[Untitled image of Old Main and DeWitt Wallace Library]

https://digitalcommons.macalester.edu/art_photo2014fall/1002/thumbnail.jp

What Is A Specialty Clinic Really Capable Of?

The actual capability of a clinic can never be known with certainty unless clear and explicit performance goals are established. In the case of Veterans Administration Medical Centers (VAMCs), one of the much-watched performance measures, the percentage of patients seen within a set number of days of their desired dates (typically anywhere from 14 to 120 days), has been criticized because of its tendency to underestimate both the backlog of patients and how long veterans seeking care are actually waiting. This paper demonstrates, via a case study in a VAMC, three of the significant ways a system dynamics simulation model can help health practitioners determine the actual capability of their services. First, this model encourages the explicit setting of specific goals for performance measures that are critical but also well understood. Second, it can provide reliable estimates of the actual performance of these services and offer means to monitor it over time. Third, it can help practitioners identify realistic goals, and formulate strategies to achieve them via scheduling policies, such as capacity allocation, no-show rescheduling, overbooking and number of appointment slots used by type of patients, just to name a few

A Hybrid Modeling And Simulation Methodology For Formulating Overbooking Policies

System dynamics modeling and discrete-event simulation have been applied in the health care industry in system improvement initiatives. Although each has its strengths and weaknesses, few studies have demonstrated how these two approaches could be brought together to improve the quality of health delivery. In this paper, a feedback-based hybrid modeling and simulation methodology has been developed, one that uses a system dynamics model for policy making and a discrete-event model for day-to-day clinic operations. This method is applied to support the formulation of overbooking policies in an Orthopedic clinic to achieve the strategic goal of a maximum appointment delay of 30 days. First, the system dynamics model is run to identify the best overall overbooking policy, which is then fed into the discrete-even model to evaluate its impact on day-to-day operations in terms of the patients\u27 time in system. In this way, the policies developed by this hybrid modeling and simulation method address both the strategic (long-term) as well operational (short-term) goals of a clinic. Additionally, the approach also demonstrates that though overbooking is a commonly practiced to mitigate the negative effects of no-shows, it also can be an effective intervention strategy to reduce appointment delays

Molecular architecture of the Chikungunya virus replication complex

To better understand how positive-strand (+) RNA viruses assemble membrane-associated replication complexes (RCs) to synthesize, process, and transport viral RNA in virus-infected cells, we determined both the high-resolution structure of the core RNA replicase of chikungunya virus and the native RC architecture in its cellular context at subnanometer resolution, using in vitro reconstitution and in situ electron cryotomography, respectively. Within the core RNA replicase, the viral polymerase nsP4, which is in complex with nsP2 helicase-protease, sits in the central pore of the membrane-anchored nsP1 RNA-capping ring. The addition of a large cytoplasmic ring next to the C terminus of nsP1 forms the holo-RNA-RC as observed at the neck of spherules formed in virus-infected cells. These results represent a major conceptual advance in elucidating the molecular mechanisms of RNA virus replication and the principles underlying the molecular architecture of RCs, likely to be shared with many pathogenic (+) RNA viruses.Ministry of Education (MOE)Published versionThis work was supported by the Singapore Ministry of Education MOE AcRF Tier 2 award MOE-T2EP30220-0009 (D.L.), Singapore Ministry of Education MOE AcRF Tier 1 award 2021-T1-002-021 (D.L.), National Institutes of Health grant R01AI148382 (W.C.), National Institutes of Health Common Fund Transformative High-Resolution Cryo-Electron Microscopy program U24 GM129541 (W.C.) and National Institutes of Health grant S10OD021600 (W.C.)

Probabilistic prediction of algal blooms from basic water quality parameters by Bayesian scale-mixture of skew-normal model

The timeliness of monitoring is essential to algal bloom management. However, acquiring algal bio-indicators can be time-consuming and laborious, and bloom biomass data often contain a large proportion of extreme values limiting the predictive models. Therefore, to predict algal blooms from readily water quality parameters (i.e. dissolved oxygen, pH, etc), and to provide a novel solution to the modeling challenges raised by the extremely distributed biomass data, a Bayesian scale-mixture of skew-normal (SMSN) model was proposed. In this study, our SMSN model accurately predicted over-dispersed biomass variations with skewed distributions in both rivers and lakes (in-sample and out-of-sample prediction R ^2 ranged from 0.533 to 0.706 and 0.412 to 0.742, respectively). Moreover, we successfully achieve a probabilistic assessment of algal blooms with the Bayesian framework (accuracy >0.77 and macro- F _1 score >0.72), which robustly decreased the classic point-prediction-based inaccuracy by up to 34%. This work presented a promising Bayesian SMSN modeling technique, allowing for real-time prediction of algal biomass variations and in-situ probabilistic assessment of algal bloom