La utilización de la investigación de operaciones como soporte a la toma de decisiones en el sector salud: Un estado del arte

The contributions of Operations Research (OR) in the healthcare field have been extensively studied in the scientific literature since the 1960s, covering decision support tools with operational, tactical, and strategic approaches. The aim of this article is to analyze the historical development of the application of OR models in healthcare. The application trends for optimization, planning, and decision- making models are studied through a descriptive literature review and a bibliometric analysis of scientific papers published between 1952 and 2016. An upward trend in the usage of operational models is observed with the predominance of resource optimization approaches and strategic decision-making for public health.Los aportes de la Investigación de Operaciones (IO) en el campo de la salud han sido ampliamente estudiados en la literatura científica desde la década de 1960, abarcando herramientas para el soporte a la decisión en enfoques operacionales, tácticos y estratégicos. El objetivo de este artículo es analizar el avance y el desarrollo histórico del uso de modelos operativos en el campo de la salud. A través de una revisión bibliográfica descriptiva y un análisis bibliométrico de artículos científicos publicados durante el periodo 1952-2016, se estudia el comportamiento de las tendencias en la aplicación de modelos operativos para la optimización, la planificación y la toma de decisiones en el sector salud. Se evidencia una tendencia creciente en el uso de modelos de IO durante el periodo estudiado, predominando las aplicaciones orientadas a la optimización de recursos y decisiones estratégicas de salud pública

A Process Modelling Framework Based on Point Interval Temporal Logic with an Application to Modelling Patient Flows

This thesis considers an application of a temporal theory to describe and model the patient journey in the hospital accident and emergency (A&E) department. The aim is to introduce a generic but dynamic method applied to any setting, including healthcare. Constructing a consistent process model can be instrumental in streamlining healthcare issues. Current process modelling techniques used in healthcare such as flowcharts, unified modelling language activity diagram (UML AD), and business process modelling notation (BPMN) are intuitive and imprecise. They cannot fully capture the complexities of the types of activities and the full extent of temporal constraints to an extent where one could reason about the flows. Formal approaches such as Petri have also been reviewed to investigate their applicability to the healthcare domain to model processes. Additionally, to schedule patient flows, current modelling standards do not offer any formal mechanism, so healthcare relies on critical path method (CPM) and program evaluation review technique (PERT), that also have limitations, i.e. finish-start barrier. It is imperative to specify the temporal constraints between the start and/or end of a process, e.g., the beginning of a process A precedes the start (or end) of a process B. However, these approaches failed to provide us with a mechanism for handling these temporal situations. If provided, a formal representation can assist in effective knowledge representation and quality enhancement concerning a process. Also, it would help in uncovering complexities of a system and assist in modelling it in a consistent way which is not possible with the existing modelling techniques. The above issues are addressed in this thesis by proposing a framework that would provide a knowledge base to model patient flows for accurate representation based on point interval temporal logic (PITL) that treats point and interval as primitives. These objects would constitute the knowledge base for the formal description of a system. With the aid of the inference mechanism of the temporal theory presented here, exhaustive temporal constraints derived from the proposed axiomatic system’ components serves as a knowledge base. The proposed methodological framework would adopt a model-theoretic approach in which a theory is developed and considered as a model while the corresponding instance is considered as its application. Using this approach would assist in identifying core components of the system and their precise operation representing a real-life domain deemed suitable to the process modelling issues specified in this thesis. Thus, I have evaluated the modelling standards for their most-used terminologies and constructs to identify their key components. It will also assist in the generalisation of the critical terms (of process modelling standards) based on their ontology. A set of generalised terms proposed would serve as an enumeration of the theory and subsume the core modelling elements of the process modelling standards. The catalogue presents a knowledge base for the business and healthcare domains, and its components are formally defined (semantics). Furthermore, a resolution theorem-proof is used to show the structural features of the theory (model) to establish it is sound and complete. After establishing that the theory is sound and complete, the next step is to provide the instantiation of the theory. This is achieved by mapping the core components of the theory to their corresponding instances. Additionally, a formal graphical tool termed as point graph (PG) is used to visualise the cases of the proposed axiomatic system. PG facilitates in modelling, and scheduling patient flows and enables analysing existing models for possible inaccuracies and inconsistencies supported by a reasoning mechanism based on PITL. Following that, a transformation is developed to map the core modelling components of the standards into the extended PG (PG*) based on the semantics presented by the axiomatic system. A real-life case (from the King’s College hospital accident and emergency (A&E) department’s trauma patient pathway) is considered to validate the framework. It is divided into three patient flows to depict the journey of a patient with significant trauma, arriving at A&E, undergoing a procedure and subsequently discharged. Their staff relied upon the UML-AD and BPMN to model the patient flows. An evaluation of their representation is presented to show the shortfalls of the modelling standards to model patient flows. The last step is to model these patient flows using the developed approach, which is supported by enhanced reasoning and scheduling

