System-wide Performance Research Articles

300,000 quitters and counting; A systematic approach to tobacco cessation

ObjectiveTo describe Kaiser Permanente Northern California's (KPNC) systematic implementation of universal tobacco screening, evidence-based interventions, and performance measures to achieve long-term smoking cessation success. MethodsWe outline seven key components of KPNC's tobacco screening and intervention program that contributed to a significant decline in smoking prevalence. We also report changes in the prevalence of current smokers within KPNC from 2014 to 2023 using linear regression analyses. ResultsKey factors driving the success of the tobacco cessation program included risk-based screening algorithms, alert prompts for at-risk patients, system-wide medical champions, performance tracking, virtual coaching, widespread messaging, and comprehensive medication management. Implementing this multifaceted approach across all facilities was associated with a significant reduction in smoking prevalence, from 8.6% in 2014 to 5.8% in 2023 (p < 0.0001). ConclusionOur comprehensive, system-wide approach resulted in substantial public health gains and highlights the potential of similar preventive strategies as healthcare systems transition toward value-based care.

Addressing Lean and Safety Readiness Assessment in Territorial Ambulatory Healthcare

Within the international healthcare landscape, it is incumbent upon all organisations to proactively address individual patient needs by offering a wide range of services. In this context, Territorial Ambulatory Healthcare (TAH) is critical to promote equal access to care as the primary point of contact between National Healthcare Systems and individuals. To achieve this goal, the adoption of Lean and Safety Management (L&amp;SM), a managerial approach to proactively reduce clinical risk and process inefficiencies, can support organisations in pursuing high quality care objectives. Despite the positive benefits associated with the adoption of this approach, the feasibility of guaranteeing a sustainable care quality over time may encounter several challenges. Among these, key contributors to failure include the lack of tailored tools and practices suitable for the healthcare environment, insufficient readiness of the context for implementation, and the absence of a comprehensive system-wide performance strategy. Recent studies in literature address these issues presenting theoretical and practical models to assess readiness levels, highlighting opportunities for future developments of these topics. The current empirical study contributes to the existing knowledge by creating a framework to assess the level of readiness for L&amp;SM implementations within TAH. While this context is essential for ensuring high-quality care and equitable access to care services, successfully implementing L&amp;SM initiatives within TAH is challenging due to the organizational complexity and the involvement of multiple, fragmented stakeholders and care providers. Addressing weak Critical Readiness Factors (CRFs) before starting a project can significantly contribute to successfully implement L&amp;SM and sustain improvement over time. This study will be conducted within the TAH setting of a healthcare organisation in Northern Italy and aims 1) to evaluate the specific care setting readiness for L&amp;SM implementations and 2) to develop practical guidelines to support the organisation in increasing its actual readiness level. The theoretical framework will be grasped integrating prior research on Lean readiness assessment, along with specific literature on L&amp;SM sustainability implementation factors within TAH. Once refined and tested with the purpose of developing a practical assessment tool, the framework will be applied within the selected setting, leading to the identification of practical guidelines to facilitate project success and improvements sustainability. Overall, this research will contribute to enrich the knowledge about L&amp;SM implementation within TAH, supporting health organisations, managers, and clinical leadership to sustainably improve care quality, enhancing the role of territorial care to foster social well-being.

Integrated urban wastewater management through on-site generation and application of ferrous carbonate

Integrated urban water management is an increasingly popular concept that cost-effectively maximizes system-wide performance by holistically considering all aspects of water and wastewater sectors. An innovative technology enabling production of high-quality bioenergy and an iron salt, ferrous carbonate (FeCO3), represents a significant opportunity for integrated urban water management. This study experimentally evaluates the effect of in-sewer FeCO3 dosing on the performance of sewers and the downstream wastewater treatment plants. Two continuous-flow laboratory-scale urban wastewater systems, each consisting of sewer reactors, a sequencing batch wastewater treatment reactor, and an anaerobic digester, were operated in parallel. After establishing comparable performance, one served as the control without any chemical dosing, while the other received a dosing of 10mgFe/L of FeCO3 in its sewer reactors. Compared to the control, the FeCO3-dosed experimental system reduced dissolved sulfide concentrations by 32.2±3.3% (at 0.58±0.05mgS/mgFe, or 1.0molFe/molS) in sewer reactors, decreased phosphate concentrations by 38.3%±3.2% (at 0.37±0.04mgP/mgFe, or 1.5molFe/molP) in sequencing batch reactors, and lowered dissolved sulfide concentrations by 72.0±4.2% (18.9±2.4mgS/L) in the anaerobic sludge digester. Iron accumulated in the sludge and improved sludge settleability by 33.9±5.5% and enhanced dewaterability of anaerobically digested sludge by 15.9±2.0%. The findings indicate multiple benefits from the integrated use of FeCO3, potentially being as a substitute for the currently used iron salts in urban wastewater systems.

