92573 Data Science in Health Care
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Subject handbook information prior to 2024 is available in the Archives.
Credit points: 6 cp
Subject level:
Undergraduate
Result type: Grade and marksRequisite(s): 96 credit points of completed study in spk(s): C10360 Bachelor of Public Health
Description
The last decade has witnessed significant advances in the amount of data people routinely generate and collect in many facets of their life, as well as improvements in the ability to use technology to analyse and understand this data. Understanding the data generated in the healthcare industry assists the discipline to become more efficient and effective, given the environment is forever attempting to reduce waste and overheads while improving outcomes and profit. Additionally, the world's population is increasing, people are living longer, and more chronic disease is prevalent, requiring new models of care and more personalised decision making driven by data. This subject exposes students to the construct of data and its journey to wisdom, and how this progression is predicated on the existence of accurate, relevant and correctly collected and organised data. Principles of good data stewardship are explored, including data processing, data governance, data standards, data privacy and data mining. First-hand exploratory interaction with data enables students to seek informative correlations, patterns and trends leading to discoveries, and identify data discovery studies of interest to them. Through hands-on data discovery experiments, students explore data as complex and heterogeneous, with new techniques and technologies raising implications for approaches to the ethical and legal collection of personal data in health care.
Subject learning objectives (SLOs)
| A. | Examine the structural and functional components of data within the healthcare environment. |
|---|---|
| B. | Analyse current trends and contemporary practices in data science in health care. |
| C. | Justify the value of multi-structured data analysis in relation to health outcomes |
| D. | Create a variety of ways in which data discovery projects can be effectively communicated in a manner appropriate for the healthcare discipline, audience and purpose. |
| E. | Determine privacy, ethical and legal implications of collecting personal healthcare data. |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the following graduate attributes:
- Evaluate the effectiveness and efficiency of health projects and programs (2.1)
- Develop creative and innovative responses to health issues (2.2)
- Demonstrates critical thinking in the development and practice of public health (5.1)
Contribution to the development of graduate attributes
This subject also contributes specifically to the following graduate attributes:
2. Adaptability: Demonstrate creative and adaptive thinking within a changeable social, political and technological environment
2.1 Demonstrate adaptable and novel thinking within changing environments to maximise outcomes for a range of individuals, communities and stakeholders
2.2 Utilise enquiry based learning to develop innovative approaches to complex issues
2.5 Apply newly generated or existing data to inform optimal care and/or service development
4. Ethics and diversity: Are ethical and responsible professionals who value the diversity of people and communities
4.2 Make use of research and data to enable responsible, ethical and equitable service provision
5. Critical thinking and practice: Translate research and evaluation into social and professional practice through critical thinking and knowledge integration
5.1 Identify appropriate information resources and apply effective and creative solutions for the improvement of individuals and communities
Teaching and learning strategies
This subject is designed to assist students understand the complexities associated with collecting, generating and analysing data in health care, and to realise the value of effective data stewardship. It is anticipated that the knowledge and skills gained in this subject will allow the graduate to appreciate common challenges associated with healthcare data and to show confidence in linking reliable data to decision making in the health service industry.
Pre-session learning
Students access online learning resources such as podcasts, videos and literature prior to attending face-to-face on-campus sessions to improve their confidence in approaching the proposed content, afford them time to construct questions, and facilitate discussion in class, resulting in shared learning, experiences and reflections. The specific sessions will be detailed in this subject outline and all resources will be located on UTSOnline.
Blend of online and face-to-face strategies
This subject benefits from both the real time delivery of content and access to resources via UTSOnline, including podcasts, videos and learning resources. Students attend weekly face-to-face on-campus sessions, comprising 1 hour lectures and 2 hour tutorials. The lecture provides a variety of models and theories for each content area, followed by tutorial discussion on which are best suited to likely situations in the health service environment. A number of industry representatives provide their experience in collecting and analysing data, and reiterate lessons learnt in data stewardship.
Case based scenarios and collaboration
Contemporary Australian and international cases are used to help students explore the approaches to generating data within a validated data stewardship framework. Real and hypothetical cases depict situations related to effective and ineffective data stewardship, as well as successful and unsuccessful attempts by healthcare organisations to become a data driven entity. Students will collaborate and use these scenarios to learn concepts, interpret existing processes and procedures related to data within the healthcare environment. Self, peer and teacher feedback is provided during the tutorials to develop students’ judgement.