Integrated Planning in Hospitals: A Review

Efficient planning of scarce resources in hospitals is a challenging task for which a large variety of Operations Research and Management Science approaches have been developed since the 1950s. While efficient planning of single resources such as operating rooms, beds, or specific types of staff can already lead to enormous efficiency gains, integrated planning of several resources has been shown to hold even greater potential, and a large number of integrated planning approaches have been presented in the literature over the past decades. This paper provides the first literature review that focuses specifically on the Operations Research and Management Science literature related to integrated planning of different resources in hospitals. We collect the relevant literature and analyze it regarding different aspects such as uncertainty modeling and the use of real-life data. Several cross comparisons reveal interesting insights concerning, e.g., relations between the modeling and solution methods used and the practical implementation of the approaches developed. Moreover, we provide a high-level taxonomy for classifying different resource-focused integration approaches and point out gaps in the literature as well as promising directions for future research

An Optimisation-based Framework for Complex Business Process: Healthcare Application

The Irish healthcare system is currently facing major pressures due to rising demand, caused by population growth, ageing and high expectations of service quality. This pressure on the Irish healthcare system creates a need for support from research institutions in dealing with decision areas such as resource allocation and performance measurement. While approaches such as modelling, simulation, multi-criteria decision analysis, performance management, and optimisation can – when applied skilfully – improve healthcare performance, they represent just one part of the solution. Accordingly, to achieve significant and sustainable performance, this research aims to develop a practical, yet effective, optimisation-based framework for managing complex processes in the healthcare domain. Through an extensive review of the literature on the aforementioned solution techniques, limitations of using each technique on its own are identified in order to define a practical integrated approach toward developing the proposed framework. During the framework validation phase, real-time strategies have to be optimised to solve Emergency Department performance issues in a major hospital. Results show a potential of significant reduction in patients average length of stay (i.e. 48% of average patient throughput time) whilst reducing the over-reliance on overstretched nursing resources, that resulted in an increase of staff utilisation between 7% and 10%. Given the high uncertainty in healthcare service demand, using the integrated framework allows decision makers to find optimal staff schedules that improve emergency department performance. The proposed optimum staff schedule reduces the average waiting time of patients by 57% and also contributes to reduce number of patients left without treatment to 8% instead of 17%. The developed framework has been implemented by the hospital partner with a high level of success

IMPLEMENTASI FIREFLY ALGORITHM PADA PENJADWALAN PASIEN OPERASI

Kedua jenis ilmu kesehatan dan ilmu lainnya dalam bidang yang berbeda, saling berinteraksi. Teknologi dan ilmu kedokteran berkembang sangat pesat dalam konteks pelayanan kesehatan yang memiliki standar minimal. Sistem penjadwalan pasien operasi di rumah sakit merupakan salah satu pelayanan kesehatan yang memiliki permasalahan yang kompleks. Efisiensi dalam penjadwalan pasien operasi diperlukan untuk mencegah keterlambatan atau pembatalan operasi. Tujuan dari penelitian ini adalah untuk memecahkan masalah penjadwalan pasien operasi pada suatu periode perencanaan dengan pendekatan metode Firefly Algorithm (FA). FA dapat mendukung proses penjadwalan dalam komputasi secara efisien sesuai dengan hasil solusi sebagai kandidat penjadwalan. FA dapat menetapkan pekerjaan yang diterima ke sumber daya yang ada seperti dokter, perawat, ruang operasi, maupun peralatan yang digunakan selama tindakan operasi berlangsung, sehingga pekerjaan dapat diselesaikan dengan waktu makespan yang minimum. Hasil dari implementasi algoritma yang diusulkan dapat menyelesaikan masalah penjadwalan pasien operasi di rumah sakit. Implementasi tersebut menghasilkan jadwal pasien yang memiliki waktu makespan minimal dalam berbagai kondisi serta dapat meningkatkan utilitas ruang operasi di rumah sakit sebesar 50,6%

