Year
2024Credit points
10Campus offering
Prerequisites
Nil
Unit rationale, description and aim
Digital health incorporates electronic medical record (EMR) systems and electronic health records (EHRs) and are essential features of a digital health interdisciplinary ecosystem. Clinicians and information communication technology (ICT) professionals need to have a sound understanding of the technical and functional aspects associated with health data standards, electronic health and medical record structures and associated information systems required to connect, collaborate and interoperate within a digital health ecosystem. Health data are the primary source of ‘truth’ for the entire data supply chain required to meet all user information management, data analytics and usage requirements. EMRs/EHR systems technical infrastructures need to maintain data integrity, be compliant with legislative requirements, preserve client privacy and maintain security measures in support of all human dignity for those served by each health service provider within the digital health ecosystem.
The aim of this unit is to introduce students to the core concepts of semantic health data exchange and information management within such ecosystem networks with a focus on compliance with health informatics standards. Students will have opportunities to explore how data standards are developed to safely and optimally meet local and national operational work, data and communication flows in order to support individual patient health journeys and patient centred care. In addition, this unit will provide students with the technical knowledge and skills needed to minimise the risk of adverse events, by addressing data integrity maintenance throughout the patient data supply chain, starting at the point of data collection, through to recording and processing data in EMR/EHR systems.
Learning outcomes
To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.
Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.
Explore the graduate capabilities.
| Learning Outcome Number | Learning Outcome Description | Relevant Graduate Capabilities |
|---|---|---|
| LO1 | Identify and describe professional and technical challenges associated with electronic health record (EHR) structural designs and associated systems use within a digital health ecosystem | GC1, GC7, GC9, GC10, GC11 |
| LO2 | Differentiate systems linked with or incorporating an EHR within an organisational ecosystem and each system’s functionalities to support a person’s health journey | GC1, GC7, GC9, GC10, GC11 |
| LO3 | Critically evaluate the link between the adoption of health data standards and health data exchange protocols relative to data integrity and the ethical use of data | GC1, GC7, GC9, GC11 |
| LO4 | Apply knowledge and skill in the use of appropriate and practical organizational, national and global digital health infrastructure resources that best support optimum and safe EHR use | GC1, GC2, GC7, GC9, GC10, GC11, GC12 |
Content
Topics will include:
- Digital health ecosystems, stakeholders & data supply chain
- Electronic Health Record structures, uses and associated systems
- Technology advancements and Information systems life cycles
- Supporting organisational, national and global infrastructures
- Standards development organisations, processes, technical and data standards
- Health data exchange schemas, transactional vs integration, use of standards
- System architectural standards & links with functionality
- Open vs proprietary systems, technology neutral databases
- Maintaining data integrity and patient safety
- Incorporating clinical guidelines & treatment protocols for decision support
- User interfaces & system navigation
- Desirable local and national digital health transformations
Learning and teaching strategy and rationale
The learning and teaching strategy for this unit is founded on active learning whereby students are provided opportunities to develop an understandings of core concepts of semantic health data exchange and information management within ecosystem networks. Active learning opportunities provide students with opportunities to practice and apply their learning in situations similar to their future profession. Learning in this mode will be online and largely asynchronous (‘anywhere, anytime learning’), as well as synchronous, for example, via live webinar scheduled periodically throughout the semester or equivalent study period. This learning approach is flexible and inclusive, allowing students the opportunity to analyse and critically evaluate the complexity associated with EHR attributes and the adoption of technical and data standards.
Students will have access to self-paced learning modules, readings, webinars, discussion forums and assessment tasks via Canvas. Students are expected to engage in readings, reflections and engage with peers over the semester or equivalent study period. These learning activities encourage students to bring their own examples to demonstrate understanding, application and engage constructively with their peers, which is particularly effective for exploring how the many EHR attributes interconnect. Students will receive regular and timely feedback on their learning, which includes information on their progress.
Students should anticipate undertaking 150 hours of study for this unit, including readings, online forum participation and assessments.