A particle swarm optimization inspired global and local stability driven predictive load balancing strategy

In distributed systems and parallel computing, optimal load balancing is difficult. These abstract addresses load balancing in distributed situations, highlighting current solutions' flaws and emphasizing the need for new ones. Load balancing research includes centralized and distributed algorithms, heuristics, and predictive models. Despite various successful methods, workload adaptability, overhead reduction, and scaling to large systems remain unresolved. This study proposes a particle swarm optimization (PSO) load balancing method that considers global and local stability considerations. The proposed method uses PSO principles to balance exploration and exploitation and allocate resources among distributed nodes. Predictive components improve preventative load management by predicting workload changes. Global and local load balancing stability criteria distinguish this study. The recommended method considers global system-wide performance indicators, local node-level characteristics, and micro-level stability to maximize system efficiency. A dual-focus technique distinguishes the proposed load balancing strategy from others, solving dynamic distributed system challenges. The study examines load balancing system advances and suggests improvements and further research. More accurate prediction modeling, stability measures, and application-specific enhancements may be studied in the future. Experimental validation and real-world implementation of the recommended approach are necessary to determine its practicality and ability to handle modern distributed computing systems.

Ride-pooling service assessment with heterogeneous travellers in non-deterministic setting

AbstractRide-pooling remains a promising emerging mode with a potential to contribute towards urban sustainability and emission reductions. Recent studies revealed complexity and diversity among travellers’ ride-pooling attitudes. So far, ride-poling analyses assumed homogeneity of ride-pooling travellers. This, as we demonstrate, leads to a false assessment of ride-pooling system performance. We experiment with an actual NYC demand from 2016 and classify travellers into four groups of various ride-pooling behaviours (value of time and penalty for sharing), as reported in the recent SP study from Netherlands. We replicate their behavioural characteristics, according to the population distribution, to obtain meaningful performance estimations. Results vary significantly from the homogeneous benchmark: mileage savings were lower, while the utility gains for travellers were greater. Observing performance of heterogeneous travellers, we find that those with a low value of time are most beneficial travellers in the pooling system, while those with an average penalty for sharing benefit the most. Notably, despite the highly variable travellers’ behaviour, the confidence intervals for the key performance indicators are reasonably narrow and system-wide performance remains predictable. Our results show that the incorrect assumption of homogeneous traits leads to a high dissatisfaction of 18.5% and a cancellation rate of 36%. Such findings shed a new light on the expected performance of large scale ride-pooling systems.

The Urban Deployment Model: A Toolset for the Simulation and Performance Characterization of Radiation Detector Deployments in Urban Environments.

Static and mobile radiation detectors can be deployed in urban environments for a range of nuclear security applications, including radiological source search-and-tracking scenarios. Modeling detector performance for such applications is challenging, as it does not depend solely on the detector capabilities themselves. Many factors must be taken into consideration, including specific source and background signatures, the topology and constraints of the deployment environment, the presence of nuisance sources, and whether detectors are mobile or static. When considering the simultaneous deployment of multiple, heterogeneous detectors, assessment of the system-wide performance requires the simulation of the individual detectors, and a system-level analysis of the detection performance. In radiological source search-and-tracking scenarios, performance is mostly dominated by the probability of encounter, which depends on the specifics of a given deployment, e.g., static vs. mobile detectors or a combination of both modalities, the number of detectors deployed, the dynamic vs. static setting of false alarm rates, and individual vs. networked operation. The Urban Deployment Model (UDM) toolset was specifically developed to cover the gap in the available generic frameworks for the simulation of radiation detector deployments at city scales. UDM provides a unified and modular framework to support the simulation and performance characterization of heterogeneous detector deployments in urban environments. This paper presents the key components along the UDM workflow.

