A virtual teaching clinic for virtual care during the COVID-19 pandemic

The COVID-19 pandemic has prompted the rapid transition of in-person outpatient care to telemedicine, and clinical training to remote learning. The endocrinology fellows at the University of Michigan maintain their own continuity-of-care clinics and rotate in the Ann Arbor Veterans Affairs (VA) Healthcare System. For these clinics, we sought to preserve patient staffing with expert attending physicians and continue the clinical training experience in a remote setting.We have adapted the online conferencing platform, Zoom, to integrate learners into a virtual teaching clinic environment. By using the Zoom “breakout room” feature, fellows are able to match staffing attending physicians to different patient cases, according to attending physicians’ areas of specialty. Similar to the traditional teaching clinic environment, our remote staffing strategy has ensured that fellows continue to provide excellent patient care and fulfill educational aims across our University and VA facilities.Outpatient clinics in other University of Michigan departments and other academic centers have inquired about or have begun utilizing our method. Even beyond COVID-19, our paradigm potentially provides a convenient virtual staffing platform to serve patient populations with geographic or transportation challenges. Following implementation, stakeholders can regularly evaluate the approach to continually improve both patient care and medical education.

Hospitalists, two decades later: Which US hospitals utilize them?

Hospitalists, or specialists of hospital medicine, have long been practicing in Canada and Europe. However, it was not until the mid-1990s, when hospitals in the U.S. started widespread adoption of hospitalists. Since then, the number of hospitalists has grown exponentially in the U.S. from a few hundred to over 50,000 in 2016. Prior studies on hospitalists have well documented benefits hospitals gain from adopting this innovative staffing strategy. However, there is a dearth of research documenting predictors of hospitals’ adoption of hospitalists. To fill this gap, this longitudinal study (2003–2015) purposes to determine organizational and market characteristics of U.S. hospitals that utilize hospitalists. Our findings indicate that private not-for-profit, system affiliated, teaching, and urban hospitals, and those located in higher per capita income markets have a higher probability of utilizing hospitalists. Additionally, large or medium, profitable hospitals, and those that treat sicker patients have a higher probability of adoption. Finally, hospitals with a high proportion of Medicaid patients have a lower probability of utilizing hospitalists. Our results suggest that hospitals with greater slack resources and those located in munificent counties are more likely to use hospitalists, while their under-resourced counterparts may experience more barriers in adopting this innovative staffing strategy.

Managing Supply in the On-Demand Economy: Flexible Workers, Full-Time Employees, or Both?

The rise of the blended workforce, which is identified as one of the top current workplace trends, is prompting firms to reevaluate their staffing strategies. A blended workforce melds as a deliberate business strategy flexible workers (for example, independent contractors or freelancers) with full-time employees. Because flexible workers are free to determine their own work schedules, the supply (total number of workers) is uncertain. In “Managing Supply in the On-Demand Economy: Flexible Workers, Full-Time Employees, or Both?,” Dong and Ibrahim examine the optimal staffing strategy for flexible workers and full-time employees to effectively balance operating costs, time variability in customer demands, and supply-side uncertainty while not compromising on the quality of service offered to customers. This work gleans insights on the appropriateness of alternative workforce models, which are especially relevant for the gig economy.

Cross-country comparison of engagement in apprenticeships: A conceptual analysis of incentives for individuals and firms

