Capacity planning and appointment scheduling for new patient oncology consults.

To ensure that patients receive timely access to care, it has become increasingly important to use existing care provider capacity as efficiently as possible and to make informed capacity planning decisions. To support this decision-making process at a regional cancer center in British Columbia (Canada), we undertook a simulation and optimization based study that investigated the simultaneous impact of the available number of new patient consultation slots, appointment scheduling policies and oncologist specialization configurations on the timeliness of patient access to care and physician workload. The key contribution of this paper is the methodological framework it provides to decision makers who manage specialty clinics to ensure that they are using their resources efficiently and making informed strategic short- and mid-term capacity planning decisions for new patient demand.

Operations research methods improve chemotherapy patient appointment scheduling.

BACKGROUND: Clinical complexity, scheduling restrictions, and outdated manual booking processes resulted in frequent clerical rework, long waitlists for treatment, and late appointment notification for patients at a chemotherapy clinic in a large cancer center in British Columbia, Canada. A 17-month study was conducted to address booking, scheduling and workload issues and to develop, implement, and evaluate solutions. METHODS: A review of scheduling practices included process observation and mapping, analysis of historical appointment data, creation of a new performance metric (final appointment notification lead time), and a baseline patient satisfaction survey. Process improvement involved discrete event simulation to evaluate alternative booking practice scenarios, development of an optimization-based scheduling tool to improve scheduling efficiency, and change management for implementation of process changes. Results were evaluated through analysis of appointment data, a follow-up patient survey, and staff surveys. RESULTS: Process review revealed a two-stage scheduling process. Long waitlists and late notification resulted from an inflexible first-stage process. The second-stage process was time consuming and tedious. After a revised, more flexible first-stage process and an automated second-stage process were implemented, the median percentage of appointments exceeding the final appointment notification lead time target of one week was reduced by 57% and median waitlist size decreased by 83%. Patient surveys confirmed increased satisfaction while staff feedback reported reduced stress levels. CONCLUSION: Significant operational improvements can be achieved through process redesign combined with operations research methods.

Benefits and challenges of health information systems for operations research: an illustrative example to improve surgical scheduling.

Operations research (OR) is playing an increasing role in the support of many health care initiatives. However one of the main challenges facing OR practitioners is the availability and the integrity of operations data. Hospital information systems (HIS) are often designed with a clinical or accounting focus and may lack the data necessary for operational studies. In this paper, we illustrate the data processing methods and data challenges faced by our team during a study of surgical scheduling practices at the Vancouver Island Health Authority. We also provide some general recommendations to improve HIS from an operations perspective. In general, more integration between operations researchers and HIS specialists are required to support ongoing operational improvements in the health care sector.

Process data: a means to measure operational performance and implement advanced analytical models.

We present the case of an ambulatory clinic in which an operational review was conducted to identify opportunities for efficiency in appointment scheduling and capacity allocation. We required process data to compare that which was planned to that which actually happened and to develop advanced analytical models. Similar to other health care studies, these data proved to be limited or non-existent. Consequently we had to conduct a time-consuming collection of operational metrics. We make recommendations for the perpetual collection of process data for modeling and simulation.

Reducing wait times through operations research: optimizing the use of surge capacity.

Widespread public demand for improved access, political pressure for shorter wait times, a stretched workforce, an aging population and overutilized equipment and facilities challenge healthcare leaders to adopt new management approaches. This paper highlights the significant benefits that can be achieved by applying operations research (OR) methods to healthcare management. It shows how queuing theory provides managers with insights into the causes for excessive wait times and the relationship between wait times and capacity. It provides a case study of the use of several OR methods, including Markov decision processes, linear programming and simulation, to optimize the scheduling of patients with multiple priorities. The study shows that by applying this approach, wait time targets can be attained with the judicious use of surge capacity in the form of overtime. It concludes with some policy insights.

A Quantitative Analysis of the Relationship Between Radiation Therapy Use and Travel Time.

