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Telemedical physician triage (TPT) is an example of a hierarchical knowledge-based service system (HKBSS) in which a second level of decision agent (telemedical physician) renders a decision on cases referred to him or her by the primary level agents (triage nurses). Managing the speed-versus-quality trade-off in such systems presents a unique challenge because of the interplay between agent knowledge and flow of work between the two levels. We develop a novel model of agent knowledge, based on the beta distribution, and deploy it in a partially observable Markov decision process model to describe the optimal policy for deciding which cases (patients) to refer to the second level for further evaluation. We show that this policy has a monotone control-limit structure that reduces the fraction of decisions made at the upper level as workload increases. Because the optimal policy is complex, we use structural insights from it to design two practical heuristics. These heuristics enable an HKBSS to adapt efficiently to workload shifts by adjusting the criteria for referring decisions to the upper level based on partial real-time queue length information. Finally, we conduct analytic and numerical analyses to derive insights into the management of a TPT system. We find that (1) the telemedical physician should evaluate more patients as congestion in the emergency room waiting area increases; (2) training that improves accuracy of the physician and/or nurses can be effective even if it only does so for a single patient type, but training that improves consistency must do so for all patient types to be effective; and (3) patient classification in triage should consider environmental and operational conditions in addition to the patient’s medical condition. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2905 . This paper was accepted by Vishal Gaur, operations management.

This paper proposes a knowledge-based decision-making system for energy bill assessment and competitive energy consumption analysis for energy savings. As humans have a tendency toward comparison between peers and self-groups, the same concept of competitive behavior is utilized to design knowledge-based decision-making systems. A total of 225 house monthly energy consumption datasets are collected for Maharashtra state, along with a questionnaire-based survey that includes socio-demographic information, household appliances, family size, and some other parameters. After data collection, the pre-processing technique is applied for data normalization, and correlation technique-based key features are extracted. These features are used to classify different house categories based on consumption. A knowledge-based system is designed based on historical datasets for future energy consumption prediction and comparison with actual usage. These comparative studies provide a path for knowledgebase system design to generate monthly energy utilization reports for significant behavior changes for energy savings. Further, Linear Programming and Genetic Algorithms are used to optimize energy consumption for different household categories based on socio-demographic constraints. This will also benefit the consumers with an electricity bill evaluation range (i.e., normal, high, or very high) and find the energy conservation potential (kWh) as well as a cost-saving solution to solve real-world complex electricity conservation problem.

The paper investigates what type of motivation can be given for adopting a knowledge-based decision theory (hereafter, KBDT). KBDT seems to have several advantages over competing theories of rationality. It is commonly argued that this theory would naturally fit with the intuitive idea that being rational is doing what we take to be best given what we know, an idea often supported by appeal to ordinary folk appraisals. Moreover, KBDT seems to strike a perfect balance between the problematic extremes of subjectivist and objectivist decision theory. We argue that these alleged advantages do not stand up to a closer scrutiny: KBDT inherits the same kinds of problems as alternative decision theoretic frameworks but doesn’t retain any of the respective advantages. Moreover, differently from other knowledge-action principles advanced in the literature, KBDT cannot fully explain the intuitive connections between knowledge and rational action. We conclude that the most serious challenge for knowledge-based decision theorists is to provide a substantive rationale for the adoption of such a view.

To meet the 2050 targets about climate change and decarbonization, accomplishing thermal comfort, Internet of Things (IoT) ecosystems are key enabling technologies to move the Built Environment (BE) towards Smart Built Environment (SBE). The first contributions of this paper conceptualise SBE from its dynamic and adaptative perspectives, considering the human habitat, and enunciate SBE as a multidimensional approach through six ways of inhabiting: defensive, projective, scientific, thermodynamic, subjective, and complex. From these premises, to analyse the performance indicators that characterise these multidisciplinary ways of inhabiting, an IoT-driven methodology is proposed: to deploy a sensor infrastructure to acquire experimental measurements; analyse data to convert them into context-aware information; and make knowledge-based decisions. Thus, this work tackles the inefficiency and high energy consumption of public buildings with the challenge of balancing energy efficiency and user comfort in dynamic scenarios. As current systems lack real-time adaptability, this work integrates an IoT-driven approach to enhance energy management and reduce discrepancies between measured temperatures and normative thresholds. Following the energy efficiency directives, the obtained results contribute to the following: understanding the complexity of the SBE by analysing its thermal performance, quantifying the potential of energy saving, and estimating its economic impact. The derived conclusions show that IoT-driven solutions allow the generation of real-data-based models on which to enhance SBE knowledge, by increasing energy efficiency and guaranteeing user comfort while minimising environmental effects and economic impact.

Background In order to support decisions regarding governance, organization and control models of the healthcare system, the Swedish government, as well as regional-level agencies, regularly commissions expert reports that are supposed to form the basis for decisions on new steering forms in healthcare. Aim The aim of this study was a) to perform a descriptive mapping of commissioned reports on Swedish healthcare governance and b) to pursue an in-depth content analysis of a strategic sample of such reports. Method Initially, 106 reports from both national and regional levels were gathered and analysed. A matrix was constructed, consisting of questions on who had commissioned the report, who had produced it, what problems the report set out to solve and what solutions were suggested. Further, questions were posed on whether the report was research-based and whether ethical assumptions and arguments were presented. Thereafter, a strategic sample of 36 reports was selected for an in-depth analysis, using inductive content analysis. Results The descriptive mapping showed that the aim of the analysed reports differed in form and content, and that they varied from giving an overview and investigating effects and consequences of new control models to more concrete goals, such as suggesting improvement measures. Academic experts involved in creating the reports often represented economics or business studies. The content analysis revealed examples of standardization in care, characterized by requirements to follow national guidelines, but also examples of requests for increased respect for professionals’ competence and experience. Further, the analysis showed how the definition of equity in care had changed, from a focus on equity in access to care in the reports produced in the 1990s to an emphasis of arguments for geographical sameness and equity in quality of care in the later reports. Discussion Two dominant trends were identified in the material, namely increased standardization and arguments for trust in the system. The great number of reports implies that the system risks requesting more information than it can handle and result in documents where the same message is recurrently repeated or create conflicts of interest and value tensions between different suggestions. Conclusion Commissioned reports can have substantial consequences for new reforms of management practices in healthcare. It is therefore important to investigate them critically. The results of our investigation may contribute to a more comprehensive and adequate model for acquiring and using expert reports regarding healthcare governance, both in Sweden and in similar healthcare systems.