DER Control and Management Strategies for Distribution Networks: A Review of Current Practices and Future Directions

It is widely recognised that improving the visibility and controllability of distributed energy resources (DERs) within electricity distribution networks will have significant benefits, particularly for the management of low-voltage (LV) and medium-voltage (MV) networks. Much work within the electricity distribution industry is currently focused on improving the visibility of DERs on LV networks. From a control-theoretic perspective, this enables closing the loop between the DER and the control room and enables a shift towards utilising data-driven model-based control strategies for DERs. The result is a system-wide performance that is closer to the theoretical optimal. In the Australian context, several jurisdictions are trialling techniques such as dynamic operating envelopes to enhance DER hosting capacity, using IEEE 2030.5-based architectures, with the implementation of distributed energy resource management (DERMS) systems at the enterprise level still quite limited. While there is significant activity focused on DER behaviour and control techniques by way of inverter grid codes and standards, the core issue of interoperability with distribution management systems (DMSs), market operators or participants, electric vehicles (EVs) or other DERs is still a work in progress. Importantly, this is also an impediment to realising distributed architectures for DER control in the grid. The unique characteristics of Australian distribution networks highlights several challenging problems for DER control and management. The objective of this paper is to provide a broad overview of DER control and management strategies in the Australian context, with an application focus on DER control in distribution network management.

Paid Priority in Service Systems: Theory and Experiments

Problem definition: Motivated by the prevalence of paid priority programs in practice, we study a service provider operating a system in which customers have random waiting costs and choose between two queues: regular (no cost) or priority (for a fee). We also consider a mechanism by which the provider redistributes a portion of priority revenue to compensate regular-queue customers for their longer waits. Methodology/results: To determine the waiting-cost-dependent equilibrium priority purchasing strategies, we establish structural results at a sample-path level and prove that they generalize. In models both with and without compensation, the equilibrium exhibits a cost-dependent, increasing-threshold structure. We also prove that compensation entails fewer priority purchases because compensating regular-queue customers makes priority less attractive. We then analyze system-wide performance. Despite the fewer priority purchases, for a fixed (low) priority fee, compensation can actually reduce equilibrium aggregate waiting cost by filtering low-waiting-cost customers out of the priority queue; however, this finding does not hold when comparing at the optimal fees. We then test our models in the laboratory. Key behavioral regularities are that low-cost subjects are overrepresented (underrepresented) in the priority (regular) queue compared with equilibrium, and subjects with low and high waiting costs tend to overbuy priority at high fees. Managerial implications: Our theoretical and behavioral results guide service providers in managing priority service systems. First, we find that compensation does not provide short-term performance benefits. Second, our experiments reveal that suboptimal customer decisions partially prevent efficient reordering of customers by waiting cost, leading to higher aggregate waiting cost than the equilibrium predicts, but still lower than under first-come, first-serve service. Finally, because customers tolerate higher fees than they should, a revenue-maximizing provider can set a higher priority fee and extract more revenue than it could if customers acted rationally. Funding: This work was supported by the Center and Laboratory for Behavioral Operations and Economics at the Naveen Jindal School of Management at The University of Texas at Dallas. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0387 .

Computationally-Affordable Unsupervised Machine Learning Algorithm to Identify the Level of Distress Severity in Pavement Functional Performance

Pavements continually experience various types of distresses that are manifested in the form of defects that will eventually lead to the pavement’s failure. Selection of appropriate preventive maintenance strategies in terms of timing and method retards the large backlog of poor conditions of pavements, therefore improves the system-wide performance in an efficient and cost-beneficial way. However, the pre-treatment functional performance condition of asphalt pavement has a significant impact on the efficacy of preventive maintenance, and to provide years of service, it is necessary to designate an appropriate preventive maintenance method according to the actual functional condition of pavement. This study presents a new unsupervised learning approach for 1) extracting high correlation factors to reduce the dimensionality of feature distress data, and 2) accurately separating attributes that define pavement functional performance into severity levels to effectively assess the practiced preventive maintenance. Three unsupervised machine learning algorithms including feature extract, relevant factor analysis, and cluster analysis were executed. Based on the severity level results of the pre-treated pavement, the effects of four treatment methods (thin overlay, slurry seal, chip seal, and crack seal) were evaluated by a statistical analysis. The preventive maintenance data from the Specific Pavement Studies-3 (SPS-3) experiment of the Long-Term Pavement Performance (LTPP) database are used for the analysis. The results demonstrated that selecting the appropriate maintenance method for the functional performance risk level of the pavement is beneficial in achieving successful treatment results.

Impact of Flexible Circuit Bonding and System Integration on Energy Resolution of Cross-Strip CZT Detectors.

