Towards optimizing hospital patient transports by automatically identifying interpretable causes of delays

The continuous financial pressure on hospitals forces them to rethink various workflows. We focus on optimizing hospital transports, within the hospital, as they count up to 30% of the overall hospital cost. In this paper, we discuss a self-learning platform that learns the causes of transport delays, in order to avoid these kinds of delays in the future. We pay special attention to the explainability of the self-learning system, such that management understands the learned causes and remains in control over the automated process. This is achieved by providing the learned causes as sentences that can be understood by non-technical personnel and allowing these causes to first be supervised before the system takes them into account. Once approved, the system will calculate how much more time should be assigned to these transports in order to avoid future delays. As a result, the scheduling of patient transportation can be automatically optimized, while management remains in full control of the process

Applying Lean Six Sigma and Systematic Layout Planning to Improve Patient Transportation Equipment Storage in an Acute Care Hospital

Purpose: The purpose of this project was to optimize the patient transportation process at an acute care hospital to achieve reduced transportation times. Methodology: A detailed Lean Six Sigma study on the patient transport and equipment handling processes helped to determine possible ways to reduce the equipment handling time which eventually reduces the patient transportation time. The Systematic Layout Planning (SLP) approach usually applied in manufacturing environments was used to identify which patient transport equipment was needed to be stored in which locations throughout the hospital footprint. The assignment of equipment to locations was determined based on frequency of use, distance, and equipment type. Findings: Key challenges were identified as lack of traceability of equipment, insufficient storage locations and storage locations with inappropriate equipment. Through SLP and statistical analysis of patient transport data, pickup locations were identified to minimize distance for high frequency trips for each mode of transport. Limitations: We provided the recommendations to the hospital to implement, but due to COVID pandemic resource issues they had not yet implemented the recommendations, although they are still planning to do so. Practical Implications and Originality/Value of Paper: The usage of the SLP approach combined with the Lean Six Sigma DMAIC method and tools was applied in the hospital environment to potentially reduce patient transport times, in what appears to be the first such research study applied to a hospital’s patient transportation system. Keywords: Patient transportation, Equipment, Systematic Layout Planning, Healthcare, Lean Six Sigma Paper Type: Case Stud

Process Mining Workshops

This open access book constitutes revised selected papers from the International Workshops held at the Third International Conference on Process Mining, ICPM 2021, which took place in Eindhoven, The Netherlands, during October 31–November 4, 2021. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 28 papers included in this volume were carefully reviewed and selected from 65 submissions. They stem from the following workshops: 2nd International Workshop on Event Data and Behavioral Analytics (EDBA) 2nd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) 2nd International Workshop on Streaming Analytics for Process Mining (SA4PM) 6th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) 4th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) 2nd International Workshop on Trust, Privacy, and Security in Process Analytics (TPSA) One survey paper on the results of the XES 2.0 Workshop is included

Process Mining Workshops

This open access book constitutes revised selected papers from the International Workshops held at the Third International Conference on Process Mining, ICPM 2021, which took place in Eindhoven, The Netherlands, during October 31–November 4, 2021. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 28 papers included in this volume were carefully reviewed and selected from 65 submissions. They stem from the following workshops: 2nd International Workshop on Event Data and Behavioral Analytics (EDBA) 2nd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) 2nd International Workshop on Streaming Analytics for Process Mining (SA4PM) 6th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) 4th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) 2nd International Workshop on Trust, Privacy, and Security in Process Analytics (TPSA) One survey paper on the results of the XES 2.0 Workshop is included

Text Similarity Between Concepts Extracted from Source Code and Documentation

Context: Constant evolution in software systems often results in its documentation losing sync with the content of the source code. The traceability research field has often helped in the past with the aim to recover links between code and documentation, when the two fell out of sync. Objective: The aim of this paper is to compare the concepts contained within the source code of a system with those extracted from its documentation, in order to detect how similar these two sets are. If vastly different, the difference between the two sets might indicate a considerable ageing of the documentation, and a need to update it. Methods: In this paper we reduce the source code of 50 software systems to a set of key terms, each containing the concepts of one of the systems sampled. At the same time, we reduce the documentation of each system to another set of key terms. We then use four different approaches for set comparison to detect how the sets are similar. Results: Using the well known Jaccard index as the benchmark for the comparisons, we have discovered that the cosine distance has excellent comparative powers, and depending on the pre-training of the machine learning model. In particular, the SpaCy and the FastText embeddings offer up to 80% and 90% similarity scores. Conclusion: For most of the sampled systems, the source code and the documentation tend to contain very similar concepts. Given the accuracy for one pre-trained model (e.g., FastText), it becomes also evident that a few systems show a measurable drift between the concepts contained in the documentation and in the source code.</p

