Using computer simulation to reduce access time for outpatient departments

Redistribution of Doctors and Decentralization of Clinics Improved Utilization of Services, Demand, and Capacity of Hamad Medical Corporation’s Staff Clinic

Background: The Staff Medical Clinic (SMC) of the Hamad Medical Corporation (HMC) serves the staff members who require healthcare services, but in a crowded environment, the SMC can only meet 75% of that demand. Overcrowding reduces productivity and service quality and increases waiting time. Furthermore, overcrowding in healthcare facilities decreases the experience and satisfaction of patients and healthcare providers.

Aim: The main objective of this study was to use simulation modeling to evaluate interventions that could improve SMC waiting time and efficiency.

Method: Eighteen months of data on SMC patient flow, staffing, and clinical sessions were collected (January 2018 to June 2019). The patient's journey through the SMC was modeled as a series of processes with assigned durations defined mathematically using the appropriate probability distribution. A simulation flow model was developed considering the locations of the staff and nearby main hospital facilities. An intervention was proposed and evaluated through a simulation. The intervention involved redistributing 25% of the SMC staff into three main satellite clinics located at the facilities where most of the SMC patients came. 

Results: The proposed intervention decreased crowding by 37%, reduced staffing requirements by 28%, and increased the number of patient slots by 22%, resulting in a net increase in the number of patients served by an average of 1250 monthly, without the need for hiring new additional staffing.

Conclusion: Redistribution of the available medical staff to three new satellite clinics reduces workload pressure at all sites and increases clinic capacity without additional costs.

Operational Modeling with Health Economics to Support Decision Making for COPD Patients

OBJECTIVE

To assess the impact of interventions for improving the management of chronic obstructive pulmonary disease (COPD), specifically increased use of pulmonary rehabilitation (PR) on patient outcomes and cost-benefit analysis.

DATA SOURCES

We used the national Hospital Episode Statistics (HES) datasets in England, local data and experts from the hospital setting, National Prices and National Tariffs, reports and the literature around the effectiveness of PR programs.

STUDY DESIGN

The COPD pathway was modeled using discrete event simulation (DES) to capture the patient pathway to an adequate level of detail as well as randomness in the real world. DES was further enhanced by the integration of a health economic model to calculate the net benefit and cost of treating COPD patients based on key sets of interventions.

DATA COLLECTION/EXTRACTION METHODS

A total of 150 input parameters and 75 distributions were established to power the model using the HES dataset, outpatient activity data from the hospital and community services, and the literature.

PRINCIPAL FINDINGS

The simulation model showed that increasing referral to PR (by 10%, 20%, or 30%) would be cost-effective (with a benefit-cost ratio of 5.81, 5.95, and 5.91, respectively) by having a positive impact on patient outcomes and operational metrics. Number of deaths, admissions, and bed days decreased (ie, by 3.56 patients, 4.90 admissions, and 137.31 bed days for a 30% increase in PR referrals) as well as quality of life increased (ie, by 5.53 QALY among 1540 patients for the 30% increase).

CONCLUSIONS

No operational model, either statistical or simulation, has previously been developed to capture the COPD patient pathway within a hospital setting. To date, no model has investigated the impact of PR on COPD services, such as operations, key performance, patient outcomes, and cost-benefit analysis. The study will support policies around extending availability of PR as a major intervention.

Implementation of simulation modelling to improve service planning in specialist orthopaedic and neurosurgical outpatient services

BACKGROUND

Advanced physiotherapist-led services have been embedded in specialist orthopaedic and neurosurgical outpatient departments across Queensland, Australia, to ameliorate capacity constraints. Simulation modelling has been used to inform the optimal scale and professional mix of services required to match patient demand. The context and the value of simulation modelling in service planning remain unclear. We aimed to examine the adoption, context and costs of using simulation modelling recommendations to inform service planning.

