54 research outputs found

    Process mining methodology for health process tracking using real-time indoor location systems

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    [EN] The definition of efficient and accurate health processes in hospitals is crucial for ensuring an adequate quality of service. Knowing and improving the behavior of the surgical processes in a hospital can improve the number of patients that can be operated on using the same resources. However, the measure of this process is usually made in an obtrusive way, forcing nurses to get information and time data, affecting the proper process and generating inaccurate data due to human errors during the stressful journey of health staff in the operating theater. The use of indoor location systems can take time information about the process in an unobtrusive way, freeing nurses, allowing them to engage in purely welfare work. However, it is necessary to present these data in a understandable way for health professionals, who cannot deal with large amounts of historical localization log data. The use of process mining techniques can deal with this problem, offering an easily understandable view of the process. In this paper, we present a tool and a process mining-based methodology that, using indoor location systems, enables health staff not only to represent the process, but to know precise information about the deployment of the process in an unobtrusive and transparent way. We have successfully tested this tool in a real surgical area with 3613 patients during February, March and April of 2015.The authors want to acknowledge the work MySphera Company and Hospital General for their invaluable support. This work was supported in part by several projects; FASyS-Absolutely Safe and Healthy Factory (Spanish Ministry of Industry. CEN-20091034), MOSAIC-Models and simulation techniques for discovering diabetes influence factors (ICT-FP7-600914) and HEARTWAYS-Advanced Solutions for Supporting Cardiac Patients in Rehabilitation (ICT-SME-315659) EU Projects; and organizations like Tecnologias para la Salud y el Bienestar (TSB S.A.) and the Universitat Politecnica de Valencia.Fernández Llatas, C.; Lizondo, A.; Montón Sánchez, E.; Benedí Ruiz, JM.; Traver Salcedo, V. (2015). Process mining methodology for health process tracking using real-time indoor location systems. Sensors. 12:29821-29840. https://doi.org/10.3390/s151229769S298212984012Weske, M., van der Aalst, W. M. P., & Verbeek, H. M. W. (2004). Advances in business process management. Data & Knowledge Engineering, 50(1), 1-8. doi:10.1016/j.datak.2004.01.001Davidoff, F., Haynes, B., Sackett, D., & Smith, R. (1995). Evidence based medicine. BMJ, 310(6987), 1085-1086. doi:10.1136/bmj.310.6987.1085Reilly, B. M. (2004). The essence of EBM. BMJ, 329(7473), 991-992. doi:10.1136/bmj.329.7473.991Weiland, D. E. (1997). Why use clinical pathways rather than practice guidelines? The American Journal of Surgery, 174(6), 592-595. doi:10.1016/s0002-9610(97)00196-7Hunter, B., & Segrott, J. (2008). Re-mapping client journeys and professional identities: A review of the literature on clinical pathways. International Journal of Nursing Studies, 45(4), 608-625. doi:10.1016/j.ijnurstu.2007.04.001Lenz, R., Blaser, R., Beyer, M., Heger, O., Biber, C., Bäumlein, M., & Schnabel, M. (2007). IT support for clinical pathways—Lessons learned. International Journal of Medical Informatics, 76, S397-S402. doi:10.1016/j.ijmedinf.2007.04.012Blaser, R., Schnabel, M., Biber, C., Bäumlein, M., Heger, O., Beyer, M., … Kuhn, K. A. (2007). Improving pathway compliance and clinician performance by using information technology. International Journal of Medical Informatics, 76(2-3), 151-156. doi:10.1016/j.ijmedinf.2006.07.006Schuld, J., Schäfer, T., Nickel, S., Jacob, P., Schilling, M. K., & Richter, S. (2011). Impact of IT-supported clinical pathways on medical staff satisfaction. A prospective longitudinal cohort study. International Journal of Medical Informatics, 80(3), 151-156. doi:10.1016/j.ijmedinf.2010.10.012Rebuge, Á., & Ferreira, D. R. (2012). Business process analysis in healthcare environments: A methodology based on process mining. Information Systems, 37(2), 99-116. doi:10.1016/j.is.2011.01.003Fernández-Llatas, C., Meneu, T., Traver, V., & Benedi, J.