Organizational Networks Research Articles

The CanPath-HDRN Canada Collaboration: Enabling Multi-jurisdictional Research in Canada

ObjectiveTo highlight the partnership between the Canadian Partnership for Tomorrow’s Health (CanPath) and Health Data Research Network (HDRN Canada), which enables researchers to link CanPath’s health and lifestyle survey data to health records and related data across multiple regions. BackgroundCanPath is a population health study of over 350,000 Canadians from seven regional cohorts across all ten provinces, making it one of the world's largest population cohorts. HDRN Canada is a network of member organizations, including provincial, territorial, and pan-Canadian data centres. ApproachThis partnership leveraged collective expertise and resources to facilitate the linkage of CanPath’s harmonized data to multi-regional health and health-related administrative data held at HDRN Canada’ s data centres. Researchers can access these data for multi-regional projects through HDRN Canada’s Data Access Support Hub, which provides a single access portal. ResultsResearchers were able to successfully link CanPath’s survey data to provincial administrative health data. The collaboration’s streamlined process for data access enhances efficiency and facilitates pan-Canadian population health research. ConclusionThis collaboration demonstrates the feasibility and value of linking population health datasets while demonstrating the challenges and opportunities associated with accessing national administrative health data within a federated health data system. ImplicationsThe partnership between CanPath and HDRN Canada has significant implications for advancing population health research in Canada. By providing researchers with access to linked data from diverse sources, the partnership enables comprehensive investigations into health determinants, disease patterns, and clinical outcomes. This enhances the scope and depth of population health research in Canada, thereby leading to a better understanding of the most pressing health challenges.

Development of a dictionary of information items inspired by common data models to support health research data access requests

A network of organizations collaborates to provide services to facilitate multi-regional research across Canada. To streamline the data access process, the network is developing a dictionary of information items (InfoItems), providing a label and definition that represent a semantic sourced from an ontology (e.g., “human birth date” from a demography ontology). This allows for a standardized and shareable means to express data requirements. Inspired by health-research common data models (CDMs) like those from the Observational Medical Outcomes Partnership (OMOP) and the Canadian Network for Observational Drug Effects (CNODES), approximately 60 InfoItems were developed by the network. As a pilot exercise, four data centres mapped their data assets to the InfoItems to ensure they are relevant to the Canadian health data context. This has laid the foundation for InfoItems as a basic data harmonization mechanism. The new InfoItems-based data dictionary promotes the standardization of data asset contents across the network’s data centres. Researchers can use the dictionary to specify a project’s data requirements, establishing details like data availability across multiple regions, for inclusion in their data access request. The InfoItems dictionary is pivotal in simplifying data access requests, promoting multi-regional research in Canada. This resource can simultaneously safeguard the quality of analytical results and optimize data extraction into existing data models (e.g., health-research CDMs). Ongoing improvements in this area aim to enhance user experience and facilitate quality research.

How DASH enables external data linkage to support multi-regional research

A network of organizations works together to support multi-regional health research across Canada. The network comprises 13+ provincial/territorial and pan-Canadian data centres, which collectively hold 500+ data assets. Although the network actively pursues new administrative or clinical data assets, linking to external research data is also a growing need in the contemporary research landscape. Data from the network’s centres can be linked to external data sources such as that from: researchers’ trials or studies; disease or population-based registries; and data sources from other organizations or custodians. Consultations held with data centers clarified their processes for linking to external data, by identifying and mapping local linkage features to a general linkage model. Local processes for data linkage, including necessary agreements and approval steps are now modelled and documented, and available to researchers and the data centres as a resource. This information helps streamline data access and linkages to data assets across the network and externally. Operationally, the network is currently working on 11 data access requests involving linkage to external data, of which three are expected to deliver final data to researchers by spring 2024. Collaboration with data centres, affiliated organizations, and researchers are foundational in the development of linkage models across the network. These models play a critical role in making linkages across data sources within Canada more efficient and standardized. Data linkages across data assets support the utility of data and health innovation.

