Decision-making Process Research Articles

Practice of a Load Shifting Algorithm for Enhancing Community-Scale RES Utilization

Amidst the recent energy crisis, the pivotal roles of resource efficiency and renewable energy sources (RES) for sustainable development have become apparent. The transition to sustainability involves decentralized energy solutions empowering local communities to generate, store, and utilize their energy, diminishing the reliance on centralized systems and potentially transforming them into resources for power flexibility. Addressing the above necessitates, amongst other elements, the adoption of advanced demand-side management (DSM) strategies. In response, we introduce a versatile algorithm investigating the impact of DSM on the community scale, designed to maximize the utilization of renewable energy produced from local installations. Integrated as an ancillary module in a research data management platform, the algorithm underwent testing using historical datasets collected from end-consumers and a small-scale RES installation. This study not only offers insights for energy stakeholders, but also establishes theoretical parameters that can inform subsequent decision-making processes in the field.

20 An Evaluation of the Quality and Readability of Online Information Regarding TMJ Dysfunction

Abstract Aim The accessibility and availability of the internet has allowed people to increasingly seek health information online, and therefore it plays an essential role in the decision-making process of the patient. The study aimed to assess the quality and readability of online health information on TMJ dysfunction. Method The search was conducted on Google using keywords: jaw pain, jaw locking, jaw discomfort, jaw clicking, jaw popping, temporomandibular joint, TMJ dysfunction, TMD and temporomandibular disorder. Ensuring Quality Information for Patient (EQIP) tool was used to assess quality of information and the application of readability score reflected through Flesch Reading Ease Score (FRES), Flesch-Kincaid Grade Level(FKGL), Gunning-Fog Index(GFI) and Simple Measure of Gobbledygook(SMOG). Results 134 websites were eligible for assessment. The overall median EQIP score was 23, with an IQR of 7 and maximum score of 30. The most common source of information was healthcare industry (n=60; 45%), followed by news services (n=19; 14%), with a median EQIP score of 20 and 25 respectively. Statistically significant differences in overall EQIP scores were found between different sources of information (p=0.004). Only 31(23%) websites achieved the recommended 6th grade or below readability level, and only 14 (10%) discussed complication rates with none mentioning mortality rates at all. Conclusions Most online information on TMJ dysfunction is of suboptimal quality and readability. This study identified areas for improvements when producing patient material regarding TMJ dysfunction. To improve patient education and awareness, content creators should strive to be informative, accurate, and patient-centric.

Enhancing Decision-Making Processes in the Complex Landscape of the Taiwanese Electronics Manufacturing Industry through a Fuzzy MCDM Approach

This research proposes a hybrid multi-criteria decision-making (MCDM) framework for workforce recruitment in Taiwan’s electronics manufacturing companies, an area with limited research. First, a comprehensive review of existing literature and interviews with industry experts were conducted to compile a list of decision-making criteria and sub-criteria relevant to workforce selection in Taiwan’s electronics industry. The Fuzzy Delphi Method (FDM) was then applied to identify and retain the most critical criteria while eliminating the less important ones. Next, Interpretive Structural Modelling (ISM) was used to calculate the interdependencies among the identified factors. Finally, based on these relationships, the Fuzzy Analytic Network Process (FANP) was employed to calculate the relative importance weights of the criteria and sub-criteria. These weights were then used to rank the criteria, identifying the most important ones and aiding in decision-making. The findings indicate that the proposed method provides a structured and assessable model for making informed decisions in workforce recruitment, particularly in the challenging environment of Taiwan’s electronics manufacturing industry, which faces a shortage of skilled labor. The research presents three primary contributions: the development of a systematic criteria selection technique using FDM, the establishment of consistent criteria relations for decision-makers using ISM, and the proposal of an application model employing the FANP method to identify and rank appropriate criteria for hiring new employees. The study highlights work attitude, adaptability to the environment, and work ability as major criteria. It also emphasizes the importance of discipline compliance, a positive attitude, and adherence to health and safety protocols as the top sub-criteria for workforce selection.