A conceptual model of BIM-based design and refurbishment, based on pre-built indicators and allowing the assessment of the building energy demand and eco-building parameters, is presented. The new approach presented in this model creates a knowledge-based decision-making environment for refurbishment strategies and quality control, in this way creating the preconditions to bridge the gap between expected and actual energy performance. The model with integration of new BIM-based optimization subsystems enables energy management and optimization processes. For a comprehensive evaluation of refurbishment measures, it is suggested to include energy efficiency, eco-efficiency, and economic parameters.

Background Clinical Decision Support Systems (CDSSs) have recently attracted attention as a method for minimizing medical errors. Existing CDSSs are limited in that they do not reflect actual data. To overcome this limitation, we propose a CDSS based on deep learning. Methods We propose the Colorectal Cancer Chemotherapy Recommender (C3R), which is a deep learning-based chemotherapy recommendation model. Our model improves on existing CDSSs in which data-based decision making is not well supported. C3R is configured to study the clinical data collected at the Gachon Gil Medical Center and to recommend appropriate chemotherapy based on the data. To validate the model, we compared the treatment concordance rate with the National Comprehensive Cancer Network (NCCN) Guidelines, a representative set of cancer treatment guidelines, and with the results of the Gachon Gil Medical Center’s Colorectal Cancer Treatment Protocol (GCCTP). Results For the C3R model, the treatment concordance rates with the NCCN guidelines were 70.5% for Top-1 Accuracy and 84% for Top-2 Accuracy. The treatment concordance rates with the GCCTP were 57.9% for Top-1 Accuracy and 77.8% for Top-2 Accuracy. Conclusions This model is significant, i.e., it is the first colon cancer treatment clinical decision support system in Korea that reflects actual data. In the future, if sufficient data can be secured through cooperation among multiple organizations, more reliable results can be obtained.

Background The most suitable and reliable inference engines for Clinical Decision Support Systems in nursing clinical practice have rarely been explored. Purpose This study examined the effect of Clinical Diagnostic Validity-based and Bayesian Decision-based Knowledge-Based Clinical Decision Support Systems on the diagnostic accuracy of nursing students during psychiatric or mental health nursing practicums. Methods A single-blinded, non-equivalent control group pretest–posttest design was adopted. The participants were 607 nursing students. In the quasi-experimental design, two intervention groups used either a Knowledge-Based Clinical Decision Support System with the Clinical Diagnostic Validity or a Knowledge-Based Clinical Decision Support System with the Bayesian Decision inference engine to complete their practicum tasks. Additionally, a control group used the psychiatric care planning system without guidance indicators to support their decision-making. SPSS, version 20.0 (IBM, Armonk, NY, USA) was used for data analysis. chi-square (χ2) test and one-way analysis of variance (ANOVA) used for categorical and continuous variables, respectively. Analysis of covariance was done to examine the PPV and sensitivity in the three groups. Results Results for the positive predictive value and sensitivity variables indicated that decision-making competency was highest in the Clinical Diagnostic Validity group, followed by the Bayesian and control groups. The Clinical Diagnostic Validity and Bayesian Decision groups significantly outperformed the control group in terms of scores on a 3Q model questionnaire and the modified Technology Acceptance Model 3. In terms of perceived usefulness and behavioral intention, the Clinical Diagnostic Validity group had significantly higher 3Q model and modified Technology Acceptance Model 3 scores than the Bayesian Decision group, which had significantly higher scores than the control group. Conclusion Knowledge-Based Clinical Decision Support Systems can be adopted to provide patient-oriented information and assist nursing student in the rapid management of patient information and formulation of patient-centered care plans.

The study assesses the effect of land use and land cover changes (LUCC) on evapotranspiration and runoff in the Shalamulun River watershed of 2,453 km2 located in Inner Mongolia Autonomic Region of China. First, Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images from 1987, 2001 and 2007 are used to quantify LUCC in the watershed. A knowledge-based decision tree (K-DT) classification technique is used to detect LUCC. By comparison of post-classification change among 1987, 2001 and 2007, the results showed significant modification and conversion of land use and cover of the watershed over the 20-year period 1987–2007. The results show that the forest area underwent the greatest change, decreasing by 159.2 km2 in the study period. At the same time, the area of farmland, barren land and residential land increased by 89.5, 46.4 and 25.3 km2, respectively. Subsequently, a two-source potential evapotranspiration (PET) model is used to estimate the potential evapotranspiration response to LUCC. Finally, the influence of LUCC on annual runoff is evaluated using a statistical method. LUCC potentially caused a decrease in annual PET and runoff. Meanwhile, the land use changes resulted in spatio-temporal variations of monthly PET in the growing season (April–September).

This paper provides some pointers on how to improve enterprise search going beyond one-size-fits-all ranking algorithms by adding knowledge-based decision making into all stages of search.