Development of a framework to facilitate a data assembly plan for multi-regional research

A network of organizations works together to facilitate multi-regional research across Canada. This network is streamlining the traditionally burdensome data access process, a major part of which is a project’s data assembly plan (DAP). A framework is proposed for the development of a DAP usable by researchers and multiple data centres across Canada. The network of 13 provincial/territorial and pan-Canadian data centres collaborated to understand variations in data request processes and local requirements. During this collaboration, partners used an iterative approach to review local forms, processes, and undertake consultations with the research community, and to identify critical components of the DAP. In April 2022, the network launched the centrally provisioned, standardized DAP form, which to date has been deployed by approximately five projects. Users have found the DAP’s unique aggregation and documentation capabilities particularly helpful. Overall, the preliminary feedback from researchers has been positive. The DAP has allowed aggregation of specific details about a project’s data requirements: cohort definition(s), data extraction(s) and analytical plans, in a single unified form. The DAP is an important component in streamlining the process for requesting data from multiple provinces/territories, organizations, and data sources. The DAP has ensured consistency across the network’s data centres that are providing data, supporting data linkage, and helping safeguard the quality of analytical results. Further process improvements are anticipated to address user experience feedback and to promote quality research.

Barriers to Care After Intimate Partner Violence Acquired Brain Injury: Current Systems Silence Survivors and Providers.

A qualitative meta-synthesis was conducted to answer the following question-What barriers are experienced by survivors of intimate partner violence and service providers when seeking or providing resources or care after experiencing brain injury (BI)? This approach was completed through 2 main phases-a systematic search and literature appraisal and reciprocal translation with interpretive triangulation of the extracted data. The databases searched were PubMed, CINAHL, and PsycInfo. The initial search yielded 559 articles. The final synthesis included 16 articles for the qualitative meta-synthesis. After data extraction, a qualitative exploratory design evaluated the experiences of survivors and service providers when connecting with resources. The findings highlight larger systemic barriers preventing smooth relationships between survivors and providers. Survivor barriers include risk-driven safety mechanisms and a lack of awareness and understanding of BI. Provider barriers are a lack of clarity within providers' scope and training limitations, diagnosis complexity, first responder's initial response to survivors, lack of open discussion, screening limitations, financial barriers, infrastructure barriers, and lack of resources. Systemic barriers include stigmatization, BI symptoms mistaken as other diagnoses like mental health, access and awareness of resources, and navigation of complex systems. This qualitative meta-synthesis demonstrates that survivors and providers face multiple individual and systemic barriers when seeking and providing resources after BI. Future research is needed to understand survivors' help-seeking behaviors and assistance needs and providers' education about BI and organizational relationships and networks. Recommendations are made for future research to understand how to reduce barriers for providers working with survivors, from trauma-informed education and communication to connecting with resources, where to concentrate, and initiatives for collaboration to increase the network of resources yet improve its accessibility.

A conceptual framework for the government big data ecosystem (‘datagov.eco’)

The public sector, private firms, and civil society constantly create data of high volume, velocity, and veracity from diverse sources. This kind of data is known as big data. As in other industries, public administrations consider big data as the “new oil" and employ data-centric policies to transform data into knowledge, stimulate good governance, innovative digital services, transparency, and citizens' engagement in public policy. More and more public organizations understand the value created by exploiting internal and external data sources, delivering new capabilities, and fostering collaboration inside and outside of public administrations. Despite the broad interest in this ecosystem, we still lack a detailed and systematic view of it. In this paper, we attempt to describe the emerging Government Big Data Ecosystem as a socio-technical network of people, organizations, processes, technology, infrastructure, standards & policies, procedures, and resources. This ecosystem supports data functions such as data collection, integration, analysis, storage, sharing, use, protection, and archiving. Through these functions, value is created by promoting evidence-based policymaking, modern public services delivery, data-driven administration and open government, and boosting the data economy. Through a Design Science Research methodology, we propose a conceptual framework, which we call ‘datagov.eco’. We believe our ‘datagov.eco’ framework will provide insights and support to different stakeholders’ profiles, including administrators, consultants, data engineers, and data scientists.

A modified CNN-IDS model for enhancing the efficacy of intrusion detection system