P-240 Is the intelligent Data Analysis Score (iDAScore) a reliable tool to predict pregnancy? Our center’s experience

Abstract Study question Is the intelligent Data Analysis Score (iDAScore) reliable in predicting a positive pregnancy test, clinical pregnancy and miscarriage rate after single vitrified-warmed blastocyst transfer (SVBT)? Summary answer There is a significant increase in positive pregnancy test and clinical pregnancy rates as iDAScore increases. The miscarriage rate is lower with increased iDAScore. What is known already With the improvement of assisted reproductive technology (ART), elective single embryo transfer has become a standard. Optimizing embryo selection is a major challenge to increase reproductive outcomes. The Gardner embryo grading system is widely adopted for the evaluation of blastocyst quality, but this tool is subject to inter- and intra-variability. In recent years, artificial intelligence has found diverse applications in healthcare. Based on time-laps sequences of > 180’000 developing embryos, iDAScore is a new deep-learning model for objective and standardized blastocyst scoring. However, its reliability in predicting pregnancy and miscarriage rates remains unknown. Study design, size, duration This monocentric retrospective pilot study was conducted from May 2023 to December 2023. A total of 206 homologous SVBT cycles from 206 patients were analyzed (one patient, one cycle). Exclusion criteria included: cycles involving preimplantation genetic testing, multiple and/or fresh blastocyst transfer, and blastocysts obtained after testicular sperm extraction. All vitrified-warmed blastocysts were scored using iDAScore version 1.0 model, ranging from 1 – 9.9. Participants/materials, setting, methods Scores were stratified into 3 groups: high-score (9.9 – 7.7), medium-score (7.6 – 5.5) and low-score (5.4 – 3.1). For each group, positive pregnancy test, clinical pregnancy and miscarriage rates were analyzed. SVBT cycles were then stratified into four maternal age groups (< 35, 35-37, 38-40, >41 years) and the same outcomes were analyzed. χ2 was used for statistics analysis. Main results and the role of chance Mean maternal age was 36.8 ± 4.9. Among the 206 vitrified-warmed blastocysts, 122 were scored as high-score, 57 as medium-score and 27 as low-score. Positive pregnancy test rate was significantly higher with increased iDAScore with 58% in the high-score group, 36.8% in the medium-score group and 17% in the low-score group (p < 0.0001). Clinical pregnancy rate, defined as the visualization of a fetal heartbeat at ultrasound scan, was also significantly higher with increased iDAScore, with 51.6% in the high-score group, 31.6% in the medium-score group and 17% in the low-score group (p < 0.0001). The miscarriage rate was lower with increased iDAScore, with 8.1% in the high-score group, 16.6% in the medium-score group and 50% in the low-score group, but this difference was not statistically significative (p 0.032). When adjusted to age group however, the miscarriage rate was found to be significantly lower in the high-score group when compared to the medium-score group (p < 0.0001); statistical analysis was not achieved in the low-score group due to the small sample size. Limitations, reasons for caution The major limitation of our study is its small sample size. Data collection is ongoing, to increase the study power. Additionally, many other factors, such as genetic, hormonal, metabolic and/or immunological, may play a role in achieving pregnancy. Wider implications of the findings Our results indicate that iDAScore may be a strong predictor of pregnancy and could be used as a complementary tool to the Gardner embryo grading system in the decision-making process of which blastocyst to transfer. Trial registration number N/A

798 Improving the Use of Ultrasound Imaging in the Diagnosis and Assessment of Paediatric Patients Admitted with Suspected Appendicitis