Assessment range
Students will be exposed to a variety of assessment modes, including quizzes, a poster, a report and a presentation. Feedback will be provided on assessments to ensure students can identify areas for development and areas of sufficient expertise. Students are encouraged to use contemporary educational technology. Some of the assessment will use a case study approach, with the student asked to assume a designated data science role in a hypothetical health service.
Early low-stakes feedback will be provided via two methods:
- during tutorial sessions, where students discuss material raised in the lecture, and ask questions of their peers and lecturing staff;
- results for the first quiz (Assessment Item 1), worth 5%, will be received prior to the census date.
Continual feedback will be provided via four methods:
- three further quizzes at the commencement of three tutorial sessions, focused on the content delivered in the preceding lectures and tutorials;
- peer communication, individual and lecturing staff contributions to the tutorial discussions, where a range of topics are controversial and will lead to debate;
- progressive assessment tasks, worth 20%, 30%, 35% and 15%.
Content (topics)
- States of data
- Nelson data to wisdom continuum
- Data stewardship
- Data wrangling
- Data governance
- Data privacy and confidentiality in a healthcare context
- Ethical and legal implications of personal healthcare data
- Information system life cycle
- Clinical communication and information exchange
- Big data
- Data standards
- Data visualisation
- Data analytics
Assessment
Assessment task 1: Individual Assignment - Multiple Choice Quizzes
| Intent: | These quizzes are designed to assess students understanding of the content delivered in the lectures and tutorials. |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): A and B This assessment task contributes to the development of graduate attribute(s): .5, 2.1, 2.2 and 5.1 |
| Type: | Quiz/test |
| Groupwork: | Individual |
| Weight: | 20% |
| Length: | 4 online quizzes throughout the semester – each of 13 minutes duration |
| Criteria: |
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Assessment task 2: Individual Assignment - Analysis of Contemporary Trends in Data Science
| Intent: | This assessment is designed to provide the students with an opportunity to explore how data transformation has changed everyday activities, and to analyse how that knowledge can assist health care in its pursuit of data driven decision making. By undertaking an analysis of a sector that is familiar to them, students will be able to make recommendations for how the healthcare discipline can improve its progress towards a data driven industry. |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): B and D This assessment task contributes to the development of graduate attribute(s): .4, .5 and 2.1 |
| Groupwork: | Individual |
| Weight: | 30% |
| Length: | An online poster + 5 recommendations (no more than 3 sentences per recommendation) |
| Criteria: |
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Assessment task 3: Individual Assignment - Quantified Self Analysis
| Intent: | This assessment is intended to humanise the exploration of data by providing a real-life case for exploring relationships in data and providing data insight into one’s own life. The data gathering exercise underpinning the assessment has been designed to collect data sufficient for working with and analysing data at increasing levels of complexity. The assessment fosters student understanding of handling their own data and being mindful of such individual personal data. |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): A, C and D This assessment task contributes to the development of graduate attribute(s): .4, 2.1, 2.2 and 5.1 |
| Groupwork: | Individual |
| Weight: | 35% |
| Length: | 2000 words |
| Criteria: |
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Assessment task 4: Group Assignment – Analysis of privacy, ethical and legal implications
| Intent: | This assessment requires students to consider the ethical and legal implications in relation to data and the privacy of data in the current healthcare environment where data collection is prolific and the consumer is often unaware of what personal data is being collected and accessed. |
|---|---|
| Objective(s): | This assessment task addresses subject learning objective(s): B and E This assessment task contributes to the development of graduate attribute(s): 2.1 and 2.2 |
| Groupwork: | Group, group and individually assessed |
| Weight: | 15% |
| Length: | 20 minutes in digital form (such as podcast, videocast, narrated powerpoint) |
| Criteria: |
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Required texts
There is no specific text for this subject, however students are encouraged to access a range of content about data in textbooks, journals, podcasts and videocasts.
Other resources
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UTS Library
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Improve your academic and English language skills
Marks for all assessment tasks such as assignments and examinations are given not only for what you write but also for how you write. If you would like the opportunity to improve your academic and English language skills, make an appointment with the HELPS (Higher Education Language & Presentation Support) Service in Student Services.
HELPS (Higher Education Language & Presentation Support)
HELPS provides assistance with English language proficiency and academic language. Students who need to develop their written and/or spoken English should make use of the free services offered by HELPS, including academic language workshops, vacation intensive courses, drop-in consultations, individual appointments and Conversations@UTS (www.ssu.uts.edu.au/helps). HELPS staff are also available for drop-in consultations at the UTS Library. Phone (02) 9514 9733.
Please see www.uts.edu.au for additional information on other resources provided to students by UTS.