As the security of computer networks in enterprises worldwide is dependent on the proper functioning of intrusion detection systems (IDSs) and intrusion prevention systems (IPSs), this effectiveness of both of them is of utmost priority. Leveraging diverse techniques, these network security systems are created to keep the reliability, the availability, and the integrity of the organizational networks safe. One plus point of using ML in intrusion detection system (IDS) is that it has successfully weeded out all the IDS attacks with a high degree of accuracy. In contrast, such systems may be believed to operate to their least competent levels when supersized data spaces have to be dealt with. In the process to solve this, application of feature selection techniques will play the crucial role to ignore non-relevant features which do not impact the issue of classification much. One more thing to keep in mind is that the ML-based IDSs often have problems with high false alarms and percentage accuracy because of the imbalanced training sets. The undertaking of this paper involves a through the analysis of the UNSW-NB15 intrusion detection data set as upon which our models will be tested and trained. We utilize two feature selection approaches: the PCA method, which is denoted as PCA, and the SVD method, called SVD. Furthermore, we categorize the datasets using these methods— Ridge Regression (RR), Stochastic Gradient Descent, and Convolutional Neural Network (CNN)-- on the transformed feature space. What is the most widely used for, is that it deals with both, binary and multiclass classification. The result measure that PCA and SVD are succeeded in getting better performance of IDS than others with enhancing the accuracy of classification models. More specifically, the RR classifier's precise was outstanding for the binary classification problem experiencing a rise in the accuracy from 98.13 % to 99.85 %. This shows the critical role of feature selection approaches and is also demonstrates the modeling capabilities of RR, SGD, and CNN classifiers and stands out as a solution to intrusion detection.

A Study of the Influence of Religious and Nationalist-Based Political Parties in the Compilation of Legal Products Based on Legal Regulations

The ideology of political parties is divided into four categories: parties with Islamic ideology, secular nationalist ideology, religious nationalist ideology, and parties with Christian ideology. The regeneration process will bring out prospective leaders who have been tested in carrying out organisational work or society, and then the emerging decisions will be directed at achieving the goals of the country. These party cadres make a reflection of political parties related to ideology, which is expected to make policies in accordance with ideology, through being regional leaders. The research method used is sociological juridical with a qualitative descriptive analysis approach to obtain the answers to the problems studied, supported by secondary data and other data. When referring to the party principles owned by each political party, in general, political parties are based on three ideologies: Islam, Nationalism and Democratic Socialism. Political parties have an obligation to be able to build their own capacity; therefore, they have sufficient capability to fulfill what has been mandated by law. The fading of ideology has weakened the ties between political parties and their members, which in turn has resulted in the weakness of organisational networks. This condition leads the political parties to face an extreme dilemma. The way to collect party funds is by making policies that benefit certain parties. However, with transactional politics, the policies taken are not pro-people. The importance of the party's stand in policymaking is marked by their close relationship with elected and appointed political officials. For this reason, the party can see its role and influence in public policy using organisational theory. This fact shows that political parties are a more powerful element in the policy-making process than they should be.

Updating and calibrating the Real-World Progression In Diabetes (RAPIDS) model in a non-Veterans Affairs population.

To present the Real-World Progression In Diabetes (RAPIDS) 2.0 Risk Engine, the only simulation model to study the long-term trajectories of outcomes arising from dynamic sequences of glucose-lowering treatments in type 2 diabetes (T2DM). The RAPIDS model's risk equations were re-estimated using a Least Absolute Shrinkage and Selection Operator (LASSO)-based regularization of features that spanned baseline data from the last two quarters of current time and interactions with age. These equations were supplemented with estimates for the impact of dipeptidyl peptidase-4 inhibitors, glucagon-like peptide-1 receptor agonists, and sodium-glucose cotransporter-2 inhibitor classes of drugs as monotherapies and their combinations with metformin based on newer trial data and comprehensive meta-analyses. The probabilistic RAPIDS 2.0 model was calibrated (N = 25 000) and validated (N = 263 816) using electronic medical records (EMR) data between 2008 and 2021 from a national network of US healthcare organizations. The EMR-based cohort had a mean age of 61 years at baseline, with 50% women, 70% non-Hispanic White individuals and 20% non-Hispanic Black individuals, and was followed for 17.5 quarters (range: 3-50). The final RAPIDS 2.0 risk engine accurately predicted the long-term trajectories of all nine biomarkers and nine outcomes in the hold-out validation sample. Similar accuracies in predictions were observed in each of the 14 subgroups studied. The RAPIDS 2.0 model demonstrated valid long-term predictions of outcomes in individuals with T2DM in the United States as a function of dynamic sequences of treatment use patterns. This highlights its potential to project long-term comparative effectiveness between alternative sequences of glucose-lowering treatment uses in the United States.

S02-4: How does the Hungarian Active Lifestyle Strategy address social inclusion?

