Flexible Service Research Articles

ACO and Social Service Organization Partnerships: Payment, Challenges, and Perspectives

Summary The integration of clinical and health-related social needs is both essential and elusive. This study highlights components of the Flexible Services Program in Massachusetts, a novel demonstration project of the Delivery System Reform Incentive Payment program under a Medicaid section 1115 waiver. The program allows the state’s Medicaid ACOs to pilot evidence-based initiatives that address a member’s health-related social needs in an effort to improve health outcomes and reduce total costs of care. An examination of payment mechanisms established in the partnerships between the ACOs and the social service organizations, combined with interviews with leaders of the ACOs, reveals strengths and outstanding challenges that must be addressed. More attention is needed to vet partnerships and to develop process or outcome measures that demonstrate meaningful impact for patients related to care utilization, equity, experience, and chronic health conditions.

Abstract PO-015: An examination of the implementation of a navigation patient navigation program to improve breast and cervical cancer screening rates of Chinese immigrant women

Abstract Background: In 2020, national breast cancer rates for female breast cancer and cervical cancer for all races were 72.8% and 81%, respectively. Chinese Americans have lower breast and cervical cancer screening rates than the national average. For example, breast and cervical cancer screening rates for Chinese American women from Chicago's Chinatown are 60% and 47%, respectfully. Patient navigators have improved screening and follow-up rates for medically underserved populations, yet investigations of cancer navigation programs and their implementation among Chinese Americans are limited. To address this gap, we used the Consolidated Framework for Implementation Research (CFIR) to examine facilitators and barriers to implementing the Chicago-based Chinatown Patient Navigation Program (CPNP) for breast and cervical cancer screening, follow-up, and treatment. Methods: Between February and April 2019, stakeholders from a local safety-net hospital, supportive care services, and a community-based organization were invited to participate in qualitative interviews to illuminate implementation processes and stakeholder perspectives of facilitators and barriers to program implementation. Interviews were audio-recorded, transcribed, and deductively coded according to CFIR domains, including 1) intervention characteristics; 2) outer setting; 3) inner setting; and 4) the implementation process. We interviewed 16 stakeholders. Results Findings suggest that perceived program benefits, such as patient navigators preparing Chinese-speaking patients for their clinic visits, providing interpreter services, being accessible to patients and stakeholders, and consistently providing high-quality flexible services facilitated stakeholder engagement in the CPNP. However, barriers to program implementation included limited regular feedback provided to stakeholders regarding their program involvement and in the early stages of the program, limited awareness among some clinical staff on navigators' roles and responsibilities, insufficient office space for the navigators, and few Chinese language patient resource materials. Conclusions: Lesson's to apply to future patient navigation programs are that patient navigators should be able to guide patients at all points of care—from scheduling to follow-up, navigator services should be high quality and complement the existing clinical workflow, clinical partners and end-users need regular and frequent communication about the navigator's responsibilities before the program's implemented, and limitations with practice-level supports (e.g., infrastructure, administration) can pose challenges to implementing the navigation program. These findings provide valuable information on the implementation of future patient navigation programs serving Chinese American and other limited-English speaking immigrant populations. Citation Format: Marquita W. Lewis-Thames, Laura S. Tom, Ivy S. Leung, Anna Yang, Melissa A. Simon. An examination of the implementation of a navigation patient navigation program to improve breast and cervical cancer screening rates of Chinese immigrant women [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PO-015.

