Decision Support Capabilities Research Articles

Revolutionizing decision support: asystematic literature review of contextual implementation models for electronic health records systems.

This study aims to assess previously developed Electronic Health Records System (EHRS) implementation models and identify successful models for decision support. A systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The data sources used were Scopus, PubMed and Google Scholar. The review identified peer-reviewed papers published in the English Language from January 2010 to April 2023, targeting well-defined implementation of EHRS with decision-support capabilities in healthcare. To comprehensively address the research question, we ensured that all potential sources of evidence were considered, and quantitative and qualitative studies reporting primary data and systematic review studies that directly addressed the research question were included in the review. By including these studies in our analysis, we aimed to provide a more thorough and reliable evaluation of the available evidence. The findings suggest that the success of EHRS implementation is determined by organizational and human factors rather than technical factors alone. Successful implementation is dependent on a suitable implementation framework and management of EHRS. The review identified the capabilities of Clinical Decision Support (CDS) tools as essential in the effectiveness of EHRS in supporting decision-making. This study contributes to the existing literature on EHRS implementation models and identifies successful models for decision support. The findings can inform future implementations and guide decision-making in healthcare facilities.

Assessing and forecasting collective urban heat exposure with smart city digital twins

Due to population growth, climate change, and the urban heat island effect, heat exposure is becoming an important issue faced by urban built environments. Heat exposure assessment is a prerequisite for mitigation measures to reduce the impact of heat exposure. However, there is limited research on urban heat exposure assessment approaches that provides fine-scale spatiotemporal heat exposure information, integrated with meteorological status and human collective exposure as they move about in cities, to enable proactive heat exposure mitigation measures. Smart city digital twins (SCDTs) provide a new potential avenue for addressing this gap, enabling fine spatiotemporal scales, human-infrastructure interaction modeling, and predictive and decision support capabilities. This study aims to develop and test an SCDT for collective urban heat exposure assessment and forecasting. Meteorological sensors and computer vision techniques were implemented in Columbus, Georgia, to acquire temperature, humidity, and passersby count data. These data were then integrated into a collective temperature humidity index. A time-series prediction model and a crowd simulation were employed to predict future short-term heat exposures based on the data accumulated by this SCDT and to support heat exposure mitigation efforts. The results demonstrate the potential of SCDT to enhance public safety by providing city officials with a tool for discovering, predicting, and, ultimately, mitigating community exposure to extreme heat.

An optimized diabetes mellitus detection model for improved prediction of accuracy and clinical decision-making

Diabetes Mellitus (DM) is an enduring metabolic illness that disturbs many individuals globally. This study addresses the global impact of Diabetes Mellitus (DM) and emphasizes the critical role of accurate DM detection in early diagnosis, effective treatment, and prevention of complications. The research introduces an optimized DM detection model, the GBM-DRU (Gradient Boosting Machine - Data Reduction Unit) network, which integrates feature engineering and ensemble learning techniques to enhance prediction accuracy and support clinical decision-making. The GBM-DRU network combines the powerful gradient boosting machine algorithm with a data reduction unit (DRU) for efficient feature selection, reducing dimensionality and improving computational efficiency. Feature engineering enhances discriminatory power, while ensemble learning methods, including bagging and boosting, improve overall model performance. Rigorous experiments on a comprehensive dataset of DM patients demonstrate that the proposed approach outperforms existing models in terms of accuracy, sensitivity, specificity, and AUC-ROC. The optimized model provides valuable insights into feature importance, aiding clinical decision-making and deepening the understanding of DM risk factors. Therefore, the GBM-DRU network, utilizing feature engineering and ensemble learning, presents a viable approach to precise diagnosis of diabetes mellitus, with favorable implications for patient outcomes, disease control, and public health campaigns. The improved prediction accuracy, feature interpretability, and clinical decision support capabilities of the model may have a beneficial effect on public health campaigns, disease management, and patient outcomes.

