Unit rationale, description and aim
Knowledge represents primary sources of truth, a crucial and valuable asset supporting the health system. The current digital health revolution has sped up the acquisition of new knowledge. The amount of new knowledge generated is far too much for any one person to be aware and make use of. All health-related knowledge needs to be readily accessible when required to support decision making at any level within the healthcare system. No clinician has the time to wade through multiple documents in order to make use of the best available evidence. In this unit, students will be introduced to the core concepts in data, information and knowledge management within a digital health ecosystem, focusing on the need to comply with a number of health informatics standards, developed to meet local, national and global professional practice and research network needs. Students will learn how knowledge can be represented as computable formalisms to enable computers to make use of and integrate clinical guidelines and protocols within health information systems and/or any health-related application or to make this knowledge accessible on-line as and when required. Key concepts will include electronic knowledge processing of timely new knowledge, governance and knowledge use at points of decision making throughout the digital health ecosystem. The aim of this unit is to equip students with the knowledge, understanding and skill required to promote compliance with scientific principles and legislative requirements in relation to health knowledge management in order to advance the preservation of data privacy and security, to support human dignity and safety in a manner that benefits the population at large.
On successful completion of this unit, students should be able to:
LO1 - Describe professional and technical challenges associated with knowledge representation and management within digital health ecosystems (GA6)
LO2 - Critically analyse the relationship between knowledge domain ontologies and computability (GA5, GA9)
LO3 - Critically evaluate the link between knowledge governance and decision support systems (GA3, GA4)
LO4 - Critically evaluate options for shareable guideline and knowledge representation formalisms (GA8)
GA3 - apply ethical perspectives in informed decision making
GA4 - think critically and reflectively
GA5 - demonstrate values, knowledge, skills and attitudes appropriate to the discipline and/or profession
GA6 - solve problems in a variety of settings taking local and international perspectives into account
GA8 - locate, organise, analyse, synthesise and evaluate information
GA9 - demonstrate effective communication in oral and written English language and visual media
Topics will include:
- Language, communication, data, information and knowledge continuum
- Bodies of knowledge, ontologies and frameworks
- Entities, objects and agent formalisms
- Problem identification & reasoning ontologies
- Knowledge discovery & representation
- Types of knowledge and knowledge management theories
- Decision theories and artificial intelligence
- Tools for knowledge processing
- Use of knowledge in applications
- Knowledge governance, validation and certification
Learning and teaching strategy and rationale
This unit uses an active learning approach to support students in the exploration of knowledge essential to the discipline. Students are provided with choice and variety in how they learn. Students are encouraged to contribute to asynchronous weekly discussions. Active learning opportunities provide students with opportunities to practice and apply their learning in situations similar to their future professions. Activities encourage students to bring their own examples to demonstrate understanding, application and engage constructively with their peers. Students receive regular and timely feedback on their learning, which includes information on their progress.
This unit will be delivered in online mode using an active learning approach whereby students are expected to engage in readings, reflections and engage with peers over a twelve-week semester or equivalent study period. Students will have access to self-paced learning modules, readings, webinars, discussion forums and assessment tasks via the ACU Learning Environment Online (LEO). While there are no formal lectures for this unit, students will be required to attend weekly one-hour online forums, which will provide opportunities to analyse and evaluate various knowledge management related concepts to meet unit learning outcomes. Online forums and chat rooms will facilitate learning by sharing experiences and findings with peers, which is particularly effective for exploring how the many knowledge related concepts and applications interconnect with electronic health records. This learning approach is flexible and inclusive, allowing students the opportunity to analyse and critically evaluate the complexity associated with knowledge management within a digital health ecosystem.
Students should anticipate undertaking 150 hours of study for this unit, including readings, online forum participation and assessments.
Assessment strategy and rationale
The assessment strategy for this unit allows students to demonstrate a critical mindset in evaluating the impact of knowledge management strategies associated with the use of electronic health records, associated enterprise systems and applications within a digital health ecosystem. In order to develop this level of capability, in the first two assessment tasks students will be required to demonstrate their knowledge on how to identify and evaluate the various concepts underpinning knowledge management within the context of a digital health ecosystem relative to the delivery of person-centred care. The final assessment task allows students to demonstrate the depth of their knowledge and understanding of work in a digital health enhanced world through a final case study assignment. The assessment tasks for this unit are designed for students to demonstrate achievement of each learning outcome.
Overview of assessments
|Brief Description of Kind and Purpose of Assessment Tasks||Weighting||Learning Outcomes||Graduate Attributes|
Assessment 1 requires students to demonstrate their knowledge of core introductory concepts and skills through an online quiz. The purpose of this assessment is to test the student's grasp of the complexities associated with desired and actual EHR functionality within a health ecosystem supporting an individual’s health journeys.
GA5, GA6, GA9
Assessment 2 requires students to apply knowledge learned in the identification and evaluation of concepts underpinning knowledge management within the context of a digital health ecosystem.
GA3, GA4, GA5, GA9
Assessment 3 requires students to apply their critical knowledge of concepts and skills learned throughout the unit and produce a case study report. The case study can be based on the student’s work situation where applicable. The purpose of this assessment is to test the student’s grasp of both theoretical and practical aspects of the unit through their problem solving and application of theoretical knowledge to real-life business problems in a given scenario (case study).
GA3, GA4, GA8
Representative texts and references
Dissanayake, P. I., Colicchio, T. K., & Cimino, J. J. (2019). Using clinical reasoning ontologies to make smarter clinical decision support systems: A systematic review and data synthesis. Journal of the American Medical Informatics Association, 27(1), 159-174. DOI: 10.1093/jamia/ocz169
Elçi, A., & Çelik Ertuğrul, D. (2020). Ontology-based smart medical solutions. Expert Systems, 37(1), 37 (2020) e12518. DOI: 10.1111/exsy.12518
Gammack, J. G., Hobbs, V., & Pigott, D. (2011). The book of informatics: Revised edition. Thomson.
Hovenga, E., & Loyd, S. (2006). Working with information and knowledge. In M. G. Harris (Ed.). Managing health services: Concepts and practice (2nd ed.). Australia: Elsevier
Hunter, W., & Liu, W. (2010). A survey of formalisms for representing and reasoning with scientific knowledge. The Knowledge Engineering Review, 25(2), 199-222. DOI: 10.1017/S0269888910000019
Riaño, D., Peleg, M., & ten Teije, A. (2019). Ten years of knowledge representation for health care (2009–2018): Topics, trends, and challenges. Artificial Intelligence in Medicine, 100, 101713. DOI: 10.1016/j.artmed.2019.101713
Swan, J., Newell, S., & Nicolini, D. (2016). Mobilizing knowledge in health care: Challenges for management and organization (First Ed.). Oxford University Press.
Turban, E., Sharda, R., & Delen, D. (2014). Decision support and business intelligence systems (ninth ed.).Pearson.
Zipperer, L. A. (2016). Knowledge management in healthcare. Routledge, Taylor & Francis Group.
Allemang, D., & Hendler, J. A. (2011). Semantic web for the working ontologist effective modeling in RDFS and OWL (second ed.). Elsevier.