A Heuristic Method to Calculate the Capacity of Residential PV-BESS in Providing Upward Flexibility Services in Energy Communities

This paper proposes a heuristic method for calculating the capacity of a set of residential photovoltaic-battery systems in providing upward flexibility services to the grid in an energy communities framework. The proposed method has been designed to calculate the upward service capacity in a few minutes, assuming a scenario where the grid operator urgently needs an upward service in a specific area. The proposed method calculates the service capacity by exploiting the PV overgeneration and the state of charge of batteries, adopting a distributed approach. If the service capacity varies relevantly over time, a centralized approach is considered allowing the service capacity to remain constant over time. An algorithm is provided that implements the proposed heuristic method that can be easily translated into a software code and solved even in the absence of specific skills and expensive high-level computational tools, i.e. using cost-effective single-board computers. The main benefits and advantages of the proposed method are due to its applicability in real-time problems and to its simplicity which makes it easy to be translated into software code and solved even in the absence of specific skills and high-level computational tools. Therefore, it is a simple and advantageous solution, especially for small energy communities. The numerical results demonstrate the effectiveness of the proposed method and algorithm, studying a set of four residential photovoltaic-battery systems and real input data. For this test case, the algorithm returns a flat service capacity of approximately 8 kW which remains perfectly constant for 1-hour. Lastly, the performance of the proposed heuristic method is compared with the solution of two optimization problems aiming at the same scope.

How Do Case Mangers Determine the Types of Services Provided to Users in the Intensive Case Management? A Longitudinal Study

Introduction Intensive case management is an essential component of community mental health care system for people with severe mental illness. We examined the association of service types with previous and future functioning and hospital admissions using a longitudinal data, to clarify the case managers’ activity. Methods The study included baseline assessment, recording and analysis of services, and follow-up assessment. At the baseline and follow-up assessments, we examined functioning (ie, Personal and Social Performance Scale: PSP) and hospital admission of users for past periods. For recording and analysis of services, we recorded services provided by case managers over 2 months, and five core services were used for the analyses: regular face-to-face contact, outreach services, hospital-based (outpatient) services, and easy contacts and coordination. Results Outreach services were significantly associated with hospital admissions at baseline and follow-up assessments. Hospital-based services were significantly associated with lower PSP scores and hospital admission at follow-up assessment. Easy contacts and coordination were associated with lower PSP scores at follow-up assessment. Conclusion In Japan, most services are provided in the outpatient department of hospital, and outreach services are relatively time consuming for case managers; thus, the indications for providing outreach services need to be carefully considered. Therefore, we suggest that case managers provide flexible services to users according to the circumstances in Japan.

Protecting Location Privacy by Multiquery: A Dynamic Bayesian Game Theoretic Approach

When using location-based services (LBSs), a user obtains points-of-interest (PoI) information by providing the LBS platform with his current geo-location. Such a search leads to potential privacy leakage if an adversary has access to his geo-data. Traditional <i>k</i>-anonymity mechanisms instruct a user to bear the overhead to report his current location together with <i>k</i> - 1 dummy locations to confuse the adversary, which only work well given a large number <i>k</i>. Aware of the common practices that a user is actually flexible in service requirement (e.g., as long as the searched PoIs are within his walking distance), we propose a novel approach to help the user gain location privacy from service flexibility for the challenging case of a small number <i>k</i>. By analyzing the strategic interaction between the user and the adversary in a dynamic Bayesian game, we prove that the user&#x2019;s equilibrium strategy depends on the adversary&#x2019;s capability of accessing geo-data. Take <i>k</i> = 2 for example, we find that if the adversary is not likely to access both geo-data, the user should report the two dummy locations at two different directions of his real location, and otherwise at the same direction. Perhaps surprisingly, the user may benefit from the adversary&#x2019;s access to more geo-data. Furthermore, we extend the game-theoretic approach for multi-query and arbitrary user location distributions. Numerical results show that our approach obviously outperforms <i>k</i>-anonymity mechanisms especially under a small number <i>k</i>.