-M. (2013). Applying Evidence-Based Medicine in Telehealth: An Interactive Pattern Recognition Approximation. International Journal of Environmental Research and Public Health, 10(11), 5671-5682. doi:10.3390/ijerph10115671Schilling, M., Richter, S., Jacob, P., & Lindemann, W. (2006). Klinische Behandlungspfade. DMW - Deutsche Medizinische Wochenschrift, 131(17), 962-967. doi:10.1055/s-2006-939876Zannini, L., Cattaneo, C., Peduzzi, P., Lopiccoli, S., & Auxilia, F. (2012). Experimenting clinical pathways in general practice: a focus group investigation with Italian general practitioners. Journal of Public Health Research, 1(2), 30. doi:10.4081/jphr.2012.e30Rubin, H. R. (2001). The advantages and disadvantages of process-based measures of health care quality. International Journal for Quality in Health Care, 13(6), 469-474. doi:10.1093/intqhc/13.6.469Liu, H., Darabi, H., Banerjee, P., & Liu, J. (2007). Survey of Wireless Indoor Positioning Techniques and Systems. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 37(6), 1067-1080. doi:10.1109/tsmcc.2007.905750Li, N., & Becerik-Gerber, B. (2011). Performance-based evaluation of RFID-based indoor location sensing solutions for the built environment. Advanced Engineering Informatics, 25(3), 535-546. doi:10.1016/j.aei.2011.02.004Curran, K., Furey, E., Lunney, T., Santos, J., Woods, D., & McCaughey, A. (2011). An evaluation of indoor location determination technologies. Journal of Location Based Services, 5(2), 61-78. doi:10.1080/17489725.2011.562927Fernández-Llatas, C., Benedi, J.-M., García-Gómez, J., & Traver, V. (2013). Process Mining for Individualized Behavior Modeling Using Wireless Tracking in Nursing Homes. Sensors, 13(11), 15434-15451. doi:10.3390/s131115434Stübig, T., Zeckey, C., Min, W., Janzen, L., Citak, M., Krettek, C., … Gaulke, R. (2014). Effects of a WLAN-based real time location system on outpatient contentment in a Level I trauma center. International Journal of Medical Informatics, 83(1), 19-26. doi:10.1016/j.ijmedinf.2013.10.001Najera, P., Lopez, J., & Roman, R. (2011). Real-time location and inpatient care systems based on passive RFID. Journal of Network and Computer Applications, 34(3), 980-989. doi:10.1016/j.jnca.2010.04.011Huang, Z., Dong, W., Ji, L., Gan, C., Lu, X., & Duan, H. (2014). Discovery of clinical pathway patterns from event logs using probabilistic topic models. Journal of Biomedical Informatics, 47, 39-57. doi:10.1016/j.jbi.2013.09.003Caron, F., Vanthienen, J., Vanhaecht, K., Limbergen, E. V., De Weerdt, J., & Baesens, B. (2014). Monitoring care processes in the gynecologic oncology department. Computers in Biology and Medicine, 44, 88-96. doi:10.1016/j.compbiomed.2013.10.015Bouarfa, L., & Dankelman, J. (2012). Workflow mining and outlier detection from clinical activity logs. Journal of Biomedical Informatics, 45(6), 1185-1190. doi:10.1016/j.jbi.2012.08.003Disco https://fluxicon.com/disco/Van der Aalst, W., Weijters, T., & Maruster, L. (2004). Workflow mining: discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering, 16(9), 1128-1142. doi:10.1109/tkde.2004.47Wantland, D. J., Portillo, C. J., Holzemer, W. L., Slaughter, R., & McGhee, E. M. (2004). The Effectiveness of Web-Based vs. Non-Web-Based Interventions: A Meta-Analysis of Behavioral Change Outcomes. Journal of Medical Internet Research, 6(4), e40. doi:10.2196/jmir.6.4.e40Bellazzi, R., Montani, S., Riva, A., & Stefanelli, M. (2001). Web-based telemedicine systems for home-care: technical issues and experiences. Computer Methods and Programs in Biomedicine, 64(3), 175-187. doi:10.1016/s0169-2607(00)00137-1Van der Aalst, W. (2012). Process Mining. ACM Transactions on Management Information Systems, 3(2), 1-17. doi:10.1145/2229156.2229157MySphera Company http://mysphera.com/Van der Aalst, W. M. P., & de Medeiros, A. K. A. (2005). Process Mining and Security: Detecting Anomalous Process Executions and Checking Process Conformance. Electronic Notes in Theoretical Computer Science, 121, 3-21. doi:10.1016/j.entcs.2004.10.01

