Jonathan Patrick

Dynamic Multipriority Patient Scheduling for a Diagnostic Resource

We present a method to dynamically schedule patients with different priorities to a diagnostic facility in a public health-care setting. Rather than maximizing revenue, the challenge facing the resource manager is to dynamically allocate available capacity to incoming demand to achieve wait-time targets in a cost-effective manner. We model the scheduling process as a Markov decision process. Because the state space is too large for a direct solution, we solve the equivalent linear program through approximate dynamic programming. For a broad range of cost parameter values, we present analytical results that give the form of the optimal linear value function approximation and the resulting policy. We investigate the practical implications and the quality of the policy through simulation.

Dynamic multi-appointment patient scheduling for radiation therapy

Seeking to reduce the potential impact of delays on radiation therapy cancer patients such as psychological distress, deterioration in quality of life and decreased cancer control and survival, and motivated by inefficiencies in the use of expensive resources, we undertook a study of scheduling practices at the British Columbia Cancer Agency (BCCA). As a result, we formulated and solved a discounted infinite-horizon Markov decision process for scheduling cancer treatments in radiation therapy units. The main purpose of this model is to identify good policies for allocating available treatment capacity to incoming demand, while reducing wait times in a cost-effective manner. We use an affine architecture to approximate the value function in our formulation and solve an equivalent linear programming model through column generation to obtain an approximate optimal policy for this problem. The benefits from the proposed method are evaluated by simulating its performance for a practical example based on data provided by the BCCA.

A Markov decision model for determining optimal outpatient scheduling

Managing an efficient outpatient clinic can often be complicated by significant no-show rates and escalating appointment lead times. One method that has been proposed for avoiding the wasted capacity due to no-shows is called open or advanced access. The essence of open access is “do today’s demand today”. We develop a Markov Decision Process (MDP) model that demonstrates that a short booking window does significantly better than open access. We analyze a number of scenarios that explore the trade-off between patient-related measures (lead times) and physician- or system-related measures (revenue, overtime and idle time). Through simulation, we demonstrate that, over a wide variety of potential scenarios and clinics, the MDP policy does as well or better than open access in terms of minimizing costs (or maximizing profits) as well as providing more consistent throughput.

Estimating the waiting time of multi-priority emergency patients with downstream blocking

To characterize the coupling effect between patient flow to access the emergency department (ED) and that to access the inpatient unit (IU), we develop a model with two connected queues: one upstream queue for the patient flow to access the ED and one downstream queue for the patient flow to access the IU. Building on this patient flow model, we employ queueing theory to estimate the average waiting time across patients. Using priority specific wait time targets, we further estimate the necessary number of ED and IU resources. Finally, we investigate how an alternative way of accessing ED (Fast Track) impacts the average waiting time of patients as well as the necessary number of ED/IU resources. This model as well as the analysis on patient flow can help the designer or manager of a hospital make decisions on the allocation of ED/IU resources in a hospital.

Needs of People With Dementia in Long-Term Care A Systematic Review

With the aging of the population and the projected increase of dementia in the coming years, it is crucial that we understand the needs of people with dementia (PWD) in order to provide appropriate care. The aim of this study is to determine, using the best evidence possible, the care needs of PWD living in long-term care (LTC). A total of 68 studies, published between January 2000 and September 2010, were identified from six databases. From the selected studies, 19 needs of PWD were identified. The existing evidence suggests that psychosocial needs such as the need to engage in daily individualized activities and care must not be ignored in LTC. This review aims to provide a clearer picture of the needs of this growing patient population.

A simulation based approximate dynamic programming approach to multi-class, multi-resource surgical scheduling

This paper presents a model and solution methodology for scheduling patients in a multi-class, multi-resource surgical system. Specifically, given a master schedule that provides a cyclic breakdown of total OR availability into specific daily allocations to each surgical specialty, the model provides a scheduling policy for all surgeries that minimizes a combination of the lead time between patient request and surgery date, overtime in the operating room and congestion in the wards. To the best of our knowledge, this paper is the first to determine a surgical schedule based on making efficient use of both the operating rooms and the recovery beds. Such a problem can be formulated as Markov Decision Process model but the size of any realistic problem makes traditional solution methods intractable. We develop a version of the Least Squares Approximate Policy Iteration algorithm and test our model on data from a local hospital to demonstrate the success of the resulting policy.

