Predicting Patient Length of Stay Using Artificial Intelligence to Assist Health Care Professionals in Resource Planning and Scheduling Decisions

Artificial Intelligence (AI) significantly revolutionizes and transforms the global healthcare industry by improving outcomes, increasing efficiency, and enhancing resource utilization. The applications of AI impact every aspect of healthcare operation, particularly resource allocation and capacity planning. This study proposes a multi-step AI-based framework and applies it to a real dataset to predict the length of stay (LOS) for hospitalized patients. The results show that the proposed framework can predict the LOS categories with an AUC of 0.85 and their actual LOS with a mean absolute error of 0.85 days. This framework can support decision-makers in healthcare facilities providing inpatient care to make better front-end operational decisions, such as resource capacity planning and scheduling decisions. Predicting LOS is pivotal in today’s healthcare supply chain (HSC) systems where resources are scarce, and demand is abundant due to various global crises and pandemics. Thus, this research’s findings have practical and theoretical implications in AI and HSC management.

Handwritten Documents Validation using Pattern Recognition and Transfer Learning

Handwritten documents in an Enterprise Resource Planning (ERP) system can come from different sources and usually have different designs, sizes, and subjects (i.e. bills, checks, invoices, etc.). Given these documents were filled manually, they have to be inspected to detect various kinds of issues (missing signature or stamp, missing name, etc.) before being saved in the ERP system or processed by an OCR engine. In this paper, the authors present a transfer learning approach to detect issues in scanned handwritten documents, using an award-winning deep convolutional neural network (InceptionV3) and different machine learning algorithms such as Logistic Regression (LR), Support Vector Machine (SVM) and Naive Bayes (NB). The experiment shows that the combination of InceptionV3 and LR got an accuracy of 91.8% for missing stamp detection. This can allow using this approach in an ERP system as an automatic verification procedure in a document processing flow.

Handwritten Documents Validation Using Pattern Recognition and Transfer Learning

Handwritten documents in an Enterprise Resource Planning (ERP) system can come from different sources and usually have different designs, sizes, and subjects (i.e. bills, checks, invoices, etc.). Given these documents were filled manually, they have to be inspected to detect various kinds of issues (missing signature or stamp, missing name, etc.) before being saved in the ERP system or processed by an OCR engine. In this paper, the authors present a transfer learning approach to detect issues in scanned handwritten documents, using an award-winning deep convolutional neural network (InceptionV3) and different machine learning algorithms such as Logistic Regression (LR), Support Vector Machine (SVM) and Naive Bayes (NB). The experiment shows that the combination of InceptionV3 and LR got an accuracy of 91.8% for missing stamp detection. This can allow using this approach in an ERP system as an automatic verification procedure in a document processing flow.

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.

The study of knowledge employee voice among the knowledge-based companies: the case of an emerging economy

PurposeA review of previous studies on the voices of employees and knowledge workers clarifies that paying attention to employees' voice is critical in human resource management. However, limited studies have been conducted on it, and much less emphasis has been placed compared to other human resource management activities such as human resource planning. Therefore, the voice of knowledge employees has been one of the critical issues that have attracted a great deal of attention recently. Nonetheless, there is no evidence of various comprehensive and integrated voice mechanisms. As a result, this study aims to design knowledge workers' voice patterns in knowledge-based companies specialising in information and communication technology (ICT) in Iran in May and June 2020.Design/methodology/approachThis study is a qualitative grounded theory research. We collected the data from a target sample of 15 experts in knowledge-based ICT companies using in-depth semi-structured interviews. Since all the participants had practised the employee voice process, they were regarded as useful data sources. Data analysis was also performed using three-step coding (open, axial and selective) by Atlas T8, which eventually led to identifying 14 components and 38 selected codes. We placed identified components in a paradigm model, including Personality Characteristics, Job Factors, Economic Factors, Cultural Factors, Organisational Policies, Organisational Structure, Climate Of Voice in the Organisation, Management Factors, Emotional Events, Communications and Networking, Contrast and Conflict and, etc. Then, the voice pattern of the knowledge staff was drawn.FindingsThe results showed that constructive knowledge voice influences the recognition of environmental opportunities and, additionally, it helps the competitive advantages among the employees. By forming the concept of knowledge staff voice, it can be concluded that paying attention to knowledge staff voice leads to presenting creative solutions to do affairs in critical situations. The presentation of these solutions by knowledge workers results in the acceptance of environmental changes, recognition and exploitation of new chances and ideas, and sharing experiences in Iranian knowledge-based companies.Practical implicationsStrengthening and expanding the voice of employees in knowledge-oriented companies can pave the way to growth and development towards a higher future that prevents the waste of tangible and intangible assets.Originality/valueCompanies' ability to engage in knowledge workers is a vital factor in human resource management and strategic management. However, the employee voice has not been involved integrally in the context of corporate.

