pubs2024

@inproceedings{Montenegro2023274,

title = {AI-Based Medical Scribe to Support Clinical Consultations: A Proposed System Architecture},

author = {L. Montenegro and L. M. Gomes and J. M. Machado},

editor = {Vale Z. Moniz N. Moniz N.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180626879&doi=10.1007%2f978-3-031-49011-8_22&partnerID=40&md5=a02dc441c31b5e5b04b0b741a3aaa2bd},

doi = {10.1007/978-3-031-49011-8_22},

issn = {03029743},

year  = {2023},

date = {2023-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {14116 LNAI},

pages = {274-285},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {AI applications in hospital frameworks can improve patient-care quality and efficient workflows and assist in digital transformation. By designing Smart Hospital infrastructures, creating an efficient framework enables patient information exchange between hospitals, point of care, and remote patient monitoring. Deep learning (DL) solutions play important roles in these infrastructures’ digital transformation process and architectural design. Literature review shows that DL solutions based on Automatic Speech Recognition (ASR) and Natural Language Processing (NLP) are rising concerning clinical data digitalisation, population health management, and improving patient care. Nevertheless, one of the literature’s shortcomings highlights the limited research using these solutions in real-world medical environments. As part of smart hospitals, smart medical scribes have been presented in several studies as a promising solution. However, just a few studies have tested it in real settings. Moreover, it was limited to non-existent studies on non-English systems, even yet to be found similar studies for European Portuguese. The proposed study evaluates NLP-based solutions in real-life Portuguese clinical settings focused on patient care for Smart Healthcare applications. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},

note = {cited By 0; Conference of 22nd EPIA Conference on Artificial Intelligence, EPIA 2023 ; Conference Date: 5 September 2023 Through 8 September 2023; Conference Code:305499},

keywords = {AI applications; Automatic speech recognition; Digital transformation; Language processing; Natural language processing; Natural languages; Patient care; Smart healthcare; Smart hospital; Systems architecture, Deep learning; Health care; Natural language processing systems; Population statistics; Speech recognition, Hospitals},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{IGustiNgurahAgungAgniPrema2023,

title = {Discovery of Hospital Billing Process in a Regional Hospital Using Process Mining},

author = {N. I Gusti Ngurah Agung Agni Prema and P. Naufal Avilandi and Fathan and R. Andreswari and J. M. F. Machado},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175616569&doi=10.1109%2fICADEIS58666.2023.10271040&partnerID=40&md5=9da901ec00a5a466f5c8da5360d3e6d7},

doi = {10.1109/ICADEIS58666.2023.10271040},

isbn = {9798350303414},

year  = {2023},

date = {2023-01-01},

journal = {ICADEIS 2023 - International Conference on Advancement in Data Science, E-Learning and Information Systems: Data, Intelligent Systems, and the Applications for Human Life, Proceeding},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Delays and complexities in the billing process for healthcare services can inconvenience patients and hinder the efficient functioning of regional hospitals. This study aims to utilize process mining techniques to analyze event logs and identify bottleneck activities within the billing process. By evaluating the process, the research aims to understand the causality behind these bottlenecks and propose effective solutions for enhancing efficiency and reducing time costs. The research employs the PM4Py open-source toolkit, including PM4Py-GPU for computationally intensive tasks. Through fitness alignments between event logs and process models, it is found that approximately 47.128 percent of traces match the process model, exhibiting a good level of conformity with an average fitness of 0.8888. Notably, the study reveals that over 3 percent of billing processes in regional hospitals exhibit repetitive occurrences of specific activities consecutively. The identification of this repetitive activity pattern prompts a deeper investigation into its root causes and implications for resource utilization and performance. By addressing these causative factors, the research aims to propose optimized approaches to streamline the billing process, thus enhancing overall efficiency and customer satisfaction levels for the hospitals. Overall, the findings of this study contribute to a comprehensive understanding of the billing process in healthcare services and provide valuable insights for hospitals to implement targeted improvements, reduce delays, and offer high-quality services to their patients. © 2023 IEEE.},