Abstract Purpose The purpose of Sarolta MONSPART Active Lifestyle Strategy is to raise proportion of physically active people with a healthy lifestyle in Hungary. There is special focus on the engagement of senior citizens and people with disadvantaged background. Project description The Strategy is the first ever national policy document dedicated to the field of overall promotion of health-enhancing physical activities. Methods The development of the Strategy had been initiated by the State Secretary for Active Lifestyle. The University of Sport Sciences had been invited for the coordination of the Strategy’ development, which is based on outcomes of international policy document analysis and national stakeholder consultations. The Strategy identifies ten priority target areas and eight horizontal priorities. “Active seniors” and “Activation of socially disadvantaged groups”, which includes people with disabilities and people with unfavourable socio-economic background are also priority areas. The implementation of the Strategy is planned to happen on the base of the proposed actions in all priority target areas. Respective governmental and non-governmental organisations have been involved into the drafting of the Strategy. Based on their proposals for implementing actions, an action plan has been drafted. Respectively these organisations are also important actors during the delivery of the proposed initiatives. The evaluation of the implementation of the Strategy will be based on the proposed indicators, that are part of the Strategy in relation to each proposed action, also reflecting on the number of successfully implemented actions and number of participants. Dissemination of the strategy has already started during the drafting phase, by the involvement of ca. 50 organisations as consultative partners. After the official approval of the Strategy, communication will be ensured by the State Secretariat for Active Lifestyle and the implementing partners. Conclusions The State Secretariat for Active Lifestyle will monitor the implementation of the action plan and collect the well-functioning methods and practices. It is expected that a relevant network of professional organisations will be created in all priority target fields and those networks will be able to provide data and feedback on the proposed actions.

Applied Crop Production Research Across the Canadian Prairies

AbstractThere is world‐class government and academic research conducted throughout the Canadian Prairies. To adapt government and academic research to local growing environments, there is a broad network of organizations across Alberta, Saskatchewan, and Manitoba that conduct applied research and demonstrations. This article provides an overview of the provincial networks and highlights one organization from each province to showcase the value of applied research that is being conducted across western Canada.

Controlling user access with scripting to mitigate cyber-attacks

With the growing threats in the dynamic digital landscape, organizations are grappling with the difficult task of managing cyber-attacks by restricting user accessibility to most communication systems. This study aimed to develop a Linux script as a proactive response to the growing of cyber-attacks in organizational settings to prevent attacking vectors based on user negligence. The primary objective was to devise inventive solutions through script automation to restrict user access to the organizational network within predefined time frames, thereby mitigating potential cyber threats. The criteria used to define predefined time frames for user access, include the restriction of users on off duty times, The scripting seeks to address cyber-attacks targeted such as cross-site scripting (XSS) and SQL injection. To achieve the goal of this paper, the necessary steps and logic required to develop the scripts were taken. For instance, there are several suitable languages such as Bash, Python, or Perl, that can be used to write script. The paper selected Bash because it excels at scripting existing command-line tools and utilities, making it a great choice for automating tasks by using Vim text editor tool. The final scripts were run, some errors were identified, and modified as needed to ensure it worked correctly. This phase was necessary to improve the script's performance, readability, and maintainability by refining code, using efficient algorithms, by incorporating best practices. Finally, the various user inputs and scenarios were deployed to put the script into production for user access management. The adopted script metrics deployed such as Script Execution Time, Resources Utilization, Success Rate and Error Rate helped in evaluating the performance, efficiency, and effectiveness of the scripts. The implementation of the script achieved an executive time of 0.032 s, success rate was 1 and error rate was 0. The practical implementation in organizations can be scaled up or down depending on the organization's size and infrastructure. The compatibility of the developed Linux script with different operating systems, hardware platforms and network architectures will have no impact in adapting to various organizational environments. The script did not show any challenges, but real-world deployment may exhibit some potential challenges and limitations that may be associated with implementing the solution, such as resource constraints and legacy system compatibility.

Highly Anion‐Conductive Viologen‐Based Two‐Dimensional Polymer Membranes as Nanopower Generators

AbstractTwo‐dimensional polymers (2DPs) and their layer‐stacked 2D covalent organic frameworks (2D COFs) membranes hold great potential for harvesting sustainable osmotic energy. The nascent research has yet to simultaneously achieve high ionic flux and selectivity, primarily due to inefficient ion transport dynamics. This is directly related to ultrasmall pore size (<3 nm), much smaller than the duple Debye length in the diluted electrolyte (6–20 nm), as well as low charge density (<4.5 mC m−2). Here, we introduce a π‐conjugated viologen‐based 2DP (V2DP) membrane possessing a large pore size of 4.5 nm, strategically enhancing the overlapping of the electric double layer, coupled with an exceptional positive surface charge density (~6 mC m−2). These characteristics enable the membrane to facilitate high anion flux while maintaining ideal selectivity. Notably, V2DP membranes realize an impressive current density of 5.5×103 A m−2, surpassing benchmarks set by previously reported nanofluidic membranes. In the practical application scenario involving the mixing of artificial seawater and river water, the V2DP membranes exhibit a considerable ion transference number of 0.70 towards Cl−, contributing to an outstanding power density of ~55 W m−2. Theoretical calculations reveal the important role of the large quantity of anion transport sites, which act as binding sites evenly located in the positively charged N‐containing pyridine rings. These binding sites enable kinematic coupling and decoupling between anions and the V2DP skeleton, establishing a continuous Cl− ion transport pathway. This work demonstrates the great promise of large‐area ultrathin 2DP membranes featuring highly organized charged ion transport networks when applied for osmotic energy conversion.