Assessment strategy and rationale
In order to pass this unit, students are expected to demonstrate achievement of every unit learning outcome, submit three graded assessment tasks, and obtain a minimum mark of 50% in graded units. In order to reward students for engagement and performance, a final graded result will be awarded.
The assessment strategy for this unit allows students to demonstrate a critical mindset in evaluating the impact of data and information management strategies associated with the use of EHRs and associated enterprise systems and apply this knowledge to a variety of work situations. In order to develop this level of capability, in the first two assessment tasks students are required to demonstrate their knowledge on how to identify and evaluate EHR functionalities within the context of a digital heath 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 digitally 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 |
|---|---|---|
Assessment Task 1: Assessment Task 1 requires students to apply their critical knowledge of core introductory concepts and skills. The purpose of this assessment task is to evaluate students’ grasp of the complexities associated with desired and actual EHR functionality within a health ecosystem supporting an individual’s health journey. Example: A written reflective journal based on contributions to online discussions throughout the unit. | 20% | LO1, LO2 |
Assessment Task 2: Assessment Task 2 requires students to apply knowledge learned, explore how the many EHR attributes work with various health informatics standards and reflect on the impact relative to desired EHR functionalities. Individual written report . | 30% | LO2, LO3, LO4 |
Assessment Task 3: Assessment Task 3 requires students to apply their knowledge of concepts and skills learned throughout the unit critically in the production of a case study report. The case study can be based on the students’ work situation where applicable. The purpose of this assessment is to evaluate students’ 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 (the case study). Case Study Report. | 50% | LO1, LO2, LO3, LO4 |
Representative texts and references
Coiera, E. (2015). Guide to health informatics (3rd ed.). CRC Press, Taylor & Francis Group.
Gold, S., Batch, A., Mcclure, R., Jiang, G., Kharrazi, H., Saripalle, R., Huser, V., Weng, C., Roderer, N., Szarfman, A., Elmqvist, N., & Gotz, D. (2018). Clinical concept value sets and interoperability in health data analytics. AMIA Annual Symposium Proceedings. AMIA Symposium, 2018, 480–489.
Parreiras, F. S. (2012). Semantic web and model-driven engineering. IEEE Press.
Pileggi, S. F., & Fernandez-Llatas, C. (2012). Semantic interoperability : Issues, solutions, and challenges. River Publishers.
Satti, F. A., Ali, T., Hussain, J., Khan, W. A., Khattak, A. M., & Lee, S. (2020). Ubiquitous health profile (UHPr): A big data curation platform for supporting health data interoperability. Computing, 102(11), 2409–2444. DOI: 10.1007/s00607-020-00837-2
Sonsilphong, S., Arch‐Int, N., Arch‐Int, S., & Pattarapongsin, C. (2016). A semantic interoperability approach to health‐care data: Resolving data‐level conflicts. Expert Systems, 33(6), 531–547. DOI: 10.1111/exsy.12167
Walonoski, J., Scanlon, R., Dowling, C., Hyland, M., Ettema, R., & Posnack, S. (2018). Validation and testing of fast healthcare interoperability resources standards compliance: Data analysis. JMIR Medical Informatics, 6(4), 97-106. DOI: 10.2196/10870
Yang, L., Cormican, K., & Yu, M. (2019). Ontology-based systems engineering: A state-of-the-art review. Computers in Industry, 111, 148–171. DOI: 10.1016/j.compind.2019.05.003
Health Level 7 (HL7) International www.hl7.org
openEHR Foundation www.openEHR.org
International Organization for Standardization. ISO/IEC 11179. Metadata registry (MDR). International Organization for Standardization
International Organization for Standardization (2019). ISO 13606:2019. EHR - Interoperability. International Organization for Standardization
International Organization for Standardization (2019). ISO/TS 21526:2019. Health informatics – Metadata repository requirements (MetaRep). International Organization for Standardization
International Organization for Standardization (2019). ISO/TS 21564:2019. Health informatics – Terminology resource map quality measures (MapQual). International Organization for Standardization