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      Healthcare utilization after a first hospitalization for COPD: a new approach of State Sequence Analysis based on the '6W' multidimensional model of care trajectories

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          Abstract

          Background

          Published methods to describe and visualize Care Trajectories (CTs) as patterns of healthcare use are very sparse, often incomplete, and not intuitive for non-experts.

          Our objectives are to propose a typology of CTs one year after a first hospitalization for Chronic Obstructive Pulmonary Disease (COPD), and describe CT types and compare patients’ characteristics for each CT type.

          Methods

          This is an observational cohort study extracted from Quebec’s medico-administrative data of patients aged 40 to 84 years hospitalized for COPD in 2013 (index date). The cohort included patients hospitalized for the first time over a 3-year period before the index date and who survived over the follow-up period. The CTs consisted of sequences of healthcare use (e.g. ED-hospital-home-GP-respiratory therapists, etc.) over a one-year period. The main variable was a CT typology, which was generated by a ‘tailored’ multidimensional State Sequence Analysis, based on the “6W” model of Care Trajectories. Three dimensions were considered: the care setting (“where”), the reason for consultation (“why”), and the speciality of care providers (“which”). Patients were grouped into specific CT types, which were compared in terms of care use attributes and patients’ characteristics using the usual descriptive statistics.

          Results

          The 2581 patients were grouped into five distinct and homogeneous CT types: Type 1 ( n = 1351, 52.3%) and Type 2 ( n = 748, 29.0%) with low healthcare and moderate healthcare use respectively; Type 3 ( n = 216, 8.4%) with high healthcare use, mainly for respiratory reasons, with the highest number of urgent in-hospital days, seen by pulmonologists and respiratory therapists at primary care settings; Type 4 ( n = 100, 3.9%) with high healthcare use, mainly cardiovascular, high ED visits, and mostly seen by nurses in community-based primary care; Type 5 ( n = 166, 6.4%) with high healthcare use, high ED visits and non-urgent hospitalisations, and with consultations at outpatient clinics and primary care settings, mainly for other reasons than respiratory or cardiovascular. Patients in the 3 highest utilization CT types were older, and had more comorbidities and more severe condition at index hospitalization.

          Conclusions

          The proposed method allows for a better representation of the sequences of healthcare use in the real world, supporting data-driven decision making.

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          Most cited references33

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          Predicting healthcare trajectories from medical records: A deep learning approach.

          Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, stored in electronic medical records are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors and models patient health state trajectories by the memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces methods to handle irregularly timed events by moderating the forgetting and consolidation of memory. DeepCare also explicitly models medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden - diabetes and mental health - the results show improved prediction accuracy.
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            Prevention of acute exacerbations of COPD: American College of Chest Physicians and Canadian Thoracic Society Guideline.

            COPD is a major cause of morbidity and mortality in the United States as well as throughout the rest of the world. An exacerbation of COPD (periodic escalations of symptoms of cough, dyspnea, and sputum production) is a major contributor to worsening lung function, impairment in quality of life, need for urgent care or hospitalization, and cost of care in COPD. Research conducted over the past decade has contributed much to our current understanding of the pathogenesis and treatment of COPD. Additionally, an evolving literature has accumulated about the prevention of acute exacerbations.
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              • Article: not found

              A deprivation index for health and welfare planning in Quebec.

              Given that one of the goals of public health policy in Quebec and Canada is to reduce social inequalities in health and welfare, it is surprising, to say the least, that most information systems in this field make no mention of people's socio-economic characteristics. The present article proposes an index to reflect the material and social dimensions of deprivation as this concept has been developed by Peter Townsend and other authors. The article describes the method used to create the index, which uses census data and tools developed by Statistics Canada to match postal codes with enumeration areas. Examples are provided of the use of the index in information systems covering three aspects of health and welfare in Quebec: deaths, hospitalizations and births. The value of the information provided by this index in planning health and social services is demonstrated.
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                Author and article information

                Contributors
                alain.vanasse@usherbrooke.ca
                josiane.courteau@usherbrooke.ca
                mireille.courteau@usherbrooke.ca
                mike.benigeri@umontreal.ca
                Yohann.Chiu@USherbrooke.ca
                Isabelle.Dufour3@USherbrooke.ca
                Simon.Couillard.Castonguay@USherbrooke.ca
                Pierre.Larivee@USherbrooke.ca
                catherine.hudon@usherbrooke.ca
                Journal
                BMC Health Serv Res
                BMC Health Serv Res
                BMC Health Services Research
                BioMed Central (London )
                1472-6963
                6 March 2020
                6 March 2020
                2020
                : 20
                : 177
                Affiliations
                [1 ]GRID grid.411172.0, ISNI 0000 0001 0081 2808, Groupe de recherche PRIMUS, , Centre de recherche du Centre hospitalier universitaire de Sherbrooke (CRCHUS), ; 3001 12e avenue nord, Sherbrooke, QC J1H 5N4 Canada
                [2 ]GRID grid.86715.3d, ISNI 0000 0000 9064 6198, Département de médecine de famille et de médecine d’urgence, , Université de Sherbrooke, ; 3001 12e avenue nord, Sherbrooke, QC J1H 5N4 Canada
                [3 ]GRID grid.14848.31, ISNI 0000 0001 2292 3357, École de santé publique de l’Université de Montréal, ; 7101 avenue du Parc, Montréal, QC H3N 1X9 Canada
                [4 ]GRID grid.86715.3d, ISNI 0000 0000 9064 6198, Service de pneumologie, Département de Médecine, , Université de Sherbrooke, ; 3001 12e avenue nord, Sherbrooke, QC J1H 5N4 Canada
                Article
                5030
                10.1186/s12913-020-5030-0
                7059729
                32143702
                724acf5f-ad78-4de6-8448-99f11f7808a4
                © The Author(s). 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 30 April 2019
                : 24 February 2020
                Funding
                Funded by: Canadian Institute of Health Research
                Award ID: CIHR #391051
                Award Recipient :
                Funded by: Fonds de recherche du Québec—Santé
                Funded by: Université de Sherbrooke
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2020

                Health & Social care
                (3–10) state sequence analysis,care trajectories,healthcare utilization,typology,copd,optimal matching,traminer,observational study,data visualization

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