Использование финансовой информации в сотрудничестве государств по выявлению и пресечению преступлений

The use of financial information in investigations plays the key role in crime combating. Efficient application of data on financial transactions facilitates identification and prevention of activities of transnational and organized criminal networks. However, limited opportunities of law enforcement authorities and the absence of access to centralized bank account registers can hinder such investigations. Enhancement of international cooperation and information exchange between law enforcement authorities and financial intelligence units (FIUs) can substantially increase the efficiency of investigations. Such cooperation includes training and professional development of employees (it is critically important to teach them contemporary financial data analysis methods and how to use cutting-edge technologies), development of new technologies. Innovative instruments of analysis of financial information are required for timely and accurate identification of criminal acts. Creation of flexible mechanisms of access to information requires development of international data exchange systems, which will enable law enforcement authorities to receive information on bank accounts without delay.

Expertise and specialization in organizations: a social network analysis

ABSTRACT Organizations rely on their employees’ expertise (i.e. very high domain-specific competence relative to peers) and specialization (i.e. moderate to high competence in tasks for which no direct peers are responsible) to foster innovation and knowledge-sharing processes. While previous research demonstrated the pivotal role of experts and specialists, there remains scant knowledge on their skill profiles and embeddedness in organizational networks. Employing social network analysis (N = 344), we explore the relationships between the reception of advice requests – quantified by in-degree centralities – and self-assessed skills, expertise, and specialization. The results suggest a halo effect, as proficiency in one skill bundle is related to advice requests in other skill bundles. Furthermore, we use an exponential random graph model (ERGM) to provide evidence of similarities in skills, tenure, and leadership responsibility between advice-giver and advice-seeker. Finally, we use optimal matching analyses to compare skill profiles of individuals in our sample with theoretically derived ideal types of skill profiles for experts and specialists. The study underscores the need to further investigate expertise and specialization, emphasizing the importance of distinguishing between diverse knowledge carriers and skill profiles within career development, talent management, and knowledge management.

Euclidean mirrors and dynamics in network time series

– Analyzing changes in network evolution is central to statistical network inference. We consider a dynamic network model in which each node has an associated time-varying low-dimensional latent vector of feature data, and connection probabilities are functions of these vectors. Under mild assumptions, the evolution of latent vectors exhibits low-dimensional manifold structure under a suitable distance. This distance can be approximated by a measure of separation between the observed networks themselves, and there exist Euclidean representations for underlying network structure, as characterized by this distance. These Euclidean representations, called Euclidean mirrors, permit the visualization of network dynamics and lead to methods for change point and anomaly detection in networks. We illustrate our methodology with real and synthetic data, and identify change points corresponding to massive shifts in pandemic policies in a communication network of a large organization.

Integrative role of attention networks in frequency-dependent modular organization of human brain.

Despite converging evidence of hierarchical organization in the cerebral cortex, with sensory-motor and association regions at opposite ends, the mechanism of such hierarchical interactions remains elusive. This organization was primarily investigated regarding the spatiotemporal dynamics of intrinsic connectivity networks (ICNs). However, more effort is needed to investigate network dynamics in the frequency domain. We aimed to examine the integrative role of brain regions in the frequency domain with graph metrics. Phase-based connectivity estimation was performed in three frequency bands (0.011-0.038, 0.043-0.071, and 0.076-0.103Hz) in the BOLD signal during rest. We applied modularity analysis to connectivity matrices and investigated those areas, which we called integrative regions, that showed frequency-domain flexibility. Integrative regions, mostly belonging to attention networks, were densely connected to higher-order cognitive ICNs in lower frequency bands but to sensory-motor ICNs in higher frequency bands. We compared the normalized participation coefficient (Pnorm) values of integrative and core regions with respect to their relation to higher-order cognition using a permutation-based t-test for multiple linear regression. Regression parameters of integrative regions in relation to three cognitive scores in executive functions, and working memory were significantly larger than those of core regions (Pfdr < 0.05) for salience ventral attention network. Parameters of integrative regions in relation to intelligence scores were significantly larger than those with core regions (Pfdr < 0.05) in dorsal attention network. Larger parameters of neuropsychological test scores in relation to these flexible parcels further indicate their essential role at an intermediate level in behavior. Results emphasize the importance of frequency-band analysis of brain networks.