METHODS

Using an implementation science approach, we undertook a prospective, qualitative evaluation to assess the use of discrete event simulation modelling recommendations for service re-design and to explore stakeholder perspectives about the role of simulation modelling in service planning. Five orthopaedic and neurosurgical services in Queensland, Australia, were selected to maximise variation in implementation effectiveness. We used the consolidated framework for implementation research (CFIR) to guide the facilitation and analysis of the stakeholder focus group discussions. We conducted a prospective costing analysis in each service to estimate the costs associated with using simulation modelling to inform service planning.

RESULTS

Four of the five services demonstrated adoption by inclusion of modelling recommendations into proposals for service re-design. Four CFIR constructs distinguished and two CFIR constructs did not distinguish between high versus mixed implementation effectiveness. We identified additional constructs that did not map onto CFIR. The mean cost of implementation was AU$34,553 per site (standard deviation = AU$737).

CONCLUSIONS

To our knowledge, this is the first time the context of implementing simulation modelling recommendations in a health care setting, using a validated framework, has been examined. Our findings may provide valuable insights to increase the uptake of healthcare modelling recommendations in service planning.

Demand, capacity, and access of the outpatient clinic: A framework for analysis and improvement

RATIONALE

While theoretical frameworks for optimization of the outpatient processes are abundant, practical step-by-step analyses to give leads for improvement, to forecast capacity, and to support decision making are sparse.

AIMS AND OBJECTIVES

This article demonstrates how to evaluate and optimize the triad of demand, (future) capacity, and access time of the outpatient clinic using a structured six-step method.

METHODS

All individual logistical patient data of an orthopaedic outpatient clinic of one complete year were analysed using a 6-step method to evaluate demand, supply, and access time. Trends in the data were retrospectively analysed and evaluated for potential improvements. A model for decision making was tested. Both the analysis of the method and actual results were considered as main outcomes.

RESULTS

More than 25 000 appointments were analysed. The 6-step method showed to be sufficient to result in valuable insights and leads for improvement. While the overall match between demand and capacity was considered adequate, the variability in capacity was much higher than in demand, thereby leading to delays in access time. Holidays and subsequent weeks showed to be of great influence for demand, capacity, and access time. Using the six-step method, several unfavourable characteristics of the outpatient clinic were revealed and a better match between demand, supply, and access time could have been reached with only minor adjustments. Last, a clinic specific prediction and decision model for demand and capacity was made using the 6-step method.

CONCLUSIONS

The 6-step analysis can successfully be applied to redesign and improve the outpatient health care process. The results of the analysis showed that national holidays and variability in demand and capacity have a big influence on the outpatient clinic. Using the 6-step method, practical improvements in outpatient logistics were easily found and leads for future decision making were contrived.

Static and dynamic appointment scheduling to improve patient access time

Appointment schedules for outpatient clinics have great influence on efficiency and timely access to health care services. The number of new patients per week fluctuates, and capacity at the clinic varies because physicians have other obligations. However, most outpatient clinics use static appointment schedules, which reserve capacity for each patient type. In this paper, we aim to optimise appointment scheduling with respect to access time, taking fluctuating patient arrivals and unavailabilities of physicians into account. To this end, we formulate a stochastic mixed integer programming problem, and approximate its solution invoking two different approaches: (1) a mixed integer programming approach that results in a static appointment schedule, and (2) Markov decision theory, which results in a dynamic scheduling strategy. We apply the methodologies to a case study of the surgical outpatient clinic of the Jeroen Bosch Hospital. We evaluate the effectiveness and limitations of both approaches by discrete event simulation; it appears that allocating only 2% of the capacity flexibly already increases the performance of the clinic significantly.

Staffs' and managers' perceptions of how and when discrete event simulation modelling can be used as a decision support in quality improvement: a focus group discussion study at two hospital settings in Sweden

OBJECTIVE

To explore healthcare staffs' and managers' perceptions of how and when discrete event simulation modelling can be used as a decision support in improvement efforts.

DESIGN

Two focus group discussions were performed.

SETTING

Two settings were included: a rheumatology department and an orthopaedic section both situated in Sweden.

