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Martin N, De Weerdt J, Fernández-Llatas C, Gal A, Gatta R, Ibáñez G, Johnson O, Mannhardt F, Marco-Ruiz L, Mertens S, Munoz-Gama J, Seoane F, Vanthienen J, Wynn MT, Boilève DB, Bergs J, Joosten-Melis M, Schretlen S, Van Acker B. Recommendations for enhancing the usability and understandability of process mining in healthcare. Artif Intell Med 2020; 109:101962. [PMID: 34756220 DOI: 10.1016/j.artmed.2020.101962] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 07/19/2020] [Accepted: 09/22/2020] [Indexed: 11/28/2022]
Abstract
Healthcare organizations are confronted with challenges including the contention between tightening budgets and increased care needs. In the light of these challenges, they are becoming increasingly aware of the need to improve their processes to ensure quality of care for patients. To identify process improvement opportunities, a thorough process analysis is required, which can be based on real-life process execution data captured by health information systems. Process mining is a research field that focuses on the development of techniques to extract process-related insights from process execution data, providing valuable and previously unknown information to instigate evidence-based process improvement in healthcare. However, despite the potential of process mining, its uptake in healthcare organizations outside case studies in a research context is rather limited. This observation was the starting point for an international brainstorm seminar. Based on the seminar's outcomes and with the ambition to stimulate a more widespread use of process mining in healthcare, this paper formulates recommendations to enhance the usability and understandability of process mining in healthcare. These recommendations are mainly targeted towards process mining researchers and the community to consider when developing a new research agenda for process mining in healthcare. Moreover, a limited number of recommendations are directed towards healthcare organizations and health information systems vendors, when shaping an environment to enable the continuous use of process mining.
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Affiliation(s)
- Niels Martin
- Research Foundation Flanders (FWO), Belgium; Hasselt University, Belgium; Vrije Universiteit Brussel, Belgium.
| | | | | | - Avigdor Gal
- Technion - Israel Institute of Technology, Israel.
| | - Roberto Gatta
- Centre Hopitalier Universitaire de Vaudois, Switzerland; Università degli Studi di Brescia, Italy.
| | | | | | | | | | | | | | - Fernando Seoane
- Karolinska Institutet, Sweden; Karolinska University Hospital, Sweden; University of Borås, Sweden.
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Leveraging semantic labels for multi-level abstraction in medical process mining and trace comparison. J Biomed Inform 2018; 83:10-24. [DOI: 10.1016/j.jbi.2018.05.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Revised: 05/16/2018] [Accepted: 05/19/2018] [Indexed: 11/17/2022]
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Zema M, Rosati S, Duran Carvajal JE, Balestra G. CPDI: An Index for measuring deviations in Clinical Pathways. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2015:1385-8. [PMID: 26736527 DOI: 10.1109/embc.2015.7318627] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Clinical Pathways (CPs) are evidence-based recommendation for treating a diagnosis and an effective instrument to decrease undesired practice variability and improve clinician performance. Deviations from CPs might just as well reduce quality of care. Moreover they can be associated to possible adverse events. In this perspective, we developed and tested a system for comparing a patient trajectory (PT) with the corresponding CP in order to recognize significant variations. To measure adherence, a Clinical Pathway Deviation Index (CPDI) was constructed as the weighted-sum of five indicators. To build the indicators three different tools for CPs modeling have been tested. Only two of them proved suitable for our system. A preliminary analysis has been carried out using data of 24 real PTs. The aim of this paper is to present the system and to characterize CPDI performances.
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Dagliati A, Tibollo V, Cogni G, Chiovato L, Bellazzi R, Sacchi L. Careflow Mining Techniques to Explore Type 2 Diabetes Evolution. J Diabetes Sci Technol 2018; 12:251-259. [PMID: 29493360 PMCID: PMC5851241 DOI: 10.1177/1932296818761751] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
In this work we describe the application of a careflow mining algorithm to detect the most frequent patterns of care in a type 2 diabetes patients cohort. The applied method enriches the detected patterns with clinical data to define temporal phenotypes across the studied population. Novel phenotypes are discovered from heterogeneous data of 424 Italian patients, and compared in terms of metabolic control and complications. Results show that careflow mining can help to summarize the complex evolution of the disease into meaningful patterns, which are also significant from a clinical point of view.
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Affiliation(s)
- Arianna Dagliati
- Istituti Clinici Scientifici Maugeri, Pavia, Italy
- University of Manchester, Manchester, UK
| | | | - Giulia Cogni
- Istituti Clinici Scientifici Maugeri, Pavia, Italy
| | | | - Riccardo Bellazzi
- Istituti Clinici Scientifici Maugeri, Pavia, Italy
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Lucia Sacchi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
- Lucia Sacchi, PhD, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata, 5, Pavia, 27100, Italy.
