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

Organizing timely treatment in multi-disciplinary care

Healthcare providers experience an increased pressure to organize their processes more efficiently and to provide coordinated care over multiple disciplines. Organizing multi-disciplinary care is typically highly constrained, since multiple appointments per patient have to be scheduled with possible restrictions between them. Furthermore, schedules of professionals from various facilities or with different skills must be aligned. Since it is important that patients are treated on time, access time targets are set on the time between referral to the facility and the actual start of the treatment. These targets may vary per patient type: e.g., urgent patients have shorter access time targets than regular patients. In this thesis, we use operations research methods to support multi-disciplinary care settings in providing timely treatments with an excellent quality of care, against affordable costs, while taking patient and employee satisfaction into account. We consider settings in rehabilitation care and radiotherapy, but the underlying planning problems are applicable to many other multi-disciplinary care settings, such as cancer care or specialty clinics. The developed models are applied to case studies in the Sint Maartenskliniek Nijmegen, the AMC Amsterdam and a BCCA cancer clinic in Vancouver, Canada. The results of the thesis demonstrate that adequate admission policies and capacity allocation to different activities and stages in complex treatment processes can improve compliance with access time targets for multi-disciplinary care systems considerably, while using the available resource capacities and taking patient and employee satisfaction into account

A careful solution: patient scheduling in health care

Koole, G.M. [Promotor

Probability Models for Health Care Operations with Application to Emergency Medicine

This thesis consists of four contributing chapters; two of which are inspired by practical problems related to emergency department (ED) operations management and the remaining two are motivated by the theoretical problem related to the time-dependent priority queue. Unlike classical priority queue, priorities in the time-dependent priority queue depends on the amount of time an arrival waits for service in addition to the priority class they belong. The mismatch between the demand for ED services and the available resources have direct and indirect negative consequences. Moreover, ED physician pay in some jurisdictions reflects pay-for-performance contracts based on operational benchmarks. To assist in capacity planning and meeting these benchmarks, in chapter 4, I built a forecasting model to produce short-term forecasts of ED arrivals. In chapter 5, I empirically investigated the effect of workload on the productivity of ED services. Specifically, under discretionary work setting, different statistical models were fitted to identify the effect of workload and census on four measures of ED service processes, namely, number discharged, length of stay, service time, and waiting time. The time-dependent priority model was first proposed by Kleinrock (1964), and, more recently, naming it accumulating priority queue (APQ), Stanford et al. (2014) derived the waiting time distributions for the various priority classes when the queue has a single server. In chapter 6, I derived expressions for the waiting time distributions for a multi-server APQ with Poisson arrivals for each class, and a common exponential service time distribution. In chapter 7, I worked with a KPI based service system where there are specific time targets by which each class of customers should commence their service and a compliance probability indicating the proportion of customers from that class meeting the target. Recognizing the fact that customer who misses their KPI target is of greater, not lesser importance, I seek to minimize a weighted sum of the expected amount of excess waiting for each class. When minimizing the total expected excess, our numerical examples lead to an easily-implemented rule of thumb for the optimal priority accumulation rates, which can have an immediate impact on health care delivery

Improve primary care performance through operations management: An application to emergency care and preventive care