Flexibility Potential of a Smart Home to Provide TSO-DSO-level Services

The high penetration of intermittent renewable-based power into modern power systems increases the need for more technical ancillary services from flexible energy resources. Smart homes could provide different flexibility services related to active power control services and therefore fulfill a part of the flexibility needs of system operators. In this regard, the estimation of the flexible capacities of each smart home's flexible device is of key importance. Correspondingly, this paper first estimates the flexible capacities of a smart home with controllable devices as flexible resources. The flexible capacity of each appliance is estimated considering its flexible and non-flexible operations. Besides, the local and system-wide flexibility services are introduced and the paper discusses whether a smart home can provide these types of services. In the simulations of this paper, the flexible capacity of each household appliance is estimated and compared to each other. Finally, the profitability of the smart home's battery energy storage multi-use is analyzed when it is providing three different types of flexibility services for the transmission system operator's needs. The results demonstrate that in some scenarios, the smart home's battery energy storage can increase its profits by providing transmission-system-level flexibility.

Technology for designing innovative processes for creating products and services of a network enterprise using an i4.0 knowledge-based system

The creation of network enterprises based on the digital technologies of the Industrie 4.0 (the 4th Industrial Revolution, i4.0) opens broad opportunities for increasing production flexibility, customer focus and continuous innovation in products and services provided. At the same time, new opportunities necessitate the development of new methods and technologies for designing innovative processes in the context of digital i4.0 platforms, all of which highlights the relevance of the presented research topic. This work aims to define technologies for designing innovative processes to create products and services using i4.0 systems which are based on multi-agent interaction of asset administration shells (AAS), displaying digital twins of product components, and the use of ontological and cognitive methods for forming and justifying design decisions. The work presented here uses the Domain-Driven Design approach, an architectural framework for building i4.0 systems, methods of ontological engineering, quality function deployment (QFD), analysis of the types and consequences of potential inconsistencies (FMEA) and processing of fuzzy sets. The paper proposes principles for identifying bounded contexts of the domain under the design activities for the stages of the life cycle and products’ subsystems (components). For bounded contexts of the domain, it is envisaged to create AAS of i4.0 systems, with the help of which the innovative process is supported and the multi-agent interaction of its participants is carried out. As cognitive tools for making design decisions, we proposed to use services for assessing the importance of the determined quality characteristics of products and minimizing deviations of the proposed solutions from the formed functional and non-functional requirements. The methods of ontological engineering and data modelling allow us to dynamically develop an innovative project and support various versions of the project in the design process. Application of the proposed technology for designing innovative processes to create products and services at network enterprises using i4.0 systems will improve the quality of design decisions, increase the dynamism and continuous design of innovative projects.

Analysis of Flexible Batch Service Queueing System to Constrict Waiting Time of Customers

When the required number of customers is available in the general bulk service (GBS) queueing system, the server begins service. Otherwise, the server will remain inactive until the number of consumers in the queue reaches that minimum required number. Customers that have already come must wait throughout this time, regardless of their arrival time. In some circumstances, like specimens awaiting testing in a clinical laboratory or perishable commodities awaiting delivery, it is necessary to finish services before the expiration date. It might only be achievable if consumers’ waiting times are kept under control. As a result, the flexible general bulk service (FGBS) rule is developed in this article to provide flexibility in batching. The effectiveness of FGBS implementation has been demonstrated using two examples: a clinical laboratory and a distribution center. To justify the suggested model, a simulation study and numerical illustration are provided.

Near-on-demand mobility. The benefits of user flexibility for ride-pooling services

Mobility-On-Demand (MoD) services have been transforming the urban mobility ecosystem. However, they raise a lot of concerns for their impact on congestion, Vehicle Miles Traveled (VMT), and competition with transit. There are also questions about their long-term survival because of inherent inefficiencies in their operations. Considering the popularity of the MoD services, increasing ride-pooling is an important means to address these concerns. Shareability depends not only on riders’ attitudes and preferences but also on operating models deployed by providers. The paper introduces an advance requests operating model for ride pooling, users may request rides in advance of their desired departure times. A platform with efficient algorithms for request matching, vehicle routing, rebalancing, and flexible user preferences is developed. Data from Chengdu, China, and New York, United States are used to evaluate the performance of the advance requests system. The impact of various design aspects of the system (e.g. advance requests horizon, vehicle capacity) on its performance is investigated. The sensitivity of the results to user preferences in terms of the level of service (time to be served and excess trip time), willingness to share and place requests in advance, and traffic conditions are explored. The results suggest that significant benefits in terms of sustainability, level of service, and fleet utilization can be realized when advance requests are along with an increased willingness to share. Furthermore, even “near-on-demand” (relative short planning horizons) operations can offer benefits for all stakeholders (users, operators, cities).