Abstract Aim To investigate the barriers to and rate of ultrasonography use to facilitate the diagnosis of suspected appendicitis and assess its effect on negative appendicectomy rates in concordance with GIRFT guidelines for children with abdominal pain. Method Retrospective single-site data on paediatric patients presenting with suspected appendicitis was collected over 6 months, using operative and admission records, imaging requests and histology results. An anonymous survey was distributed amongst Surgical clinicians to determine the perceived impact of access to and influencers of ultrasonography use for children. Results Of 91 patients, 35 underwent a diagnostic laparoscopy. The operation findings were: 23% (8) not acute appendicitis, 23% (8) perforated or complex appendicitis and 54% (19) “other” acute appendicitis. 85% (30) of patients who were surgically managed did not have an ultrasound before theatre. 23% (21) of the total cohort underwent an ultrasound and none of the scans suggested acute appendicitis. Of these, 76% (16) of patients were managed non-operatively. Of the 5 patients who underwent an ultrasound and proceeded to laparoscopy; none had intraoperative findings of appendicitis. Further analysis aims to investigate why ultrasound was utilised for only 23% of patients and to clarify the decision-making processes behind a “straight to theatre” versus an “imaging-first” approach. Conclusions The negative paediatric appendicectomy rate in this District General Hospital was 23%. According to this data, ultrasonography is a reliable rule-out tool for suspected appendicitis and more frequent use could reduce unnecessary operations.

Factors influencing the reimbursement of cancer drugs in Europe: A scoping review.

Reimbursement process of oncology drugs in Europe occurs within a complex decision-making process that varies between Member States. Distinctions between the States trigger societal debates since it is necessary to balance access to medicines and health systems sustainability. We aimed to review the evidence concerning factors associated with the reimbursement decision or Health Technology Agency recommendation of oncology drugs in Europe. A systematic literature search was performed in two databases from inception to august 2023. Screening and data extraction were performed by pairs. Thirteen articles were included and encompassed data from 11 nations. Seven articles showed that cost-effective (C-E) drugs and lower Incremental Cost-Effectiveness Ratios (ICERs) had higher likelihood of reimbursement. Disease severity might influence the reimbursement decision with financial agreements. Improvement in clinical outcomes, substantial clinical benefit (p < 0.01) or overall survival gains (p < 0.05) were positively associated. Orphan drug designation impact varies between countries but positive decisions are usually achieved under specific conditions. Clinical and C-E uncertainty frequently led to reimbursement with financial agreements or outcomes-based conditions. Sociodemographic factors as: social health insurance system, higher Gross Domestic Product and larger elderly population were positively associated with reimbursement (p < 0.01). There is a need for further research into key determinants of reimbursement decisions in Europe and the development of drug access models that can effectively address and overcome costs and effectiveness uncertainties.

A Local-Scale, Post-Fire Assessment in a Double-Burned Area: A Case Study from Peloponnisos, Greece

In the summer of 2021, Greece experienced significant forest fires and mega-fires across multiple regions, leading to human casualties and damage to the natural environment, infrastructure, livestock, and agriculture. The current study aims to assess the ecosystem condition in terms of the natural regeneration and soil conditions of an area burnt by a forest fire (2021), specifically in the Ancient Olympia region situated in West Peloponnese (Ilia Prefecture), Greece. A standardized field sampling methodology was applied to record natural regeneration at chosen sites where a forest fire had also previously occurred (in 2007), resulting in the natural re-growth of the Pinus halepensis forest. Furthermore, an analysis was conducted on the geochemical, mineralogical, and sedimentological properties of soils obtained from this location. The findings of the research demonstrate the decline in the established natural regeneration of the Pinus halepensis forest and the overall tree layer. Species characteristic of post-fire ecological succession were observed in the shrub and herb layers, displaying varying coverage. The examination of soil mineralogy, sedimentology, and geochemistry indicated that the soil characteristics in the area are conducive to either natural or artificial regeneration. Ultimately, recommendations for landscape rehabilitation strategies are provided to inform decision-making processes, considering future climate conditions.

