A Novel Deep Learning Model For Hotel Demand and Revenue Prediction amid COVID-19

The COVID-19 pandemic has cast a substantial impact on the tourism and hospitality sector. Public policies such as travel restrictions and stay-at-home orders had significantly affected tourist activities and service businesses' operations and profitability. It is essential to develop interpretable forecasting models to support managerial and organizational decision-making. We developed DemandNet, a novel deep learning framework for predicting time series data under the influence of the COVID-19 pandemic. The DemandNet framework has the following unique characteristics. First, it selects the top static and dynamic features embedded in the time series data. Second, it includes a nonlinear model which can provide interpretable insight into the previously seen data. Third, a novel prediction model is developed to leverage the above characteristics to make robust long-term forecasts. We evaluated DemandNet using daily hotel demand and revenue data from eight cities in the US between 2013 and 2020. Our findings reveal that DemandNet outperforms the state-of-art models and can accurately predict the effect of the COVID-19 pandemic on hotel demand and revenue. © 2022 IEEE Computer Society. All rights reserved.

Leadership and decision-making under uncertainty

This article in the journal "Gruppe. Interaktion. Organisation. (GIO)" summarizes the challenges of leadership and decision making under uncertainty. These challenges are illustrated using the example of corona management in Germany and related to problems in organizational decision-making processes. The main aim of the text is to shed light on the dilemmas of decision-making using a combination of organizational psychological and organizational sociological perspectives and to explain these dilemmas with reference to the social context of decision making. The final section gives recommendations for an organizational and leadership culture that meets the requirements of decision-making under uncertainty. (PsycInfo Database Record (c) 2023 APA, all rights reserved) Abstract (German) Dieser Artikel der Zeitschrift Gruppe. Interaktion. Organisation. (GIO)" gibt einen Uberblick uber die Herausforderungen des Fuhrens und Entscheiden unter Unsicherheit. Diese Herausforderungen werden am Beispiel des deutschen Corona-Managements illustriert und auf Problemlagen in organisationalen Entscheidungsprozessen bezogen. Der Text verfolgt vorrangig den Anspruch, Dilemmata des Entscheidens in einer Kombination organisationspsychologischer und organisationssoziologischer Perspektiven herauszuarbeiten und vor dem Hintergrund sozialer Kontextbedingungen aufzuschliesen, gibt aber auch Empfehlungen fur die Gestaltung einer Organisations- und Fuhrungskultur, die den Anforderungen des Entscheidens unter Unsicherheit Rechnung tragt. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Synthesis of Evidence to Support EMS Personnel's Mental Health During Disease Outbreaks: A Scoping Review.

Emergency medical services (EMS) personnel are at high risk for adverse mental health outcomes during disease outbreaks. To support the development of evidence-informed mitigation strategies, we conducted a scoping review to identify the extent of research pertaining to EMS personnel's mental health during disease outbreaks and summarized key factors associated with mental health outcomes. We systematically searched three databases for articles containing keywords within three concepts: EMS personnel, disease outbreaks, and mental health. We screened and retained original peer-reviewed articles that discussed, in English, EMS personnel's mental health during disease outbreaks. Where inferential statistics were reported, the associations between individual and work-related factors and mental health outcomes were synthesized. Twenty-five articles were eligible for data extraction. Our findings suggest that many of the contributing factors for adverse mental health outcomes are related to inadequacies in fulfilling EMS personnel's basic safety and informational needs. In preparation for future disease outbreaks, resources should be prioritized toward ensuring adequate provisions of personal protective equipment and infection prevention and control training. This scoping review serves as a launching pad for further research and intervention development.

Investigating the cognitive capacity constraints of an ICU care team using a systems engineering approach.

BACKGROUND: ICU operational conditions may contribute to cognitive overload and negatively impact on clinical decision making. We aimed to develop a quantitative model to investigate the association between the operational conditions and the quantity of medication orders as a measurable indicator of the multidisciplinary care team's cognitive capacity. METHODS: The temporal data of patients at one medical ICU (MICU) of Mayo Clinic in Rochester, MN between February 2016 to March 2018 was used. This dataset includes a total of 4822 unique patients admitted to the MICU and a total of 6240 MICU admissions. Guided by the Systems Engineering Initiative for Patient Safety model, quantifiable measures attainable from electronic medical records were identified and a conceptual framework of distributed cognition in ICU was developed. Univariate piecewise Poisson regression models were built to investigate the relationship between system-level workload indicators, including patient census and patient characteristics (severity of illness, new admission, and mortality risk) and the quantity of medication orders, as the output of the care team's decision making. RESULTS: Comparing the coefficients of different line segments obtained from the regression models using a generalized F-test, we identified that, when the ICU was more than 50% occupied (patient census > 18), the number of medication orders per patient per hour was significantly reduced (average = 0.74; standard deviation (SD) = 0.56 vs. average = 0.65; SD = 0.48; p < 0.001). The reduction was more pronounced (average = 0.81; SD = 0.59 vs. average = 0.63; SD = 0.47; p < 0.001), and the breakpoint shifted to a lower patient census (16 patients) when at a higher presence of severely-ill patients requiring invasive mechanical ventilation during their stay, which might be encountered in an ICU treating patients with COVID-19. CONCLUSIONS: Our model suggests that ICU operational factors, such as admission rates and patient severity of illness may impact the critical care team's cognitive function and result in changes in the production of medication orders. The results of this analysis heighten the importance of increasing situational awareness of the care team to detect and react to changing circumstances in the ICU that may contribute to cognitive overload.

Skilled Nursing Facility Organizational Characteristics Are More Strongly Associated With Multiparticipant Therapy Provision Than Patient Characteristics.