Quality financial inclusion and its determinants in South Africa: evidence from survey data

PurposeThe study measured quality financial inclusion, a more comprehensive measure of financial inclusion, and examined its determinants at a consumer level in South Africa.Design/methodology/approachThis study leveraged on FinScope 2015 survey data to compute a quality financial inclusion index using polychoric principal component analysis. Subsequently, a heteroscedasticity consistent ordinary least squares regression model was employed to assess determinants of quality financial inclusion.FindingsThe empirical findings indicated that gender, education, financial literacy, income, location and geographical proximity determine quality financial inclusion. These findings could inform policymakers and financial services providers on how quality financial inclusion can be promoted through tailoring financial products for various socio-demographic groups.Research limitations/implicationsDue to data limitations, the study was confined to South Africa and did not capture digital financial inclusion. Hence, future studies could replicate the study in Sub-Saharan Africa's context and compute an index that captures digital financial inclusion.Practical implicationsThese findings could inform policymakers and financial services providers on how quality financial inclusion can be promoted through tailoring financial products for various socio-demographic groups.Originality/valueThis study proposed a more comprehensive measure of quality financial inclusion from a demand-side perspective by accounting for important dimensions that include diversity, affordability, appropriateness and flexibility of financial products and services.

FOLIO and Smart Libraries: Meeting the future needs of Chinese Libraries

The vision and concept of FOLIO have gained the attention and expectation from libraries. Its open and flexible service platform provides an approach to the realization of the Smart Libraries in the future. On the basis of summarising the needs of Smart Libraries in China, the paper expounds the architecture and ideas of providing smart services based on FOLIO. By analyzing the problems and challenges in the implementation and localization of FOLIO in China, the author points out that the“Chinese Alliance for Library Service Platform”(also called YunHan Alliance) should play a very important role in running the Chinese FOLIO community. It should steward the direction of the community, carry out the minimum open source software application suite, cultivate an open platform ecology, so as to support the new business models for the prosperity of Chinese libraries in the future.

Virtual power plant containing electric vehicles scheduling strategies based on deep reinforcement learning

Virtual power plants (VPPs), which aggregate customer-side flexibility resources, provide an effective way for customers to participate in the electricity market, and provide a variety of flexible technologies and services to the market. Importantly, VPPs can provide services to electric vehicle (EV) charging stations. In this paper, we constructed a deep reinforcement learning (DRL) based Stackelberg game model for a VPP with EV charging stations. Considering the interests of both sides of the game, soft actor-critic (SAC) algorithm is used for the VPP agent and twin delay deep deterministic policy gradient (TD3) algorithm is used for the EV charging station agent. By alternately training the network parameters of the agents, the strategy and solution at the equilibrium of the game are calculated. Results of cases demonstrate that the VPP agent can learn the strategy of selling electricity to EVs, optimize the scheduling of distributed energy resources (DERs), and bidding strategy for participation in the electricity market. Meanwhile, the EV aggregation agent can learn scheduling strategies for charging and discharging EVs. When the EV aggregator uses a deterministic strategy and the virtual power plant uses a stochastic strategy, energy complementarity is achieved and the overall operating economy is improved.