Intangible Cultural Heritage in Tourism: Research Review and Investigation of Future Agenda

Intangible cultural heritage (ICH) can be a valuable tourism resource for both government and local communities. However, the complex definition and the massive and fragmented nature of ICH data make it hard to review and conclude research trends and future directions of ICH tourism. In this study, 85 keywords extracted from ICH definitions are input in the Web of Science database before collecting papers indexed in the Social Sciences Citation Index, the Arts and Humanities Citation Index, and the Conference Proceedings Citation Index-Social Science and Humanities. Later, a systematic literature review of 418 ICH tourism studies from 76 countries published between 2000 and 2021 were conducted based on three groups of questions. The findings mainly illustrated that: (1) Currently research in ICH tourism is mainly composed of three themes: resource planning and sustainability, the impact of tourism development, and tourist behavior and destination marketing; (2) topics related to food tourism, sacred knowledge, traditional management systems, traditional management systems, legends, and myths can achieve high impact; (3) in the last five years, scholars have reduced using the official full name of ICH in tourism studies, while the category of “social practices, rituals and festive events” has become a hot topic since 2010; (4) ecotourism, culinary tourism, festival tourism, and religious tourism are the most discussed in ICH tourism research, and they will still be intensive topics in near future; (5) future directions in ICH tourism research are resultant of three vectors: place making, technology, and environment. The results present a comprehensive picture of current popular ICH topics and predict future directions in the field of ICH tourism. The systematic review of literature can help contribute to both theoretical construction, heritage preservation, and tourism practices.

Geological Resource Planning and Environmental Impact Assessments Based on GIS

The purpose is to study the geological resource planning and environmental impact assessments based on the geographic information system (GIS). In this study, the land resources of Yinan County in southeastern Shandong Province are taken as the research object. Based on a GIS, the current situation of land resource development is analyzed, land resource planning is carried out, and environmental impact mitigation measures are evaluated and analyzed through the environmental impact. The results obtained depict the distribution of cultivated land; the development area is 1617.31 hm2, of which 577.32 hm2 is cultivated land, 30.43 hm2 is garden land, 399.66 hm2 is forest land, 40.87 hm2 is urban and rural construction land, 10.11 hm2 is traffic water conservancy and other construction land, and 558.92 hm2 is natural reserve land. In the layout of the construction land, the development area is 841.94 hm2, of which 175.44 hm2 is cultivated land, 47.88 hm2 is garden land, 100.54 hm2 is forest land, 0.1 hm2 is other agricultural land, 90.45 hm2 is urban and rural construction land, 3.66 hm2 is traffic water conservancy and other construction land, 11.33 hm2 is water area, and 412.54 hm2 is natural reserve land. The impact of the implementation of planning on most indicators is positive and beneficial, while the impact of negative indicators is relatively small. It is revealed that the implementation of the plan has little impact on most of the ecological environment indicators. Construction and cultivated land development further improve the level of urbanization. In the process of planning implementation, corresponding measures should be taken to slow down or eliminate the negative development of the ecological environment.

