인 인버터타입 압축기 시스템

ABSTRACT Air compressor, as an essential equipment used in the factory and plant operations, accounts for around 30% of the total electricity consumption in U.S.A, thereby being proposed advanced technologies to reduce electricity consumption. When the fluctuation of the compressed airflow rate is small, the system stability is increased followed by the reduction of the electricity consumption which results in the efficient design of the energy system. In the statistical analysis, the normal distribution, log normal distribution, gamma distribution or the like are generally used to identify system characteristics. However a single distribution may not fit well the data with long tail, representing sudden air flow rate especially in extremes. In this paper, authors decouple the compressed airflow rate into two parts to present a mixture of distribution function and suggest a method to reduce the electricity consumption. This reduction stems from the fact that a general pareto distribution estimates more accurate quantile value than a gaussian distribution when an airflow rate exceeds over a large number.

Establishing and implementing best practice to reduce unplanned admissions in those aged 85 years and over through system change [Establishing System Change for Admissions of People 85+ (ESCAPE 85+)]: a mixed-methods case study approach

BackgroundIn England, between 2007/8 and 2009/10, the rate of unplanned hospital admissions of people aged 85 years and above rose from 48 to 52 per 100. There was substantial variation, with some areas showing a much faster rate of increase and others showing a decline.ObjectivesTo identify system characteristics associated with higher and lower increases in unplanned admission rates in those aged 85 years and over; to develop recommendations to inform providers and commissioners; and to investigate the challenges of starting to implement these recommendations.DesignMixed-methods study using routinely collected data, in-depth interviews and focus groups. Data were analysed using the framework approach, with themes following McKinsey’s 7S model. Recommendations derived from our findings were refined and prioritised through respondent validation and consultation with the project steering group. The process of beginning to implement these recommendations was examined in one ‘implementation site’.ParticipantsSix study sites were selected based on admission data for patients aged 85 years and above from primary care trusts: three where rates of increase were among the most rapid and three where they had slowed down or declined. Each ‘improving’ or ‘deteriorating’ site comprised an acute hospital trust, its linked primary care trust/clinical commissioning group, the provider of community health services, and adult social care. At each site, representatives from these organisations at strategic and operational levels, as well as representatives of patient groups, were interviewed to understand how policies had been developed and implemented. A total of 142 respondents were interviewed.ResultsBetween 2007/8 and 2009/10, average admission rates for people aged 85 years and over rose by 5.5% annually in deteriorating sites and fell by 1% annually in improving sites. During the period under examination, the population aged 85 years and over in deteriorating sites increased by 3.4%, compared with 1.3% in improving sites. In deteriorating sites, there were problems with general practitioner access, pressures on emergency departments and a lack of community-based alternatives to admission. However, the most striking difference between improving and deteriorating sites was not the presence or absence of specific services, but the extent to which integration within and between types of service had been achieved. There were also overwhelming differences in leadership, culture and strategic development at the system level. The final list of recommendations emphasises the importance of issues such as maximising integration of services, strategic leadership and adopting a system-wide approach to reconfiguration.ConclusionsRising admission rates for older people were seen in places where several parts of the system were under strain. Places which had stemmed the rising tide of admissions had done so through strong, stable leadership, a shared vision and strategy, and common values across the system.Future workResearch on individual components of care for older people needs to take account of their impact on the system as a whole. Areas where more evidence is needed include the impact of improving access and continuity in primary care, the optimal capacity for intermediate care and how the frail elderly can best be managed in emergency departments.Study registrationUK Clinical Reasearch Network 12960.Funding detailsThe National Institute for Health Research Health Services and Delivery Research programme.

Ship design evaluation subject to carbon emission policymaking using a Markov decision process framework

This paper outlines a novel ship design evaluation framework rooted in Markov decision analysis and derived metrics. The framework synthesizes concepts from dynamic network optimization, decision theory, and scenario analysis to holistically manage exogenous uncertainty and value ship system changeability. A Markov decision process is used to analyze development and operational paths over a ship׳s life cycle and to identify system characteristics consistent within high performing designs. Decision metrics then contextualize a fuller extent of design engineer and operator preferences toward tradeoffs between value creation and active ship management. The case study specifically examines future scenarios subject to carbon emission regulations and uncertainty surrounding enforcement of the Energy Efficient Design Index. Results inform decisions about when, where, and how to incorporate the changeability that maximizes expected life cycle rewards.

Predictor-Response Analysis of Fiber Optic Enzymatic Biosensors Constructed with Nonmodified E. coli BL21 (DE3) pGELAF+ Sensing 1,2-Dichloroethane.