Purpose: A fundamental assumption of the apprenticeship model is that there are benefits to both employers and individual learners. This paper offers a broad conceptual interrogation of an inherent assumption in the apprenticeship model, in that it provides incentives for participation to both individual learners and employers. Approach: This study combines the analysis of literature and available data and draws upon apprenticeship models in ten nations: Australia, Brazil, Denmark, Egypt, England, Finland, Germany, India, Malaysia and South Africa. Findings: For individuals, incentives to undertake apprenticeship may be linked to the process and outcomes of that learning, such as the appeal of learning through doing; the opportunities for occupational socialization; the possibility of progression to employment or to additional education; and learning while earning. The analysis of incentives for employers shows a range of reasons related to their short-term interests and the needs of the production processes, technologies, and associated skills; longer-term benefits for the company’s staffing strategy; and the opportunity to make a contribution to the wider education and economic systems. Despite all the potential incentives, many firms consider apprenticeships too costly, risky, and complex to justify the investment. However, when firms are making decisions under the umbrella of chambers or associations, they are more likely to coordinate their skills investment strategies around collectively beneficial outcomes. Conclusion: The links to the labour market and specifically to employers are a key challenge for sustaining apprenticeship systems, as well as for the task of researching them. As such, policy maker (and researcher) engagement with apprenticeship should account for the capacity and commitment of employers. Another key challenge for apprenticeship is related to the relative attractiveness of this pathway within E&T and labour market system for individuals. What is clear from this study is that the development of a strong apprenticeship system requires the buy-in of both employers and individual learners, and as such the necessity to identify and effectively implement incentives cannot be underestimated. Governments can play a key role in realizing the potential incentives for both employers and learners, thereby yielding benefits of all parties engaged in apprenticeships.

The 24-Hour Intensivists Staffing Model Improves the Outcome for Nighttime Admitted Patients: A Matched Historical Control Study

Introduction: We previously showed that a “10-hour daytime on-site” and “nighttime (NT) on-call” staffing strategy was associated with higher mortality for intensive care unit (ICU) patients admitted during NT than it was for patients admitted during office hours (OH). In here, we evaluated the clinical effects of a 24-hour intensivist staffing model. Methods: We formed an intervention group of 3034 consecutive ICU patients hospitalized from January 2013 to December 2015, and a control group of 2891 patients from our previous study (2009-2011). We applied propensity score matching (PSM) for whole and subgroup analyses adjusting for confounding factors. We compared clinical outcomes of patients under the 2 staffing models using multivariate logistic regression and survival analyses. Results: After PSM, we balanced the clinical data between the complete cohorts and the subgroups. Comparison of ICU survivals between the intervention and control cohorts yielded no significant differences. However, the intervention was significantly associated with a higher ICU survival in the NT (5:30 pm-07:30 am) admission patients ( P = .049) than in those admitted during OH (07:30 am to 5:30 pm; P = .456). Additionally, the intervention shortened the LOSHOS ( P = .001) and/or LOSICU ( P < .001), reduced the hospital ( P = .672) and/or ICU ( P = .004) expenses, and resulted in earlier mechanical ventilation extubation ( P = .442) as compared to the same variables in the control group, especially for NT admissions. Conclusions: The 24-hour intensivists staffing could significantly improve ICU outcomes, especially for NT-admission patients in high-acuity, high-volume ICUs with frequent NT admissions.

C3I cooperative decision system simulation and optimization based on genetic algorithm for surface warship formation

The C3I cooperative decision system is the guarantee for combat capabilities of surface warship formations. At present, research on the C3I system simulation lays more emphasis on finding structural logic defects by executing the simulation model, lacking the application of intelligent optimization algorithms to optimize parameters in the system. In this paper, the cooperative decision-making process of surface warship formation defense system is studied. Meanwhile, modelling and optimization methods for cooperative decision system are proposed. Based on simulation models built on the ExtendSim platform, this paper optimizes the staffing strategy of decision makers based on the genetic algorithm, to improve the per capita decision efficiency. The optimized staffing strategy meets objectives and requirements. The research in this paper can provide a scientific and objective reference for relevant decision-makers and researchers.

Predictive analytics in practice: A novel simulation application for addressing patient flow challenges in today's emergency departments