Projects Never Fail: A Critical Review on Estimation of Project Scheduling and Project Costing

Uncertainty remains common in all projects. It is need to realize this uncertainty and have to minimize the effect of this uncertainty to achieve better project outcomes. To realize the project on truthful base it is required to develop project schedule and estimate project costing on reality bases. A lot of project scheduling and costing techniques and tools are used to measure the accuracy. The new systematic techniques increase project outcomes and also reduce the uncertainty from the projects.  This study will leads to examine thoroughly project scheduling and project costing. Then this study will guide project managers how to develop a project schedule and what factors are effecting on the project scheduling and a sample project schedule will also provide for project managers and students of project management. After that the major sources of project costing and the method to calculate the project cost will also provide. And the sample project costing sheet is also develop in this study. Both project scheduling and project costing will develop the professionalism among project managers and students of project managers which they can never think before this study and also enhance project outcomes. Keywords: Project Scheduling, Project Costing, Uncertainty Handling and Project Succes

PENJADWALAN RUANG OPERASI RUMAH SAKIT DENGAN METODE NON-DOMINATED SORTING GENETIC ALGORITHM II (NSGA-II)

Penjadwalan operasi pasien merupakan aktifitas penting pada kegiatan operasional rumah sakit, karena menentukan waktu pasien-pasien tertangani dengan baik. Permasalahan ini dimodelkan sebagai masalah optimasi multi-obyektif yaitu meminimalkan waktu yang digunakan saat tindakan operasi. Masalah penjadwalan operasi dirumuskan sebagai masalah mixed integer programming (MIP), sehingga variabel merepresentasikan jadwal kasus operasi yang layak untuk ruangan tertentu dalam satu hari. Tahapan dalam merumuskan solusi heuristik, yaitu menentukan fungsi objektif sebagai solusi fraksional, menentukan solusi integer dengan mengubah solusi fraksional, dan meningkatkan kualitas solusi menggunakan local branching, formulasi MIP tersebut didasarkan pada variabel time-index. Model optimasi penjadwalan pasien dapat diselesaikan dengan metode berbasis global search. Metode tersebut dapat menghasilkan sejumlah jadwal operasi non-dominated yang mendekati pareto front dalam satu proses. Walaupun mampu mendekati pareto front, isu utama dari metode berbasis global search adalah bagaimana menjaga diversitas jadwal operasi. Penelitian ini menerapkan algoritma Non-dominated Sorting Genetic Algorithm II (NSGA-II). Berdasarkan hasil uji coba, dalam hal isu diversity pada penjadwalan operasi, metode NSGA II mampu menghasilkan himpunan pareto optimal dengan tingkat diversitas terbaik sebesar 0.597. Hal ini menunjukkan bahwa metode usulan berbasis NSGA-II mampu menghasilkan jadwal operasi pasien yang beragam dengan berbagai kombinasi

An Integrated Framework for Staffing and Shift Scheduling in Hospitals

Over the years, one of the main concerns confronting hospital management is optimising the staffing and scheduling decisions. Consequences of inappropriate staffing can adversely impact on hospital performance, patient experience and staff satisfaction alike. A comprehensive review of literature (more than 1300 journal articles) is presented in a new taxonomy of three dimensions; problem contextualisation, solution approach, evaluation perspective and uncertainty. Utilising Operations Research methods, solutions can provide a positive contribution in underpinning staffing and scheduling decisions. However, there are still opportunities to integrate decision levels; incorporate practitioners view in solution architectures; consider staff behaviour impact, and offer comprehensive applied frameworks. Practitioners’ perspectives have been collated using an extensive exploratory study in Irish hospitals. A preliminary questionnaire has indicated the need of effective staffing and scheduling decisions before semi-structured interviews have taken place with twenty-five managers (fourteen Directors and eleven head nurses) across eleven major acute Irish hospitals (about 50% of healthcare service deliverers). Thematic analysis has produced five key themes; demand for care, staffing and scheduling issues, organisational aspects, management concern, and technology-enabled. In addition to other factors that can contribute to the problem such as coordination, environment complexity, understaffing, variability and lack of decision support. A multi-method approach including data analytics, modelling and simulation, machine learning, and optimisation has been employed in order to deliver adequate staffing and shift scheduling framework. A comprehensive portfolio of critical factors regarding patients, staff and hospitals are included in the decision. The framework was piloted in the Emergency Department of one of the leading and busiest university hospitals in Dublin (Tallaght Hospital). Solutions resulted from the framework (i.e. new shifts, staff workload balance, increased demands) have showed significant improvement in all key performance measures (e.g. patient waiting time, staff utilisation). Management team of the hospital endorsed the solution framework and are currently discussing enablers to implement the recommendation

