Contributions de la Gestion des connaissances à la performance des organisations de soins de santé, revue systématique

Health care is considered a mainstay to keep humans alive. It is therefore essential to manage their health by diagnosing and treating the disease, in addition to preventing it. Knowledge management (KM) in the field of health plays an important role in the implementation of different processes to ensure the existence and performance of health systems. The strategic goal of knowledge management is to convert knowledge into competitive advantages and creative potential that can be measured as success and performance. The purpose of this research work is to shed light on the factors that influence the adoption of KM in healthcare. Based on a search strategy, the systematic review offers a broad analysis of 23 research articles assessed by publication quality in indexed journals between 2011 and 2021. The results provide conclusions regarding the most studied KM factors, namely knowledge sharing and applications, and the studied KM processes, which are knowledge acquisition and protection. This review study attempts to identify and present research on KM factors as a performance lever for healthcare organizations. This research hopes to help the implementation of KM System and in particular medical managers, as well as suppliers to be aware of these factors, when implementing medical procedures and devices. Keywords: Knowledge management (KM), KM process, healthcare organizations, organizational performance, knowledge management system (KMS); JEL Classification: M15 Paper type: Theoretical researchLes soins de santé sont considérés comme un pilier pour maintenir les humains en vie. Il est donc indispensable de gérer leur santé en diagnostiquant et en traitant la maladie, en plus de la prévenir. La gestion des connaissances (KM) dans le domaine de la santé joue un rôle important dans la mise en œuvre de différents processus pour garantir l'existence et la performance de systèmes de santé. L'objectif stratégique de la gestion des connaissances est de convertir les connaissances en avantages concurrentiels et potentiel de créativité qui peuvent être mesurés en tant que succès et performance.  Le but de ce travail de recherche est de mettre en lumière les facteurs qui influencent l’adoption de KM en ce qui concerne les soins de santé. Sur la base d'une stratégie de recherche, la revue systématique propose une large analyse de 23 articles de recherche évalués par la qualité de publication dans des revues indexées entre 2011-2021. Les résultats fournissent des conclusions concernant les facteurs de GC les plus étudiés, à savoir le partage et les applications des connaissances, et les processus de KM étudiés, qui sont l'acquisition et la protection des connaissances. Cette étude de revue tente de déterminer et de présenter la recherche sur les facteurs de KM comme un levier de performance des organisations de santé. Cette recherche espère aider à la mise en œuvre de KM System et en particulier les responsables médicaux, ainsi que les fournisseurs à être conscients de ces facteurs, lors de la mise en œuvre des procédures et dispositifs médicaux. Mots clé : Gestion des connaissances (KM), Processus de KM, organismes de soins de santé, performance organisationnelle, système de gestion des connaissances (KMS) ; Classification JEL :  M15 Type de l’article : Recherche théoriqu

Improving Prediction Accuracy of Breast Cancer Survivability and Diabetes Diagnosis via RBF Networks trained with EKF models

The continued reliance on machine learning algorithms and robotic devices in the medical and engineering practices has prompted the need for the accuracy prediction of such devices. It has attracted many researchers in recent years and has led to the development of various ensembles and standalone models to address prediction accuracy issues. This study was carried out to investigate the integration of EKF, RBF networks and AdaBoost as an ensemble model to improve prediction accuracy. In this study we proposed a model termed EKF-RBFN-ADABOOST