Flexibility services and strategy of integration of distribution system users in the electricity market

Flexibility services and strategy of integration of distribution system users in the electricity market

Balanced resource allocation for VNF service chain provisioning in inter-datacenter elastic optical networks

Network Function Virtualization (NFV) is a novel technology that enables flexible and cost-effective network services by replacing the hardware-based middleboxes with software instances named virtual network functions (VNFs). In NFV, each requested Service Function Chain (SFC) is an ordered sequence of VNFs that must be placed optimally on the geographically distributed data centers (DCs). For the popular Elastic Optical Networks (EONs) with flexible bandwidth management, a major challenge is optimal placement of the VNF-based Service Chain (VNF-SC) on data centers and steering traffic through deployed VNFs in the substrate network. In this paper, we study both the problems of VNF placement and Routing/Spectrum Allocation (RSA) for inter-DC EONs with the aim of minimizing the total network cost while considering the balanced resource utilization to reduce the blocking probability. We first formulate the problem as an Integer Linear Programming (ILP) to solve the problem exactly, and then propose a heuristic algorithm based on the resource-balancing and fragmentation-aware solution to reduce the blocking probability. The simulation results verify that the proposed algorithm outperforms an existing algorithm.

The Costs and Benefits of Choice: Family Managers in Directly Funded Home Care

Directly funded (DF) home care, or consumer directed home care, gives program users a budget to choose their own services. Set in the Canadian province of Manitoba, our study examines the local DF program “Self and Family Managed Care”, which does not allow program users to hire and pay a family member. Incorporating a disability lens into care and aging studies, we share findings from a qualitative study based on 24 semi-structured interviews with DF users. We focus on the experiences of family managers, that is, representatives acting as a decision maker for an older adult. About half of the family managers in this study care for people living with dementia or cognitive decline. We identify two main themes: 1) service flexibility in DF reduces caregiver strain, 2) family managers tend to hire agencies rather than individuals to avoid administrative burden. Our discussion highlights the costs of DF from the perspective of caregivers as administrative burden (financial paperwork, finding workers, choosing a ‘good’ agency), and the benefits as flexibility (choosing workers, trusting workers, setting schedules, assigning work). We also consider the goals of family managers to enhance quality of life and avoid long-term residential care, in contrast to younger self-managers who desire control and autonomy. We recommend that DF programs need to reduce administrative work for users, support users in making informed choices, and find better ways to support, acknowledge and value the work of family managers and substitute decision makers.

Optimizing Traffic Engineering for Resilient Services in NFV-Based Connected Autonomous Vehicles.

The massive amount of data generated daily by various sensors equipped with connected autonomous vehicles (CAVs) can lead to a significant performance issue of data processing and transfer. Network Function Virtualization (NFV) is a promising approach to improving the performance of a CAV system. In an NFV framework, Virtual Network Function (VNF) instances can be placed in edge and cloud servers and connected together to enable a flexible CAV service with low latency. However, protecting a service function chain composed of several VNFs from a failure is challenging in an NFV-based CAV system (VCAV). We propose an integer linear programming (ILP) model and two approximation algorithms for resilient services to minimize the service disruption cost in a VCAV system when a failure occurs. The ILP model, referred to as TERO, allows us to obtain the optimal solution for traffic engineering, including the VNF placement and routing for resilient services with regard to dynamic routing. Our proposed algorithms based on heuristics (i.e., TERH) and reinforcement learning (i.e., TERA) provide an approximation solution for resilient services in a large-scale VCAV system. Evaluation results with real datasets and generated network topologies show that TERH and TERA can provide a solution close to the optimal result. It also suggests that TERA should be used in a highly dynamic VCAV system.

