Demand and Capacity Modelling of Acute Services Using Simulation and Optimization Techniques

The level of difficulty that hospital management have been experiencing over the past decade in terms of balancing demand and capacity needs has been at an unprecedented level in the UK. Due to shortage of capacity, hospitals are unable to treat patients, and in some cases, patients are transferred to other hospitals, outpatient referrals are delayed, and accident and emergency (A&E) waiting times are prolonged. So, it’s time to do things differently, because the current status quo is not an option. A whole hospital level decision support system (DSS) was developed to assess and respond to the needs of local populations. The model integrates every component of a hospital (including A&E, all outpatient and inpatient specialties) to aid with efficient and effective use of scarce resources. An individual service or a specialty cannot be assumed to be independent, they are all interconnected. It is clear from the literature that this level of generic hospital simulation model has never been developed before (so this is an innovative DSS). Using the Hospital Episode Statistics and local datasets, 768 forecasting models for the 28 outpatient and inpatient specialties are developed (to capture demand). Within this context, a variety of forecasting models (i.e. ARIMA, exponential smoothing, stepwise linear regression and STLF) for each specialty of outpatient and inpatient including the A&E department were developed. The best forecasting methods and periods were selected by comparing 4 forecasting methods and 3 periods (i.e. daily, weekly and monthly) according to forecast accuracy values calculated by the mean absolute scaled error (MASE). Demand forecasts were then used as an input into the simulation model for the entire hospital (all specialties). The generic hospital simulation model was developed by taking into account all specialties and interactions amongst the A&E, outpatient and inpatient specialties. Six hundred observed frequency distributions were established for the simulation model. All distributions used in the model were based on age groups. Using other inputs (i.e. financial inputs, number of follow ups, etc.), the hospital was therefore modelled to measure key output metrics in strategic planning. This decision support system eliminates the deficiencies of the current and past studies around modelling hospitals within a single framework. A new output metric which is called ‘demand coverage ratio’ was developed to measure the percentage of patients who are admitted and discharged with available resources of the associated specialty. In addition, a full factorial experimental design with 4 factors (A&E, elective and non-elective admissions and outpatient attendance) at 2 levels (possible 5% and 10% demand increases) was carried out in order to investigate the effects of demand increases on the key outputs (i.e. demand coverage ratio, bed occupancy rate and total revenue). As a result, each factor is found to affect total revenue, as well as the interaction between elective and non-elective admissions. The demand coverage ratio is affected by the changes in outpatient demands as well as A&E arrivals and non-elective admissions. In addition, the A&E arrivals, non-elective admissions and elective admissions are most important for bed occupancy rates, respectively. After an exhaustive review of the literature we notice that an entire hospital model has never been developed that combines forecasting, simulation and optimization techniques. A linear optimization model was developed to estimate the required bed capacity and staff needs of a mid-size hospital in England (using essential outputs from forecasting and forecasting-simulation) for each inpatient elective and non-elective specialty. In conclusion, these results will bring a different perspective to key decision makers with a decision support tool for short and long term strategic planning to make rational and realistic plans. This hospital decision support system can become a crucial instrument for decision makers for efficient service in hospitals in England and other parts of the world

Access and Resource Management for Clinical Care and Clinical Research in Multi-class Stochastic Queueing Networks.

In healthcare delivery systems, proper coordination between patient visits and the health care resources they rely upon is an area in which important new planning capabilities are very valuable to provide greater value to all stakeholders. Managing supply and demand, while providing an appropriate service level for various types of care and patients of differing levels of urgency is a difficult task to achieve. This task becomes even more complex when planning for (i) stochastic demand, (ii) multi-class customers (i.e., patients with different urgency levels), and (iii) multiple services/visit types (which includes multi-visit itineraries of clinical care and/or clinical research visits that are delivered according to research protocols). These complications in the demand stream require service waiting times and itineraries of visits that may span multiple days/weeks and may utilize many different resources in the organization (each resource with at least one specific service being provided). The key objective of this dissertation is to develop planning models for the optimization of capacity allocation while considering the coordination between resources and patient demand in these multi-class stochastic queueing networks in order to meet the service/access levels required for each patient class. This control can be managed by allocating resources to specific patient types/visits over a planning horizon. In this dissertation, we control key performance metrics that relate to patient access management and resource capacity planning in various healthcare settings with chapters devoted to outpatient services, and clinical research units. The methods developed forecast and optimize (1) the access to care (in a medical specialty) for each patient class, (2) the Time to First Available Visit for clinical research participants enrolling in clinical trials, and (3) the access to downstream resources in an itinerary of care, which we call the itinerary flow time. We also model and control how resources are managed, by incorporating (4) workload/utilization metrics, as well as (5) blocking/overtime probabilities of those resources. We control how to allocate resource capacity along the various multi-visit resource requirements of the patient itineraries, and by doing so, we capture the key correlations between patient access, and resource allocation, coordination, and utilization.PhDIndustrial and Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116770/1/jivan_1.pd