    The application of process mining to care pathway analysis in the NHS

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    Background: Prostate cancer is the most common cancer in men in the UK and the sixth-fastest increasing cancer in males. Within England survival rates are improving, however, these are comparatively poorer than other countries. Currently, information available on outcomes of care is scant and there is an urgent need for techniques to improve healthcare systems and processes. Aims: To provide prostate cancer pathway analysis, by applying concepts of process mining and visualisation and comparing the performance metrics against the standard pathway laid out by national guidelines. Methods: A systematic review was conducted to see how process mining has been used in healthcare. Appropriate datasets for prostate cancer were identified within Imperial College Healthcare NHS Trust London. A process model was constructed by linking and transforming cohort data from six distinct database sources. The cohort dataset was filtered to include patients who had a PSA from 2010-2015, and validated by comparing the medical patient records against a Case-note audit. Process mining techniques were applied to the data to analyse performance and conformance of the prostate cancer pathway metrics to national guideline metrics. These techniques were evaluated with stakeholders to ascertain its impact on user experience. Results: Case note audit revealed 90% match against patients found in medical records. Application of process mining techniques showed massive heterogeneity as compared to the homogenous path laid out by national guidelines. This also gave insight into bottlenecks and deviations in the pathway. Evaluation with stakeholders showed that the visualisation and technology was well accepted, high quality and recommended to be used in healthcare decision making. Conclusion: Process mining is a promising technique used to give insight into complex and flexible healthcare processes. It can map the patient journey at a local level and audit it against explicit standards of good clinical practice, which will enable us to intervene at the individual and system level to improve care.Open Acces