El propósito principal de esta tesis es aplicar el método de gestión de operaciones para mejorar el rendimiento de los responsables de proporcionar atención sanitaria en relación con dos componentes principales de la atención primaria: atención de urgencia y atención primaria. Durante muchos años, en la atención sanitaria se han aplicado los sistemas de gestión de operaciones (OM) y de investigación de operaciones (OR) con la finalidad de mejorar la eficiencia en la prestación de los servicios sanitarios. El núcleo del sistema de atención médica es la atención sanitaria, cuyas funciones principales incluyen el suministro de un punto de entrada, la prestación de atención médica y preventiva fundamental y ayudar a los pacientes a coordinar y a integrar la atención, aspectos que son fundamentales de cara a mejorar no solo el resultado sanitario de los pacientes, sino también el rendimiento en términos de coste de todo el sistema sanitario (Starfield 1998). En un estudio sobre el rendimiento de la atención primaria y del sistema de salud (Schoen et al., 2004), en EE. UU. se registró un índice de utilización del departamento de urgencias (ED) muy superior al de otros tres países, el cual venía acompañado de un menor porcentaje de adultos que dispusieran de un doctor, un lugar o una clínica habitual donde acudir al caer enfermos. Por este motivo, el capítulo 2 de esta disertación aborda la mejora del departamento de salas de urgencia a través del rediseño del proceso. Otro hallazgo fundamental de la encuesta es que Canadá cuenta con el menor índice de chequeos en términos de prueba de Papanicolaou y mamografías. Debido a la importancia de la atención preventiva para salvar vidas y reducir costes, el capítulo 3 de esta disertación analiza cómo mejorar el programa de atención preventiva financiado por el gobierno a través del diseño de la red. El capítulo 2 establece el contexto de un departamento de urgencias (ED) en un hospital terciario con un censo anual de 55 000 pacientes, y analiza la forma en la que el proceso de rediseño de una prueba sanguínea específica tiene un determinado impacto sobre la congestión del ED. De forma más específica, analizamos en cambio en tres magnitudes de rendimiento después de que el análisis de la muestra de sangre del paciente para determinar los niveles de troponina fuera trasladada del laboratorio central del laboratorio al interior del ED. Mediante la teoría de la asignación de colas de prioridad, generamos hipótesis sobre las siguientes medidas de rendimiento: tiempo de espera (definido como la diferencia de tiempo entre el registro de entrada del paciente y la asignación de cama), tiempo de servicio (definido como la diferencia de tiempo entre la asignación de cama y la distribución, el metabolismo y la eliminación de un fármaco) y calidad del servicio (definido como el índice de revisión de los pacientes tras 72 horas). Mediante un modelo de diferencias en diferencias, determinamos que el rediseño del proceso está asociado con unas mejoras estadísticamente significativas en casi todas las mediciones de rendimiento operativo. Concretamente, encontramos que la adopción de POCT está asociada a una reducción del 21,6 % en el tiempo de servicio entre los pacientes objeto de la prueba durante las horas punta, y en una reducción de entre el 5,9 % y el 35,5 % en el tiempo de espera en función de la categoría de prioridad del paciente durante esas mismas horas punta. Además, encontramos que la adopción de un POCT estaba asociada con una mejora de la calidad del servicio, puesto que la probabilidad de recaída pronosticada se redujo en un 0,64 % durante su uso. También descubrimos importantes efectos indirectos a través de todo el sistema en pacientes que no habían sido objeto de un POCT (pacientes que no son objeto de prueba). En otras palabras, la adopción de un POCT está asociada con una reducción del tiempo de espera entre estos pacientes que no son objeto de prueba de un 4,73 % y a una reducción del 11,6 % en el tiempo de espera en función de la categoría de prioridad de los pacientes durante las horas punta. Al examinar el impacto del POCT entre ambas poblaciones de pacientes, tanto los que fueron sometidos a la prueba como los que no, se pudo determinar que esta investigación es única a la hora de identificar los grandes beneficios en el sistema que pueden lograrse a través del rediseño del proceso asociado al ED. El tercer capítulo de esta tesis emplea un modelo de elección de preferencias para analizar las prioridades del cliente en la atención preventiva desde la perspectiva de la configuración del servicio. Aplicamos el modelo en el contexto de un programa de chequeos asociados con el cáncer de mama financiado por el gobierno en Montreal (Canadá), con el fin de identificar las contrapartidas que reciben los participantes del programa a la hora de acceder a un conjunto de instalaciones con diferentes configuraciones de servicio basadas en sus auténticas preferencias. De forma más concreta, analizamos estas preferencias en relación con el tiempo de espera para obtener cita, el tiempo de desplazamiento a la clínica en la que se vaya a practicar el chequeo, la disponibilidad del aparcamiento de la clínica, el horario de apertura de la clínica, el tiempo de espera dentro de la clínica el día del chequeo, la preparación del personal de enfermería, el proceso de chequeo y el tiempo de espera para recibir el resultado. Pudimos comprobar que la preparación del personal de enfermería (es decir, si son capaces de responder preguntas relacionadas con el chequeo o con el cáncer de mama) y el tiempo de espera para obtener una cita eran los factores más determinantes a la hora de elegir una clínica, seguidos de cerca por la disponibilidad de aparcamiento. Mediante el análisis de clases latentes también podemos confirmar que, al contrario de lo apuntado por otras investigaciones, no existe una heterogeneidad clara entre los participantes del programa. Nuestro modelo Arena de simulación muestra que tener en cuenta las preferencias del cliente en el diseño de las configuraciones del servicio mejorará notablemente tanto el nivel de congestión como el índice de participación en las nuevas pruebas. Como conclusión de ambos capítulos, esta tesis trata de generar implicaciones en términos de gestión en lo que respecta a la configuración de la atención sanitaria que puedan ayudar a mejorar la calidad del servicio mediante el uso de un enfoque de metodología empírica. Vemos que pueden acometerse importantes mejoras en los servicios existentes a través del rediseño del proceso de servicio y de la comprensión de las preferencias del cliente, sin necesidad de revisar todo el sistema de atención sanitaria