Analyst-driven development of an open-source simulation tool to address poor uptake of O.R. in healthcare

Computer simulation studies of health and care problems have been reported extensively in the academic literature, but the one-off research projects typically undertaken have failed to create an enduring legacy of widespread use by healthcare practitioners. Simulation and other modelling tools designed and developed to be used routinely have not fared much better either. Following a review of the literature and a survey of frontline analysts in the UK NHS, we found that one reason for this is because simulation tools have, to date, not been developed with the requirements of the end-user in the heart of the development process. Starting with a thorough needs assessment of NHS based healthcare analysts, this study outlines a set of practical design principles to guide development of simulation software tool for conducting patient flow simulation studies. The overall requirement is that patient flow be modelled over a number of inter-connected points of delivery while capturing the stochastic nature of patient arrivals and hospital length of stay, as well as the dynamic delays to patient discharge and transfer of care between different points of care delivery. In ensuring a cost-free solution that is both versatile and user-friendly, and coded in an increasingly popular language among the envisaged end users, the tool was implemented is the R programming language and software environment, with the user interface implemented in the interactive R-Shiny application. The talk will provide an overview of the project lifecycle including an illustrative example of an empirical simulation study concerning the centralisation of an acute stroke pathway

HOSX: Hospital operations excellence model

Hospital performance can be evaluated in four categories: (i) quality of care, (ii) process of care (iii) financial and (iv) operations productivity. Of these, ‘quality of care’ is the most widely reported and studied measure of performance, and focuses primarily on the clinical outcomes of the patient. In contrast, operations productivity and efficiency is the least studied measure, and currently there is limited ability to evaluate how efficiently the hospital has used its resources to deliver healthcare services. Cost containment in the healthcare industry is a challenging problem, and there is a lack of models and methods to benchmark hospital operating costs. Every hospital claims they are unique, and hence comparative assessments across hospitals cannot be made effectively. This research presents a performance framework for hospital operations to be called HOSx: Hospital Operations Excellence Model, used to measure and evaluate the operations productivity of hospitals. A key part of this research is healthcare activity data extracted from Medicare Provider Analysis and Review (MedPAR) database and the Healthcare Provider Cost Reporting Information System (HCRIS), both of which are maintained by the Center for Medicare Services (CMS). A key obstacle to hospital productivity measurement is defining a standard unit of output. Traditionally used units of output are inpatient day, adjusted patient day (APD) and adjusted discharge, which are reasonable estimators of patient volume, but are fundamentally limited in that they assume that all patients are equivalent. This research develops a standardized productivity output measure for a Hospital Unit of Care (HUC), which is defined as the resources required to provide one general medical/surgical inpatient day. The HUC model views patient care as a series of healthcare related activities that are designed to provide the needed quality of care for the specific disease. A healthcare activity is defined as a patient centric activity prescribed by physicians and requiring the direct use of hospital resources. These resources include (i) clinical staff (ii) non-clinical staff (iii) equipment (iv) supplies and (v) facilities plus other indirect resources. The approach followed here is to derive a roll-up equivalency parameter for each of the additional care/services activities that the hospital provides. Six HUC components are proposed: (i) case-mix adjusted inpatient days (ii) discharge disposition (iii) intensive care (iv) nursery (v) outpatient care and (vi) ancillary services. The HUC is compatible with the Medicare Cost Report data format. Model application is demonstrated on a set of 17 honor roll hospitals using data from MedPar 2011. An expanded application on 203 hospitals across multiple U.S. states shows that the HUC is significantly better correlated than the more traditional APD to hospital operating costs. The HUC measure will facilitate the development of an array of models and methods to benchmark hospital operating costs, productivity and efficiency. This research develops two hospital operations metrics. The first is the Hospital Resource Efficiency (HRE), which is defined as operating cost per Hospital Unit of Care, and the second is the Hospital Productivity Index, which benchmarks performance across the reference set of hospitals. Productivity analysis of all 203 hospitals in our database was conducted using these two measures. Specific factors studied include (i) functional areas (ii) patient volume (iii) geographical location. The results provide for the first time a ranking of most productive hospitals in each state – New Jersey, Pennsylvania, Nebraska, South Dakota and Washington as well as an interstate ranking. This research also provides detailed analysis of all outlier hospitals and causes of productivity variance in hospitals. The final output, the Hospital Total Performance Matrix combines clinical performance with productivity to identify the leading U.S. hospitals

