Digital Transformation in Healthcare: How the Potential of Digital Health Is Tackled to Transform the Care Process of Intensive Care Patients Across All Healthcare Sectors

Necesidades en familiares de pacientes críticos de una institución de IV nivel en Montería, Colombia

Enfermería Global ◽

10.6018/eglobal.423121 ◽

2021 ◽

Vol 20 (1) ◽

pp. 285-304

Author(s):

Jorge Luis Herrera Herrera ◽

Yolima Judith Llorente Pérez ◽

Sadith José Suarez Mendoza ◽

Edinson Oyola López

Keyword(s):

Intensive Care ◽

Critically Ill Patients ◽

Care Service ◽

Care Process ◽

Data Card ◽

Cross Sectional ◽

Intensive Care Patients ◽

Sociodemographic Data ◽

The Family ◽

Level Institution

Objetivo: Determinar las necesidades en familiares de pacientes críticos de una institución de IV nivel en Montería, Colombia.Metodología: Investigación descriptiva, transversal con enfoque cuantitativo. Para la recolección de la información se aplicó el Cuestionario de Necesidades de los Familiares de Pacientes de Cuidados Intensivos y una cédula de datos sociodemográficos.Resultados: Las necesidades que se determinaron fueron la información sincera respecto al estado y progreso del paciente y recibir explicación del equipamiento que está utilizándose. La dimensión que presentó mayores necesidades fue la de comunicación.Conclusiones: El familiar de una persona ingresada en un servicio de cuidado intensivo debe ser tomado en cuenta en el proceso de atención. Objective: To determine the needs in relatives of critically ill patients of an IV level institution in Montería, Colombia.Methodology: Descriptive, cross-sectional research with a quantitative approach. For the collection of information, the Questionnaire of Needs of the Relatives of Intensive Care Patients and a sociodemographic data card were applied.Results: The needs that were determined were honest information regarding the state and progress of the patient and receive an explanation of the equipment being used. The dimension that presented the greatest needs was that of communication.Conclusions: The family of a person admitted to an intensive care service should be taken into account in the care process.

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A novel survival algorithm in COVID-19 intensive care patients: the classification and regression tree (CRT) method

African Health Sciences ◽

10.4314/ahs.v21i3.16 ◽

2021 ◽

Vol 21 (3) ◽

pp. 1083-1092

Author(s):

Sevinç Dağıstanlı ◽

Süleyman Sönmez ◽

Murat Ünsel ◽

Emre Bozdağ ◽

Ali Kocataş ◽

...

Keyword(s):

Intensive Care ◽

Decision Tree ◽

Clinical Laboratory ◽

Laboratory Data ◽

Regression Tree ◽

Classification And Regression Tree ◽

Care Process ◽

Intensive Care Patients ◽

Training And Research ◽

The City

Background/aim: The present study aimed to create a decision tree for the identification of clinical, laboratory and radio- logical data of individuals with COVID-19 diagnosis or suspicion of Covid-19 in the Intensive Care Units of a Training and Research Hospital of the Ministry of Health on the European side of the city of Istanbul. Materials and methods: The present study, which had a retrospective and sectional design, covered all the 97 patients treated with Covid-19 diagnosis or suspicion of COVID-19 in the intensive care unit between 12 March and 30 April 2020. In all cases who had symptoms admitted to the COVID-19 clinic, nasal swab samples were taken and thoracic CT was per- formed when considered necessary by the physician, radiological findings were interpreted, clinical and laboratory data were included to create the decision tree. Results: A total of 61 (21 women, 40 men) of the cases included in the study died, and 36 were discharged with a cure from the intensive care process. By using the decision tree algorithm created in this study, dead cases will be predicted at a rate of 95%, and those who survive will be predicted at a rate of 81%. The overall accuracy rate of the model was found at 90%. Conclusions: There were no differences in terms of gender between dead and live patients. Those who died were older, had lower MON, MPV, and had higher D-Dimer values than those who survived. Keywords: Survival algorithm; COVID-19 intensive care patients; CRT method.

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