Modelo matemático para tempo de transformação de prioridades no deslocamento intra-hospitalar

Orientador: Prof. Dr. José Eduardo Pécora JúniorCoorientador: Prof. Dr. Gustavo Valentim LochDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia de Produção. Defesa : Curitiba, 26/05/2021Inclui referências: p. 75-79Área de concentração: Pesquisa OperacionalResumo: O transporte intra-hospitalar de pacientes realizado em macas é um processo importante e que requer atenção pois o tempo desse deslocamento pode influenciar no agravamento do estado de saúde dos pacientes. Existe um número de funcionários (maqueiros) para realizar o deslocamento e cada paciente tem um nível de prioridade para ser deslocado. Cada nível de prioridade tem um tempo máximo em que o paciente deve ser deslocado e quando esse tempo é ultrapassado o paciente recebe uma prioridade mais urgente. Esse tempo se trata do tempo de transformação de prioridades. Baseado na solicitação de um hospital canadense e em um artigo inicial do tema, foi realizada uma expansão do modelo para, após esse tempo limite, o paciente receber um nível de prioridade mais urgente para ser atendido mais rapidamente. O problema abordado será o tempo de espera para deslocamento e foi resolvido como sendo um sistema clássico de máquinas paralelas (parallel machine). Apesar do tema ter algumas abordagens, nenhuma toma como base a transformação de níveis de prioridade na fila de atendimento. Portanto, neste trabalho foi proposto um modelo de programação linear inteira mista para a transformação de prioridades no deslocamento intra-hospitalar utilizando as instâncias propostas de um artigo inicial sobre o tema. Foi realizada a comparação entre 3 tipos de testes (do modelo original, modelo com transformação de prioridades e o modelo desenvolvido nesse trabalho com transformação e melhoria de tempo) e para análise dos resultados vários aspectos foram levados em consideração: maior tempo de término entre as atividades, função objetivo, tempo de atraso médio por prioridade, tempo de resposta por prioridade, tempo ocioso dos maqueiros e atraso por prioridade. Sabe-se que o modelo aqui desenvolvido apresentou melhoras em alguns aspectos e a maior contribuição do estudo foi a sugestão de modificação dos tempos de prioridade, principalmente na prioridade 4 que é a mais urgente, reduzindo assim os atrasos recorrentes. O estudo desses tempos é um dos pontos fundamentais para a tomada de decisão do atendimento do hospital em questão, bem como a busca de um deslocamento sem demoras e atrasos, respeitando a necessidade hospitalar de cada paciente.Abstract: Intra-hospital patient transport on stretchers is an important process that requires attention because the time of this displacement can influence the worsening of the patients' health status. There are a number of employees (stretcher bearers) to perform the displacement and each patient has a priority level to be moved. Each priority level has a maximum time in which the patient must be moved, and when this time is exceeded, the patient receives a more urgent priority. This time is the priority transformation time. Based on a request from a Canadian hospital and an early article on the subject, an expansion of the model was made so that after this time limit, the patient would receive a more urgent priority level to be seen more quickly. The problem addressed will be the displacement waiting time and was solved as a classic parallel machine system. Although the subject has some approaches, none of them is based on the transformation of priority levels in the queue. Therefore, in this work, a mixed integer linear programming model was proposed for the transformation of priorities in intra-hospital displacement using the proposed instances from an initial paper on the subject. A comparison between 3 types of tests (the original model, the model with priority transformation and the model developed in this work with transformation and time improvement) was performed and to analyze the results several aspects were taken into consideration: longest completion time between activities, objective function, average delay time per priority, response time per priority, idle time of waiters and delay per priority. It is known that the model developed here presented improvements in some aspects and the greatest contribution of the study was the suggestion to modify the priority times, especially in priority 4 which is the most urgent, thus reducing recurring delays. The study of these times is one of the fundamental points for decision making in the care of the hospital in question, as well as the search for a displacement without delays and delays, respecting the hospital needs of each patient