Scheduling Elective Surgeries in Multiple Operating Rooms

This thesis focuses on the problem of designing appointment schedules in a surgery center with multiple operating rooms. The conditions under which overlapping surgeries in the surgeons’ schedule (i.e. parallel surgery processing) at the lowest cost are investigated with respect to three components of the total cost: waiting time, idle time, and overtime. A simulation optimization method is developed to find the near-optimal appointment schedules for elective surgical procedures in the presence of uncertain surgery durations. The analysis is performed in three steps. First, three near-optimal operating room schedules are found for different cost configurations based on the secondary data of surgery durations obtained from the Canadian Institute for Health Information. Second, these near-optimal appointment schedules are used to test a parallel scheduling policy where each surgeon has overlapping surgeries scheduled in two operating rooms for the entire session (480 minutes) and only attends the critical portions of surgeries in the two operating rooms. Lastly, another parallel scheduling policy is tested where each surgeon has overlapping surgeries scheduled for half of the session duration (240 minutes) and only has surgeries scheduled in one operating room for the remaining time. These two policies are tested using simulation with scenarios for parallelizable portions of surgeries varying from 0.1 to 0.9 at 0.1 increments and three cost configurations. In the simulated scenarios, the total cost is calculated as the weighted sum of patient waiting time, surgeon idle time, surgeon overtime, operating room idle time, and operating room overtime. Out of the nine scenarios for each policy and each cost configuration, the parallelizable portion of surgeries that result in the lowest total cost is identified. The results from both policies indicate that implementing parallel scheduling policies for surgery types with higher parallelizable portions results in surgeons remaining idle for longer periods during the session. This idle time cost is justified by a decrease in other cost components for surgeries with parallelizable portions 50% or less; however, the total cost is higher for surgeries with parallelizable portions over 50%. In addition, it has been observed that overlapping surgeries with lower parallelizable portions is more expensive than overlapping those over with 50%. Therefore, it is concluded that the surgery types that allow parallel surgery scheduling policies to be implemented at the lowest cost have 50% of their duration parallelizable

Multi-objective Operating Room Planning and Scheduling

abstract: Surgery is one of the most important functions in a hospital with respect to operational cost, patient flow, and resource utilization. Planning and scheduling the Operating Room (OR) is important for hospitals to improve efficiency and achieve high quality of service. At the same time, it is a complex task due to the conflicting objectives and the uncertain nature of surgeries. In this dissertation, three different methodologies are developed to address OR planning and scheduling problem. First, a simulation-based framework is constructed to analyze the factors that affect the utilization of a catheterization lab and provide decision support for improving the efficiency of operations in a hospital with different priorities of patients. Both operational costs and patient satisfaction metrics are considered. Detailed parametric analysis is performed to provide generic recommendations. Overall it is found the 75th percentile of process duration is always on the efficient frontier and is a good compromise of both objectives. Next, the general OR planning and scheduling problem is formulated with a mixed integer program. The objectives include reducing staff overtime, OR idle time and patient waiting time, as well as satisfying surgeon preferences and regulating patient flow from OR to the Post Anesthesia Care Unit (PACU). Exact solutions are obtained using real data. Heuristics and a random keys genetic algorithm (RKGA) are used in the scheduling phase and compared with the optimal solutions. Interacting effects between planning and scheduling are also investigated. Lastly, a multi-objective simulation optimization approach is developed, which relaxes the deterministic assumption in the second study by integrating an optimization module of a RKGA implementation of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to search for Pareto optimal solutions, and a simulation module to evaluate the performance of a given schedule. It is experimentally shown to be an effective technique for finding Pareto optimal solutions.Dissertation/ThesisPh.D. Industrial Engineering 201