Access to primary healthcare services for the Roma population in Serbia: a secondary data analysis

BackgroundSerbia has proclaimed access to healthcare as a human right. In a context wherein the Roma population are disadvantaged, the aim of this study was to assess whether the Roma population are able to effectively access primary care services, and if not, what barriers prevent them from doing so. The history of the Roma in Serbia is described in detail so as to provide a context for their current vulnerable position.MethodsDisaggregated data were analyzed from three population groups in Serbia; the general population, the Roma population, and the poorest quintile of the general population not including the Roma. The effective coverage framework, which incorporates availability, affordability, accessibility, acceptability, and effectiveness of health services, was used to structure the secondary data analysis. Acute respiratory infection (ARI) in children less than five years of age was used as an example as this is the leading cause of death in children under 5 years old in Serbia.ResultsRoma children were significantly more likely to experience an ARI than either the general population or the poorest quintile of the general population, not including the Roma. All three population groups were equally likely to not receive the correct treatment regime of antibiotics. An analysis of the factors that affect quality of access to health services reveal that personal documentation is a statistically significant problem; availability of health services is not an issue that disproportionately affects the Roma; however the geographical accessibility and affordability are substantive issues that disproportionately affect the Roma population. Affordability of services affected the Roma and the poorest quintile and affordability of medications significantly affected all three population groups. With regards to acceptability, mothers from all three population groups are equally likely to recognize the importance of seeking treatment.ConclusionsThe Roma should be assisted in applying for personal documentation, the geographical accessibility of clinics needs to be addressed, and the costs of healthcare visits and medications should be reviewed. Areas for improvement specific to ARI are the costs of antibiotics and the diagnostic accuracy of providers. A range of policy recommendations are outlined.

Using data envelopment analysis for assessing the performance of pediatric emergency department physicians

In attempting to measure the performance of providers in a service industry such as health care, it is crucial that the measurement tool recognize both the efficiency and quality of service provided. We develop a Data Envelopment Analysis (DEA) model to help assess the performance of emergency department (ED) physicians at a partner hospital. The model incorporates efficiency measures as inputs and quality measures as outputs. We demonstrate the importance of a nuanced approach that recognizes the heterogeneity of patients that an ED physician encounters and the important role s/he plays as a mentor for physicians in training. In the study, patients were grouped according to their presenting complaint and ED physicians were assessed on each group separately. Performance variations were evident between physicians within each complaint group as well as between groups. A secondary grouping divided patients based on whether the attending physician was assisted by a trainee. Almost all ED physicians showed better performance scores when not assisted by trainees or ED fellows.

A simulation model for capacity planning in community care

Sustainable health care requires the building of sufficient capacity in order to ensure that patients receive the right care in a timely fashion. Often the efficient use of available capacity at one level (ie, acute care) is hindered by insufficient capacity at a downstream level (ie, long-term care (LTC)). This paper provides a simulation that helps determine the necessary downstream capacity in LTC in order to maintain smooth patient flow out of the hospitals in the region while still maintaining wait times within a target for those accessing LTC directly from the community. The model is complicated by multiple demand classes, client preferences, competing performance metrics, clients transferring between servers (ie, LTC facilities), significant wait time-dependent reneging and non-homogeneous servers. We provide policy recommendations for capacity planning in the region both for LTC and for supportive housing.

Models and Methods for Improving Patient Access

This chapter provides an overview of the work that has been done to date in the operations research community to improve patient access. Broadly speaking methods for improving patient access can be broken into the management of “within day” scheduling (determining start times for a sequence of n appointments) and “advanced scheduling” (determining the optimal number of days to book in advance). We provide an overview of the research to date in both streams focusing on the variety of applications that have been explored and the methodologies that have been applied. We present policy implications based on current research as well as the gaps in research that point to where additional work remains to be done.