Design and conceptual proposal of an intelligent clinical decision support system for the diagnosis of suspicious obstructive sleep apnea patients from health profile

Obstructive Sleep Apnea (OSA) is a chronic sleep-related pathology characterized by recurrent episodes of total or partial obstruction of the upper airways during sleep. It entails a high impact on the health and quality of life of patients, affecting more than one thousand million people worldwide, which has resulted in an important public health concern in recent years. The usual diagnosis involves performing a sleep test, cardiorespiratory polygraphy, or polysomnography, which allows characterizing the pathology and assessing its severity. However, this procedure cannot be used on a massive scale in general screening studies of the population because of its execution and implementation costs; therefore, causing an increase in waiting lists which would negatively affect the health of the affected patients. Additionally, the symptoms shown by these patients are often unspecific, as well as appealing to the general population (excessive somnolence, snoring, etc.), causing many potential cases to be referred for a sleep study when in reality are not suffering from OSA. This paper proposes a novel intelligent clinical decision support system to be applied to the diagnosis of OSA that can be used in early outpatient stages, quickly, easily, and safely, when a suspicious OSA patient attends the consultation. Starting from information related to the patient’s health profile (anthropometric data, habits, comorbidities, or medications taken), the system is capable of determining different alert levels of suffering from sleep apnea associated with different apnea-hypopnea index (AHI) levels to be studied. To that end, a series of automatic learning algorithms are deployed that, working concurrently, together with a corrective approach based on the use of an Adaptive Neuro-Based Fuzzy Inference System (ANFIS) and a specific heuristic algorithm, allow the calculation of a series of labels associated with the different levels of AHI previously indicated. For the initial software implementation, a data set with 4600 patients from the Álvaro Cunqueiro Hospital in Vigo was used. The results obtained after performing the proof tests determined ROC curves with AUC values in the range 0.8–0.9, and Matthews correlation coefficient values close to 0.6, with high success rates. This points to its potential use as a support tool for the diagnostic process, not only from the point of view of improving the quality of the services provided, but also from the best use of hospital resources and the consequent savings in terms of costs and time.Xunta de Galicia | Ref. ED481A-2020/03

Design of an intelligent decision support system applied to the diagnosis of obstructive sleep apnea

Obstructive sleep apnea (OSA), characterized by recurrent episodes of partial or total obstruction of the upper airway during sleep, is currently one of the respiratory pathologies with the highest incidence worldwide. This situation has led to an increase in the demand for medical appointments and specific diagnostic studies, resulting in long waiting lists, with all the health consequences that this entails for the affected patients. In this context, this paper proposes the design and development of a novel intelligent decision support system applied to the diagnosis of OSA, aiming to identify patients suspected of suffering from the pathology. For this purpose, two sets of heterogeneous information are considered. The first one includes objective data related to the patient’s health profile, with information usually available in electronic health records (anthropometric information, habits, diagnosed conditions and prescribed treatments). The second type includes subjective data related to the specific OSA symptomatology reported by the patient in a specific interview. For the processing of this information, a machine-learning classification algorithm and a set of fuzzy expert systems arranged in cascade are used, obtaining, as a result, two indicators related to the risk of suffering from the disease. Subsequently, by interpreting both risk indicators, it will be possible to determine the severity of the patients’ condition and to generate alerts. For the initial tests, a software artifact was built using a dataset with 4400 patients from the Álvaro Cunqueiro Hospital (Vigo, Galicia, Spain). The preliminary results obtained are promising and demonstrate the potential usefulness of this type of tool in the diagnosis of OSA.Xunta de Galicia | Ref. ED481A-2020/03

Proceso asistencial integrado de esclerosis lateral amiotrófica

O proceso asistencial integrado da esclerose lateral amiotrófica, elaborouse co obxectivo de crear un proceso de traballo común en todas as áreas para facilitar a asistencia sanitaria ás persoas diagnosticadas desta enfermidade. Establécense actuacións como o asesoramento continuo, as consultas en acto único, a coordinación asistencial, tanto entre especialidades como coa atención primaria e a coordinación administrativa do sistema socio sanitario. Neste proceso participaron profesionais das diferentes áreas sanitarias especialistas en neuroloxía, endocrinoloxía, neumoloxía, psicoloxía clínica, rehabilitación, traballo social e hospitalización a domicilioEl proceso asistencial integrado de la esclerosis lateral amiotrófica, se elaboró con el objetivo de crear un proceso de trabajo común en todas las áreas para facilitar la asistencia sanitaria a las personas diagnosticadas de esta enfermedad. Se establecen actuaciones como el asesoramiento continuo, las consultas en acto único, la coordinación asistencial, tanto entre especialidades como con la atención primaria y la coordinación administrativa del sistema socio sanitario. En este proceso participaron profesionales de las diferentes áreas sanitarias especialistas en neurología, endocrinología, neumología, psicología clínica, rehabilitación, trabajo social y hospitalización a domicili