Assessment of ChatGPT-3.5's Knowledge in Oncology: Comparative Study with ASCO-SEP Benchmarks

Background ChatGPT (Open AI) is a state-of-the-art large language model that uses artificial intelligence (AI) to address questions across diverse topics. The American Society of Clinical Oncology Self-Evaluation Program (ASCO-SEP) created a comprehensive educational program to help physicians keep up to date with the many rapid advances in the field. The question bank consists of multiple choice questions addressing the many facets of cancer care, including diagnosis, treatment, and supportive care. As ChatGPT applications rapidly expand, it becomes vital to ascertain if the knowledge of ChatGPT-3.5 matches the established standards that oncologists are recommended to follow. Objective This study aims to evaluate whether ChatGPT-3.5’s knowledge aligns with the established benchmarks that oncologists are expected to adhere to. This will furnish us with a deeper understanding of the potential applications of this tool as a support for clinical decision-making. Methods We conducted a systematic assessment of the performance of ChatGPT-3.5 on the ASCO-SEP, the leading educational and assessment tool for medical oncologists in training and practice. Over 1000 multiple choice questions covering the spectrum of cancer care were extracted. Questions were categorized by cancer type or discipline, with subcategorization as treatment, diagnosis, or other. Answers were scored as correct if ChatGPT-3.5 selected the answer as defined by ASCO-SEP. Results Overall, ChatGPT-3.5 achieved a score of 56.1% (583/1040) for the correct answers provided. The program demonstrated varying levels of accuracy across cancer types or disciplines. The highest accuracy was observed in questions related to developmental therapeutics (8/10; 80% correct), while the lowest accuracy was observed in questions related to gastrointestinal cancer (102/209; 48.8% correct). There was no significant difference in the program’s performance across the predefined subcategories of diagnosis, treatment, and other (P=.16, which is greater than .05). Conclusions This study evaluated ChatGPT-3.5’s oncology knowledge using the ASCO-SEP, aiming to address uncertainties regarding AI tools like ChatGPT in clinical decision-making. Our findings suggest that while ChatGPT-3.5 offers a hopeful outlook for AI in oncology, its present performance in ASCO-SEP tests necessitates further refinement to reach the requisite competency levels. Future assessments could explore ChatGPT’s clinical decision support capabilities with real-world clinical scenarios, its ease of integration into medical workflows, and its potential to foster interdisciplinary collaboration and patient engagement in health care settings.

Data Warehousing Revolution: AI-driven Solutions

The contemporary landscape of data warehousing is undergoing a revolutionary transformation propelled by the integration of Artificial Intelligence (AI). This paper explores the intersection of AI and data warehousing, unraveling the dynamics that fuel this revolution and examining the profound implications for businesses and data management practices. The traditional paradigm of data warehousing has relied on structured data and predefined schemas, limiting its adaptability to the dynamic nature of modern datasets. The advent of AI injects a new dimension, enabling data warehouses to evolve into intelligent, adaptive entities capable of handling diverse data types, volumes, and velocities. This abstract encapsulates the essence of the research, delving into key themes that define the AI-driven revolution in data warehousing. The paper begins by surveying the historical trajectory of data warehousing, highlighting the challenges posed by the increasing complexity and heterogeneity of contemporary data sources. As businesses grapple with unstructured data, streaming data, and the need for real-time insights, the limitations of traditional data warehousing architectures become apparent. The introduction of AI-driven solutions revolutionizes data warehousing in several dimensions. Machine learning algorithms are harnessed for automating data integration, cleansing, and transformation processes, mitigating the manual labor associated with traditional ETL (Extract, Transform, Load) methods. Deep learning techniques, such as neural networks, unlock the potential to uncover complex patterns within massive datasets, enhancing predictive analytics and decision support capabilities. Moreover, the abstract explores the role of AI in enabling self-optimizing data warehouses. Adaptive query optimization, automated indexing, and real-time performance tuning emerge as pivotal components, ensuring that data warehouses evolve in response to changing workloads and user patterns. Ethical considerations and responsible AI practices within the context of data warehousing are also addressed. The abstract concludes by underlining the transformative impact of AI-driven solutions on the efficiency, agility, and strategic value of data warehousing, offering a glimpse into the future where intelligent data warehouses play a central role in shaping data-driven enterprises.

Analysis of Enterprise Accounting Data Management Evaluation Model Based on Maturity Modeling

Abstract This paper takes the capability of accounting data management in Company D as the research object and constructs a maturity model of accounting data management. The idea of the maturity model’s gradient is used to divide key process domains and practices and then create a set of accounting data management index systems. Meanwhile, the fuzzy element evaluation and analysis method is applied to the evaluation of accounting data management maturity of enterprises, and “maturity level, evaluation index, and quantitative value” are defined as the basic elements of fuzzy elements. Finally, based on the evaluation results, the evaluation indicators at all levels of the accounting data management maturity model of Company D are analyzed. The results show that the evaluation value of the cost management capability and decision support capability of Company D is 3.1678 and 2.9518, which can ensure the authenticity, timeliness and reliability of accounting data to a certain extent. This paper provides new ideas for the research of enterprise accounting management evaluation, which has certain theoretical significance and practical value.