    Process mining for healthcare: Characteristics and challenges

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    Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.This work is partially supported by ANID FONDECYT 1220202, Dirección de Investigación de la Vicerrectoría de Investigación de la Pontificia Universidad Católica de Chile - PUENTE [Grant No. 026/ 2021]; and Agencia Nacional de Investigación y Desarrollo [Grant Nos. ANID-PFCHA/Doctorado Nacional/2019–21190116, ANID-PFCHA/ Doctorado Nacional/2020–21201411]. With regard to the co-author Hilda Klasky, this manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-accessplan).Peer ReviewedArticle signat per 55 autors/es: Jorge Munoz-Gama (a)* , Niels Martin (b,c)* , Carlos Fernandez-Llatas (d,g)* , Owen A. Johnson (e)* , Marcos Sepúlveda (a)* , Emmanuel Helm (f)* , Victor Galvez-Yanjari (a)* , Eric Rojas (a) , Antonio Martinez-Millana (d) , Davide Aloini (k) , Ilaria Angela Amantea (l,q,r) , Robert Andrews (ab), Michael Arias (z) , Iris Beerepoot (o) , Elisabetta Benevento (k) , Andrea Burattin (ai), Daniel Capurro (j) , Josep Carmona (s) , Marco Comuzzi (w), Benjamin Dalmas (aj,ak), Rene de la Fuente (a) , Chiara Di Francescomarino (h) , Claudio Di Ciccio (i) , Roberto Gatta (ad,ae), Chiara Ghidini (h) , Fernanda Gonzalez-Lopez (a) , Gema Ibanez-Sanchez (d) , Hilda B. Klasky (p) , Angelina Prima Kurniati (al), Xixi Lu (o) , Felix Mannhardt (m), Ronny Mans (af), Mar Marcos (v) , Renata Medeiros de Carvalho (m), Marco Pegoraro (x) , Simon K. Poon (ag), Luise Pufahl (u) , Hajo A. Reijers (m,o) , Simon Remy (y) , Stefanie Rinderle-Ma (ah), Lucia Sacchi (t) , Fernando Seoane (g,am,an), Minseok Song (aa), Alessandro Stefanini (k) , Emilio Sulis (l) , Arthur H. M. ter Hofstede (ab), Pieter J. Toussaint (ac), Vicente Traver (d) , Zoe Valero-Ramon (d) , Inge van de Weerd (o) , Wil M.P. van der Aalst (x) , Rob Vanwersch (m), Mathias Weske (y) , Moe Thandar Wynn (ab), Francesca Zerbato (n) // (a) Pontificia Universidad Catolica de Chile, Chile; (b) Hasselt University, Belgium; (c) Research Foundation Flanders (FWO), Belgium; (d) Universitat Politècnica de València, Spain; (e) University of Leeds, United Kingdom; (f) University of Applied Sciences Upper Austria, Austria; (g) Karolinska Institutet, Sweden; (h) Fondazione Bruno Kessler, Italy; (i) Sapienza University of Rome, Italy; (j) University of Melbourne, Australia; (k) University of Pisa, Italy; (l) University of Turin, Italy; (m) Eindhoven University of Technology, The Netherlands; (n) University of St. Gallen, Switzerland; (o) Utrecht University, The Netherlands; (p) Oak Ridge National Laboratory, United States; (q) University of Bologna, Italy; (r) University of Luxembourg, Luxembourg; (s) Universitat Politècnica de Catalunya, Spain; (t) University of Pavia, Italy; (u) Technische Universitaet Berlin, Germany; (v) Universitat Jaume I, Spain; (w) Ulsan National Institute of Science and Technology (UNIST), Republic of Korea; (x) RWTH Aachen University, Germany; (y) University of Potsdam, Germany; (z) Universidad de Costa Rica, Costa Rica; (aa) Pohang University of Science and Technology, Republic of Korea; (ab) Queensland University of Technology, Australia; (ac) Norwegian University of Science and Technology, Norway; (ad) Universita degli Studi di Brescia, Italy; (ae) Lausanne University Hospital (CHUV), Switzerland; (af) Philips Research, the Netherlands; (ag) The University of Sydney, Australia; (ah) Technical University of Munich, Germany; (ai) Technical University of Denmark, Denmark; (aj) Mines Saint-Etienne, France; (ak) Université Clermont Auvergne, France; (al) Telkom University, Indonesia; (am) Karolinska University Hospital, Sweden; (an) University of Borås, SwedenPostprint (published version

    Process mining for healthcare: Characteristics and challenges

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    [EN] Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.This work is partially supported by ANID FONDECYT 1220202, Direccion de Investigacion de la Vicerrectoria de Investigacion de la Pontificia Universidad Catolica de Chile-PUENTE [Grant No. 026/2021] ; and Agencia Nacional de Investigacion y Desarrollo [Grant Nos. ANID-PFCHA/Doctorado Nacional/2019-21190116, ANID-PFCHA/Doctorado Nacional/2020-21201411] . With regard to the co-author Hilda Klasky, this manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE) . The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan)Munoz Gama, J.; Martin, N.; Fernández Llatas, C.; Johnson, OA.; Sepúlveda, M.; Helm, E.; Galvez-Yanjari, V.... (2022). Process mining for healthcare: Characteristics and challenges. Journal of Biomedical Informatics. 127:1-15. https://doi.org/10.1016/j.jbi.2022.10399411512