547 Creation of a Machine Learning Model to Predict Cognitive Outcomes Post Deep Brain Stimulation Surgery for Parkinson’s Disease

Abstract Background Parkinson’s disease (PD) is the second most common neurodegenerative disorder and continues to affect an increasingly large portion of the world’s population. Limitations of the medical treatment of PD with levodopa have led to deep brain stimulation (DBS) therapy being used to alleviate symptoms in advanced PD patients. Despite its efficacy with motor symptoms of PD, DBS has been noted to lead to worsening of cognitive outcomes in a percentage of cases. There is currently no predictive model for individual patient cognitive outcomes post DBS. Aim The aim of this study is the creation of a machine learning (ML) model which can help facilitate decision making for clinicians regarding offering DBS to patients. ML has the potential to analyse multiple variables and produce a predictive model for neurocognitive outcomes. The volume of variables analysed by ML is beyond the remit of calculations that can be performed by individual medical professionals for each patient. Method This study used mild cognitive impairment (MCI) as the outcome, at six months post DBS surgery to measure neurocognitive impact. Clinical and radiomics variables were used to train the models. Results Two predictive models were successfully generated – random forest and logistic regression, which showed an accuracy of 0.87 and 0.74 respectively. Conclusions The study concluded that it is possible to use ML to generate a prediction model for outcomes post DBS. The model can help optimise the decision-making process behind offering DBS surgery to patients and lend insight into the possible causes of poor outcomes.

Modelling and Optimization of Residential Electricity Load under Stochastic Demand

The paper considers a modelling framework for a set of households in residential areas; using electricity as a form of energy for domestic consumption. Considering the demand and availability of units for electricity consumption, optimal decisions for electricity load allocation are paramount to sustain energy management. We formulate this problem as a stochastic decision-making process model where electricity demand is characterized by Markovian demand. The loading and operational framework is governed by the demand and supply phenomena; where shortage costs are realized when demand exceeds supply. Empirical data for electricity consumption was collected from fifty households in two residential areas within the suburbs of Kampala in Uganda. Data collection was made at hourly intervals over a period of four months. The major problem focussed on determining an optimal electricity loading decision in order to minimize consumption costs as demand changes from one state to another. Considering a multi-period planning horizon, an optimal decision was determined for loading or not loading additional electricity units using Markov decision process approach. The model was tested; whose results demonstrated the existence of an optimal state-dependent decision and consumption costs considering the case study used in this study. The proposed model can be cost-effective for managers in the electricity industry. Improved efficiency and utilization of resources for the distribution systems of electricity to residential areas was realised; with subsequent enhanced reliability of service to essential customers of the energy market.

XRAInet: AI-based decision support for pneumothorax and pleural effusion management.

This study aimed to develop and assess the performance of an artificial intelligence(AI)-driven decision support system, XRAInet, in accurately identifying pediatric patients with pleural effusion or pneumothorax and determining whether tube thoracostomy intervention is warranted. In this diagnostic accuracy study, we retrospectively analyzed a data set containing 510 X-ray images from 170 pediatric patients admitted between 2005 and 2022. Patients were categorized into two groups: Tube (requiring tube thoracostomy) and Conservative (managed conservatively). XRAInet, a deep learning-based algorithm, was trained using this data set. We evaluated its performance using various metrics, including mean Average Precision (mAP), recall, precision, and F1 score. XRAInet, achieved a mAP score of 0.918. This result underscores its ability to accurately identify and localize regions necessitating tube thoracostomy for pediatric patients with pneumothorax and pleural effusion. In an independent testing data set, the model exhibited a sensitivity of 64.00% and specificity of 96.15%. In conclusion, XRAInet presents a promising solution for improving the detection and decision-making process for cases of pneumothorax and pleural effusion in pediatric patients using X-ray images. These findings contribute to the expanding field of AI-driven medical imaging, with potential applications for enhancing patient outcomes. Future research endeavors should explore hybrid models, enhance interpretability, address data quality issues, and align with regulatory requirements to ensure the safe and effective deployment of XRAInet in healthcare settings.

