Knowledge Management (3cr)

Evaluation scale

0-5

Content scheduling

1. Fundamentals of data based management and decision-making
2. Data sources and the information base in social and healthcare management
3. Applied analytics, visualisation, and AI

Objective

The student - is able to analyse and assess the development of the data and knowledge base in the field of health care and social services - applies knowledge-based management methods and digital data sources in the strategic and operational management of an organisation - is able to analyse and examine the opportunities and limitations of data, key performance indicators, and analytics, particularly in supporting effectiveness and predictive decision-making

Content

- Principles of data-driven and predictive decision-making in knowledge management - Identification, evaluation, and utilisation of key data sources and data repositories in health care and social services as well as in management - Applications of information and data analytics: key performance indicators, forecasting, visualisation - The role of predictive information – identifying future service needs, phenomena, and resource requirements

Location and time

The study unit is delivered in a non-stop format and can be completed at your own pace between 1 September and 6 December 2026. Some assignments are assessed automatically, but others require teacher assessment. Assessment is carried out primarily during the first week of each month.

The deadline for completing the assignments is 6 December 2026 at 21:00.

Materials

The learning materials used in the study unit are available in Moodle or are accessed via the learning platform through external sources. As supplementary materials to support learning, it is recommended that students make use, for example, of reliable online sources, peer-reviewed research articles, up-to-date news coverage, and library resources.

The use of artificial intelligence in course assignments is instructed on an assignment-by-assignment basis. Students must clearly state which AI-based tool has been used, with which prompts, what has been done with the responses received, and where these are evident in the learning assignment. Students are always responsible for the integrity, accuracy, and originality of the work they submit.

Teaching methods

This study unit is intended for Open University of Applied Sciences students and cross-institutional students, not for Metropolia’s degree students.

The study unit is delivered entirely online in the Moodle learning environment. The implementation consists of three different themes and modules: their theoretical sections, external materials, and various assignments that support and assess learning.

Employer connections

The study unit is linked to working life through materials and applied tasks. The student is able to apply the theoretical content of the study unit in learning assignments, integrating it with their own work experience.

Exam schedules

The completion and retake opportunities for the study unit assignments are predefined through the available number of attempts and are always described on an assignment-by-assignment basis. The student confirms when they have used the attempts they wish to use and requests that the study attainment be recorded. Based on their own work plan, the teacher communicates the assessment timetable, and the student may, if they wish, schedule the submission of assignments accordingly.

Completion alternatives

No elective or alternative modes of completion.

Student workload

The study unit consists of three modules, totalling 3 credits. One credit corresponds to 27 hours of student work.