Master of Health Data Analytics

Purpose

The purpose of the Master of Health Data Analytics is to develop skills in health data analytics to address the growing burden of noncommunicable and communicable diseases in low- and middle-income countries. Thus, the course aims to develop a critical mass of professionals and build capacity in the African continent borne out of a need highlighted by 120 participants from different countries in Africa, who attended the summer/winter schools in data analytics. The need for the skills has also been highlighted by the Western Cape Government Department of Health and Wellness data centre, which requires individuals with specific skills in health data analytics. Given the location, the institution is firmly placed to contribute the requisite skills to the department.

This qualification will be implemented online at the institution and other Sub-Saharan universities through joint research collaboration. It envisages improving the online academic skills, offerings, and competitiveness of participating institutions by including online qualifications in the basket of offerings (flexible learning offering).

The qualification will advance the collaboration between the government (Health Sector), industry, and universities for mutual benefits and contribute to national imperatives using applied research. Applied research that makes an immediate contribution to business and society is a strategic drive for the institutions. The qualification also benefits from extending the university's intellectual footprint in both the African and European countries where the partnering institutions are based.

Rationale

There is a rapid growth in demand for Data Scientists globally with an estimated shortfall of two million skilled workers in 2017. Institutions are positioning themselves to offer training and research leadership required to take advantage of data analytics. The need to train professionals to close the gap in health data analytics has also been identified. An increasing amount of personal and population data has led to a need for complex analytical techniques to make sense of these data.

Although many institutions in developing countries are offering courses in mathematics or statistics relevant to data science to address the skills shortage, there is inadequate focus on higher-level data analytics skills in health. These new foci present several critical challenges in the global south. Firstly, there is limited capacity to train, teach, research and deploy the right techniques for health data analytics in many universities. Secondly, the diversity and multi-disciplinary nature and technical requirements in data analytics make it a challenge even for the better-endowed universities to develop and maintain a talented team to offer these courses. The lack of health data analysts means Africa cannot adequately harness the data revolution's potential for the quality of health for her citizens.

The COVID-19 pandemic demonstrated the need for individuals who are skilled in data analytics. Indeed, in the global south, most healthcare organisations do not have the necessary skills to capture, analyse, and synthesize the valuable information and knowledge that can be derived from big data. Currently, and to the best of our knowledge, there are no health data analytics programmes offered online in Africa. This qualification is targeted at graduates both from the healthcare professions and the sciences who are interested in expanding their knowledge and skills in data analytics. The online nature of this programme allows for access to individuals across the African continent and beyond. Due to the nature of the offering of this programme, individuals who are often in permanent positions will also have access. Graduates from this Programme would be able to source employment from health data centres in their specific countries, as well as the private and educational sectors.

Recognition of Prior Learning (RPL)

The institution's RPL policy for postgraduate study makes provision for RPL for advanced standing on a case-by-case basis. Each application is considered and approved in collaboration with the relevant Faculty, supervisor (content specialist/academic), RPL Unit and the institution's Quality Assurance office. Relevant research experience and formal and non-formal learning are evaluated.

RPL for access

• Prospective learners who wish to be admitted to the qualification using the RPL process must demonstrate 'knowledge, skills, competence, and academic readiness.
• The current RPL policy makes provision for RPL for advanced standing on a case-by-case basis. Advanced standing entails the consideration of 'evidence of learning achievement through work and/or other experience'.
• The institution's RPL Policy recommends that 10% of the cohort of learners admitted to a qualification can be admitted through its RPL process.
• Applicants must be over the age of 23 years.

Access to postgraduate studies through Senate discretion is common practice at many universities. It usually entails candidates producing evidence of relevant learning achievement through work and/or other experience. Each application is considered and approved in collaboration with the relevant faculty, supervisor (content specialist/academic), RPL Unit and the Quality Assurance Office. Relevant research experience, formal and non-formal learning, current research as well as conferences attended are evaluated. The Final decision rests with the Senate Higher Degrees Committee in terms of its standing Orders on recommendation from the Faculty Higher Degrees Committee which it is awarded and accepted.

Entry Requirements

The minimum entry requirement for this qualification is

• Bachelor of Science Honours in Applied Statistics, NQF Level 8.

Or

• Bachelor of Commerce Honours in Statistics, NQF Level 8.