Operations research as a decision-making tool in the health sector: A state of the art

The contributions of Operations Research (OR) in the healthcare field have been extensively studied in the scientific literature since the 1960s, covering decision support tools with operational, tactical, and strategic approaches. The aim of this article is to analyze the historical development of the application of OR models in healthcare. The application trends for optimization, planning, and decision- making models are studied through a descriptive literature review and a bibliometric analysis of scientific papers published between 1952 and 2016. An upward trend in the usage of operational models is observed with the predominance of resource optimization approaches and strategic decision-making for public health.Los aportes de la Investigación de Operaciones (IO) en el campo de la salud han sido ampliamente estudiados en la literatura científica desde la década de los 60, abarcando el soporte a la decisión en enfoques operacionales, tácticos y estratégicos. Se presenta un resumen del desarrollo histórico de la IO en el campo de la salud y se listan los principales modelos aplicados en los últimos años, identificando el principal enfoque utilizado, y el potencial aporte a la toma de decisiones en el campo de la salud

Robust Optimization Framework to Operating Room Planning and Scheduling in Stochastic Environment

Arrangement of surgical activities can be classified as a three-level process that directly impacts the overall performance of a healthcare system. The goal of this dissertation is to study hierarchical planning and scheduling problems of operating room (OR) departments that arise in a publicly funded hospital. Uncertainty in surgery durations and patient arrivals, the existence of multiple resources and competing performance measures are among the important aspect of OR problems in practice. While planning can be viewed as the compromise of supply and demand within the strategic and tactical stages, scheduling is referred to the development of a detailed timetable that determines operational daily assignment of individual cases. Therefore, it is worthwhile to put effort in optimization of OR planning and surgical scheduling. We have considered several extensions of previous models and described several real-world applications. Firstly, we have developed a novel transformation framework for the robust optimization (RO) method to be used as a generalized approach to overcome the drawback of conventional RO approach owing to its difficulty in obtaining information regarding numerous control variable terms as well as added extra variables and constraints into the model in transforming deterministic models into the robust form. We have determined an optimal case mix planning for a given set of specialties for a single operating room department using the proposed standard RO framework. In this case-mix planning problem, demands for elective and emergency surgery are considered to be random variables realized over a set of probabilistic scenarios. A deterministic and a two-stage stochastic recourse programming model is also developed for the uncertain surgery case mix planning to demonstrate the applicability of the proposed RO models. The objective is to minimize the expected total loss incurred due to postponed and unmet demand as well as the underutilization costs. We have shown that the optimum solution can be found in polynomial time. Secondly, the tactical and operational level decision of OR block scheduling and advance scheduling problems are considered simultaneously to overcome the drawback of current literature in addressing these problems in isolation. We have focused on a hybrid master surgery scheduling (MSS) and surgical case assignment (SCA) problem under the assumption that both surgery durations and emergency arrivals follow probability distributions defined over a discrete set of scenarios. We have developed an integrated robust MSS and SCA model using the proposed standard transformation framework and determined the allocation of surgical specialties to the ORs as well as the assignment of surgeries within each specialty to the corresponding ORs in a coordinated way to minimize the costs associated with patients waiting time and hospital resource utilization. To demonstrate the usefulness and applicability of the two proposed models, a simulation study is carried utilizing data provided by Windsor Regional Hospital (WRH). The simulation results demonstrate that the two proposed models can mitigate the existing variability in parameter uncertainty. This provides a more reliable decision tool for the OR managers while limiting the negative impact of waiting time to the patients as well as welfare loss to the hospital