Application of GIS in Land Policy and Planning Strategies in Rural Revitalization

In recent years, there has been a growing emphasis on reviving rural areas in China, which has become a crucial strategic direction for the nation's economic and social development. Currently, rural revitalization is a major endeavor in China, and the successful implementation of land policy and planning strategies is crucial to its achievement. This study seeks to examine the use of Geographic Information System (GIS) in crafting land policy and planning strategies for rural rejuvenation. By harnessing GIS technology, a thorough evaluation and analysis of rural land resources can be undertaken, furnishing a scientific basis for formulating land policy. Furthermore, Geographical Information Systems (GIS) provide opportunities for spatial analysis, simulation, and decision support capabilities to implement rural land policy and planning strategies. Nevertheless, the use of GIS technology presents challenges and limitations in terms of data acquisition and technological implementation. To promote the sustainable development of the rural economy, future research efforts should concentrate on further enhancing the utilization of GIS technology in rural revitalization.

Development of a watershed health assessment framework integrating ecological, social, cultural, economic and policy attributes

ABSTRACT Watershed health assessment is vital for effective resource management, necessitating an integrated framework that captures the multidimensional nature of watershed health and guides protection and restoration efforts. This study presents the development of the Integrated Watershed Health Assessment Framework (IWHF), incorporating ecological, social, cultural, economic and policy attributes to comprehensively assess watershed health as a socio-ecological system. By integrating primary and secondary data, including literature reviews and expert opinions, the IWHF captures the multidimensional nature of watershed health and provides a practical tool for sustainable watershed management. The results, evaluated through the Analytic Hierarchy Process (AHP) within the context of Multi-Criteria Decision Making (MCDM), prioritize ecological factors (48.80%), followed by economic considerations (21.90%), socio-cultural factors (16.91%) and policy aspects (12.39%). This highlights the critical importance of preserving ecological integrity, emphasizing sustainable economic practices, inclusive community engagement and well-defined policy frameworks. The IWHF's holistic and flexible approach, comprehensive integration, and enhanced decision support capabilities set it apart from other frameworks, contributing to the understanding, management, and preservation of watershed health while guiding policy direction and optimizing the utilization of watershed resources.

Decision-support system for safety and security assessment and management in smart cities

AbstractCounter-terrorism measures and preparedness play a critical role in securing mass gatherings, soft targets, and critical infrastructures within urban environments. This paper introduces a comprehensive Decision Support System developed as part of the S4AllCitites project, designed to seamlessly integrate with existing legacy systems in Smart Cities. The system encompasses urban pedestrian and vehicular evacuation, incorporating predictive models to anticipate the progression of incendiary and mass shooting attacks, alongside a probabilistic model for threat assessment in the case of improvised explosive devices. A notable achievement of this research is the successful implementation and deployment of the system in operational environments through pilot studies. It empowers public and private security operators with real time decision support capabilities during both prevention and intervention stages of potential attacks. The decision support information provided encompasses various aspects, including optimal evacuation strategies, estimated egress times, pedestrian movement profiles, probability assessments, and the potential impact of different terrorist threats in terms of casualties. Additionally, the system offers real-time insights into the status of the traffic network under normal and unusual conditions, enabling efficient emergency management throughout its progression. This includes the ability to identify optimal intervention routes and assess the impact of anomalous traffic resulting from evacuations.