An Experimental Performance Evaluation of Cloud-API-Based Applications

Cloud Application Programming Interfaces (APIs) have been developed to link several cloud computing applications together. API-based applications are widely used to provide flexible and reliable services over cloud platforms. Recently, a huge number of services have been attached to cloud platforms and widely utilized during a very short period of time. This is due to the COVID-19 lockdowns, which forced several businesses to switch to online services instantly. Several cloud platforms have failed to support adequate services, especially for extended and real-time-based applications. Early testing of the available platforms guarantees a level of suitability and reliability for the uploaded services. In this work, we first selected two different API-based applications from education and professional taxonomies, the two most recently used applications that have switched to the cloud environment. Then, we aimed to evaluate the performance of different API-based applications under different cloud platforms, in order to measure and validate the ability of these platforms to support these services. The advantages and drawbacks of each platform were experimentally investigated for each application.

Generalized Nash equilibrium analysis of transmission and distribution coordination in coexistence of centralized and local markets

The increasing scale of distributed energy resources connected to distribution networks can not only solve the operational problems in the distribution system, but it can also supply the transmission system with flexibility and ancillary services. Thus, the design of a market framework to suit this situation is required. This paper builds a generalized Nash game model with shared constraints for the joint day-ahead market where the centralized market coexists with the local market. In this model, the transmission system operator clears the centralized market assuming that resources of distribution systems are fixed, the distribution system operator clears the local market assuming that resources of the transmission system are fixed, and the clearing of the two markets are conducted simultaneously. Subsequently, the generalized Nash game model can be reformulated to a variational inequality problem that can be solved by the Goldstein–Levitin–Polyak projection method, and more than one generalized Nash equilibrium solution is obtained by the parametrized variational inequality approach. Finally, case studies on two systems, which consist of a 30-bus transmission system and 33-bus distribution system, and a practical 343-bus transmission system and 110-bus distribution system, respectively, are used to verify the effectiveness of the proposed method.

The pickup and delivery problem with synchronized en-route transfers for microtransit planning

Microtransit and other flexible transit fleet services can reduce costs by incorporating transfers. However, transfers are costly to users if they must get off a vehicle and wait at a stop for another pickup. A mixed integer linear programming model (MILP) is proposed to solve pickup and delivery problems with vehicle-synchronized en-route transfers (PDPSET). The transfer location is determined by the model and can be located at any candidate node in the network rather than a static facility defined in advance. The transfer operation is strictly synchronized between vehicles within a hard time window. A heuristic algorithm is proposed to solve the problem with an acceptable solution in a much shorter computation time than commercial software. Two sets of synthetic numerical experiments are tested: small-scale instances based on a 5x5 grid network, and large-scale instances of varying network sizes up to 250x250 grids to test scalability. The results show that adding synchronized en-route transfers in microtransit can further reduce the total cost by 10% on average and maximum savings can reach up to 19.6% in our small-scale test instances. The heuristic on average has an optimality gap less than 1.5% while having a fraction of the run time and can scale up to 250x250 grids with run times within 1 min. Two large-scale examples demonstrate that over 50% of vehicle routes can be further improved by synchronized en-route transfers and the maximum savings in vehicle travel distance that can reach up to 20.37% for the instance with 100 vehicles and 300 requests on a 200x200 network.

Uncertainty modelling of an industry facility as a multi-energy demand response provider

While the latest European energy regulations emphasise the active power system participation of the household level end-users, the large industrial facilities are still not fully exploiting all the market opportunities to decrease their costs and become more competitive. Significant cost reduction can be achieved by offering flexibility services in the electricity market. This is especially valid in the case when the industrial consumers are multi-energy hubs where shifting and optimising usage of input energy vectors creates additional opportunities. Research gaps were identified and a price responsive demand response model for a multi-energy industry facility under uncertainty was developed. The uncertainty aspects are modelled both by the robust optimisation and by the two-stage stochastic optimisation. Additionally, we develop a linear energy flow-based model of an industrial steam system which better encompasses losses and makes the model more realistic. The model is validated on a real-world case of a multi-energy industry facility and the results indicate that cost savings of up to 18 % can be achieved compared to the passive and deterministic, mass flow-based business-as-usual behaviour.