Cadmium zinc telluride (CZT) detectors enable high spatial resolution and high detection efficiency and are utilized for many gamma-ray and X-ray spectroscopy applications. In this article, we describe a stable bonding process and report on the characterization of cross-strip CZT detectors before and after bonding to flexible circuit. The bonding process utilizes gold stud bonding and polymer epoxy technique to bond the flexible circuits to two CZT crystals and form a detector module in an anode-cathode-cathode-anode (ACCA) configuration. The readout electronics is optimized in terms of shaper setting and steering electrode voltage. The average full-width half maximum (FWHM) energy resolution at 662 keV of 110 CZT crystals tested individually was 3.5% ± 0.59% and 4.75% ± 0.48% prebonded and post-bonded, respectively. No depth correction was performed in this study. The average FWHM energy resolution at 662 keV of the scaled-up system with 80 CZT crystals was 4.40% ± 0.53%, indicating the scaled-up readout electronics and stacking of the modules does not deteriorate performance. The proper shielding and grounding of the scaled-up system slightly improved the system-wide performance. The FWHM energy resolution at 511 keV of the scaled-up system was 5.85% ± 0.73%.

Viewing the Role of Alternate Care Service Pathways in the Emergency Care System through a Causal Loop Diagram Lens

Globally, Emergency Care Systems (ECS) are a critical resource that needs to be used judiciously as demand can easily exceed supply capacity. Sub-optimal ECS use contributes to Emergency Department (ED) crowding; this adversely affects ECS as well as system-wide service performance. Alternate Care Service Pathways (ACSPs) are innovations intended to mitigate ED crowding by re-routing less-urgent cases to sites of care other than the ED. As in other countries, policymakers in Singapore need to respond to increasing ED utilization and are evaluating the introduction of ACSPs. However, developing ACSPs is costly, entails tinkering with established critical services, and runs the risk of unintended adverse consequences. Through a Causal Loop Diagram (CLD) developed in four stages, we present a view of the current Singapore ECS and the intended role of ACSPs in relieving its stress. This exercise suggests that to be successful ACSPs must change the prevailing mental model of the ED as a “one-stop shop” but should focus on integrating with primary care. The discussions stimulated by the development, critiquing, and revision of the CLD highlighted the importance of accounting for the reservations of stakeholders for changes. The CLD has enhanced shared understanding and will be used to guide quantitative simulation modeling to promote informed policy.

Effects of inertia distribution on regional frequency heterogeneity

Heterogeneous inertia distribution can result in large regional frequency deviations and inter-area oscillations that exceed pro-tection limits configured based on system-wide averaged performance. This paper examines how the spatial distribution of inertia affects frequency heterogeneity. An investigation into the effects of different fast frequency response (FFR) schemes on frequency heterogeneity is also presented. The frequency heterogeneity is quantified by calculating cosine similarity between regional frequency trajectories. The key results are obtained using a two-area model considering varying inertia distributions. A key finding is that the localness of regional frequency is independent of the inertia of a specific area, nor of the total system inertia. The inertia ratio, described as the ratio of the disturbance area inertia to that of the non-disturbance area, is shown to have a strong correlation with the frequency heterogeneity. It has been shown that generators are likely to be highly synchronous if the inertia ratio is greater than 2.5. Providing derivative FFR within the disturbance area always demonstrates benefits regard-ing frequency heterogeneity inhibition, whereas droop scheme typically introduces deterioration in frequency heterogeneity.

Hierarchical control for stochastic network traffic with reinforcement learning

This study proposes a hierarchical control framework to maximize the throughput of a road network driven by travel demand with uncertainties. In the upper level, a perimeter controller regulates the traffic influx into the core road network. The upper level uses a reinforcement learning algorithm that learns and responds to the traffic dynamics in the core road network without the need for an underlying system model and macroscopic fundamental diagram. The lower level is a local signal control system that regulates the spatial distribution of traffic flow within the core network. The results show that the hierarchical control framework can improve road network throughput by coordinating control actions conducted at the two levels. The improvement in system-wide performance is validated by a range of performance metrics and macroscopic flow-accumulation patterns realized under different control settings. The study contributes to the management of urban road networks with advanced computing technologies.