METHOD FOR DETERMINING THE RATIONAL NUMBER OF UAV FLOTILLA TAKING INTO ACCOUNT THE RELIABILITY OF THE AIRCRAFT

The conducted studies showed that projects of analysis and assessment of emergencies at critical infrastructure facilities have been initiated in Ukraine and abroad. The purpose of such a system is the formation of data for the development of architecture, demonstration analytics and a prototype of decision support capabilities, taking into account the use of currently available data and analytical methodologies. Unmanned aerial vehicles (UAVs), which work with ground and air control points in emergencies, are planned as the basis for the construction of such a system. The subject of the study is methods of determining the rational number of UAV flotillas taking into account the assigned tasks and characteristics. Currently, there is no method for determining the rational number of a UAV flotilla, taking into account the reliability of the aircraft. An urgent scientific and technical task is the task of creating a rational number of the UAV flotilla, taking into account the reliability of the aircraft and the necessary quality of monitoring the situation in emergencies. The purpose of the article is to develop a method for determining the rational number of UAV flotillas taking into account the reliability of the aircraft and the necessary quality of monitoring the situation in emergencies. Research methods – provisions of risk theory, probability theory, combinatorics, mathematical apparatus of reliability theory, mathematical methods of optimization. Research results: an analysis of factors affecting the structure of the UAV grouping was carried out; the dependence of task performance on the probability of UAV failure under the influence of interfering factors was obtained; it is shown that in conditions with a low impact of interfering factors, the reliability of individual UAVs does not have a significant impact on the performance of assigned tasks. It has been established that with increasing influence of interfering factors, the probability of completing tasks depends on the reliability of the aircraft. Conclusions: The proposed method makes it possible to create a rational number of UAV flotillas taking into account the reliability of the aircraft and the necessary quality of monitoring the situation with a rational number of UAVs in emergencies.

Effect of Management Information System (MIS) on Decision-Making in the Academic Sector

The current era is the information era; people are running on society 5.0, where machines are integrated with humans for a healthy, comfortable, easy, and long life. In general, information is blood, and MIS is body; blood flows for life which can be compared with the flow of information on the organization as per the requirement for decision-making to grow the organization to meet its desired outcome. The overall objective of this research is to assess the effect of MIS on decision-making among academic institutions. This machine can function effectively and efficiently only with the help of data and information for humanitarian assistance for various purposes. Nowadays, every organization cannot run smoothly without the use of MIS. It signifies the dependability of decision-making on MIS. This research has attempted to identify necessary variables. They are used to evaluate the influence of management information systems on decision-support capabilities in the organization.
 This study also discusses the concept, attributes, characteristics, types of MIS, and the MIS model, and finally, highlights the effect of MIS in decision-making in academic institutions. At the same time, different models and figures are presented to enrich the discussion and to highlight the status of each MIS and DSS information system in an organization's decision-making process. Here fifteen related articles are incorporated to review the title to ensure the research gap and where conceptual review approach based on descriptive content analysis using long-term classroom discussion. This research will create an awareness to develop an integrated MIS among academic institutions to ensure success through rational scientific decision-making.

1743. Antimicrobial and Diagnostic Stewardship Targeting Urinary Tract Infections in the Emergency Department: A Pharmacist-driven Quality Improvement Project at a Tertiary Military Treatment Facility

Abstract Background With the increasing interest in ambulatory stewardship, we designed a quality improvement project to enhance guideline concordant antimicrobial and diagnostic stewardship practices targeting urinary tract infections (UTI) in the ED of a Military Treatment Facility. Between two data collection phases (P1, P2), we provided education on national guidelines, local protocols and antibiograms, modified order-sets, and implemented enhancements targeting urine collection. The primary outcome was a change in guideline-concordant practices between P1 and P2. Methods This prospective pharmacist-driven study had two 3-month data collection periods including ED encounters that resulted in a urinalysis (UA), urine culture (UCx) and antibiotics for UTI upon discharge. We collected data on participant demographics, clinical course and therapy details via chart review. Culture callback and corresponding interventions were made within 72 hours. Based on P1 results, we broadened our scope in P2 to include a diagnostic stewardship arm (Figure 1). This project was IRB approved under a non-research determination. Results There were no significant changes in guideline-concordant prescribing or diagnostic practices between P1 and P2 (Figure 2). Inappropriate antibiotic selection was primarily due to use of cephalosporins and FQs as first line agents (Table 1). The primary pharmacy intervention was discontinuation of therapy (P1 29.1%, P2 39.8%). One-third of patients receiving antibiotics were asymptomatic, a majority of which had a positive UA (P1 68.4%, P2 73.3%). There was a high rate of UCx contamination which improved in P3 but did not reach statistical significance (P1 25.6%, P2 17.2%, p=0.19). Across all P2 encounters, 51.4% of UCx were deemed inappropriate. Conclusion Significant guideline-discordant prescribing persisted despite interventions. An overreliance on UA findings results in treatment of asymptomatic bacteriuria. Education and order-set modification alone are insufficient for a lasting impact on prescribing practices especially where there is a high rate of provider turnover. An EMR with embedded clinical and alert-based decision support capabilities are likely to make a sustained impact in curbing inappropriate treatment of asymptomatic bacteriuria. Disclosures All Authors: No reported disclosures.

