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Contact centers for patient and referring physician are important to large medical-centers such as the Mayo Clinic's Central Appointment Office (CAO). The aim of this case study is to report the process and results of a major process improvement effort, designed to simultaneously improve service quality and efficiency.

Discrete-event simulation and optimization are used and linked to significant service improvements.

The process improvement efforts led to about a 70 percent improvement in patient service performance as measured by average answering-speed (ASA) and average abandonment rate (AAR). This was achieved without adding additional staff, despite call volume increasing by 12 percent. Evaluating process improvement projects is difficult owing to the “phased” implementation of changes. Thus, there is no true control against which to compare. Additionally, the results are based on a single case study.

Evaluation of process improvement projects is difficult due to the “phased” implementation of changes. Thus, there is no true control to compare against.

Contact center data and operations research methods, such as discrete-event simulation and optimization, can be integrated with change management, which results in significant process improvements in medical call-centers.

Structured quantitative modeling of contact centers can be an important extension to traditional quality and process improvement techniques.

Time waiting for service is a major concern for consumers, and excessive waiting for a pre‐scheduled appointment is especially annoying. This is an on‐going problem because appointment scheduling is a challenging task, mainly due to the uncertainties associated with service times. Prior studies have focused mainly on a single scheduling period (i.e. either a morning or afternoon); this paper uses a more realistic model that represents an on‐going, multi‐period scheduling environment where clients can be scheduled days or even weeks into the future. Two main objectives will be considered; the best scheduling rule to use in a multi‐period environment, and the best placement of appointment slots that are left open for urgent clients. Both of these have been studied in a single period environment, and results here will be compared to those. It will be shown that in some cases earlier findings from the one‐period environment are robust and perform well in a multi‐period environment, while in other cases the one‐period findings do not apply.

Parallel processing of regional anesthesia may improve operating room (OR) efficiency in patients undergoes upper extremity surgical procedures. The purpose of this paper is to evaluate whether performing regional anesthesia outside the OR in parallel increases total cases per day, improve efficiency and productivity.

Data from all adult patients who underwent regional anesthesia as their primary anesthetic for upper extremity surgery over a one-year period were used to develop a simulation model. The model evaluated pure operating modes of regional anesthesia performed within and outside the OR in a parallel manner. The scenarios were used to evaluate how many surgeries could be completed in a standard work day (555 minutes) and assuming a standard three cases per day, what was the predicted end-of-day time overtime.

Modeling results show that parallel processing of regional anesthesia increases the average cases per day for all surgeons included in the study. The average increase was 0.42 surgeries per day. Where it was assumed that three cases per day would be performed by all surgeons, the days going to overtime was reduced by 43 percent with parallel block. The overtime with parallel anesthesia was also projected to be 40 minutes less per day per surgeon.

Key limitations include the assumption that all cases used regional anesthesia in the comparisons. Many days may have both regional and general anesthesia. Also, as a case study, single-center research may limit generalizability.

Perioperative care providers should consider parallel administration of regional anesthesia where there is a desire to increase daily upper extremity surgical case capacity. Where there are sufficient resources to do parallel anesthesia processing, efficiency and productivity can be significantly improved.

Simulation modeling can be an effective tool to show practice change effects at a system-wide level.

Service managers are continually challenged with balancing customer demand and service capacity. Recent studies have raised awareness of various demand and capacity management practices available to services, but little numerical work has been done to identify how these decisions work together and how they relate to one another. For instance, reducing prices may attract customers during a slow period, but the extent of impact this should have on cross‐training staff is not clear. A simulation based on theoretical and empirical insights explores the impact of various decisions on profitability and operations. The decisions modelled include the impact of: automation, customer participation, cross training employees, informing customers about the operation, and others. It is shown that demand and capacity decisions do indeed impact on each other – sometimes in ways that are not initially obvious. Results provide useful thought‐starters for service managers striving to improve their operations.

The purpose of this paper is to report on the development of a qualitative systems model developed to understand why average emergency department (ED) length of stay (LOS) was rapidly increasing while the number of ED visits was relatively constant. The paper's focus was to identify systemic causes for poor patient flow so that the model could then be used to evaluate improvement options using a more complete view of the causal structure for the ED delays.

In this case study, a disciplined system dynamics approach was used that included development of a dynamic hypothesis, causal loop and stock and flow diagramming, interviews with system experts, and data collection and analysis.

Results support the dynamic hypothesis that an aging population and shortages of resources to treat chronically ill patients (among other dynamics) were causing longer average LOS. Older and sicker patients were consuming more ED resources and causing less acute patients to leave without being seen or to avoid visiting the ED in the first place. In essence, the ED was acting as a safety valve for the wider health care system as many parts of this wider system became overloaded.

