Reingreso hospitalario en Medicina Interna

Martínez, J. L. Alonso; Díez, B.; Agara, M. Echegaray; Echezarreta, M. A. Urbieta; Arencibia, C. González

doi:10.4321/s0212-71992001000500005

Cited by 16 publications

(8 citation statements)

References 20 publications

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Section: Introductionmentioning

confidence: 99%

“…Nearly 39% of readmissions could be prevented in older adults, and among the most common reasons for readmission are worsening in the chronic disease, iatrogenia and inadequate ambulatory management (Alonso Martínez et al 2001). What is more, the time between readmissions could be attributed to patient conditions at home, education during the intervals between hospitalisations and finally to social and healthcare support factors (Maciá Soler et al 2011).…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of the effectiveness of hospital discharge planning and follow‐up in the primary care of patients with chronic obstructive pulmonary disease

Abad‐Corpa

Royo-Morales

Iniesta-Sánchez

et al. 2012

Journal of Clinical Nursing

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of the effectiveness of hospital discharge planning and follow‐up in the primary care of patients with chronic obstructive pulmonary disease

Abad‐Corpa

Royo-Morales

Iniesta-Sánchez

et al. 2012

Journal of Clinical Nursing

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“…The data analyzed comes from a reference hospital with a population of around 290,000 inhabitants for the period between June 2015 and February 2018 (both included) for a total of 33 months. The number of emergencies attended at this period was 143,803 with a return rate of: (1) This means that 4.32% of patients returned to the emergency department in less than 72 h. The main characteristics of the data are:…”

Section: Discussionmentioning

confidence: 93%

“…The study of the quality system of hospitals is a necessary activity for the improvement of the services they offer [1]. In recent years, different public and private institutions have established a set of activity and quality indicators that allow the evaluation, monitoring and comparison of the activities of hospital emergency services [2].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Techniques to Analyze Patient Returns to the Emergency Department

Cabezuelo

2020

JPM

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The study of the quality of hospital emergency services is based on analyzing a set of indicators such as the average time of first medical attention, the average time spent in the emergency department, degree of completion of the medical report and others. In this paper, an analysis is presented of one of the quality indicators: the rate of return of patients to the emergency service less than 72 h from their discharge. The objective of the analysis was to know the variables that influence the rate of return and which prediction model is the best. In order to do this, the data of the activity of the emergency service of a hospital of a reference population of 290,000 inhabitants were analyzed, and prediction models were created for the binary objective variable (rate of return to emergencies) using the logistic regression techniques, neural networks, random forest, gradient boosting and assembly models. Each of the models was analyzed and the result shows that the best model is achieved through a neural network with activation function tanh, algorithm levmar and three nodes in the hidden layer. This model obtains the lowest mean squared error (MSE) and the best area under the curve (AUC) with respect to the rest of the models used.

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“…Numerous studies highlight the relevance of hospital readmissions [ 1 , 2 , 3 , 4 , 5 ]. Hospital readmissions provide information on the evolution of patient health status following initial hospital care and can thus be an indicator of the outcome of the care process.…”

Section: Introductionmentioning

confidence: 99%

Acute Poisoning Readmissions to an Emergency Department of a Tertiary Hospital: Evaluation through an Active Toxicovigilance Program

Romo

Pérez

Alvira

et al. 2022

JCM

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The aim of this study is to investigate hospital readmissions during 1 year after acute poisoning cases (APC), analyze the temporal behavior of early readmissions (ER) (in the month after the index episode) and predict possible ER. A descriptive analysis of the patients with APC assisted between 2011 and 2016 in the Emergency Department of Hospital La Paz is presented, and various methods of inferential statistics were applied and confirmed by Bayesian analysis in order to evaluate factors associated with total and early readmissions. Out of the 4693 cases of APC included, 968 (20.6%) presented, at least one readmission and 476 (10.1%) of them were ER. The mean age of APC with readmission was 41 years (12.7 SD), 78.9% had previous psychiatric pathology and 44.7% had a clinical history of alcohol addiction. Accidental poisoning has been a protective factor for readmission (OR 0.50; 0.26–0.96). Type of toxin (“drug of abuse” OR 8.88; 1.17–67.25), history of addiction (OR 1.93; 1.18–3.10) and psychiatric history (OR 3.30; 2.53–4.30) are risk factors for readmissions during the first year. Women showed three or more readmissions in a year. The results of the study allow for identification of the predictors for the different numbers of readmissions in the year after the index APC, as well as for ERs.

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Reingreso hospitalario en Medicina Interna

Cited by 16 publications

References 20 publications

Evaluation of the effectiveness of hospital discharge planning and follow‐up in the primary care of patients with chronic obstructive pulmonary disease

Evaluation of the effectiveness of hospital discharge planning and follow‐up in the primary care of patients with chronic obstructive pulmonary disease

Application of Machine Learning Techniques to Analyze Patient Returns to the Emergency Department

Acute Poisoning Readmissions to an Emergency Department of a Tertiary Hospital: Evaluation through an Active Toxicovigilance Program

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