PARTICIPANTS

Healthcare staff and managers (n=13) from the two settings.

INTERVENTIONS

Two workshops were performed, one at each setting. Workshops were initiated by a short introduction to simulation modelling. Results from the respective simulation model were then presented and discussed in the following focus group discussion.

RESULTS

Categories from the content analysis are presented according to the following research questions: how and when simulation modelling can assist healthcare improvement? Regarding how, the participants mentioned that simulation modelling could act as a tool for support and a way to visualise problems, potential solutions and their effects. Regarding when, simulation modelling could be used both locally and by management, as well as a pedagogical tool to develop and test innovative ideas and to involve everyone in the improvement work.

CONCLUSIONS

Its potential as an information and communication tool and as an instrument for pedagogic work within healthcare improvement render a broader application and value of simulation modelling than previously reported.

Reducing postponements of elective pediatric cardiac procedures: analysis and implementation of a discrete event simulation model

BACKGROUND

This study describes the use of discrete event simulation (DES) to model and analyze a large academic pediatric and test cardiac center. The objective was to identify a strategy, and to predict and test the effectiveness of that strategy, to minimize the number of elective cardiac procedures that are postponed because of a lack of available cardiac intensive care unit (CICU) capacity.

METHODS

A DES of the cardiac center at The Children's Hospital of Philadelphia was developed and was validated by use of 1 year of deidentified administrative patient data. The model was then used to analyze strategies for reducing postponements of cases requiring CICU care through improved scheduling of multipurpose space. Each of five alternative scenarios was simulated for ten independent 1-year runs.

RESULTS

Reductions in simulated elective procedure postponements were found when a multipurpose procedure room (the hybrid room) was used for operations on Wednesday and Thursday, compared with Friday (as was the real-world use). The reduction Wednesday was statistically significant, with postponements dropping from 27.8 to 23.3 annually (95% confidence interval 18.8-27.8). Thus, we anticipate a relative reduction in postponements of 16.2%. Since the implementation, there have been two postponements from July 1 to November 21, 2014, compared with ten for the same time period in 2013.

CONCLUSIONS

Simulation allows us to test planned changes in complex environments, including pediatric cardiac care. Reduction in postponements of cardiac procedures requiring CICU care is predicted through reshuffling schedules of existing multipurpose capacity, and these reductions appear to be achievable in the real world after implementation.

Scalable approximate policies for Markov decision process models of hospital elective admissions

OBJECTIVE

To demonstrate the feasibility of using stochastic simulation methods for the solution of a large-scale Markov decision process model of on-line patient admissions scheduling.

METHODS

The problem of admissions scheduling is modeled as a Markov decision process in which the states represent numbers of patients using each of a number of resources. We investigate current state-of-the-art real time planning methods to compute solutions to this Markov decision process. Due to the complexity of the model, traditional model-based planners are limited in scalability since they require an explicit enumeration of the model dynamics. To overcome this challenge, we apply sample-based planners along with efficient simulation techniques that given an initial start state, generate an action on-demand while avoiding portions of the model that are irrelevant to the start state. We also propose a novel variant of a popular sample-based planner that is particularly well suited to the elective admissions problem.

RESULTS

Results show that the stochastic simulation methods allow for the problem size to be scaled by a factor of almost 10 in the action space, and exponentially in the state space. We have demonstrated our approach on a problem with 81 actions, four specialities and four treatment patterns, and shown that we can generate solutions that are near-optimal in about 100s.

CONCLUSION

Sample-based planners are a viable alternative to state-based planners for large Markov decision process models of elective admissions scheduling.

Reducing outpatient waiting time: a simulation modeling approach

Objectives

The objective of this study was to provide a model for reducing outpatient waiting time by using simulation.

Materials and Methods

A simulation model was constructed by using the data of arrival time, service time and flow of 357 patients referred to orthopedic clinic of a general teaching hospital in Tehran. The simulation model was validated before constructing different scenarios.