Biosensor technology can lack methods to iteratively validate system outputs (i.e., signals) concomitantly with the development of mathematical models. We evaluated a nonmodified fiber optic enzymatic biosensor (FOEB-Escherichia coli BL21 (DE3) pGELAF+) sensing dichloroethane with a predictor-response statistical form. The linear regression technique applied with MATLAB functions correlated FOEB parameters to sensing responses that could be used to identify system characteristics and interactions. A FOEB specific metric (i.e.normalized sensitivity) is shown to be significant as a mixed sensing correlation metric suggesting that similar development parameters could be related to engineering design paradigms for biosensor(or whole cell biosensor) systems.

Evolutionary Optimization of Directed Self-Assembly of Triblock Copolymers on Chemically Patterned Substrates.

Directed self-assembly of block copolymers on chemical patterns is of considerable interest for sublithographic patterning. The concept of pattern interpolation, in which a subset of features patterned on a substrate is multiplied through the inherent morphology of an ordered block copolymer, has enabled fabrication of extremely small, defect-free features over large areas. One of the central challenges in design of pattern interpolation strategies is that of identifying system characteristics leading to ideal, defect-free directed assembly. In this work we demonstrate how a coarse-grained many-body model of block copolymers, coupled to an evolutionary computation (EC) strategy, can be used to design and optimize substrate-copolymer combinations for use in lithographic patterning. The proposed approach is shown to be significantly more effective than traditional algorithms based on random searches, and its results are validated in the context of recent experimental observations. The coupled simulation-evolution method introduced here provides a general and efficient method for potential design of complex device-oriented structures.

Identifying product and process configuration requirements in a decentralised service delivery system

Designing new service solutions and improving the performance of existing ones have always played a central role in service management. In order to maintain the capability to create innovative solutions, a necessary step is to extend the closed management models towards understanding the systemic nature of services and how systemic characteristics affect service operations management. The main purpose of the study is to identify system characteristics in a service delivery system and to find out how they affect the product and process configurations on the operational level. The main contribution of the study is providing new insight into product and process configurations and how these static artefacts require dynamic information in order to work properly. The results are based on an empirical case study in a decentralised healthcare service delivery system. The practical and future research implications aim at adding systems thinking into service system research and service operations management.

Identifying system characteristics of autonomous wind turbines: Measurement system design

In this study, an autonomous wind turbine system has been simulated. Meteorological data gathered at the turbine's location and the turbine's characteristics have been measured in one-second periods of time. Based on these measurement values, simulation values and measured values have been compared synchronously, using a software program prepared in a graphic media. The study initially used the matlab program for turbine simulation. However, the matlab program ran too slowly because of the study's large amount of data calculations. Moreover, observation of measured data simulation results on the same graph and simultaneous Internet results proved to be inefficient. For these reasons, a new program, Turbine Analyzer, was prepared, using Delphi programming language. Simulation results taken from matlab and Turbine Analyzer were compared, and the newly written program was tested. In this study, an original system, created to conduct turbine tests, has achieved characteristics tests of small-scale (20 kW and below) autonomous wind turbines.

Weather-driven model indicative of spatiotemporal power laws

In the traditional Drossel-Schwabl forest fire model (DS model), the frequency distributions of fire size and fire interval follow a power law and an exponential law, respectively. However, it is found that the frequency-interval distribution of actual forest fires is not exponential, but a power law with periodical fluctuations which may be caused by the daily cycle of weather parameters. Therefore, a weather driven forest fire model (WD model) is built considering actual hourly weather records, with which the fire igniting probability is calculated. The simulation results indicate that the frequency-interval distribution of the WD model agrees with that of actual forest fire data and, at the same time, the frequency-size distributions of the WD and the DS models are in accordance with each other. In the further analysis of the temporal property of weather data, it is found that the change of weather data also exhibits a power-law relation with periodic fluctuations, implying that the external driving from weather parameters is the essential reason for the power-law distribution of fire intervals. The results suggest that natural systems may be coupled with each other and that the decoupling of systems is important to identifying system characteristics.

A comparative study on damage detection in speed-up and coast-down process of grinding spindle-typed rotor-bearing system

Abstract In this paper, the damage detection from non-stationary mechanical vibration signals collected during acceleration and deceleration was performed. A laboratory grinding spindle-typed rotor-bearing system was equipped, and modal testing was carried out to identify system characteristics and operating range. Time–frequency analysis method such as Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), Wigner–Ville Distribution (WVD), and Discrete Wavelet Transform (DWT) was applied to vibration data of normal rotor and shaft-cracked rotor in speed-varying process and the results of feature extraction from shaft-cracked condition were also compared with those in normal condition.

Designing Dynamic Strategies for the Efficient Usage of Systems and Tools

Increasing innovation in technology has led to the rapid development of systems and tools, and evolution of working environment and style. Strategies were developed to improve the efficiency of systems and tools. The objective is to formulate a framework for strategy building. The phases are: (1) identifying system characteristics, (2) identifying user characteristics, (3) expert analyses, (4) extracting expert analyses, and (5) building strategy set.