PURPOSE: To model and quantify the relationship between radiation therapy (RT) use and travel time to RT services. METHODS AND MATERIALS: Population-based registries and databases were used to identify both incident cancer patient and patients receiving RT within 1 year of diagnosis (RT1y) in British Columbia, Canada, between 1992 and 2011. The effects of age, gender, diagnosis year, income, prevailing wait time, and travel duration for RT on RT1y were assessed. Significant factors from univariate analyses were included in a multivariable logistic regression model. The shape of the travel time-RT1y curve was represented by generalized additive and segmented regression models. Analyses were conducted for breast, lung, and genitourinary cancer separately and for all cancer sites combined. RESULTS: After adjustment for age, gender, diagnosis year, income, and prevailing wait times, increasing travel time to the closest RT facility had a negative impact RT1y. The shape of the travel time-RT1y curve varied with cancer type. For breast cancer, the odds of RT1y were constant for the first 2 driving hours and decreased at 17% per hour thereafter. For lung cancer, the odds of RT1y decreased by 16% after 20 minutes and then decreased at 6% per hour. Genitourinary cancer RT1y was relatively independent of travel time. For all cancer sites combined, the odds of RT1y were constant within the first 2 driving hours and decreased at 7% per hour thereafter. CONCLUSIONS: Travel time to receive RT has a different impact on RT1y for different tumor sites. The results provide evidence-based insights for the configuration of catchment areas for new and existing cancer centers providing RT.

Dynamic scheduling with due dates and time windows: an application to chemotherapy patient appointment booking.

We study a scheduling problem in which arriving patients require appointments at specific future days within a treatment specific time window. This research is motivated by a study of chemotherapy scheduling practices at the British Columbia Cancer Agency (Canada). We formulate this problem as a Markov Decision Process (MDP). Since the resulting MDPs are intractable to exact methods, we employ linear-programming-based Approximate Dynamic Programming (ADP) to obtain approximate solutions. Using simulation, we compare the performance of the resulting ADP policies to practical and easy-to-use heuristic decision rules under diverse scenarios. The results indicate that ADP is promising in several scenarios, and that a specific easy-to-use heuristic performs well in the idealized chemotherapy scheduling setting we study.

Developing an adaptive policy for long-term care capacity planning.

This paper describes a refined methodology for determining long-term care (LTC) capacity levels over a multi-year planning horizon based on a previous study. The problem is to find a capacity level in each year during the planning horizon to meet a wait time service level criterion. Instead of a static policy for capacity planning, we proposal an adaptive policy, where the capacity level required in this year depends on the achieved service level in the last year as the state of the LTC system. We aggregate service levels into a few groups for tractability. Our methodology integrates a discrete event simulation for describing the LTC system and an optimization algorithm to find required capacity levels. We illustrate this methodology through a case study. The results show that the refined methodology overcomes the problems observed in the previous study. It also improves resource utilization greatly. To execute this adaptive policy in practice requires availability of surge or temporary capacity.

Residential long-term care capacity planning: the shortcomings of ratio-based forecasts.

This paper uses observations from two British Columbia studies to illustrate the shortcomings of widely used ratio-based approaches for residential long-term care capacity planning. It shows that capacity plans based on a fixed ratio of beds per population over age 75 may result in either excess capacity or long wait times for admission. It then investigates the use of linear regression models to obtain a "best" ratio by relating optimal plans derived by rigorous analytical methods to population characteristics and shows that no single ratio applies broadly. While the use of regression is promising, finding these "best" ratios is too analytically complex for general practice. The paper concludes by providing and evaluating an easy-to-use planning method, which we call the average flow model (AFM). The AFM combines demand forecasts with length-of-stay estimates to produce enhanced capacity plans. The AFM is transparent, easily implemented in a spreadsheet and well suited for "what if?" analyses.

Cet article porte sur les observations de deux études britanno-colombiennes afin de dégager les lacunes des démarches de planification de la capacité fondée sur les ratios, grandement utilisées dans les centres d'hébergement et de soins de longue durée. L'article montre que la planification fondée sur un ratio fixe de lits par habitant âgé de plus de 75 ans peut donner lieu soit à un excès de capacité, soit à de longs temps d'attente pour l'admission. L'article examine aussi l'utilisation de modèles de régression linéaire afin d'obtenir le «meilleur¼ ratio en associant des planifications optimales, dérivées de méthodes analytiques rigoureuses, aux caractéristiques de la population et démontre qu'aucun ratio unique ne peut s'appliquer à toutes les situations. Bien que l'emploi de la régression soit prometteur, la recherche des «meilleurs¼ ratios est trop complexe du point de vue analytique. L'article conclut en fournissant et en évaluant une méthode de planification facile à utiliser, que nous nommons le modèle du débit moyen (MDM). Le MDM combine les prévisions de la demande aux estimations des durées de séjour afin de produire une meilleure planification de la capacité. Le MDM est transparent, facile à utiliser dans un tableur et adéquat pour les analyses de simulation.