Owing to the systemic causes for the patient treatment delay problem in the ED, simple local solutions are unlikely to be effective. The system model can be used as a basis to understand the underlying dynamics of the systemic causes for poor patient flow and identify robust and long‐term solutions.

The paper presents a process for developing a dynamic model to engage the various participants in a health care system in understanding the causes for delays and poor patient flow. The modeling approach can be used as a means for health care managers/administrators to identify improvement options that address the systemic problems.

As reimbursements fall and costs for services climb, organizations are forced to follow the painful motto of doing more with less. A solution could be the adaptation of industrial business process improvement (BPI) methods such as Lean to the hospital setting (HS). The purpose of this paper is to analyze if Lean approaches related in the literature provide sufficient methodological support for other practitioners to reproduce the reported results.

The authors analyzed the published Lean literature in the HS using a methodological maturity-level framework and what the authors defined as the 11 characteristic activities of BPI.

The literature analysis reveals that a Lean approach with a high-methodological maturity level that includes the 11 characteristic activities of BPI has never been reported. Considering this, the paper suggests a meta model for a high-methodological maturity-level Lean method based on the characteristic activities of BPI.

This is the first study on the Lean approach in the HS that evidences the absence of a robust Lean methodology in the literature. For Lean to be adopted and implemented by hospital practitioners a structured robust method should be provided.

The need to match highly variable demand for healthcare services with existing capacity has been a significant health system challenge experienced by the COVID-19 pandemic. Despite mounting research on various COVID-19 responses in the recent literature, there has not been a structured review examining key determinants of health system volume flexibility. The focus of the present research is to review recent literature based on a conceptual framework developed based on the volume flexibility literature on COVID-19. The review also provides a meaningful way to guide practice and future research in the area.

A literature review was performed guided by the theoretical frameworks developed in the previous volume of flexibility literature. Selected research papers were reviewed and analyzed to identify key determinants of health system volume flexibility.

Seven determinants of health system volume flexibility under three domains (demand management, capacity management, and performance) included demand management strategies, integration, capacity management strategies, workforce management, technology, quality of disease outcomes, and health system knowledge.

The research provides both health systems researchers as well as practitioners with a foundational conceptual framework and knowledge related to health system volume flexibility. In addition, this research identifies future research areas to expand knowledge relevant to health system volume flexibility.

In the process industries, it is essential to have a well-articulated manufacturing strategy within companies. However, to facilitate manufacturing strategy development, it is important to start with a good characterisation of the material transformation system and company production capabilities. The paper aims to discuss these issues.

A grounded theory approach, with inspiration from configuration modelling, attempted to characterize the material transformation system as a set of variables. The variable development was based on a literature review and the knowledge base of five industry experts. Two exploratory mini-case studies were carried out, primarily to illustrate the use of the model, but additionally to test its industrial usability.

A set of 31 variables was developed, and related measures and scales were tentatively defined. Two mini-cases supported the usability of the model. The model, focussing on company generic process capabilities, is a conceptual taxonomy and the study’s theoretical contribution.

The lucidity of the definitions and scales for the variables are open to further refinement, and the limited discussions of variable relationships in this study are addressed in an agenda for further research.

The model can be deployed as a facilitative instrument in the analysis of company material transformation systems and may serve as a platform in further discussions on companies’ strategy development.

The model is a new instrument for analysing company generic process capabilities and an effort to build new theory rather than to test an existing one.

A major problem of operational control in the services industry is the integration of customers in the delivery process. The aim of this paper is to develop a method that allows service companies to evaluate the impact of customer integration on operational control in service processes.

The development of the proposed method follows a design science approach. Thus, the method is conceptualised on the basis of production, services and information systems research. A case study of loan processing in a bank serves to evaluate the applicability of the method.

As a result of this study, customer integration should be included into operational control following three steps: identification of the type of customer integration; quantification and characterisation of the impact of the integration; and identification of the appropriate mechanisms of operational control to deal with the customer integration better. The results of the case study show that customer integration has an impact on certain activities within a service process only but the results can be used to enhance operational control.

The method can be used by process managers of service companies to identify the impact of customer integration on operational control. Thus, decisions within operational control and consequently the overall productivity of a service process can be improved.

The paper delivers a new insight how customer integration and operational control can be linked in service processes. Thus, a theoretical gap in service operations literature is filled. Furthermore, the case study demonstrates how the method can be used in practice.