A Hybrid Modelling Framework for Real-time Decision-support for Urgent and Emergency Healthcare

In healthcare, opportunities to use real-time data to support quick and effective decision-making are expanding rapidly, as data increases in volume, velocity and variety. In parallel, the need for short-term decision-support to improve system resilience is increasingly relevant, with the recent COVID-19 crisis underlining the pressure that our healthcare services are under to deliver safe, effective, quality care in the face of rapidly-shifting parameters. A real-time hybrid model (HM) which combines real-time data, predictions, and simulation, has the potential to support short-term decision-making in healthcare. Considering decision-making as a consequence of situation awareness focuses the HM on what information is needed where, when, how, and by whom with a view toward sustained implementation. However the articulation between real-time decision-support tools and a sociotechnical approach to their development and implementation is currently lacking in the literature. Having identified the need for a conceptual framework to support the development of real-time HMs for short-term decision-support, this research proposed and tested the Integrated Hybrid Analytics Framework (IHAF) through an examination of the stages of a Design Science methodology and insights from the literature examining decision-making in dynamic, sociotechnical systems, data analytics, and simulation. Informed by IHAF, a HM was developed using real-time Emergency Department data, time-series forecasting, and discrete-event simulation. The application started with patient questionnaires to support problem definition and to act as a formative evaluation, and was subsequently evaluated using staff interviews. Evaluation of the application found multiple examples where the objectives of people or sub-systems are not aligned, resulting in inefficiencies and other quality problems, which are characteristic of complex adaptive sociotechnical systems. Synthesis of the literature, the formative evaluation, and the final evaluation found significant themes which can act as antecedents or evaluation criteria for future real-time HM studies in sociotechnical systems, in particular in healthcare. The generic utility of IHAF is emphasised for supporting future applications in similar domains

Optimising cardiac services using routinely collected data and discrete event simulation

Background: The current practice of managing hospital resources, including beds, is very much driven by measuring past or expected utilisation of resources. This practice, however, doesn’t reflect variability among patients. Consequently, managers and clinicians cannot make fully informed decisions based upon these measures which are considered inadequate in planning and managing complex systems. Aim: to analyse how variation related to patient conditions and adverse events affect resource utilisation and operational performance. Methods: Data pertaining to cardiac patients (cardiothoracic and cardiology, n=2241) were collected from two major hospitals in Oman. Factors influential to resource utilisation were assessed using logistic regressions. Other analysis related to classifying patients based on their resource utilisation was carried out using decision tree to assist in predicting hospital stay. Finally, discrete event simulation modelling was used to evaluate how patient factors and postoperative complications are affecting operational performance. Results: 26.5% of the patients experienced prolonged Length of Stay (LOS) in intensive care units and 30% in the ward. Patients with prolonged postoperative LOS had 60% of the total patient days. Some of the factors that explained the largest amount of variance in resource use following cardiac procedure included body mass index, type of surgery, Cardiopulmonary Bypass (CPB) use, non-elective surgery, number of complications, blood transfusion, chronic heart failure, and previous angioplasty. Allocating resources based on patient expected LOS has resulted in a reduction of surgery cancellations and waiting times while overall throughput has increased. Complications had a significant effect on perioperative operational performance such as surgery cancellations. The effect was profound when complications occurred in the intensive care unit where a limited capacity was observed. Based on the simulation model, eliminating some complications can enlarge patient population. Conclusion: Integrating influential factors into resource planning through simulation modelling is an effective way to estimate and manage hospital capacity.Open Acces