Optimizing nursing human resource planning in British Columbia.

This paper describes a linear programming hierarchical planning model that determines the optimal number of nurses to train, promote to management and recruit over a 20 year planning horizon to achieve specified workforce levels. Age dynamics and attrition rates of the nursing workforce are key model components. The model was developed to help policy makers plan a sustainable nursing workforce for British Columbia, Canada. An easy to use interface and considerable flexibility makes it ideal for scenario and "What-If?" analyses.

Reducing patient wait times and improving resource utilization at British Columbia Cancer Agency's ambulatory care unit through simulation.

We consider an ambulatory care unit (ACU) in a large cancer centre, where operational and resource utilization challenges led to overcrowding, excessive delays, and concerns regarding safety of critical patient care duties. We use simulation to analyze the simultaneous impact of operations, scheduling, and resource allocation on patient wait time, clinic overtime, and resource utilization. The impact of these factors has been studied before, but usually in isolation. Further, our model considers multiple clinics operating concurrently, and includes the extra burden of training residents and medical students during patient consults. Through scenario analyses we found that the best outcomes were obtained when not one but multiple changes were implemented simultaneously. We developed configurations that achieve a reduction of up to 70% in patient wait times and 25% in physical space requirements, with the same appointment volume. The key findings of the study are the importance of on time clinic start, the need for improved patient scheduling; and the potential improvements from allocating examination rooms flexibly and dynamically among individual clinics within each of the oncology programs. These findings are currently being evaluated for implementation by senior management.

Reducing Wait Times through Operations Research: Optimizing the Use of Surge Capacity.

Widespread public demand for improved access, political pressure for shorter wait times, a stretched workforce, an aging population and overutilized equipment and facilities challenge healthcare leaders to adopt new management approaches. This paper highlights the significant benefits that can be achieved by applying operations research (OR) methods to healthcare management. It shows how queuing theory provides managers with insights into the causes for excessive wait times and the relationship between wait times and capacity. It provides a case study of the use of several OR methods, including Markov decision processes, linear programming and simulation, to optimize the scheduling of patients with multiple priorities. The study shows that by applying this approach, wait time targets can be attained with the judicious use of surge capacity in the form of overtime. It concludes with some policy insights.

Using operations research to plan the british columbia registered nurses' workforce.

The authors explore the power and flexibility of using an operations research methodology known as linear programming to support health human resources (HHR) planning. The model takes as input estimates of the future need for healthcare providers and, in contrast to simulation, compares all feasible strategies to identify a long-term plan for achieving a balance between supply and demand at the least cost to the system. The approach is illustrated by using it to plan the British Columbia registered nurse (RN) workforce over a 20-year horizon. The authors show how the model can be used for scenario analysis by investigating the impact of decreasing attrition from educational programs, changing RN-to-manager ratios in direct care and exploring how other changes might alter planning recommendations. In addition to HHR policy recommendations, their analysis also points to new research opportunities.

Improving the efficiency of hospital porter services, part 2: schedule optimization and simulation model.

This article is the second of a 2-part series on a study of porter operations at Vancouver General Hospital, Vancouver, British Columbia, Canada. Part 1 describes the importance of efficient porter services, the system's operation at the time of the study, the challenges faced in carrying out the study, the performance measures developed, the recommendations, and the outcomes. Part 2 describes the simulation model that measured the impact of system changes and the linear programming model developed to improve porter schedules.

Improving the efficiency of hospital porter services, part 1: study objectives and results.

This article is the first of a 2-part series reporting the results of a 7-month study of porter operations at Vancouver General Hospital, Vancouver, British Columbia, Canada. Part 1 describes the importance of efficient porter services, the system's operation at the time of the study, the challenges faced in carrying out the study, the performance measures developed, the recommendations, and the outcomes. Part 2 describes the simulation model that measured the impact of system changes and the linear programming model developed to improve porter schedules.