Spatial Evolution of Coastal Tourist City Using the Dyna-CLUE Model in Koh Chang of Thailand during 1990–2050

Spatial evolution can be traced by land-use change (LUC), which is a frontier issue in the field of geography. Using the limited areas of Koh Chang in Thailand as the research case, this study analyzed the simulation of its spatial evolution from a multi-scenario perspective on the basis of the 1900–2020 thematic mapper/operational land imager (TM/OLI) remote sensing data obtained through the transfer matrix model, and modified LUC and the dynamic land-use change model (Dyna-CLUE). Over the past 30 years, the expansion of recreation areas and urban and built-up land has been very high (2944.44% and 486.99%, respectively) along the western coast of Koh Chang, which replaced the original mangrove forests, orchards, and communities. Logistic regression analysis of important variables affecting LUC revealed that population density variables and coastal plain topography significantly affected LUC, which showed strong β coefficients prominently in the context of a coastal tourist city. The results of the LUC and logistic regression analyses were used to predict future LUCs in the Dyna-CLUE model to simulate 2050 land-use in three scenarios: (1) natural evolution scenario, where a large patch expansion of agricultural land extends along the edge of the entire forest boundary around the island, particularly the southwestern areas of the island that should be monitored; (2) reserved area protection scenario, where the boundary of the conservation area is incorporated into the model, enabling forest preservation in conjunction with tourism development; and (3) recreation area growth scenario, where the southern area is the most susceptible to change at the new road crossing between Khlong Kloi village to Salak Phet village, and where land-use of the recreation area type is expanding. The model-projected LUC maps provide insights into possible changes under multiple pathways, which could help local communities, government agencies, and stakeholders jointly allocate resource planning in a systematic way, so that the development of various infrastructures to realize the potential impact on the environment is a sustainable coastal tourist city development.

Relationship between Precipitation Characteristics at Different Scales and Drought/Flood during the Past 40 Years in Longchuan River, Southwestern China

In this study, the temporal and spatial patterns of rainfall in the Longchuan River basin from 1977 to 2017 were analyzed, to assess the feature of precipitation. Based on the daily precipitation time series, the Lorenz curve, precipitation concentration index (PCI), precipitation concentration degree (PCD), and the precipitation concentration period (PCP) were used to evaluate the precipitation distribution characteristics. The PCI, PCD and PCP in five categories, defined by the fixed thresholds, were proposed to investigate the concentrations, and the average values indicated the higher concentrations in the higher intensities. The indices showed strong irregularity of daily and monthly precipitation distributions in this basin. The decrease in the PCD revealed an increase in the proportion of precipitation in the dry season. The rainy days of slight precipitation in the upper and lower basins with significant downward trends (−13.13 d/10 a, −7.78 d/10 a) led to longer dry spells and an increase in the risk of drought, even severe in the lower area. In the upper basin, the increase in rainfall erosivity was supported by the upward trend in the PCIw of heavy precipitation and the simple daily intensity index (SDII) of extreme precipitation. Moreover, the PCP of light precipitation, moderate precipitation, and heavy precipitation concentrated earlier at the end of July. The results of this study can provide beneficial reference information to water resource planning, reservoir operation, and agricultural production in the basin.

Healthcare’s Sustainable Resource Planning Using Neutrosophic Goal Programming

We develop neutrosophic goal programming models for sustainable resource planning in a healthcare organization. The neutrosophic approach can help examine the imprecise aspiration levels of resources. For deneutrosophication, the neutrosophic value is transformed into three intervals based on the truth, falsity, and indeterminacy-membership functions. Then, a crisp value is derived. Moreover, multi-choice goal programming is also used to get a crisp value. The proposed models seek to draw a strategic plan and long-term vision for a healthcare organization. Accordingly, the specific aims of the proposed flexible models are meant to evaluate hospital service performance and to establish an optimal plan to meet the growing patient needs. As a result, sustainability’s economic and social goals will be achieved so that the total cost would be optimized, patients’ waiting time would be reduced, high-quality services would be offered, and appropriate medical drugs would be provided. The simplicity and feasibility of the proposed models are validated using real data collected from the Al-Amal Center for Oncology, Aden, Yemen. The results obtained indicate the robustness of the proposed models, which would be valuable for planners who could guide healthcare staff in providing the necessary resources for optimal annual planning.