note = {cited By 0; Conference of 5th International Conference on Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2023 ; Conference Date: 2 August 2023 Through 3 August 2023; Conference Code:193233},

keywords = {Celonis; Effective solution; Event logs; Healthcare services; Hospital biling; Mining techniques; PM4Py; Process mining; Process-models; Time cost, Customer satisfaction; Data mining; Efficiency; Health care, Hospitals},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Neves2016273,

title = {Length of hospital stay and quality of care},

author = {J. Neves and V. Abelha and H. Vicente and J. Neves and J. Machado},

editor = {Kacprzyk J. Skulimowski A.M.J. Papadopoulos G.A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84975720683&doi=10.1007%2f978-3-319-27478-2_19&partnerID=40&md5=e94d0fa5c4bd31c39abe6b1aef60148b},

doi = {10.1007/978-3-319-27478-2_19},

issn = {21945357},

year  = {2016},

date = {2016-01-01},

journal = {Advances in Intelligent Systems and Computing},

volume = {416},

pages = {273-287},

publisher = {Springer Verlag},

abstract = {The relationship between Length Of hospital Stay (LOS) and Quality-of- Care (QofC) is demanding and difficult to assess. Indeed, a multifaceted intertwining network of countless services and LOS factors is available, which may range from organizational culture to hospital physicians availability, without discarding the possibility of lifting the foot on intermediate care services, to the customs and cultures of the people. On health policy terms, LOS remains a measurable index of efficiency, and most of the studies that have been undertaken show that QoC or health outcomes do not appear to be compromised by reductions in LOS times. Therefore, and in order to assess this statement, a Logic Programming based methodology to Knowledge Representation and Reasoning, allowing the modeling of the universe of discourse in terms of defective data, information and knowledge is used, being complemented with an Artificial Neural Networks based approach to computing, allowing one to predict for how long a patient should remain in a hospital or at home, during his/her illness experience. © Springer International Publishing Switzerland 2016.},

note = {cited By 3; Conference of 9th International Conference on Knowledge, Information and Creativity Support Systems, KICSS 2014 ; Conference Date: 6 November 2014 Through 8 November 2014; Conference Code:164129},

keywords = {Computation theory; Computer circuits; Knowledge representation; Logic programming; Neural networks; Reconfigurable hardware, Health outcomes; Health policy; Intermediate cares; Knowledge representation and reasoning; Length of hospital stays; Organizational cultures; Quality of care; Universe of discourse, Hospitals},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Viana2012251,

title = {Step towards simulation and monitoring of hospital waiting lists},

author = {M. Viana and O. Oliveira and A. Abelha and J. Machado},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84899008754&partnerID=40&md5=26ef3a69986fd26fcfd230dcf0809614},

year  = {2012},

date = {2012-01-01},

journal = {ESM 2012 - 2012 European Simulation and Modelling Conference: Modelling and Simulation 2012},

pages = {251-254},

publisher = {EUROSIS},

address = {Essen},

abstract = {Nowadays, the key to competitive advantage is being able to identify, summarize and categorize data. Currently, organizations should be able to interpret and convert information in a differentiating factor for those who are responsible for decision-making can take advantage of it. The main aim of this paper is to simulate and monitor real clinical data from Hospital Geral Santo Antonio (HGSA) in order to find trends and indicators that can support decision-making. It was made some experiments in ICU arena and in the hospital waiting lists (surgery and appointment). In the experimental phase it was used the open source BI tool Pentaho Suite in order to proceed the Knowledge Discovery (KD) process. It was considered an efficient process to clinical data simulation and monitoring, such as Pentaho BI tool. © 2012 EUROSIS-ETI.},

note = {cited By 1; Conference of 26th European Simulation and Modelling Conference, ESM 2012 ; Conference Date: 22 October 2012 Through 24 October 2012; Conference Code:104382},

keywords = {Clinical data; Competitive advantage; Efficient process; ETL process; Open source tools; Open sources; Simulation and monitoring; Waiting lists, Competition; Competitive intelligence; Data mining; Data warehouses; Decision making; Modal analysis; Tools, Hospitals},

pubstate = {published},

tppubtype = {inproceedings}

}