Co-creation of a new clinical data science professional development programme
This article will also be published as: Professors Ang Davies & Berne Ferry, Co-creation of a new clinical data science professional development programme. London, UK: The Bulletin of the Royal College of Pathologists, Issue 196, 2021.
An innovative collaborative project, led by The University of Manchester in collaboration with the National School of Healthcare Science, seeks to create a new educational programme in clinical data science with the health and social care workforce.
The prevalence and utility of health data has never been as evident and critical as over the last 18 months. Building a healthcare workforce that has a strong understanding and skillset in data science will enable the full potential of this data to be harnessed for the best possible clinical decision making and outcomes for patients.
Health Education England (HEE), through the National School of Healthcare Science (NSHCS) in HEE, is working with The University of Manchester (UoM) to develop a flexible programme of continuing professional development in clinical data science. This innovative initiative, funded by HEE, is being developed in collaboration with clinical partners at The Christie Hospital and the wider healthcare workforce to support the NHS cancer programme, NHS long-term workforce development plans, the People Plan and the Richards Report.
This educational programme will support the development of data science, statistics, machine learning and programming capabilities, as well as introductory and advanced genomic courses across within the healthcare science workforce and beyond to many healthcare professions in the NHS, including medicine, nursing, pharmacy and allied health professionals. Healthcare professionals will be able to take individual modules as well as — if desired — combined modules that could lead to a 60-credit postgraduate qualification in clinical data science. There is a plethora of health data science courses offered by other institutions, but this is the first programme that, to our knowledge, will be co-created with and be specifically designed for health and social care professionals.
Demonstrating the importance of clinical data science
The current COVID-19 pandemic clearly illustrates the importance of developing widespread health data science capability across the workforce. During the pandemic, digital technologies have been harnessed to support symptom reporting, surveillance monitoring and contact tracing. This data is being reported widely and publicly via dashboards and visualisations to scientists, medical professionals and the general public alike. Terms such as PCR and R-rate are now known and being used by the public, indicating the explosion of interest in both genomics and health data.
The scaling up of data initiatives, aggregation and visualisation of health data, both in the UK and globally, has been critical in supporting successful research and public health surveillance throughout the pandemic. Aggregated datasets and visualisations provided by the organisation Our World in Data allow the monitoring of cases, deaths and vaccinations, not only in the UK but across the world, propelling research by sharing data openly under full creative commons licenses and via open-source data repositories such as Github.
Linkage of data has also been of real importance during the pandemic, allowing health and social care providers to understand pre-existing health conditions that might cause vulnerability to COVID-19. Innovative projects such as the Greater Manchester Care Record, led by Health Innovation Manchester and the GM Health and Social Care Partnership, have been accelerated by the pandemic, bringing together complete patient records from ten localities across Greater Manchester enabling the best possible decision making and outcomes for patients. The record also indicates if a patient has COVID-19, as this might affect their ongoing treatment, monitoring or medication.
How do we prepare the workforce to best utilise all of this data?
Time is a key factor in upskilling existing staff. New training programmes will need to rapidly develop data science specialists from existing areas of the healthcare science workforce to support growing areas, such as cancer sciences and precision medicine delivery. Protected time will be required to allow these digital champions the time and space to develop the additional knowledge and expertise that complements their clinical work and enables them to contribute effectively to enable data-led digital transformation.
Through the development of this flexible programme of clinical data science, we will offer continuing professional development in data science, statistics, genomics and programming, and other areas as defined by this programme of work. Early adopters and pioneers in clinical data science will require support in their journey. This might be achieved through peer support or through the development of national communities of practice, exemplified by the pioneering Topol Fellowship programme.
Co-creation of a curriculum
The educational development team comprises of academics (Professor Ang Davies, Dr Alan Davies, Fran Hooley, UoM), a learning technologist, a project manager and an information systems programme manager (Dr Phil Couch, UoM). This team is working closely with Professor Berne Ferry and colleagues at the NSHCS, and other key stakeholders within the healthcare workforce to develop the curriculum and infrastructure to deliver this cutting-edge educational programme.
Phil Couch, with research software engineers at UoM, are developing the Manchester eLab software. This software can be used to create virtual environments for teaching and research. eLabs provide students and researchers with access to information and tools, such a Jupyter notebooks, through standard web browsers. This makes it simple for users to work from remote locations using a wide range of devices. The eLab software has been designed to make it simple to create eLabs at other institutions by automating many of the complex tasks using standard images. The current software is used for a diverse range of activities, including teaching programming and statistics.
Working with a world-leading specialist cancer centre
To incorporate world-leading expertise in real-world clinical data science, the development team includes colleagues from The Christie Hospital, a specialist cancer hospital, with which UoM has existing collaborations. The Christie Hospital is pioneering a team science approach within clinical practice, often encompassing multidisciplinary teams including data scientists, clinicians and research software engineers, to utilise approaches such as machine learning to support clinical decision making.
A three-phase approach
The creation of the curriculum for the new programme is being informed by a rigorous three phase approach, outlined below, a digital survey will be widely distributed via relevant stakeholders and networks.
To date, some key themes have come through during the interviews and survey participation, including: database management, statistics and machine learning methodologies, skills in R and Python, and data interpretation and interrogation.
Once the areas of focus are decided for the curriculum, the next steps will involve detailed descriptions of each module to be taught, inclusion of learning outcomes and then the learning materials themselves will be created. It is envisaged that the course is likely to be fully online to accommodate the working schedules and study requirements of busy healthcare professionals.
Next steps
A clinical advisory board and also a number of key stakeholders have been involved to date. We envisage that in late 2021, a number of smaller curriculum development groups will be established to focus on specific parts of the curriculum and content creation. The academic team is delivering a fully online machine learning workshop for the second cohort of Topol Digital Fellows from September to December 2021, providing an opportunity to test some of the new Jupyter Notebooks which will contain teaching content related to data science and machine learning algorithms and will also test the virtual laboratory infrastructure. The team aims to launch the first module of the new clinical data science programme in data engineering in September 2022. If you’re interested in finding out more about this programme you can contact Angela Davies (angela.davies@manchester.ac.uk) and we would welcome your contributions in shaping this new clinical data science programme — you can participate in the digital survey here.