Or

• Bachelor of Science Honours in Computer Science, NQF Level 8.

Or

• Bachelor of Data Science, NQF Level 8, NQF Level 8.

Or

• Bachelor of Science Honours in Data Science, NQF Level 8.

Or

• Bachelor of Health Sciences Honours, NQF Level 8.

Or

• Bachelor of Health Sciences Honours in Physiology, NQF Level 8.

Or

• Postgraduate Diploma in Data Science, NQF Level 8.

Or

• Postgraduate Diploma in Data Analytics, NQF Level 8.

This qualification consists of the following compulsory modules at NQF Level 9 totalling 180 Credits.

Compulsory modules, Level 9, 180 Credits

• Data Analytics in Health Systems and Policies, 20 Credits.
• Health Data, 20 Credits.
• Applied Health Data Analytics, 20 Credits.
• Health Data Management, 20 Credits.
• Applied Statistics and Data Mining, 20 Credits.
• Research Methods, 20 Credits.
• Research Mini-thesis, 60 Credits.

1. Demonstrate the ability to engage with health data analytics in creating value-based health systems and policies.
2. Demonstrate the ability to select the appropriate health data.
3. Demonstrate the ability to design methodologies, techniques, processes, or technologies for using health data in complex health decision-making.
4. Demonstrate the ability to engage with current research and practices used in health data analytics.
5. Make autonomous ethical decisions in health data use and integrate and evaluate health data in governance procedures.
6. Conduct and complete a research project using health data analytics.
7. Communicate and defend ideas, and techniques, taking full responsibility for processes or technologies used in health data analytics.
8. Implement health data analytics interventions in their organizations.

Associated Assessment Criteria for Exit Level Outcome 1

• Critically evaluate the value of applying health data analytics in creating value-based health systems and policies.
• Generate and apply health data, using data analytics procedures.
• Discuss the importance of health data management in the Health Data Management.

Associated Assessment Criteria for Exit Level Outcome 2

• Critically engage and select appropriate data through assignments and case studies to address specific questions related to health.
• Apply appropriate tools and techniques to manage the data.
• Evaluate the role of data governance and related policies in health settings, which includes relevant policies and technologies for data security.

Associated Assessment Criteria for Exit Level Outcome 3

• Apply techniques, processes, or technologies in complex health decision-making with various assessment activities such as case studies and discussion groups.
• Identify and apply emerging data analytics models in health data.

Associated Assessment Criteria for Exit Level Outcome 4

• Critically analyze and present summaries of current research and practices used in health data analytics.

Associated Assessment Criteria for Exit Level Outcome 5

• Apply ethical principles and governance structures when using health information through a case study.

Associated Assessment Criteria for Exit Level Outcome 6

• Submit a research proposal and written research report under supervision for external evaluation which focuses on health data.

Associated Assessment Criteria for Exit Level Outcome 7

• Communicate and reason ideas related to health data analytics through presentations, assignments, case studies, and small group discussions.

Associated Assessment Criteria for Exit Level Outcome 8

• Submit a report indicating the application of health data interventions in the workplace.

INTEGRATED ASSESSMENT

The qualification will use both formative and summative assessments.

Formative Assessment

Formative assessment will be continuous, with regular feedback from tutors and lecturers. It will include assignments, presentations, and projects. These activities will be marked by individual lecturers and given different weightings (e.g. 10%) as part of formative assessment. Feedback will be provided to learners to enable them to see their performance and make improvements where necessary. A minimum of two formative assessment tasks will be given in each module which counts at least 60% towards the final assessment mark.

Summative Assessment

Summative assessment will occur at the end of each module, at the end of a semester. It will be in the form of various assessment tasks written examinations, presentations, assignments, and case studies usually written or submitted in May/June and in October/November of each year. The summative assessment will contribute 40% to the final assessment mark.

In the research methods module, the final assessment task will be in the form of a submission of the proposal. The final assessment task will be in the form of a research report in the mini-thesis research module. The research report will be assessed by two examiners, one of which will be external to the university.

This form of assessment will cover the work done in a semester and determine learners' progression from one level to another. Modules with a summative assessment will include a final task geared towards integrating knowledge, skills, and attitudes related to health data analytics education and practice. All summative assessments will be externally moderated.