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Ma Y, Shi M, Wei J. Cost and accuracy aware scientific workflow retrieval based on distance measure. Inf Sci (N Y) 2015. [DOI: 10.1016/j.ins.2015.03.055] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Peek N, Combi C, Marin R, Bellazzi R. Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes. Artif Intell Med 2015; 65:61-73. [DOI: 10.1016/j.artmed.2015.07.003] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2015] [Revised: 07/17/2015] [Accepted: 07/17/2015] [Indexed: 10/23/2022]
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Syed H, Das AK. Identifying Chemotherapy Regimens in Electronic Health Record Data Using Interval-Encoded Sequence Alignment. Artif Intell Med 2015. [DOI: 10.1007/978-3-319-19551-3_17] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Defossez G, Rollet A, Dameron O, Ingrand P. Temporal representation of care trajectories of cancer patients using data from a regional information system: an application in breast cancer. BMC Med Inform Decis Mak 2014; 14:24. [PMID: 24690482 PMCID: PMC3983896 DOI: 10.1186/1472-6947-14-24] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2013] [Accepted: 03/27/2014] [Indexed: 12/04/2022] Open
Abstract
Background Ensuring that all cancer patients have access to the appropriate treatment within an appropriate time is a strategic priority in many countries. There is in particular a need to describe and analyse cancer care trajectories and to produce waiting time indicators. We developed an algorithm for extracting temporally represented care trajectories from coded information collected routinely by the general cancer Registry in Poitou-Charentes region, France. The present work aimed to assess the performance of this algorithm on real-life patient data in the setting of non-metastatic breast cancer, using measures of similarity. Methods Care trajectories were modeled as ordered dated events aggregated into states, the granularity of which was defined from standard care guidelines. The algorithm generates each state from the aggregation over a period of tracer events characterised on the basis of diagnoses and medical procedures. The sequences are presented in simple form showing presence and order of the states, and in an extended form that integrates the duration of the states. The similarity of the sequences, which are represented in the form of chains of characters, was calculated using a generalised Levenshtein distance. Results The evaluation was performed on a sample of 159 female patients whose itineraries were also calculated manually from medical records using the same aggregation rules and dating system as the algorithm. Ninety-eight per cent of the trajectories were correctly reconstructed with respect to the ordering of states. When the duration of states was taken into account, 94% of the trajectories matched reality within three days. Dissimilarities between sequences were mainly due to the absence of certain pathology reports and to coding anomalies in hospitalisation data. Conclusions These results show the ability of an integrated regional information system to formalise care trajectories and automatically produce indicators for time-lapse to care instatement, of interest in the planning of care in cancer. The next step will consist in evaluating this approach and extending it to more complex trajectories (metastasis, relapse) and to other cancer localisations.
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Affiliation(s)
- Gautier Defossez
- Unité d'épidémiologie, biostatistique et registre général des cancers de Poitou-Charentes, Faculté de médecine, Centre Hospitalier Universitaire de Poitiers, Université de Poitiers, 6, rue de la milétrie, Poitiers, Cedex BP 199 86034, France.
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Álvarez MR, Félix P, Cariñena P. Discovering metric temporal constraint networks on temporal databases. Artif Intell Med 2013; 58:139-54. [DOI: 10.1016/j.artmed.2013.03.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2012] [Revised: 03/06/2013] [Accepted: 03/17/2013] [Indexed: 10/26/2022]
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Yang X, Han R, Guo Y, Bradley J, Cox B, Dickinson R, Kitney R. Modelling and performance analysis of clinical pathways using the stochastic process algebra PEPA. BMC Bioinformatics 2012; 13 Suppl 14:S4. [PMID: 23095226 PMCID: PMC3439723 DOI: 10.1186/1471-2105-13-s14-s4] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Background Hospitals nowadays have to serve numerous patients with limited medical staff and equipment while maintaining healthcare quality. Clinical pathway informatics is regarded as an efficient way to solve a series of hospital challenges. To date, conventional research lacks a mathematical model to describe clinical pathways. Existing vague descriptions cannot fully capture the complexities accurately in clinical pathways and hinders the effective management and further optimization of clinical pathways. Method Given this motivation, this paper presents a clinical pathway management platform, the Imperial Clinical Pathway Analyzer (ICPA). By extending the stochastic model performance evaluation process algebra (PEPA), ICPA introduces a clinical-pathway-specific model: clinical pathway PEPA (CPP). ICPA can simulate stochastic behaviours of a clinical pathway by extracting information from public clinical databases and other related documents using CPP. Thus, the performance of this clinical pathway, including its throughput, resource utilisation and passage time can be quantitatively analysed. Results A typical clinical pathway on stroke extracted from a UK hospital is used to illustrate the effectiveness of ICPA. Three application scenarios are tested using ICPA: 1) redundant resources are identified and removed, thus the number of patients being served is maintained with less cost; 2) the patient passage time is estimated, providing the likelihood that patients can leave hospital within a specific period; 3) the maximum number of input patients are found, helping hospitals to decide whether they can serve more patients with the existing resource allocation. Conclusions ICPA is an effective platform for clinical pathway management: 1) ICPA can describe a variety of components (state, activity, resource and constraints) in a clinical pathway, thus facilitating the proper understanding of complexities involved in it; 2) ICPA supports the performance analysis of clinical pathway, thereby assisting hospitals to effectively manage time and resources in clinical pathway.