A Throughput Performance Analysis Method for Multimode Underwater Acoustic Communication Network Based on Markov Decision Process

The multimode underwater acoustic communication network is a novel form of underwater acoustic communication that adjusts its communication mode to enhance overall performance. Current performance analysis methods are primarily applied to single-mode networks and assume uniform communication capability across all nodes, making them unsuitable for multimode networks. This paper investigates the multimode communication of the physical layer, considering factors such as the marine environment, the node transmitting sound source level, and the transmitting distance. A decoding conflict model is proposed to support multimode concurrent transmission scenarios. The communication mode is designed to be compatible with the channel and node characteristics. Additionally, using a Markov decision process, this paper establishes a performance evaluation and analysis model for multimode underwater acoustic networks to determine throughput performance limits in real underwater environments. Simulations across various scenarios validate that the throughput performance limits obtained by this method are more accurate under multimode networks, with an improvement in accuracy of over 67.5% compared to existing methods.

A Hybrid Handover Scheme for Vehicular VLC/RF Communication Networks

Visible light communication (VLC) is a promising complementary technology to its radio frequency (RF) counterpart to satisfy the high quality-of-service (QoS) requirements of intelligent vehicular communications by reusing LED street lights. In this paper, a hybrid handover scheme for vehicular VLC/RF communication networks is proposed, to balance QoS and handover costs by considering the vertical handover and horizontal handover together, and judging from the mobile state of the vehicle. A Markov decision process (MDP) is formulated to describe this hybrid handover problem, with a cost function balancing the handover consumption, delay, and reliability. A value iteration algorithm was applied to solve the optimal handover policy. The simulation results demonstrated the performance of the proposed hybrid handover scheme in comparison to other benchmark schemes.

Unveiling the Enigma: Empowering Citizen Engagement in Decision-Making Deciphering Albania's Case

This article aims to fill the gap and analyze citizen involvement in developing and transitional democracy, focusing specifically on Albania, a country in the Western Balkans. The objective of this article is to examine the models, trends, obstacles and opportunities offered by citizen participation, as well as to evaluate the determinant factors and their impact on the transformative processes of democratization and the fragile development of Albania. The article explores the participation of citizens in the decision-making process, and their role in policy-making and democratic decision-making and tries to understand how citizens can be more actively involved in the decision-making process and how this involvement can increase the legitimacy and effectiveness of policies. In this study, we use qualitative and quantitative methods to analyze existing practices of citizen involvement in the decision-making process at different levels of local and central government. We focus on the tools and mechanisms that can be used to encourage the involvement of citizens in the decision-making process. The results of this study aim to provide recommendations for policymakers and practitioners at different levels of government to improve the involvement of citizens in the process of decision-making. In conclusion, through these recommendations, we aim to help increase transparency, responsibility and legitimacy of decision-making processes and create an environment where citizens feel involved and heard in decision-making that affects life. Received: 8 March 2024 / Accepted: 13 June 2024 / Published: 02 July 2024

Expanding the intellectual capabilities of OLAP technology using neural networks

The article highlights the main characteristics, features and structure of Online Analytical Processing systems based on the same technology that perform online analytical processing of data. This technology allows analysts to explore and navigate a multidimensional indicator structure called an online analytical processing cube (data cube). Indicators (measures) of data cube play an important role in the decision-making process. To solve certain problems, these measures often need to be classified or grouped. Moreover, empty measures are common in data cube. This fact negatively affects strategic decision making. Unfortunately, online analytical processing itself is not well suited for classifying, clustering, and predicting empty measures of data cube in the presence of large data. In this regard, today there is a need to use new technologies to solve such problems. Such technologies include neural networks. The article discusses the problem of integrating online analytical processing and a neural network, showing the possibilities and advantages of such integration. It mentions that in the case of big data, the integration of OLAP and neural networks is very effective in solving problems of classification, clustering and empty measure prediction of data cube. An architectural and technological model for the integration of online analytical processing and neural networks is presented.