Results

In this study 10 scenarios were presented for reducing outpatient waiting time. Patients waiting time was divided into three levels regarding their physicians. These waiting times for all scenarios were computed by simulation model. According to the final scores the 9th scenario was selected as the best way for reducing outpatient's waiting time.

Conclusions

Using the simulation as a decision making tool helps us to decide how we can reduce outpatient's waiting time. Comparison of outputs of this scenario and the based- case scenario in simulation model shows that combining physician's work time changing with patient's admission time changing (scenario 9) would reduce patient waiting time about 73.09%. Due to dynamic and complex nature of healthcare systems, the application of simulation for the planning, modeling and analysis of these systems has lagged behind traditional manufacturing practices. Rapid growth in health care system expenditures, technology and competition has increased the complexity of health care systems. Simulation is a useful tool for decision making in complex and probable systems.

Tactical resource allocation and elective patient admission planning in care processes

Tactical planning of resources in hospitals concerns elective patient admission planning and the intermediate term allocation of resource capacities. Its main objectives are to achieve equitable access for patients, to meet production targets/to serve the strategically agreed number of patients, and to use resources efficiently. This paper proposes a method to develop a tactical resource allocation and elective patient admission plan. These tactical plans allocate available resources to various care processes and determine the selection of patients to be served that are at a particular stage of their care process. Our method is developed in a Mixed Integer Linear Programming (MILP) framework and copes with multiple resources, multiple time periods and multiple patient groups with various uncertain treatment paths through the hospital, thereby integrating decision making for a chain of hospital resources. Computational results indicate that our method leads to a more equitable distribution of resources and provides control of patient access times, the number of patients served and the fraction of allocated resource capacity. Our approach is generic, as the base MILP and the solution approach allow for including various extensions to both the objective criteria and the constraints. Consequently, the proposed method is applicable in various settings of tactical hospital management.

Process analysis to reduce MRI access time at a German University Hospital

QUALITY PROBLEM OR ISSUE

Long access times for magnetic resonance imaging (MRI) can negatively impact the quality of care provided to patients. We investigated improving access by reducing MRI processing time.

INITIAL ASSESSMENT

Data were collected for scans (n= 360) performed over 3 weeks (April-May 2008) at the University Hospital of Mannheim, Germany. Average access time, excluding emergencies, was 44 (±44) days for outpatients and 3 (±5) days for inpatients. Factors influencing total MRI processing time were identified using multivariate linear regression. In addition to region scanned, the total MRI processing time was significantly related to performing multiple scans (β = 33.57, P< 0.01), using oral contrast media (β = 13.58, P< 0.01), placing an intravenous (IV) catheter (β = 5.00, P= 0.04) and scanning patients ≤8 years old (β = 0.41, P= 0.03). Contrary to prior perceptions, emergency cases (5.6%) and late arrivals (12.8% >5 min late) were less than expected.

CHOICE OF SOLUTION

Increasing scheduling flexibility to address non-modifiable process variation and completing preparatory activities outside the scanner room were identified as process improvement targets.

IMPLEMENTATION

Scheduling was adapted to utilize three expected total MRI processing times and IV placement was moved outside the scanner room.

EVALUATION

Planned hardware and software upgrades were completed concurrent to the process improvements. As a result, it was not possible to accurately measure the effect of implementing the scheduling and preparatory activity changes.

LESSONS LEARNED

Clinical study team members' prior perceptions of workflow obstacles did not match the study findings. Utilizing insiders and outsiders during process analysis may limit bias in identification of process improvement opportunities.

Managing health care decisions and improvement through simulation modeling

Simulation modeling is a way to test changes in a computerized environment to give ideas for improvements before implementation. This article reviews research literature on simulation modeling as support for health care decision making. The aim is to investigate the experience and potential value of such decision support and quality of articles retrieved. A literature search was conducted, and the selection criteria yielded 59 articles derived from diverse applications and methods. Most met the stated research-quality criteria. This review identified how simulation can facilitate decision making and that it may induce learning. Furthermore, simulation offers immediate feedback about proposed changes, allows analysis of scenarios, and promotes communication on building a shared system view and understanding of how a complex system works. However, only 14 of the 59 articles reported on implementation experiences, including how decision making was supported. On the basis of these articles, we proposed steps essential for the success of simulation projects, not just in the computer, but also in clinical reality. We also presented a novel concept combining simulation modeling with the established plan-do-study-act cycle for improvement. Future scientific inquiries concerning implementation, impact, and the value for health care management are needed to realize the full potential of simulation modeling.