    Discovery of Transport Operations from Geolocation Data

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    Os dados de geolocalização identificam a localização geográfica de pessoas ou objetos e são fundamentais para empresas que dependem de veículos, como empresas logísticas e de transportes. Com o avanço da tecnologia, a recolha de dados de geolocalização tornou-se cada vez mais acessível e económica, gerando novas oportunidades de inteligência empresarial. Este tipo de dados tem sido utilizado principalmente para caracterizar o veículo em termos de posicionamento e navegação, mas também pode ter um papel preponderante na avaliação de desempenho em relação às atividades e operações executadas. A abordagem proposta consiste numa metodologia com várias etapas que recebe dados de geolocalização como entrada e permite a análise do processo de negócio no final. Em primeiro lugar, a preparação dos dados é aplicada para lidar com uma série de questões relacionadas com ruído e erros nos dados. Depois, a identificação dos eventos estacionários é realizada com base nos estados estacionários dos veículos. Em seguida, é realizada a inferência de operações com base numa análise espacial, que permite descobrir os locais onde os eventos estacionários ocorrem com frequência. Finalmente, as operações identificadas são classificadas com base nas suas características, e a sequência de eventos pode ser estruturada. A aplicação de técnicas de process mining é então possível e a consequente extração de conhecimento do processo. As etapas da metodologia também podem ser utilizadas separadamente para enfrentar desafios específicos, dando mais flexibilidade à sua aplicação. Três estudos de caso distintos são apresentados para demonstrar a eficácia e transversalidade da solução. Fluxos de dados de geolocalização em tempo real de autocarros de duas redes distintas de transporte público são usados para demonstrar a detecção de operações relacionadas com os veículos e comparar as distintas abordagens propostas por este trabalho. As operações dos autocarros produzem uma sequência estruturada de eventos que descreve o comportamento dos mesmos. Esse comportamento é mapeado por meio da aplicação de técnicas de process mining, para descobrir oportunidades de análise e gargalos no processo. Complementarmente, os dados de geolocalização de uma empresa de logística internacional são explorados para a monitorização de processos logísticos, nomeadamente para detecção de operações de logística em tempo real, demonstrando a eficácia da solução proposta para resolver problemas específicos da indústria. Os resultados deste trabalho revelam novas possibilidades no uso de dados de geolocalização e o seu potencial para gerar conhecimento acerca do processo. A exploração de dados de geolocalização nos contextos logísticos e de transportes públicos apresenta-se como uma oportunidade para melhorar a monitorização e gestão das operações baseadas em veículos. Isso pode originar melhorias na eficiência do processo e, consequentemente, maior lucro e melhor qualidade do serviço.Geolocation data identifies the geographic location of people or objects, and is fundamental for businesses relying on vehicles such as logistics and transportation. With the advance of technology, collecting geolocation data has become increasingly accessible and affordable, raising new opportunities for business intelligence. This type of data has been used mainly for characterizing the vehicle in terms of positioning and navigation, but it can also showcase its performance regarding the executed activities and operations. The proposed approach consists on a multi-step methodology that receives geolocation data as an input and allows the analysis of the business process in the end. Firstly, the preparation of the data is applied to handle a number of issues related to outliers, data noise, and missing or erroneous information. Then, the identification of stationary events is performed based on the motionless states of the vehicles. Next, the inference of operations based on a spatial analysis is performed, which allows the discovery of the locations where stationary events occur frequently. Finally, the identified operations are classified based on their characteristics, and the sequence of events can be structured into an event log. The application of process mining techniques is then possible and the consequently extraction of process knowledge. The steps of the methodology can also be used separately to tackle specific challenges, giving more flexibility to its application. Three distinct case studies are presented to demonstrate the effectiveness and transversality of the solution. Real-time geolocation data streams of buses from two distinct public transport networks are used to demonstrate the detection of vehicle-based operations and compare the distinct approaches proposed by this work. The buses operations produce a structured sequence of events that describes the behaviour of the buses. This behaviour is mapped through the application of process mining techniques uncovering analysis opportunities and discovering bottlenecks in the process. Geolocation data from an international logistics company is exploited for monitoring logistics processes, namely for detecting vehicle-based operations in real time, showing the effectiveness of the proposed solution to solve specific industry problems. The results of this work reveal new possibilities for geolocation data and its potential to generate process knowledge. The exploitation of geolocation data in the public transport and logistics contexts poses as an opportunity for improving the monitoring and management of vehicle-based operations. This can lead to into improvements in the process efficiency and consequently higher profit and better service quality

    Approaches and Solutions to Hospital Emergency Department Overcrowding Including Failure Mode and Effect Analysis as a Risk Assessment Technique of Real-time Locating System