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Affiliation(s)
- Xian Yang
- Department of Computing, Imperial College London, London, SW7 2AZ, UK
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Huang Z, Lu X, Duan H. On mining clinical pathway patterns from medical behaviors. Artif Intell Med 2012; 56:35-50. [PMID: 22809825 DOI: 10.1016/j.artmed.2012.06.002] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2011] [Revised: 05/21/2012] [Accepted: 06/10/2012] [Indexed: 11/19/2022]
Abstract
OBJECTIVE Clinical pathway analysis, as a pivotal issue in ensuring specialized, standardized, normalized and sophisticated therapy procedures, is receiving increasing attention in the field of medical informatics. Clinical pathway pattern mining is one of the most important components of clinical pathway analysis and aims to discover which medical behaviors are essential/critical for clinical pathways, and also where temporal orders of these medical behaviors are quantified with numerical bounds. Even though existing clinical pathway pattern mining techniques can tell us which medical behaviors are frequently performed and in which order, they seldom precisely provide quantified temporal order information of critical medical behaviors in clinical pathways. METHODS This study adopts process mining to analyze clinical pathways. The key contribution of the paper is to develop a new process mining approach to find a set of clinical pathway patterns given a specific clinical workflow log and minimum support threshold. The proposed approach not only discovers which critical medical behaviors are performed and in which order, but also provides comprehensive knowledge about quantified temporal orders of medical behaviors in clinical pathways. RESULTS The proposed approach is evaluated via real-world data-sets, which are extracted from Zhejiang Huzhou Central hospital of China with regard to six specific diseases, i.e., bronchial lung cancer, gastric cancer, cerebral hemorrhage, breast cancer, infarction, and colon cancer, in two years (2007.08-2009.09). As compared to the general sequence pattern mining algorithm, the proposed approach consumes less processing time, generates quite a smaller number of clinical pathway patterns, and has a linear scalability in terms of execution time against the increasing size of data sets. CONCLUSION The experimental results indicate the applicability of the proposed approach, based on which it is possible to discover clinical pathway patterns that can cover most frequent medical behaviors that are most regularly encountered in clinical practice. Therefore, it holds significant promise in research efforts related to the analysis of clinical pathways.
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Affiliation(s)
- Zhengxing Huang
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Zhou Yiqin building 510, Zheda road 38#, Hangzhou, 310008 Zhejiang, China
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Forestier G, Lalys F, Riffaud L, Trelhu B, Jannin P. Classification of surgical processes using dynamic time warping. J Biomed Inform 2011; 45:255-64. [PMID: 22120773 DOI: 10.1016/j.jbi.2011.11.002] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2011] [Revised: 11/09/2011] [Accepted: 11/10/2011] [Indexed: 10/15/2022]
Abstract
In the creation of new computer-assisted intervention systems, Surgical Process Models (SPMs) are an emerging concept used for analyzing and assessing surgical interventions. SPMs represent Surgical Processes (SPs) which are formalized as symbolic structured descriptions of surgical interventions using a pre-defined level of granularity and a dedicated terminology. In this context, one major challenge is the creation of new metrics for the comparison and the evaluation of SPs. Thus, correlations between these metrics and pre-operative data are used to classify surgeries and highlight specific information on the surgery itself and on the surgeon, such as his/her level of expertise. In this paper, we explore the automatic classification of a set of SPs based on the Dynamic Time Warping (DTW) algorithm. DTW is used to compute a similarity measure between two SPs that focuses on the different types of activities performed during surgery and their sequencing, by minimizing time differences. Indeed, it turns out to be a complementary approach to the classical methods that only focus on differences in the time and the number of activities. Experiments were carried out on 24 lumbar disk herniation surgeries to discriminate the surgeons level of expertise according to a prior classification of SPs. Supervised and unsupervised classification experiments have shown that this approach was able to automatically identify groups of surgeons according to their level of expertise (senior and junior), and opens many perspectives for the creation of new metrics for comparing and evaluating surgeries.