Strategies to improve end-of-life decision-making and palliative care following hip fracture in frail older adults: a scoping review.

Although surgery is the gold standard following a hip fracture, the potential for rehabilitation and survival rates are low in frail older patients. Some patients may derive more benefit from palliative care. The objectives of this review were to identify the available strategies to improve end-of-life decision-making and palliative care for frail patients with hip fractures and to synthetise their level of support. We conducted a scoping review of the scientific and grey literature, searching seven databases and websites of associations. We included all study designs, expert opinion articles and clinical practice guidelines (CPGs). Data were synthetised according to the Approach to Patient with Limited Life Expectancy and Hip Fracture framework. The number of research items and their level of evidence were tabulated for each of the recommended strategies. Of the 10591 items identified, 34 were eligible. The majority of included articles were original research studies (n = 15). Half of the articles and CPGs focused on intervention categories (55%) such as goals of care discussion and comfort care, followed by factors to consider in the end-of-life decision-making process (25%) and prognosis assessments (20%), mainly through the estimation of life expectancy. The level of evidence for these strategies remains low, given the limited number of prospective studies supporting them. This scoping review highlighted that end-of-life care in frail older patients with a hip fracture remains understudied. The strategies identified could be prioritised for future research to improve the well-being of the target population while promoting sustainable resource management.

Exploring Financial Behavior: A Qualitative Investigation into Psychological Factors Influencing Risk Preferences and Investment Decisions

This qualitative study investigates the psychological determinants influencing risk preferences and investment decisions. Grounded in phenomenology and interpretivism, the research aims to explore subjective experiences related to financial behavior. Employing purposive sampling, participants were selected based on diverse criteria. Data collection involved semi-structured interviews, participant observation, and document analysis. Thematic analysis revealed intricate interplays between personal experiences, cognitive biases, emotions, social influences, and financial literacy, shaping individuals' risk perceptions and decision-making processes. Findings underscore the multifaceted nature of financial behavior, highlighting the significance of personality traits, cognitive styles, and decision-making biases. Risk-averse individuals prioritized capital preservation, while sensation seekers pursued high-risk, high-reward investments. Moreover, individuals' self-efficacy influenced their investment strategies. Cognitive biases, such as overconfidence and loss aversion, further impacted investment decisions. The study emphasizes the importance of considering psychological factors in designing personalized financial interventions and educational programs. Future research directions include longitudinal studies, cross-cultural comparisons, and interdisciplinary approaches integrating insights from psychology, economics, and finance. This research contributes to the advancement of knowledge in behavioral finance and informs practical implications for financial advisors and policymakers, aiming to enhance individuals' financial well-being and decision-making competence.

Optimizing Patient Stratification in Healthcare: A Comparative Analysis of Clustering Algorithms for EHR Data

Advanced data analytics are increasingly being employed in healthcare research to improve patient classification and personalize medicinal therapies. In this paper, we focus on the critical problem of clustering electronic health record (EHR) data to enable appropriate patient categorization. In the era of personalized medicine, optimizing patient classification is critical to healthcare analytics. This research presents a comparative assessment of different clustering algorithms for Electronic Health Record (EHR) data, with the goal of improving the efficacy and productivity of patient clustering methods. Our study focuses on Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) as a Multi-Criteria Decision-Making (MCDM) strategy, includes an in-depth assessment of eight clustering algorithms: K-Means, DBSCAN, Hierarchical Clustering, Mean Shift, Affinity Propagation, Spectral Clustering, Gaussian Mixture Models (GMM), as well as Self-Organizing Maps. The evaluation factors used for evaluation in this research are Cluster Quality Metrics, Scalability, Robustness to Noise, Cluster Shape and Density, Interpretability, Cluster Number, Dimensionality, and Consistency and Stability. These criteria and alternatives were chosen after conducting a thorough assessment of the literature and consulting with domain experts. All participated specialists actively engaged in the decision-making process, bringing unique insights into the best clustering algorithms for healthcare data. The results of this study illustrate each algorithm’s strengths and weaknesses in the setting of patient stratification, providing insight into their performance across multiple dimensions. The fuzzy TOPSIS MCDM strategy is a reliable instrument for synthesizing expert opinions and methodically evaluating the found clustering alternatives. This study advances healthcare analytics by giving practitioners and researchers with informative perspectives on the selection of clustering algorithms designed to address the unique problems of patient stratification utilizing EHR data.