Factors Relating to Patient Visit Time With a Physician

This study sought to identify factors that increase or decrease patient time with a physician, determine which combinations of factors are associated with the shortest and longest visits to physicians, quantify how much physicians contribute to variation in the time they spend with patients, and assess how well patient time with a physician can be predicted. Data were acquired from a modified replication of the 1997–1998 National Ambulatory Medical Care Survey, administered by the Kentucky Ambulatory Network to 56 primary care clinicians at 24 practice sites in 2001 and 2002. A regression tree and a linear mixed model (LMM) were used to discover multivariate associations between patient time with a physician and 22 potentially predictive factors. Patient time with a physician was related to the number of diagnoses, whether non-illness care was received, and whether the patient had been seen before by the physician or someone at the practice. Approximately 38% of the variation in patient time with a physician was accounted for by predictive factors in the tree; roughly 33% was explained by predictive factors in the LMM, with another 12% linked to physicians. Knowledge of patient characteristics and needs could be used to schedule office visits, potentially improving patient flow through a clinic and reducing waiting times.

Improving clinical access and continuity through physician panel redesign

BACKGROUND

Population growth, an aging population and the increasing prevalence of chronic disease are projected to increase demand for primary care services in the United States.

OBJECTIVE

Using systems engineering methods, to re-design physician patient panels targeting optimal access and continuity of care.

DESIGN

We use computer simulation methods to design physician panels and model a practice's appointment system and capacity to provide clinical service. Baseline data were derived from a primary care group practice of 39 physicians with over 20,000 patients at the Mayo Clinic in Rochester, MN, for the years 2004-2006. Panel design specifically took into account panel size and case mix (based on age and gender).

MEASURES

The primary outcome measures were patient waiting time and patient/clinician continuity. Continuity is defined as the inverse of the proportion of times patients are redirected to see a provider other than their primary care physician (PCP).

RESULTS

The optimized panel design decreases waiting time by 44% and increases continuity by 40% over baseline. The new panel design provides shorter waiting time and higher continuity over a wide range of practice panel sizes.

CONCLUSIONS

Redesigning primary care physician panels can improve access to and continuity of care for patients.

Reducing access times for an endoscopy department by an iterative combination of computer simulation and linear programming

To reduce the access times of an endoscopy department, we developed an iterative combination of Discrete Event simulation and Integer Linear Programming. We developed the method in the Endoscopy Department of the Academic Medical Center in Amsterdam and compared different scenarios to reduce the access times for the department. The results show that by a more effective allocation of the current capacity, all procedure types will meet their corresponding performance targets in contrast to the current situation. This improvement can be accomplished without requiring additional equipment and staff. Currently, our recommendations are implemented.

Reducing access times for an endoscopy department by an iterative combination of computer simulation and linear programming

To reduce the access times of an endoscopy department, we developed an iterative combination of Discrete Event simulation and Integer Linear Programming. We developed the method in the Endoscopy Department of the Academic Medical Center in Amsterdam and compared different scenarios to reduce the access times for the department. The results show that by a more effective allocation of the current capacity, all procedure types will meet their corresponding performance targets in contrast to the current situation. This improvement can be accomplished without requiring additional equipment and staff. Currently, our recommendations are implemented.