Modified Bureau of Public Roads Link Function

The Bureau of Public Roads (BPR) function is a widely used link cost function in transportation planning because of its simple mathematical form, easily measurable field variables, and consistent performance. However, the BPR function is deterministic and does not capture the stochastic relation between travel time and traffic flow. The present study develops a modified BPR (MBPR) function by incorporating travel time uncertainty (TTU) in the deterministic BPR function. In the MBPR function, the effect of TTU is incorporated using two parameters, [Formula: see text] and [Formula: see text]. A nonlinear optimization problem is formulated, and the generalized reduced gradient method is used to calibrate the BPR and the MBPR function. The applicability of the proposed MBPR function is demonstrated using empirical data collected for an urban arterial in India and simulated data developed for a real-world urban road network. The proposed MBPR function captures the heterogeneity in travel time for different traffic flow values. The function (a) captures the variability in travel time under oversaturated conditions and (b) captures the time-dependent relation between traffic volume and delay. The physical meaning of [Formula: see text] and [Formula: see text] in the context of inter-day heterogeneity, infrastructure potential, and traffic flow heterogeneity are discussed. The practical application of MBPR as an analytical tool for system-wide performance evaluation is demonstrated by investigating the impact of traffic signal control on travel times using a before–after perspective. Compared with the before case, a 13% reduction in travel time is observed for the after case. Therefore, the installation of traffic signal control has reduced congestion.

COVID-19 impacts on mobility and travel demand

Since the beginning of the COVID-19 pandemic, many travel restriction policies were implemented to reduce further spread of the virus. These measures significantly affected travel demand to levels which could not have been anticipated by most planners in transportation agencies. As the pandemic has proven to have significant short-term impacts, it is anticipated that some of these impacts may translate to longer-term impacts on overall travel behavior and the movement of people and goods. Beyond the pandemic, the observed travel patterns during this period also provides a great opportunity for planners to assess policies such as work from home and remote learning as strategies to manage travel demand. This study provides a scenario analysis framework to re-evaluate travel demand forecasts under uncertain future conditions using the Maryland Statewide Transportation Model (MSTM). Model parameters associated with working from home, household income, changes in discretionary travel, distance learning, increased e-commerce, vehicle occupancy and mode choice were identified. Parameter values were assigned under the various scenarios using employer surveys on workforce teleworking and observed data on e-commerce growth and shopping behavior. The main findings of this study capture the sensitivities of systemwide vehicle miles travel, and vehicle hours travel under different scenarios and implications on future investment decisions.The study found that future investments under the scenarios remain beneficial to systemwide performance and therefore justified. Although this study focuses on the state of Maryland, the scenario framework and parameter definitions can be used in other states or agencies within a travel demand model environment.

Equity-oriented integrated optimization of train timetable and stop plans for suburban railways system

In the suburban railway system, when oversaturation occurs in the railway line or the train skips the same station continuously, passengers waiting at different stations may receive a varying share of train resources. As a result, some passengers have to endure a very long waiting time, causing issues of inequity. Thus, this paper focuses on improving the system-wide equity performance through integrated optimization of the train timetable and stop plan while ensuring travel efficiency and cost rationality. As a part of the study, a novel equation to estimate equity performance by considering the difference in passenger travel utility is developed. Then, an integrated optimization model combining the train timetable and stop plan is proposed to minimize total travel time, train running time, and equity performance. Unlike other studies in the literature, this paper does not define any initial set of stopping plans, and synchronously optimizes the train timetable and stops the plan. This facilitates obtaining better solutions by expanding the solution space. Furthermore, it overcomes the NP-hardness of the investigated problem by using the adaptive large-scale neighborhood search (ALNS) algorithm to solve the multi-objective mixed-integer linear programming model. Finally, this paper discusses the original and optimized scheduling indicators of a real-world case based on the Shanghai Jinshan railway and performs a sensitivity analysis on equity weights. The results show that the proposed approach can improve the overall benefits of efficiency, cost, and equity by 23%. Specifically, in terms of equity, the passenger travel utility variance was reduced by 42.8%. This analysis provides essential information to railway managers in formulating the train operation plan.

Speculative Container Scheduling for Deep Learning Applications in a Kubernetes Cluster