Does the Bangladesh Equity Market Expose to Disposition Effects Bias under Different Market Conditions?

The study provides an intuitive investigation into the disposition effect within frontier markets such as Bangladesh, particularly focusing on its behavior during various market conditions. The study’s novelty lies in its application of a methodological framework of the disposition effect measure of Weber and Camerer, aiming to understand the disposition effect through different market conditions. Dow Theory is applied to disparate bullish and bearish intermediate periods. Disposition effects persist for the entire study period, as well as the different market conditions except for the bearish Bangladesh equity market. The bullish and crisis markets exhibit a rather high disposition effect due to their respective market volatility. Stronger disposition effects are more pronounced for a crisis market in relation to a bullish market. In addition, the disposition effect in Bangladesh’s equity market oscillates in crisis periods. The documentation of the disposition effect in the Bangladesh equity market across market conditions suggests that investors’ psychology plays a crucial role in their decision processes. Individuals and professional investors should carefully design an appropriate strategy to control their decision-making process since the presence of disposition effects may impair the risk-return payoffs.

Implementing the information system for older adult patients post-discharge self-management: a qualitative study.

Discharging older adult patients from the hospital poses risks due to their vulnerable conditions, complex instructions and limited health literacy. Insufficient information about medication side effects adds to patient concerns. To address this, a post-discharge information summary system was developed. While it has shown positive impacts, concerns exist regarding implementation fidelity. This study employed a theory-driven approach to understand health providers' perspectives on effective implementation. Individual semi-structured interviews were conducted via telephone with nurses, doctors and pharmacists from local public hospitals. All interviews were audio-recorded and transcribed verbatim. Theoretical Domains Framework (TDF) was applied for direct content analysis. Belief statements were generated by thematic synthesis under each of the TDF domains. A total of 98 participants were interviewed. Out of the 49 belief statements covering eight TDF domains, 19 were determined to be highly relevant to the implementation of the post-discharge information summary system. These TDF domains include knowledge, skills, social/professional role and identity, beliefs about consequences, intentions, memory, attention and decision processes, environmental context and resources and social influences. Our study contributes to the understanding of determinants in implementing discharge interventions for older adult patients' self-care. Our findings can inform tailored strategies for frontline staff, including aligning programme rationale with stakeholders, promoting staff engagement through co-creation, reinforcing positive programme outcomes and creating default settings. Future research should employ rigorous quantitative designs to examine the actual impact and relationships among these determinants.

Synergizing ChatGPT and experiential learning: unravelling TOC based production planning and control variants through the dice game

Amidst debates around the impact of Artificial Intelligence (AI) technologies like ChatGPT in education, our study explores their role in enhancing the ‘theory of experiential learning’, particularly in Production and Operations Management (POM). We demonstrate how Goldratt’s Dice Game, as an experiential learning aid, allows undergraduate students in a senior-level production planning and control (PPC) course to apply knowledge and skills in a dynamic, interactive setting. This study presents how these students, supported by ChatGPT's insights, gain a deeper understanding of the DBR system, focusing on buffer management, internal (i.e. a dominant capacity constraint), external (i.e. market demand constraint), and interactive decision-making processes. We detail manual and Excel-based simulation models for Drum-Buffer-Rope (DBR) variants, reflecting on experiential learning outcomes. Concluding with managerial implications, our research advocates for the synergy of ChatGPT-aided theoretical learning with experiential models, presenting a comprehensive approach for understanding POM fundamentals such as Production Planning & Control (PPC) systems.