How work context affects operating room processes: using data mining and computer simulation to analyze facility and process design

The complexity of the operating room (OR) requires that both structural (eg, department layout) and behavioral (eg, staff interactions) patterns of work be considered when developing quality improvement strategies. In our study, we investigated how these contextual factors influence outpatient OR processes and the quality of care delivered. The study setting was a German university-affiliated hospital performing approximately 6000 outpatient surgeries annually. During the 3-year-study period, the hospital significantly changed its outpatient OR facility layout from a decentralized (ie, ORs in adjacent areas of the building) to a centralized (ie, ORs in immediate vicinity of each other) design. To study the impact of the facility change on OR processes, we used a mixed methods approach, including process analysis, process modeling, and social network analysis of staff interactions. The change in facility layout was seen to influence OR processes in ways that could substantially affect patient outcomes. For example, we found a potential for more errors during handovers in the new centralized design due to greater interdependency between tasks and staff. Utilization of the mixed methods approach in our analysis, as compared with that of a single assessment method, enabled a deeper understanding of the OR work context and its influence on outpatient OR processes.

[Economic benefits of overlapping induction: investigation using a computer simulation model]

The aim of this study was to investigate the potential economic benefit of overlapping anaesthesia induction given that all patient diagnosis-related groups (AP DRG) are used as the model for hospital reimbursement. A computer simulation model was used for this purpose. Due to the resource-intensive production process, the operating room (OR) environment is the most expensive part of the supply chain for surgical disciplines. The economical benefit of a parallel production process (additional personnel, adaptation of the process) as compared to a conventional serial layout was assessed. A computer-based simulation method was used with commercially available simulation software. Assumptions for revenues were made by reimbursement based on AP DRG. Based on a system analysis a model for the computer simulation was designed on a step-by-step abstraction process. In the model two operating rooms were used for parallel processing and two operating rooms for a serial production process. Six different types of surgical procedures based on historical case durations were investigated. The contribution margin was calculated based on the increased revenues minus the cost for the additional anaesthesia personnel. Over a period of 5 weeks 41 additional surgical cases were operated under the assumption of duration of surgery of 89+/-4 min (mean+/-SD). The additional contribution margin was CHF 104,588. In the case of longer surgical procedures with 103+/-25 min duration (mean+/-SD), an increase of 36 cases was possible in the same time period and the contribution margin was increased by CHF 384,836. When surgical cases with a mean procedural time of 243+/-55 min were simulated, 15 additional cases were possible. Therefore, the additional contribution margin was CHF 321,278. Although costs increased in this simulation when a serial production process was changed to a parallel system layout due to more personnel, an increase of the contribution margin was possible, especially with procedures of shorter duration (<120 min). For longer surgical times, the additional costs for the workforce result in a reduced contribution margin depending on the models chosen to handle overtime of the technical OR personnel. Important advantages of this approach for simulation are the use of the historical production data and the reflection of the specificities of the local situation. Computer simulation is an ideal tool to support operation room management, particularly regarding the planning of resource allocation and the coordination of workflow.

The SQUIRE (Standards for QUality Improvement Reporting Excellence) guidelines for quality improvement reporting: explanation and elaboration

As the science of quality improvement in health care advances, the importance of sharing its accomplishments through the published literature increases. Current reporting of improvement work in health care varies widely in both content and quality. It is against this backdrop that a group of stakeholders from a variety of disciplines has created the Standards for QUality Improvement Reporting Excellence, which we refer to as the SQUIRE publication guidelines or SQUIRE statement. The SQUIRE statement consists of a checklist of 19 items that authors need to consider when writing articles that describe formal studies of quality improvement. Most of the items in the checklist are common to all scientific reporting, but virtually all of them have been modified to reflect the unique nature of medical improvement work. This "Explanation and Elaboration" document (E & E) is a companion to the SQUIRE statement. For each item in the SQUIRE guidelines the E & E document provides one or two examples from the published improvement literature, followed by an analysis of the ways in which the example expresses the intent of the guideline item. As with the E & E documents created to accompany other biomedical publication guidelines, the purpose of the SQUIRE E & E document is to assist authors along the path from completion of a quality improvement project to its publication. The SQUIRE statement itself, this E & E document, and additional information about reporting improvement work can be found at http://www.squire-statement.org.