Optimization of online patient scheduling with urgencies and preferences

We consider the online problem of scheduling patients with urgencies and preferences on hospital resources with limited capacity. To solve this complex scheduling problem effectively we have to address the following sub problems: determining the allocation of capacity to patient groups, setting dynamic rules for exceptions to the allocation, ordering timeslots based on scheduling efficiency, and incorporating patient preferences over appointment times in the scheduling process. We present a scheduling approach with optimized parameter values that solves these issues simultaneously. In our experiments, we show how our approach outperforms standard scheduling benchmarks for a wide range of scenarios, and how we can efficiently trade-off scheduling performance and fulfilling patient preferences

Dynamic constraint satisfaction approach to hospital scheduling optimization

L' elaborato tratta il problema dell'Hospital Scheduling Optimization : l'ottimizzazione della gestione delle risorse di un ospedale attraverso una schedulazione efficiente dei singoli pazienti ospiti della struttura, mirando a massimizzare il throughput dei pazienti assistiti mantenendo, nello stesso tempo, il Minimum Service Level garantito. Dopo una trattazione sullo stato dell'arte degli approcci risolutivi a questo problema l'elaborato descrive il metodo di risoluzione mediante l'uso dei Dynamic Constraint Satisfaction Problems proponendo prima di tutto una modellazione dettagliata della realtà ospedaliera e, successivamente, un'implementazione in Java. Sono inoltre descritti i risultati ottenuti dai vari tipi di simulazione, che ricalcano gli scenari tipici dell'ambiente ospedaliero, mirate a misurare le performance di quest'implementazion

Stateless Two-Stage Multiple Criteria Scheduling in Nuclear Medicine

Examination in nuclear medicine exhibits scheduling difficulties due to its intricate clinical issues, such as varied radiopharmaceuticals for different diseases, machine preparation and length of scan, and patients’ and hospital’s criteria and/or limitations. Many scheduling methods exist but are limited for nuclear medicine. In this paper, we present stateless two-stage scheduling to cope with multiple criteria decision making. The first stage mostly deals with patients’ conditions. The second stage concerns more the clinical condition and its correlations with patients’ preference which presents more complicated intertwined configurations. A greedy algorithm is proposed in the second stage to determine the (time slot and patient) pair in linear time. The result shows practical and efficient scheduling for nuclear medicine

Lusíadas dental field lab: launching a new brand in dental care - conceiving a short-term expansion plan

Following the proposed scope of the project, goals and deliverables were set under three distinct dimensions, comprising an analysis of both the market and the company, the creation of a winning Value Proposition, and the conceiving of recommendations for an expansion strategy. An Internal Analysis was conducted to assess Lusíadas Dental’s current capabilities and key figures, supporting the evaluation with an examination of the Consumer Decision Journey to better understand the existent efforts within the distinct stages of the process: Awareness, Consideration, Purchase and Loyalty. With the intuit to comprehend the trends shaping the industry, tendencies of supply and demand were studied along with the thorough examination of the national competitors’ key figures and positioning strategies. In addition, both the market’s best practices and failing companies were analyzed to better understand the success path that the brand should follow. The understanding of consumers’ preferences was critical to create the hypothesis surrounding both the Value Proposition and the Expansion Plan. Following this rationale, an in-depth survey was conducted to better acknowledge clients’ motivations and needs regarding Dental Care services. The Value Proposition’s recommendations were organized into three different spheres that together comprise the critical elements for the implementation of the Lusíadas Dental brand in the market. Across the dimensions —Clinical, Operational and Infrastructure —several recommendations were considered, representing the key takeaways of each element for the efficient execution of the brand. Distinct solutions were considered in what regards the expansion plan of the group, contemplating hypotheses such as capacity expansion within units, units’ expansion with the creation of clinics and the promotion of strategic alliances with other existing Dental Care players

A genetic algorithm for dynamic scheduling in emergency departments with priorities

A hospital is a very complex environment and its management is a hard task. The point we explore in this thesis is the management of the patient flow for the Emergency Department. The main contribution of the thesis is twofold: on the one hand, we design a model of the Emergency Department as close as possible to the real environment; on the other hand, we design a genetic algorithm for finding the optimal schedule of patients’ care. The choice of this approach is due to both the dynamic nature of the environment and the tight constraints on computational time, which favour the use of an any-time algorithm. We show through simulation that the model is sound and that the genetic algorithm is effective for the scheduling problem and could be easily applied to a real Emergency Departmen

Adaptive resource allocation for efficient patient scheduling

Objective Efficient scheduling of patient appointments on expensive resources is a complex and dynamic task. A resource is typically used by several patient groups. To service these groups, resource capacity is often allocated per group, explicitly or implicitly. Importantly, due to fluctuations in demand, for the most efficient use of resources this allocation must be flexible. Methods We present an adaptive approach to automatic optimization of resource calendars. In our approach, the allocation of capacity to different patient groups is flexible and adaptive to the current and expected future situation. We additionally present an approach to determine optimal resource openings hours on a larger time frame. Our model and its parameter values are based on extensive case analysis at the Academic Medical Hospital Amsterdam. Results and conclusion We have implemented a comprehensive computer simulation of the application case. Simulation experiments show that our approach of adaptive capacity allocation improves the performance of scheduling patients groups with different attributes and makes efficient use of resource capacity

Online Clinic Appointment Scheduling

Health care is a fast growing industry in the United States. Appointment scheduling is one of the key processes in this industry. This thesis focused on on-line appointment system for clinics. The objective of this thesis is to maximize patients\u27 preferences and the number of patients seen during normal business hours. This is a multi-objective problem to balance the trade-off between overtime and patients\u27 preferences.To achieve the objective, a simulation model was built to compare four policies proposed. Based on simulation results, it was found that most of non-dominated solutions were close both minimum objective values, so policies proposed were helpful for the clinics to balance overtime and patients\u27 preferences