Methods to Improve the Prediction Accuracy and Performance of Ensemble Models

The application of ensemble predictive models has been an important research area in predicting medical diagnostics, engineering diagnostics, and other related smart devices and related technologies. Most of the current predictive models are complex and not reliable despite numerous efforts in the past by the research community. The performance accuracy of the predictive models have not always been realised due to many factors such as complexity and class imbalance. Therefore there is a need to improve the predictive accuracy of current ensemble models and to enhance their applications and reliability and non-visual predictive tools. The research work presented in this thesis has adopted a pragmatic phased approach to propose and develop new ensemble models using multiple methods and validated the methods through rigorous testing and implementation in different phases. The first phase comprises of empirical investigations on standalone and ensemble algorithms that were carried out to ascertain their performance effects on complexity and simplicity of the classifiers. The second phase comprises of an improved ensemble model based on the integration of Extended Kalman Filter (EKF), Radial Basis Function Network (RBFN) and AdaBoost algorithms. The third phase comprises of an extended model based on early stop concepts, AdaBoost algorithm, and statistical performance of the training samples to minimize overfitting performance of the proposed model. The fourth phase comprises of an enhanced analytical multivariate logistic regression predictive model developed to minimize the complexity and improve prediction accuracy of logistic regression model. To facilitate the practical application of the proposed models; an ensemble non-invasive analytical tool is proposed and developed. The tool links the gap between theoretical concepts and practical application of theories to predict breast cancer survivability. The empirical findings suggested that: (1) increasing the complexity and topology of algorithms does not necessarily lead to a better algorithmic performance, (2) boosting by resampling performs slightly better than boosting by reweighting, (3) the prediction accuracy of the proposed ensemble EKF-RBFN-AdaBoost model performed better than several established ensemble models, (4) the proposed early stopped model converges faster and minimizes overfitting better compare with other models, (5) the proposed multivariate logistic regression concept minimizes the complexity models (6) the performance of the proposed analytical non-invasive tool performed comparatively better than many of the benchmark analytical tools used in predicting breast cancers and diabetics ailments. The research contributions to ensemble practice are: (1) the integration and development of EKF, RBFN and AdaBoost algorithms as an ensemble model, (2) the development and validation of ensemble model based on early stop concepts, AdaBoost, and statistical concepts of the training samples, (3) the development and validation of predictive logistic regression model based on breast cancer, and (4) the development and validation of a non-invasive breast cancer analytic tools based on the proposed and developed predictive models in this thesis. To validate prediction accuracy of ensemble models, in this thesis the proposed models were applied in modelling breast cancer survivability and diabetics’ diagnostic tasks. In comparison with other established models the simulation results of the models showed improved predictive accuracy. The research outlines the benefits of the proposed models, whilst proposes new directions for future work that could further extend and improve the proposed models discussed in this thesis

Kanta-järjestelmän käyttöönotto terveyspalvelualan yrityksissä

Tutkimus selvitti Kanta-järjestelmän käyttöönottoa terveyspalvelualan yrityksissä. Taustalla on ajankohtainen ja yhteiskunnallisesti merkittävä tietojärjestelmäuudistus, missä sähköisessä muodossa asiakastietonsa kirjaavat yritykset terveyspalvelualalla liittyvät Kansalliseen Terveysarkistoon (Kantaan). Mitä dynaamisempi ja adaptiivisempi yritys on kompleksisissa toimintaympäristönsä muutoksissa, sitä paremmat mahdollisuudet sillä on proaktiivisella käyttäytymisellään kääntää tilanne edukseen. Tutkimuksen tavoitteena oli auttaa ymmärtämään pienyritysten asemaa ja toiminnan mahdollisuuksia laajassa yhteiskunnallisessa muutoksessa. Aihetta ei ole aikaisemmin tutkittu yritysten näkökulmasta. Tutkimuksen perusjoukon muodostivat Suomen Kuntoutusyrittäjät ry:n jäsenyritykset ja tarkemmin yrityksissä Kanta-järjestelmän käyttöönotosta vastanneet henkilöt. Tutkimus toteutettiin verkkokyselynä 29.4.2019 – 9.5.2019. Vastausprosentti oli 18,5 prosenttia. Strukturoidut vastaukset analysoitiin ja koodattiin SPSS-ohjelmalla. Väittämät analysoitiin regressioanalyysilla. Avoimien kysymysten sanallisista osioista tehtiin taulukkomatriisit. Tulosten mukaan yritykset toimivat dynaamisesti ja adaptiivisesti toimintaympäristönsä suhteen. Kanta-järjestelmän jo käyttöön ottaneet yritykset olivat hieman dynaamisempia kuin ne, jotka eivät olleet tehneet käyttöönottoa. Enemmän adaptiivisuutta osoittaneet yritykset tekivät useammin yhteistyötä muiden toimijoiden kanssa, kuin ne, jotka ilmoittivat, etteivät tee yhteistyötä. Yleisin syy Kanta-järjestelmän käyttöönottamuuteen oli tarvittavien resurssien puuttuminen. Pääasiallisimmat tavoitteet yhteistyön tekemiseen liittyivät yrittäjänä toimimiseen, tietojärjestelmiin sekä asiakkaisiin. Tulevaisuudessa yhteistyön muodot muuttuvat toimijoiden siirtyessä yhä lisääntyvässä määrin teknologiavälitteiseen kommunikaatioon ja sähköisiin palveluverkostoihin