Increasing competitiveness through the implementation of lean management in healthcare

The main aim of this paper was two-fold: first, to design a participative methodology that facilitates lean management implementation in healthcare by adopting the action research approach; second, to illustrate the usefulness of this methodology by applying it to the sleep unit of a public hospital in Spain. This methodology proposes the implementation of lean management in its broadest sense: adopting both lean principles and some of its practical tools or practices in order to achieve competitive advantage. The complete service value chain was considered when introducing changes through lean management implementation. This implementation involved training and involving staff in the project (personnel pillar), detecting and analysing “waste” in value chain processes (processes pillar) and establishing control and measurement mechanisms in line with objectives (key performance indicators pillar) and putting in place improvement actions to achieve these objectives. The application of this methodology brought about an improvement in the management of patient flow in terms of effectiveness, efficiency and quality but also an internal transformation towards lean culture

Increasing Competitiveness through the Implementation of Lean Management in Healthcare

The main aim of this paper was two-fold: first, to design a participative methodology that facilitates lean management implementation in healthcare by adopting the action research approach; second, to illustrate the usefulness of this methodology by applying it to the sleep unit of a public hospital in Spain. This methodology proposes the implementation of lean management in its broadest sense: adopting both lean principles and some of its practical tools or practices in order to achieve competitive advantage. The complete service value chain was considered when introducing changes through lean management implementation. This implementation involved training and involving staff in the project (personnel pillar), detecting and analysing “waste” in value chain processes (processes pillar) and establishing control and measurement mechanisms in line with objectives (key performance indicators pillar) and putting in place improvement actions to achieve these objectives. The application of this methodology brought about an improvement in the management of patient flow in terms of effectiveness, efficiency and quality but also an internal transformation towards lean culture

Stratification for Identification of Prognostic Categories in the Acute RESpiratory Distress Syndrome (SPIRES) Score

OBJECTIVES: To develop a scoring model for stratifying patients with acute respiratory distress syndrome into risk categories (Stratification for identification of Prognostic categories In the acute RESpiratory distress syndrome score) for early prediction of death in the ICU, independent of the underlying disease and cause of death. DESIGN: A development and validation study using clinical data from four prospective, multicenter, observational cohorts. SETTING: A network of multidisciplinary ICUs. PATIENTS: One-thousand three-hundred one patients with moderate-to-severe acute respiratory distress syndrome managed with lung-protective ventilation. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The study followed Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guidelines for prediction models. We performed logistic regression analysis, bootstrapping, and internal-external validation of prediction models with variables collected within 24 hours of acute respiratory distress syndrome diagnosis in 1,000 patients for model development. Primary outcome was ICU death. The Stratification for identification of Prognostic categories In the acute RESpiratory distress syndrome score was based on patient's age, number of extrapulmonary organ failures, values of end-inspiratory plateau pressure, and ratio of Pao2to Fio2assessed at 24 hours of acute respiratory distress syndrome diagnosis. The pooled area under the receiver operating characteristic curve across internal-external validations was 0.860 (95% CI, 0.831-0.890). External validation in a new cohort of 301 acute respiratory distress syndrome patients confirmed the accuracy and robustness of the scoring model (area under the receiver operating characteristic curve = 0.870; 95% CI, 0.829-0.911). The Stratification for identification of Prognostic categories In the acute RESpiratory distress syndrome score stratified patients in three distinct prognostic classes and achieved better prediction of ICU death than ratio of Pao2to Fio2at acute respiratory distress syndrome onset or at 24 hours, Acute Physiology and Chronic Health Evaluation II score, or Sequential Organ Failure Assessment scale. CONCLUSIONS: The Stratification for identification of Prognostic categories In the acute RESpiratory distress syndrome score represents a novel strategy for early stratification of acute respiratory distress syndrome patients into prognostic categories and for selecting patients for therapeutic trials