The value of probabilistic forecasting in emergency medical resource planning under uncertainty

PurposeMost epidemic transmission forecasting methods can only provide deterministic outputs. This study aims to show that probabilistic forecasting, in contrast, is suitable for stochastic demand modeling and emergency medical resource planning under uncertainty.Design/methodology/approachTwo probabilistic forecasting methods, i.e. quantile regression convolutional neural network and kernel density estimation, are combined to provide the conditional quantiles and conditional densities of infected populations. The value of probabilistic forecasting in improving decision performances and controlling decision risks is investigated by an empirical study on the emergency medical resource planning for the COVID-19 pandemic.FindingsThe managerial implications obtained from the empirical results include (1) the optimization models using the conditional quantile or the point forecasting result obtain better results than those using the conditional density;(2) for sufficient resources, decision-makers' risk preferences can be incorporated to make tradeoffs between the possible surpluses and shortages of resources in the emergency medical resource planning at different quantile levels;and (3) for scarce resources, the differences in emergency medical resource planning at different quantile levels greatly decrease or disappear because of the existing of forecasting errors and supply quantity constraints.Originality/valueVery few studies concern probabilistic epidemic transmission forecasting methods, and this is the first attempt to incorporate deep learning methods into a two-phase framework for data-driven emergency medical resource planning under uncertainty. Moreover, the findings from the empirical results are valuable to select a suitable forecasting method and design an efficient emergency medical resource plan.

Forecasting ward-level bed requirements to aid pandemic resource planning: Lessons learned and future directions.

During the COVID-19 pandemic, there has been considerable research on how regional and country-level forecasting can be used to anticipate required hospital resources. We add to and build on this work by focusing on ward-level forecasting and planning tools for hospital staff during the pandemic. We present an assessment, validation, and deployment of a working prototype forecasting tool used within a modified Traffic Control Bundling (TCB) protocol for resource planning during the pandemic. We compare statistical and machine learning forecasting methods and their accuracy at one of the largest hospitals (Vancouver General Hospital) in Canada against a medium-sized hospital (St. Paul's Hospital) in Vancouver, Canada through the first three waves of the COVID-19 pandemic in the province of British Columbia. Our results confirm that traditional statistical and machine learning (ML) forecasting methods can provide valuable ward-level forecasting to aid in decision-making for pandemic resource planning. Using point forecasts with upper 95% prediction intervals, such forecasting methods would have provided better accuracy in anticipating required beds on COVID-19 hospital units than ward-level capacity decisions made by hospital staff. We have integrated our methodology into a publicly available online tool that operationalizes ward-level forecasting to aid with capacity planning decisions. Importantly, hospital staff can use this tool to translate forecasts into better patient care, less burnout, and improved planning for all hospital resources during pandemics.

Cultural Challenges of ERP Implementation in Middle-Eastern Oil & Gas Sector: An Action Research Approach.

The purpose of this paper was to examine the cultural barriers that existed at various stages of the Enterprise Resource Planning (ERP) implementation process, using the Middle-Eastern oil and gas sector as a case study. Due to a variety of cultural implications, ERP implementation rates in the oil and gas sector in Middle-Eastern developing countries are extremely low. Although the literature highlighted numerous ERP implementation theories that attempted to overcome the cultural complexities of ERP systems, there are few studies that have framed these complexities using action research theory in order to provide potential solutions to these challenges, particularly in Middle-Eastern developing countries where cultural settings are distinct from those in Western developed countries. Action research AR, in conjunction with documentation, observations, and interviews, aided in the exploration of the culturally complex barriers encountered during the pre-implementation (plan and propose), implementation (do), and post-implementation (assess and improve) stages of ERP projects conducted within a Middle-Eastern oil and gas organisation. This article confirms numerous cultural implications at each stage of the ERP implementation process, including team conflict, managerial authority, and a lack of an IT culture, all of which contributed to the project's delay. Other impediments, such as a lack of commitment to training and technophobia, persisted throughout the post-implementation phase and the subsequent follow-up experience under the recent COVID-19 pandemic. This article contributes to theory and practise by highlighting the culturally complex barriers that underpin many ERP implementations in the Middle Eastern oil and gas sector. This information can assist practitioners and researchers in developing future research and ideas to mitigate future ERP implementation challenges in this region.

Human Resource Planning in Health Care: Outlining a Basic Model and Related Complexities.

The effectiveness of the health care system is largely dependent on the knowledge, skills, and motivation of health care workers, which was particularly evident during the COVID-19 pandemic. The systemic planning of human resources is therefore an important condition for ensuring the sustainability and efficiency of the health care system. This article focuses on outlining a basic model of human resource planning in health care and the investigation of related complexities. An in-depth analysis framework based on various materials and evidence is proposed in order to outline the factors that influence human resource planning in health care. In order to achieve greater credibility of the research results, the in-depth analytical process employs an extensive review of the literature and carries out an investigation of numerous sources and materials, in both the national and international contexts. The purpose of the human resource planning initiatives in health care is to calculate the needed number of health care workers in the future, on the basis of past and current data, and based on assumptions about future trends in supply and demand. The research findings reveal that this is a very challenging task, as there are typically many unknowns in future planning, and, in addition, planners often face a lack of reliable data and systemic deficiencies. Moreover, the study indicates that unplanned and delayed solutions concerning the human resource needs in health care can only alleviate problems, but in no way can they replace effective strategic measures and timely structural changes within the health care ecosystem.