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    Emergency Departments (ED) are highly dynamic environments comprising complex multi-dimensional patient-care processes. In recent decades, there has been increased pressure to improve ED services, while taking into account various aspects such as clinical quality, operational efficiency, and cost performance. Overcrowding has become a major barrier to receiving a proper and timely emergency care in many acute hospitals throughout the world. Patients often face long waiting times to be seen and treated. Those who require admission may even wait longer. The scope of this research is to focus on ED factors that lead to overcrowding and their management. Technology is being cited as one of the management tools, specifically the utilization of Radio Frequency Identification (RFID) for tracking patients as their journey progresses through an ED. Like any technology, RFID has potential and pitfalls. The author chose to use Failure Mode and Effect Analysis (FMEA) as a tool to explore the possible failures of RFID technology as it is utilized in one of the ED in Riyadh Military Hospital (RMH). This particular ED has been used as a case study to explore those failures and, with the use of FMEA, propose a set of recommendations to address those failures and improve the design and implementation of RFID. The experience of RMH-ED was explored through interviews and a survey in which 100 participants took part. The survey touched upon various aspects of this experience. This was due to the various roles of the surveyed staff who were involved with this technology. These roles ranged from front line clinical staff to administrative staff, management staff and technical support staff. Data analysis showed convincing evidence of the positive impact RFID had on managing ED overcrowding. However, and as expected, there are some pitfalls and failures that FMEA helped identifying and suggested potential solutions to them. RFID is a small link in the chain of other technological innovations and solutions. It is by no means capable of solving the problems associated with ED overcrowding by itself. Most of the search carried out by the author identified large variation in approaches to dealing with the issue of ED overcrowding. Those ranged from applying more human resources to altering the pathways of managing patients journey through healthcare system to applying more intermediate layers of management to ease the pressure of the Emergency departments. Other approaches included some aspects of technology such as development of early warning systems that have not been widely adopted and remained as isolated efforts

    Crossing Borders - Digital Transformation and the U.S. Health Care System

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    The Value of Integrated Information Systems for U.S. General Hospitals

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    Each year, huge investments into healthcare information systems (HIS) are being made all over the world. Despite the enormous cost for the hospitals, the overall benefits and costs of the healthcare information systems have not been deeply assessed. In recent years, much previous research has investigated the link between the implementation of Information Systems and the performance of organizations. Although the value of Healthcare Information System or Healthcare Information Technology (HIS/HIT) has been found in many studies, some questions remain unclear. Do HIS/HIT systems influence different hospitals the same way? How to understand and explain the mechanism that HIS/HIT improves the performance of hospitals? To address these questions, our research will: 1) Identify the bottlenecks of the current healthcare system which affects the operation efficiency (mismatch between demand and service provided); 2) Adopt the institutional theory to explain the process of implementing HIS/HIT and the possible outcomes; 3) Conduct an empirical study, to expose issues of current healthcare system and the value of the HIS/HIT, and to identify the factors that affect the performance of different hospitals; and 4) Design a decision support system for hospitals. Based on institutional theory, we explain the empirical findings from 2014 HIMSS database. To solve the mismatch between the patient needs and doctor’s schedule, we will propose a business model for a new integrated information management system. It gives the physicians and patients a comprehensive picture needed to understand the type of different patients. A classification schema will be designed to provide recommendations for scheduling decision, and it is supported by the interactive system

    Human-centred artificial intelligence for mobile health sensing:challenges and opportunities

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    Advances in wearable sensing and mobile computing have enabled the collection of health and well-being data outside of traditional laboratory and hospital settings, paving the way for a new era of mobile health. Meanwhile, artificial intelligence (AI) has made significant strides in various domains, demonstrating its potential to revolutionize healthcare. Devices can now diagnose diseases, predict heart irregularities and unlock the full potential of human cognition. However, the application of machine learning (ML) to mobile health sensing poses unique challenges due to noisy sensor measurements, high-dimensional data, sparse and irregular time series, heterogeneity in data, privacy concerns and resource constraints. Despite the recognition of the value of mobile sensing, leveraging these datasets has lagged behind other areas of ML. Furthermore, obtaining quality annotations and ground truth for such data is often expensive or impractical. While recent large-scale longitudinal studies have shown promise in leveraging wearable sensor data for health monitoring and prediction, they also introduce new challenges for data modelling. This paper explores the challenges and opportunities of human-centred AI for mobile health, focusing on key sensing modalities such as audio, location and activity tracking. We discuss the limitations of current approaches and propose potential solutions
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