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Neumuth T, Loebe F, Jannin P. Similarity metrics for surgical process models. Artif Intell Med 2011; 54:15-27. [PMID: 22056273 DOI: 10.1016/j.artmed.2011.10.001] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2010] [Revised: 08/18/2011] [Accepted: 10/04/2011] [Indexed: 11/28/2022]
Abstract
OBJECTIVE The objective of this work is to introduce a set of similarity metrics for comparing surgical process models (SPMs). SPMs are progression models of surgical interventions that support quantitative analyses of surgical activities, supporting systems engineering or process optimization. METHODS AND MATERIALS Five different similarity metrics are presented and proven. These metrics deal with several dimensions of process compliance in surgery, including granularity, content, time, order, and frequency of surgical activities. The metrics were experimentally validated using 20 clinical data sets each for cataract interventions, craniotomy interventions, and supratentorial tumor resections. The clinical data sets were controllably modified in simulations, which were iterated ten times, resulting in a total of 600 simulated data sets. The simulated data sets were subsequently compared to the original data sets to empirically assess the predictive validity of the metrics. RESULTS We show that the results of the metrics for the surgical process models correlate significantly (p<0.001) with the induced modifications and that all metrics meet predictive validity. The clinical use of the metrics was exemplarily, as demonstrated by assessment of the learning curves of observers during surgical process model acquisition. CONCLUSION Measuring similarity between surgical processes is a complex task. However, metrics for computing the similarity between surgical process models are needed in many uses in the field of medical engineering. These metrics are essential whenever two SPMs need to be compared, such as during the evaluation of technical systems, the education of observers, or the determination of surgical strategies. These metrics are key figures that provide a solid base for medical decisions, such as during validation of sensor systems for use in operating rooms in the future.
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Affiliation(s)
- Thomas Neumuth
- Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, Germany.
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Gooch P, Roudsari A. Computerization of workflows, guidelines, and care pathways: a review of implementation challenges for process-oriented health information systems. J Am Med Inform Assoc 2011; 18:738-48. [PMID: 21724740 PMCID: PMC3197986 DOI: 10.1136/amiajnl-2010-000033] [Citation(s) in RCA: 64] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2010] [Accepted: 05/27/2011] [Indexed: 11/03/2022] Open
Abstract
OBJECTIVE There is a need to integrate the various theoretical frameworks and formalisms for modeling clinical guidelines, workflows, and pathways, in order to move beyond providing support for individual clinical decisions and toward the provision of process-oriented, patient-centered, health information systems (HIS). In this review, we analyze the challenges in developing process-oriented HIS that formally model guidelines, workflows, and care pathways. METHODS A qualitative meta-synthesis was performed on studies published in English between 1995 and 2010 that addressed the modeling process and reported the exposition of a new methodology, model, system implementation, or system architecture. Thematic analysis, principal component analysis (PCA) and data visualisation techniques were used to identify and cluster the underlying implementation 'challenge' themes. RESULTS One hundred and eight relevant studies were selected for review. Twenty-five underlying 'challenge' themes were identified. These were clustered into 10 distinct groups, from which a conceptual model of the implementation process was developed. DISCUSSION AND CONCLUSION We found that the development of systems supporting individual clinical decisions is evolving toward the implementation of adaptable care pathways on the semantic web, incorporating formal, clinical, and organizational ontologies, and the use of workflow management systems. These architectures now need to be implemented and evaluated on a wider scale within clinical settings.
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Affiliation(s)
- Phil Gooch
- Centre for Health Informatics, School of Informatics, City University London, London, UK.
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Juarez JM, Campos M, Morales A, Palma J, Marin R. Applications of Temporal Reasoning to Intensive Care Units. JOURNAL OF HEALTHCARE ENGINEERING 2010. [DOI: 10.1260/2040-2295.1.4.615] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Juarez JM, Campos M, Palma J, Palacios F, Marin R. Severity Evaluation Support for Burns Unit Patients Based on Temporal Episodic Knowledge Retrieval. Artif Intell Med 2009. [DOI: 10.1007/978-3-642-02976-9_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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