SYKKPRA30KB Nursing Patients with Acute, Critical and Chronic Diseases 2 Course description

The course builds and expands on the course SYKK/SYKPPRA20. In this course, the students will practice independence in planning, carrying out and assessing nursing in acute and chronically ill patients. The prevention of complications and early detection of deterioration in the patients’ condition are key elements. Quality development, patient safety and ethics and health gudiance are also part of the course.

Passed the courses:

• SYKK/SYKPPRA10 The Fundamentals of Nursing, 15 credits
• SYKK/SYPP1400 Diseases and Health Deficits, 10 credits
• SYKK/SYKP1300 Pharmacology and Administration of Medicine, 5 credits (only from year group 2022 in the academic year 2023-2024)
• Part 1 of SYKK/SYKPPRA20 Nursing Patients with Acute, Critical and Chronic Diseases

or equivalent.

After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills and competence:

Knowledge

The student is capable of

• explaining care pathways in the event of relevant diseases and treatments
• under supervision, contributing to equal nursing services independent of patients’ gender, ethnicity, religion and view of life, functional impairment, sexual orientation, gender identity, gender expression and age
• explaining the significance of nutrition in the event of disease and challenges linked to obesity, malnutrition and disease-related undernourishment
• assessing factors related to an increased risk of patient injuries or unwanted incidents and contributing to work processes to promote quality improvement and patient safety
• identifying ethical dilemmas in practice and reflecting on different choices of action
• explaining the significance of next-of-kin for the patients’ health and quality of life both when it comes to majority and minority cultures
• is capable of explaining key concepts in the nurse’s pedagogical responsibilities: guidance, counselling, health guidance and shared decision-making
• is capable of explaining how the patient’s health competence is significant for life style changes and shared decision-making

Skills

The student is capable of

• interpreting the patients’ experiences and reactions such as insecurity, fear, discomfort and exhaustion
• applying mapping, assessment, documentation and communication tools in nursing practice
• carrying out and explaining nursing to patients with the most common symptoms, diseases, care pathways and treatment in the nursing practice
• identifying signs of change/deterioration at an early stage, and implementing necessary measures
• implementing national knowledge-based professional procedures and national guidelines
• applying different approaches and methods in health guidance adapted to the individual's needs
• communicating across language barriers with the help of a professional interpreter
• applying professional knowledge and scientific methods to elucidate a delimited issue relevant to the practical training establishment
• using technology and digital solutions to support patients’ and next-of-kin's resources, mastering possibilities and participation
• is capable of applying educational principles when imparting information, teaching and counselling patients and next-of-kin

Competence

The student

• is capable of reflecting on how unwanted incidents can occur and discussing this in relation to professional responsibility in the practice of nursing
• is familiar with quality indicators and standard terminology in the documentation of nursing
• is capable of identifying different ethical issues and dilemmas, making ethical considerations, safeguarding the patient’s dignity and integrity and promoting the patient and next-of-kin’s right of co-determination and autonomy
• is familiar with innovative thinking in e-health, welfare and care technology
• is capable of reflecting on the practical training establishment’s procedures and methods and taking the initiative to engage in dialogue about the implementation of new knowledge and new work methods
• is capable of reflecting on the connection between care pathways, patient safety and equal health services

Practical training: in the specialist health service (medicine/surgery) (7 weeks), includes SF unit and seminars.

Practical training has requirements for attendance (90 %), self-presentation for the start of the practice and self-assessment for the mid- and final assessment, SF unit (wound) and digital seminar (nutrition).

This course provides a broad introduction to Artificial Intelligence (AI), with methodologies and techniques that can be applied to different application domains. The course will balance theoretical approaches and practical tasks. Two broad AI areas will be covered, namely supervised and unsupervised methods. Among those, standard methods for regression, classification and clustering will be covered, e.g. support vector machines, nearest neighbor, decision tree, K-means, agglomerative and hierarchical clustering. An introduction to the usage of artificial neural networks and backpropagation algorithm will be provided.

None

On successful completion of the course, the student should have the following learning outcomes defined in terms of knowledge, skills and general competence.

Knowledge

The student:

• Knows how the field of artificial intelligence developed historically
• Is familiar with the main artificial intelligence theories and has a practical understanding of the development and use of artificial intelligence
• Can reflect on the practical, social and ethical implications of the development of artificial intelligence
• Has an understanding of the current application areas of artificial intelligence

Skills

The student:

• Has the theoretical and practical skills to build simple artificial intelligence systems
• Can use a variety of state-of-the-art artificial intelligence techniques in different application domains
• Can evaluate the technical quality and practical value of various types of artificial intelligence

Competence

The student:

• Has both theoretical and practical understanding of artificial intelligence methods
• Can discuss the relevance, strengths and limitations of artificial intelligence methods
• Is able to solve real-life problems using artificial intelligence methods

The course consists of lectures and seminars on techniques and methods, as well as a project to be carried out in groups of 2 to 4 students. The project will be chosen from a portfolio of available problems, either from industry partners or by the research groups. The students will work in groups and will submit an academic report and give an oral presentation. Lab sessions supporting the assignments will be provided.

3 compulsory assignments done in groups of 2-4 students must be approved in order to be admitted to the final exam.