Examining the Evolution of Management Accounting: A Qualitative Review

This qualitative review investigates the evolution of management accounting, focusing on its historical development, theoretical frameworks, and contemporary practices. The research aims to provide insights into the adaptive nature of management accounting in response to changing organizational needs, technological advancements, and societal trends. The methodology involves a comprehensive literature review encompassing historical analysis, theoretical perspectives, and empirical studies in the field of management accounting. Key findings reveal the historical roots of management accounting tracing back to the Industrial Revolution, with significant milestones including the emergence of cost accounting techniques, budgeting systems, and advanced methodologies like activity-based costing and strategic management accounting. The review also identifies theoretical frameworks such as agency theory, contingency theory, and institutional theory, offering diverse perspectives on the role and functioning of management accounting within organizations. Contemporary practices in management accounting emphasize strategic analysis, risk management, performance measurement, and the integration of financial and non-financial metrics. Technological advancements, particularly in information technology, have revolutionized decision support capabilities, enabling real-time monitoring and analysis of organizational performance. The synthesis of literature underscores the dynamic and multifaceted nature of management accounting, highlighting its pivotal role in facilitating strategic decision-making, enhancing organizational performance, and driving sustainable growth. The findings contribute to a deeper understanding of management accounting evolution and offer implications for practice and future research.

Measuring, Modeling, and Forecasting the Mental Wealth of Nations.

The COVID-19 pandemic has exposed the deep links and fragility of economic, health and social systems. Discussions of reconstruction include renewed interest in moving beyond GDP and recognizing “human capital”, “brain capital”, “mental capital”, and “wellbeing” as assets fundamental to economic reimagining, productivity, and prosperity. This paper describes how the conceptualization of Mental Wealth provides an important framing for measuring and shaping social and economic renewal to underpin healthy, productive, resilient, and thriving communities. We propose a transdisciplinary application of systems modeling to forecast a nation's Mental Wealth and understand the extent to which policy-mediated changes in economic, social, and health sectors could enhance collective mental health and wellbeing, social cohesion, and national prosperity. Specifically, simulation will allow comparison of the projected impacts of a range of cross-sector strategies (education sector, mental health system, labor market, and macroeconomic reforms) on GDP and national Mental Wealth, and provide decision support capability for future investments and actions to foster Mental Wealth. Finally, this paper introduces the Mental Wealth Initiative that is harnessing complex systems science to examine the interrelationships between social, commercial, and structural determinants of mental health and wellbeing, and working to empirically challenge the notion that fostering universal social prosperity is at odds with economic and commercial interests.

Breaking Down Barriers to Precision Oncology Care

Breaking Down Barriers to Precision Oncology Care

Towards a blueprint for decision support in connected health: scenarios in maternal and child health

ABSTRACT This paper explores the potential of connected health solutions to solve the problems currently facing healthcare systems around the world with a particular interest in their decision support capabilities. Leveraging three selected projects in which we have been involved in the area of maternal and child health, the paper proposes a blueprint for connected health decisions in a variety of settings, namely: home-based, community-based, ward-based scenarios as well as the specific scenario of low-income countries. This blueprint can be used to frame discussions on connected health solutions and discuss their decision support potential.

BaNTERA: A Bayesian Network for Third-Party Excavation Risk Assessment

Third-party damage constitutes a major threat to underground natural gas pipeline safety; in the U.S., between 2016 and 2020, it caused eleven fatalities, twenty-nine injuries, and $124M USD in property damage losses. Several research studies have been carried out to identify the causes and contextual factors leading to third-party damage. However, there is a lack of models that are not only causally-based, but also comprehensive and suitable for modeling the probabilities of a pipe hit and subsequent damage. This paper presents the development process and results of building BaNTERA, a probabilistic Bayesian network model for third-party excavation risk assessment in the U.S. BaNTERA’s capabilities for risk-informed decision support are presented in three ways: verification of the model’s performance, validation of its damage rate predictions with historical industry data, and application in multiple case study scenarios. Preliminary results indicate that BaNTERA offers valuable insight including and beyond a probability estimation of third-party damage. Using the best available industry data and previous models derived from multiple sources, different inference methods can assist in pipeline damage prevention and risk mitigation. As such, BaNTERA represents a promising holistic and rigorous tool for addressing third-party excavation damage in natural gas pipelines.

Participatory Systems Modelling for Youth Mental Health: An Evaluation Study Applying a Comprehensive Multi-Scale Framework.