The analysis of the impact of different supply chain factors using statistical perspective

Supply Chain Management (SCM) is an essential part of modern business and involves the coordination of procurement, production, and logistics. Effective SCM allows organizations to remain competitive and reduce costs. With the growth of the global marketplace, SCM is becoming increasingly important for businesses to compete. The most important aspect of SCM is to understand the components of the supply chain, which form a network of organizations involved in value-added processes and activities. The supply chain network consists of multiple components, indicating that the success of an organization's SCM depends on the combination of multiple influencing factors. This paper utilizes the supply chain data of several well-known companies, uses a dozen variables such as inventory turnover ratio, lead time, and supplier count as independent variables, and analyzes the effects of these different supply chain influencing factors on operational efficiency, supply chain resilience, and supplier relationship through multiple linear regression models. This study finds that many different supply chain factors, like transportation cost efficiency, delivery time, and others, have the greatest impact on the above-mentioned factors. This analysis may help companies to comprehensively assess their supply chain maturity and strategically enhance their supply chain attributes to achieve their business development goals.

The history of smoking cessation support in Hungary: A narrative review.

Tobacco use remains the largest preventable cause of death worldwide, including Hungary with a smoking-related death rate in 2019 of 360/100000 (age-standardized death rate), among the highest in the World Health Organization (WHO) European Region. Despite the well-formulated tobacco control interventions defined by the WHO Framework Convention on Tobacco Control (WHO FCTC) in place, smoking prevalence in 2019 was high (27%) and has not decreased since 2014. Therefore, greater emphasis should be placed on addressing and treating smokers. This narrative review summarizes the progress in smoking cessation support in Hungary to identify strengths and areas for improvement. A literature search was conducted in the Hungarian Arcanum Digital Science Library. After 2012, data were derived from the National Methodology Center for Cessation Support reports. The National Korányi Institute for Pulmonology established the first organized network of cessation counselling services in 1987 in outpatient pulmonary clinics (OPCs) sponsored by a State Insurance tender. By 1999, individual behavioral counselling with medication was accessible to 130 healthcare providers, due to the support of pharmaceutical companies. Since 2005, the National Health Insurance Fund has financed smoking cessation support in OPCs, albeit at a low value. Having recognized that OPCs are overburdened by the organizational tasks of cessation support and that funding is intermittent, from 2020, the counselling service has transferred to the existing network of health promotion offices, although without specific funding for cessation programs and communication. Adequate and regular funding for established counselling services and nicotine withdrawal treatment is essential to achieve progress in tobacco control. The role of healthcare professionals is outstanding; therefore, individual responsibilities should be recognized.

Strategies and practices for organizational learning in integrated care

PurposeThe purpose of this study is to identify important strategies and practices supporting inter-organizational learning (IOL) in integrated care. The two research questions ask how organizational network architectures can help involved organizations overcome the barriers of IOL in integrated care (RQ1) and what design recommendations can strengthen the processes of IOL in integrated care (RQ2).Design/methodology/approachThis study applies a qualitative design to analyze an improvement initiative in a regional, integrated care service for elderly patients with multiple illnesses in Norway. An inductive thematic analysis for the triangulating of qualitative data from different sources was applied. Patterns within the data were organized into themes, categories and subcategories. No software was applied.FindingsThe identified characteristics of the organizational network architectures supporting IOL in integrated care in the case under study were: equality of the involved parties, shared goals, recognition of expertise and the abilities to coordinate, design IOL processes and make joint decisions (RQ1). The categories of practices supporting the process of IOL were: insight into complex realities, contradictions, iteration, motivation and prototypes (RQ2).Originality/valueThis study offers much-needed insight into a successful approach for IOL in integrated care. The results offer strategies to be considered when building organizational networks for the improvement of integrated care and relevant practices useful when designing IOL processes in such care services. We believe such knowledge has important implications for policymakers, frontline personnel, education, research and leaders.