Objectives: To develop a flexible software application that uses predictive analytics to enable emergency department (ED) decision-makers in virtually any environment to predict the effects of operational interventions and enhance continual process improvement efforts. To demonstrate the ability of the application's core simulation model to recreate and predict site-specific patient flow in two very different EDs: a large academic center and a freestanding ED. To describe how the application was used by a freestanding ED medical director to match ED resources to patient demand. Methods: The application was developed through a public-private partnership between University of Florida Health and Roundtable Analytics, Inc., supported by a National Science Foundation Small Business Technology Transfer (STTR) grant. The core simulation technology was designed to be quickly adaptable to any ED using data routinely collected by most electronic health record systems. To demonstrate model accuracy, Monte Carlo studies were performed to predict the effects of management interventions in two distinct ED settings. At one ED, the medical director conducted simulation studies to evaluate the sustainability of the current staffing strategy and inform his decision to implement specific interventions that better match ED resources to patient demand. After implementation of one intervention, the fidelity of the model's predictions was evaluated. Results: A flexible, cloud-based software application enabling ED decision-makers to predict the effects of operational decisions was developed and deployed at two qualitatively distinct EDs. The application accurately recreated each ED's throughput and faithfully predicted the effects of specific management interventions. At one site, the application was used to identify when increasing arrivals will dictate that the current staffing strategy will be less effective than an alternative strategy. As actual arrivals approached this point, decision-makers used the application to simulate a variety different interventions; this directly informed their decision to implement a new strategy. The observed outcomes resulting from this intervention fell within the range of predictions from the model. Conclusion: This application overcomes technical barriers that have made simulation modeling inaccessible to key decision-makers in emergency departments. Using this technology, ED managers with no programming experience can conduct customized simulation studies regardless of their ED's volume and complexity. In two very different case studies, the fidelity of the application was established and the application was shown to have a direct positive effect on patient flow. The effective use of simulation modeling promises to replace inefficient trial-and-error approaches and become a useful and accessible tool for hea

Predictive analytics in practice: A novel simulation application for addressing patient flow challenges in today's emergency departments

Objectives: To develop a flexible software application that uses predictive analytics to enable emergency department (ED) decision-makers in virtually any environment to predict the effects of operational interventions and enhance continual process improvement efforts. To demonstrate the ability of the application's core simulation model to recreate and predict site-specific patient flow in two very different EDs: a large academic center and a freestanding ED. To describe how the application was used by a freestanding ED medical director to match ED resources to patient demand. Methods: The application was developed through a public-private partnership between University of Florida Health and Roundtable Analytics, Inc., supported by a National Science Foundation Small Business Technology Transfer (STTR) grant. The core simulation technology was designed to be quickly adaptable to any ED using data routinely collected by most electronic health record systems. To demonstrate model accuracy, Monte Carlo studies were performed to predict the effects of management interventions in two distinct ED settings. At one ED, the medical director conducted simulation studies to evaluate the sustainability of the current staffing strategy and inform his decision to implement specific interventions that better match ED resources to patient demand. After implementation of one intervention, the fidelity of the model's predictions was evaluated. Results: A flexible, cloud-based software application enabling ED decision-makers to predict the effects of operational decisions was developed and deployed at two qualitatively distinct EDs. The application accurately recreated each ED's throughput and faithfully predicted the effects of specific management interventions. At one site, the application was used to identify when increasing arrivals will dictate that the current staffing strategy will be less effective than an alternative strategy. As actual arrivals approached this point, decision-makers used the application to simulate a variety different interventions; this directly informed their decision to implement a new strategy. The observed outcomes resulting from this intervention fell within the range of predictions from the model. Conclusion: This application overcomes technical barriers that have made simulation modeling inaccessible to key decision-makers in emergency departments. Using this technology, ED managers with no programming experience can conduct customized simulation studies regardless of their ED's volume and complexity. In two very different case studies, the fidelity of the application was established and the application was shown to have a direct positive effect on patient flow. The effective use of simulation modeling promises to replace inefficient trial-and-error approaches and become a useful and accessible tool for hea

Coalition Formation Based Staffing Strategy Development

In this paper, we propose game theoretical models for developing staffing strategies, i.e., strategies which support managers’ decision making on hiring, head hunting, staff reassignment, and implementation of staff development policy in enterprises. Our staffing models are hedonic coalition formation games with newly proposed stability concepts calledinvitation based stabilities, in which players’ activities of changing their coalitions are motivated based on invitation from other coalitions. These stabilities capture behaviors of managers, employees, and contractors depending on the balance of power in business situations. We analyze the existence of stable coalition structures under invitation based stabilities, and provide several sufficient conditions with natural interpretations as staffing strategies.