The youth mental health sector is persistently challenged by issues such as service fragmentation and inefficient resource allocation. Systems modelling and simulation, particularly utilizing participatory approaches, is offering promise in supporting evidence-informed decision making with limited resources by testing alternative strategies in safe virtual environments before implementing them in the real world. However, improved evaluation efforts are needed to understand the critical elements involved in and to improve methods for implementing participatory modelling for youth mental health system and service delivery. An evaluation protocol is described to evaluate the feasibility, value, impact, and sustainability of participatory systems modelling in delivering advanced decision support capabilities for youth mental health. This study applies a comprehensive multi-scale evaluation framework, drawing on participatory action research principles as well as formative, summative, process, and outcome evaluation techniques. Novel data collection procedures are presented, including online surveys that incorporate gamification to enable social network analysis and patient journey mapping. The evaluation approach also explores the experiences of diverse stakeholders, including young people with lived (or living) experience of mental illness. Social and technical opportunities will be uncovered, as well as challenges implementing these interdisciplinary methods in complex settings to improve youth mental health policy, planning, and outcomes. This study protocol can also be adapted for broader international applications, disciplines, and contexts.

558 Nurse-driven fluid resuscitation protocol - a quality improvement initiative

IntroductionThe migration of paper medical records to electronic health record (EHR) has been in process for a few years now and most facilities have achieved this successfully. EHR has streamlined care and documentation. Further developments such as Computerized Physician Order Entry (CPOE) has been noted as one of the most promising functionalities of Health Information Technology (HIT), as it allows providers to enter orders, medications, diagnostic tests, and procedures, with the intent of improving the clarity and specificity of physician orders, facilitating the rapid communication of orders to pharmacies, and providing significantly enhanced decision support capabilities compared to traditional handwritten orders.1In our experience, initiation of CPOE has been beneficial in many ways, namely in decreasing medication errors related to handwritten orders. However, in the clinical scenario of acute resuscitation of a critically injured burn patient, the CPOE structure did not address each and every need that would arise, especially if fluid titration was necessary. Nursing staff were left unsure of what to do in terms of their role in adjusting fluids and assessing for adequacy of resuscitation. This led to gaps in care in which potential critical situations needed to be addressed. For instance, the possible development of abdominal compartment syndrome and how to respond to it was not part of CPOE set that was implemented. This was placing the patient at risk by delaying initiation of hemodynamic monitoring and delaying electrolyte replacement as well.The goal of this study is to report the outcomes of this quality improvement initiative and to describe the resultant research that is in place to evaluate its effectiveness.MethodsThis is a QI project that will identify and describe how a protocol was developed post- CPOE implementation to address gaps in nursing care during fluid resuscitation of critically ill burned patientsResultsWe created a protocol that allows the nurse to have better insight into what is happening with the patient and what physician orders are most pertinent at any particular time. The protocol sets parameters that alerts the nurse when additional intervention is necessary. For instance, monitoring for abdominal compartment syndrome begins once resuscitation exceeds 6 mL/kg/TBSA and allowing for the nurse to call the primary physician for hypotension that is refractory to fluid bolus. This was not clear before and nurses were not intervening appropriately, which resulted in gaps in delivery of care. We have not had to report any adverse or sentinel event related to fluid resuscitation since the implementation of this protocol.ConclusionsA nurse-driven protocol helped address gaps in care for nurses at the bedside during fluid resuscitation

An investigation for integration of deep learning and digital twins towards Construction 4.0

PurposeThe purpose of this paper is to investigate the potential integration of deep learning (DL) and digital twins (DT), referred to as (DDT), to facilitate Construction 4.0 through an exploratory analysis.Design/methodology/approachA mixed approach involving qualitative and quantitative analysis was applied to collect data from global industry experts via interviews, focus groups and a questionnaire survey, with an emphasis on the practicality and interoperability of DDT with decision-support capabilities for process optimization.FindingsBased on the analysis of results, a conceptual model of the framework has been developed. The research findings validate that DL integrated DT model facilitating Construction 4.0 will incorporate cognitive abilities to detect complex and unpredictable actions and reasoning about dynamic process optimization strategies to support decision-making.Practical implicationsThe DL integrated DT model will establish an interoperable functionality and develop typologies of models described for autonomous real-time interpretation and decision-making support of complex building systems development based on cognitive capabilities of DT.Originality/valueThe research explores how the technologies work collaboratively to integrate data from different environments in real-time through the interplay of the optimization and simulation during planning and construction. The framework model is a step for the next level of DT involving process automation and control towards Construction 4.0 to be implemented for different phases of the project lifecycle (design–planning–construction).