Matching Items (2)

Filtering by

Clear all filters

153607-Thumbnail Image.png

A framework for screening experiments and modelling in complex systems


Complex systems are pervasive in science and engineering. Some examples include complex engineered networks such as the internet, the power grid, and transportation networks. The complexity of such systems arises

Complex systems are pervasive in science and engineering. Some examples include complex engineered networks such as the internet, the power grid, and transportation networks. The complexity of such systems arises not just from their size, but also from their structure, operation (including control and management), evolution over time, and that people are involved in their design and operation. Our understanding of such systems is limited because their behaviour cannot be characterized using traditional techniques of modelling and analysis.

As a step in model development, statistically designed screening experiments may be used to identify the main effects and interactions most significant on a response of a system. However, traditional approaches for screening are ineffective for complex systems because of the size of the experimental design. Consequently, the factors considered are often restricted, but this automatically restricts the interactions that may be identified as well. Alternatively, the designs are restricted to only identify main effects, but this then fails to consider any possible interactions of the factors.

To address this problem, a specific combinatorial design termed a locating array is proposed as a screening design for complex systems. Locating arrays exhibit logarithmic growth in the number of factors because their focus is on identification rather than on measurement. This makes practical the consideration of an order of magnitude more factors in experimentation than traditional screening designs.

As a proof-of-concept, a locating array is applied to screen for main effects and low-order interactions on the response of average transport control protocol (TCP) throughput in a simulation model of a mobile ad hoc network (MANET). A MANET is a collection of mobile wireless nodes that self-organize without the aid of any centralized control or fixed infrastructure. The full-factorial design for the MANET considered is infeasible (with over 10^{43} design points) yet a locating array has only 421 design points.

In conjunction with the locating array, a ``heavy hitters'' algorithm is developed to identify the influential main effects and two-way interactions, correcting for the non-normal distribution of the average throughput, and uneven coverage of terms in the locating array. The significance of the identified main effects and interactions is validated independently using the statistical software JMP.

The statistical characteristics used to evaluate traditional screening designs are also applied to locating arrays.

These include the matrix of covariance, fraction of design space, and aliasing, among others. The results lend additional support to the use of locating arrays as screening designs.

The use of locating arrays as screening designs for complex engineered systems is promising as they yield useful models. This facilitates quantitative evaluation of architectures and protocols and contributes to our understanding of complex engineered networks.




Date Created
  • 2015

609-Thumbnail Image.png

Screening, Brief Intervention and Referral to Treatment (SBIRT): Implementation in the Adolescent Inpatient Psychiatric Setting


Background: The cost of substance use (SU) in the United States (U.S.) is estimated at $1.25 trillion annually. SU is a worldwide health concern, impacting physical and psychological health of

Background: The cost of substance use (SU) in the United States (U.S.) is estimated at $1.25 trillion annually. SU is a worldwide health concern, impacting physical and psychological health of those who use substances, their friends, family members, communities and nations. Screening, Brief Intervention (BI) and Referral to Treatment (SBIRT) provides an evidence-based (EB) framework to detect and treat SU. Evidence shows that mental health (MH) providers are not providing EB SU management. Federally grant-funded SBIRT demonstrated evidence of decreased SU and prevention of full disorders. Implementation outcomes in smaller-scale projects have included increased clinician knowledge, documentation and interdisciplinary teamwork.

Objective: To improve quality of care (QOC) for adolescents who use substances in the inpatient psychiatric setting by implementing EB SBIRT practices.

Methods: Research questions focused on whether the number of SBIRT notes documented (N=170 charts) increased and whether training of the interdisciplinary team (N=26 clinicians) increased SBIRT knowledge. Individualized interventions used existing processes, training and a new SBIRT Note template. An SBIRT knowledge survey was adapted from a similar study. A pre-and post-chart audit was conducted to show increase in SBIRT documentation. The rationale for the latter was not only for compliance, but also so that all team members can know the status of SBIRT services. Thus, increased interdisciplinary teamwork was an intentional, though indirect, outcome.

Results: A paired-samples t-test indicated clinician SBIRT knowledge significantly increased, with a large effect size. The results suggest that a short, 45-60-minute tailored education module can significantly increase clinician SBIRT knowledge. Auditing screening & BI notes both before and after the study period yielded important patient SU information and which types of SBIRT documentation increased post-implementation. The CRAFFT scores of the patients were quite high from a SU perspective, averaging over 3/6 both pre- and post-implementation, revealing over an 80% chance that the adolescent patient had a SU disorder. Most patients were positive for at least one substance (pre- = 47.1%; post- = 65.2%), with cannabis and alcohol being the most commonly used substances. Completed CRAFFT screenings increased from 62.5% to 72.7% of audited patients. Post-implementation, there were two types of BI notes: the preexisting Progress Note BI (PN BI) and the new Auto-Text BI (AT BI), part of the new SBIRT Note template introduced during implementation. The PN BIs not completed despite a positive screen increased from 79.6% to 83.7%. PN BIs increased 1%. The option for AT BI notes ameliorated this effect. Total BI notes completed for a patient positive for a substance increased from 20.4% to 32.6%, with 67.4% not receiving a documented BI. Total BIs completed for all patients was 21.2% post-implementation.

Conclusion: This project is scalable throughout the U.S. in MH settings and will provide crucial knowledge about positive and negative drivers in small-scale SBIRT implementations. The role of registered nurses (RNs), social workers and psychiatrists in providing SBIRT services as an interdisciplinary team will be enhanced. Likely conclusions are that short trainings can significantly increase clinician knowledge about SBIRT and compliance with standards. Consistent with prior evidence, significant management involvement, SBIRT champions, thought leaders and other consistent emphasis is necessary to continue improving SBIRT practice in the target setting.

Keywords: adolescents, teenagers, youth, alcohol, behavioral health, cannabis, crisis, documentation, drug use, epidemic, high-risk use, illicit drugs, implementation, mental health, opiates, opioid, pilot study, psychiatric inpatient hospital, quality improvement, SBIRT, Screening, Brief Intervention and Referral to Treatment, substance use, unhealthy alcohol use, use disorders




Date Created
  • 2019-05-02