In the past decade, we have witnessed a dramatically increasing volume of data collected from varied sources. The explosion of data has transformed the world as more information is available for collection and analysis than ever before. To maximize the utilization, various machine and deep learning models have been developed, e.g. CNN [1] and RNN [2], to study data and extract valuable information from different perspectives. While data-driven applications improve countless products, training models for hyperparameter tuning is still a time-consuming and resource-intensive process. Cloud computing provides infrastructure support for the training of deep learning applications. The cloud service providers, such as Amazon Web Services [3], create an isolated virtual environment (virtual machines and containers) for clients, who share physical resources, e.g., CPU and memory. On the cloud, resource management schemes are implemented to enable better sharing among users and boost the system-wide performance. However, general scheduling approaches, such as spread priority and balanced resource schedulers, do not work well with deep learning workloads. In this project, we propose SpeCon, a novel container scheduler that is optimized for shortlived deep learning applications. Based on virtualized containers, such as Kubernetes [4] and Docker [5], SpeCon analyzes the common characteristics of training processes. We design a suite of algorithms to monitor the progress of the training and speculatively migrate the slow-growing models to release resources for fast-growing ones. Specifically, the extensive experiments demonstrate that SpeCon improves the completion time of an individual job by up to 41.5%, 14.8% system-wide and 24.7% in terms of makespan.

Evolution of a Systematic Approach to Smoking Cessation in Ontario's Regional Cancer Centres.

Smoking cessation after a cancer diagnosis can significantly improve a person’s prognosis, treatment efficacy and safety, and quality of life. In 2012, Cancer Care Ontario (now part of Ontario Health) introduced a Framework for Smoking Cessation, to be implemented for new ambulatory cancer patients at the province’s 14 Regional Cancer Centres (RCCs). Over time, the program has evolved to become more efficient, use data for robust performance management, and broaden its focus to include new patient populations and additional data collection. In 2017, the framework was revised from a 5As to a 3As brief intervention model, along with an opt-out approach to referrals. The revised model was based on emerging evidence, feedback from stakeholders, and an interim program evaluation. Results showed an initial increase in referrals to cessation services. Two indicators (tobacco use screening and acceptance of a referral) are routinely monitored as part of Ontario Health’s system-wide performance management approach, which has been identified as a key driver of change among RCCs. Due to the COVID-19 pandemic, many RCCs reported a decrease in these indicators. RCCs that were able to maintain a high level of smoking cessation activities during the pandemic offer valuable lessons, including the opportunity to swiftly leverage virtual care. Future directions for the program include capturing data on cessation outcomes and expanding the intervention to new populations. A focus on system recovery from COVID-19 will be paramount. Smoking cessation must remain a core element of high-quality cancer care, so that patients achieve the best possible health benefits from their treatments.

MIMO architecture for fast convergence of distributed online optimization in smart grids

This paper proposes an architectural solution to enhance the resilience of distributed decision-making and control in microgrids and smart grids with respect to communication delays. The paper develops a resource allocation framework to optimally place the multiple-input multiple-output (MIMO) communication technology on critical channels to reduce the communication bottle neck and expedite the convergence speed of distributed online optimization algorithms, which require convergence in the cyber network before the solution can be implemented on the physical grid. The paper shows that optimal placement of the MIMO technology can decrease communication transmission delays, which in turn improves the reliability of power system operational protocols, such as distributed frequency regulation. Illustrative case studies using data from real-world power systems show that only upgrading the critical communication channels is sufficient to achieve system-wide performance improvement.

Impact of 'Enhanced' Intermediate Care Integrating Acute, Primary and Community Care and the Voluntary Sector in Torbay and South Devon, UK.

Introduction:Intermediate care (IC) was redesigned to manage more complex, older patients in the community, avoid admissions and facilitate earlier hospital discharge. The service was ‘enhanced’ by employing GPs, pharmacists and the voluntary sector to be part of a daily interdisciplinary team meeting, working alongside social workers and community staff (the traditional model).Methods:A controlled before-and-after study, using mixed methods and a nested case study. Enhanced IC in one locality (Coastal) is compared with four other localities where IC was not enhanced until the following year (controls), using system-wide performance data (N = 4,048) together with ad hoc data collected on referral-type, staff inputs and patient experience (N = 72).Results:Coastal showed statistically significant increase in EIC referrals to 11.6% (95%CI: 10.8%–12.4%), with a growing proportion from GPs (2.9%, 95%CI: 2.5%–3.3%); more people being cared for at home (10.5%, 95%CI: 9.8%–11.2%), shorter episode lengths (9.0 days, CI 95%: 7.6–10.4 days) and lower bed-day rates in ≥70 year-olds (0.17, 95%CI: 0.179–0.161). The nested case study showed medical, pharmacist and voluntary sector input into cases, a more holistic, coordinated service focused on patient priorities and reduced acute hospital admissions (5.5%).Discussion and conclusion:Enhancing IC through greater acute, primary care and voluntary sector integration can lead to more complex, older patients being managed in the community, with modest impacts on service efficiency, system activity, and notional costs off-set by perceived benefits.