A Simulation Study of Centralized versus Decentralized Healthcare Admission Processes

With an expanding number of healthcare clinics and a growing trend of consolidation, the possibility of a centralized location performing admission for multiple clinics has been presented as a possible method to save on operational costs. This centralized approach would cause significant changes in the admission process for decentralized clinics that have their own admission processes, and the effect on quality of care cannot be ignored when deciding which method to use. To determine the characteristics under which a centralized or decentralized admission system would be better, a discrete event simulation methodology is designed and utilized to compare the alternative approaches. Using the model and real-world data, a better understanding of the criteria that works best for each system can be gained and used as a guide for clinical organizations considering this choice. An experimental performance evaluation investigates factors including arrival rate per day, the mixture of patients for each clinic, the percentage of patients who have multiple appointments, the travel time to clinics, and the number of clinics in each system. Overall these experiments reveal that a centralized system can obtain the same or faster wait times than the decentralized system with less staffing in certain scenarios such as an increase in the number of clinics and number of multiple appointment patients. However, the centralized system with fewer staff can result in slightly higher maximum wait times than the decentralized model. A validation case study, supports the results and demonstrates the usefulness of the simulation methodology

Modeling change in a health system: Implications on patient flows and resource allocations

This work is motivated by the recent changes in the health system in Turkey, which is a consolidation of health insurance funds, and its implications on the resource allocations and the flow of patients in the system. Our aim is to provide a model to find the best reallocation of resources between the hospitals and the best patient-hospital match to minimize the costs. © 2005 CIM

A systematic literature review of operational research methods for modelling patient flow and outcomes within community healthcare and other settings

An ambition of healthcare policy has been to move more acute services into community settings. This systematic literature review presents analysis of published operational research methods for modelling patient flow within community healthcare, and for modelling the combination of patient flow and outcomes in all settings. Assessed for inclusion at three levels – with the references from included papers also assessed – 25 “Patient flow within community care”, 23 “Patient flow and outcomes” papers and 5 papers within the intersection are included for review. Comparisons are made between each paper’s setting, definition of states, factors considered to influence flow, output measures and implementation of results. Common complexities and characteristics of community service models are discussed with directions for future work suggested. We found that in developing patient flow models for community services that use outcomes, transplant waiting list may have transferable benefits

Statistical methods for NHS incident reporting data

The National Reporting and Learning System (NRLS) is the English and Welsh NHS’ national repository of incident reports from healthcare. It aims to capture details of incident reports, at national level, and facilitate clinical review and learning to improve patient safety. These incident reports range from minor ‘near-misses’ to critical incidents that may lead to severe harm or death. NRLS data are currently reported as crude counts and proportions, but their major use is clinical review of the free-text descriptions of incidents. There are few well-developed quantitative analysis approaches for NRLS, and this thesis investigates these methods. A literature review revealed a wealth of clinical detail, but also systematic constraints of NRLS’ structure, including non-mandatory reporting, missing data and misclassification. Summary statistics for reports from 2010/11 – 2016/17 supported this and suggest NRLS was not suitable for statistical modelling in isolation. Modelling methods were advanced by creating a hybrid dataset using other sources of hospital casemix data from Hospital Episode Statistics (HES). A theoretical model was established, based on ‘exposure’ variables (using casemix proxies), and ‘culture’ as a random-effect. The initial modelling approach examined Poisson regression, mixture and multilevel models. Overdispersion was significant, generated mainly by clustering and aggregation in the hybrid dataset, but models were chosen to reflect these structures. Further modelling approaches were examined, using Generalized Additive Models to smooth predictor variables, regression tree-based models including Random Forests, and Artificial Neural Networks. Models were also extended to examine a subset of death and severe harm incidents, exploring how sparse counts affect models. Text mining techniques were examined for analysis of incident descriptions and showed how term frequency might be used. Terms were used to generate latent topics models used, in-turn, to predict the harm level of incidents. Model outputs were used to create a ‘Standardised Incident Reporting Ratio’ (SIRR) and cast this in the mould of current regulatory frameworks, using process control techniques such as funnel plots and cusum charts. A prototype online reporting tool was developed to allow NHS organisations to examine their SIRRs, provide supporting analyses, and link data points back to individual incident reports