OBJECTIVES: Multiparticipant physical and occupational therapy provision has fluctuated significantly in skilled nursing facilities (SNFs) under shifts in Medicare reimbursement policy. Multiparticipant therapy includes group (2-6 individuals per therapist) and concurrent therapy (2 individuals per therapist). This study uses recent patient-level data to characterize multiparticipant therapy provision in SNFs to help anticipate shifts under new Medicare policy and the COVID-19 pandemic. METHODS: This secondary analysis used data on 1,016,984 post-acute rehabilitation stays in SNFs in 2018. This analysis identified patient predictors (eg, demographic, clinical) and organizational predictors (eg, ownership, quality, staffing) of receiving multiparticipant therapy using mixed-effects logistic regression. Among individuals who received any multiparticipant therapy, those patient or facility factors associated with high rates of multiparticipant therapy provision were also determined. RESULTS: Less than 3% of individuals received multiparticipant therapy in 2018. Patient functional and cognitive impairment and indicators of market regulation were associated with lower odds of multiparticipant therapy. Effect sizes for organizational factors associated with multiparticipant therapy provision were generally larger compared with patient factors. High multiparticipant therapy provision was concentrated in <2% of SNFs and was positively associated with for-profit ownership, contract staffing, and low 5-star quality ratings. CONCLUSION: SNF organizational characteristics tended to have stronger associations with multiparticipant therapy provision than patient factors, suggesting that changes in patient case-mix, as expected during the COVID-19 pandemic, may have less of an impact on multiparticipant therapy provision than organizational factors. Results suggest that for-profit SNFs in states with higher market regulation, SNFs providing high volumes of therapy, and SNFs utilizing high proportions of assistants and contract staff may be more responsive to Medicare policy by increasing multiparticipant therapy provision. IMPACT: This study may help identify SNFs that are more likely to increase multiparticipant therapy provision under new Medicare payment policy.

Scarce Resource Allocation in a Pandemic: A Protocol to Promote Equity, Timeliness, and Transparency.

Shortages of equipment, medication, and staff under coronavirus disease 2019 may force hospitals to make wrenching decisions. Although bioethical guidance is available, published procedures for decision-making processes to resolve the time-sensitive conflicts are rare. Failure to establish decision-making procedures before scarcities arise exposes clinicians to moral distress and potential legal liability, entrenches existing systemic biases, and leaves hospitals without processes to guarantee transparency and consistency in the application of ethical guidelines. Formal institutional processes can reduce the panic, inequity, and irresolution that arise from confronting ethical conflicts under duress. Drawing on expertise in critical care medicine, bioethics, and political science, we propose a decision-making protocol to ensure fairness in the resolution of conflict, timely decision-making, and accountability to improve system response.

Nimble, efficient and evolving: the rapid response of the National Collaborating Centres to COVID-19 in Canada. / Agiles, efficients et en évolution : la réponse rapide des Centres de collaboration nationale à la COVID-19 au Canada.

Since December 2019, there has been a global explosion of research on COVID-19. In Canada, the six National Collaborating Centres (NCCs) for Public Health form one of the central pillars supporting evidence-informed decision making by gathering, synthesizing and translating emerging findings. Funded by the Public Health Agency of Canada and located across Canada, the six NCCs promote and support the use of scientific research and other knowledges to strengthen public health practice, programs and policies. This paper offers an overview of the NCCs as an example of public health knowledge mobilization in Canada and showcases the NCCs' contribution to the COVID-19 response while reflecting on the numerous challenges encountered.

The explosion of research on COVID-19 in Canada and around the world called for an improved capacity to support evidence-informed decision making (EIDM). Canada is fostering various mechanisms to achieve this goal; the National Collaborating Centres (NCCs) for Public Health are central to supporting EIDM during the pandemic. The NCCs, a network of networks anchored on six unique knowledge hubs, are well connected to provincial, territorial, local and international partners. In response to COVID-19, the NCCs are making an important contribution to building knowledge, skills and capacity in the public health sector, and to supporting public health professionals in synthesizing and using evidence-informed knowledge in policy and practice.

L'explosion de la recherche menée sur la COVID-19 au Canada et ailleurs dans le monde a nécessité une augmentation de la capacité à soutenir la prise de décisions informées par les données probantes. Parmi les divers mécanismes préconisés par le Canada pour atteindre cet objectif, les Centres de collaboration nationale (CCN) en santé publique jouent un rôle essentiel pendant la pandémie pour soutenir la prise de décisions informées par les données probantes. Les CCN, qui constituent un réseau de réseaux s'appuyant sur six centres de connaissances, ont des liens étroits avec plusieurs partenaires provinciaux, territoriaux, locaux et internationaux. Pour lutter contre la COVID-19, les CCN renforcent de façon significative les connaissances, les compétences et les capacités en santé publique et soutiennent les professionnels en santé publique en synthétisant des connaissances fondées sur des données probantes pour leur intégration aux politiques et aux pratiques.

Challenges in Pandemic Disaster Preparedness: Experience of a Saudi Academic Medical Center.

In December 2019, a pneumonia of unknown etiology was detected in Wuhan, China. This outbreak was then declared an international public emergency in January 2020 by the World Health Organization (WHO), and the announcement activated disaster management plans worldwide. This global crisis created several challenges for the health-care sector. This study reviews the challenges faced by a middle-sized urban academic hospital that are likely present to some extent in all health-care sectors, regardless of their existing disaster plans and policies. While preparing this Saudi academic hospital with a capacity of 192 beds for the emerging pandemic, obstacles arose despite its extensive prior disaster planning and training. Specifically, these challenges were related to health-care workers, supplies, and patient care. We review the actions taken to overcome and resolve these difficulties and provide future planning suggestions for each area to potentially assist other hospitals in their disaster planning and preparedness.