Discussion
The use of an iterative approach for competency definition is advocated by Greenhalgh and Macfarlane.13 Our approach was similar to that of Jidkov et al which used an iterative method to develop 20 universal health informatics competencies for postgraduate medical education. The approach featured a literature review, content analysis and expert review.14 As opposed to a purely topic-based approach, competency approaches have been gaining more attention in both nursing and medical education.12 This is not without some issues however, such as problems related to identifying priorities as well as defining and measuring how competencies are met.15 Therefore one should aim to strike a balance between covering the most salient areas and keeping the number of competencies to the smallest number required. Another threat to competency models is that of clinician burnout in postgraduate education.16 As pointed out in the responses, there is a potential additional burden on clinical informaticians as they already have professional membership as clinicians and allied health professionals, with codes of conduct and clinical competencies to maintain. Consideration is required in terms of how clinical informaticians will be required to meet the competencies defined in the framework. Several participants indicated that a light touch is preferable to more intense examinations. It should also be recognised that participants may work in one (or several) domains deeply and not have experience spanning all the domains depending on their role.
The mixed-methods approach chosen appears to be appropriate for generating a core competency framework, as it allowed us to take a systematic and structured approach to co-designing the framework with no major issues. The combination of qualitative and quantitative methods contributed to the robustness of the final output. There already exist specific frameworks for certain informatics subdomains, such as TIGER for nurses12 and ELIXIR17 for bio-informaticians. In addition there are international frameworks and recommendations for informatics education such as18 and.19 Although there is some overlap between the CCF and competencies identified in such international frameworks, the CCF adopts a UK-specific perspective, taking into consideration the unique characteristics of UK health and social care. Considering this local perspective is crucial to the development of competency frameworks.4 12 20 The Topol review specifically identifies the FCI as ideally positioned to recruit, retain and credentialise NHS data science specialists.1
Participants felt that the framework should be open to as many professional groups as possible. It was also highlighted that practitioners may work in depth in a few domains and may not span all the domains. However, it was considered that at entry level, an awareness of the other domains would be sufficient and that practice-level skills may not be required in all the domains. The participants generally felt that all the competencies were ‘core’ competencies depending on the level that a practitioner was expected to engage with them. There was some contention in terms of the more technical aspects (specifically around domain 3, ‘working with data and analytical methods’) where some felt that informaticians should have more extensive skills (ie, being able to build software), whereas others put forward that clinicians may struggle to meet this requirement. As it stands the framework only stipulates an awareness of such issues rather than practical experience.
The evolving technical requirements of informatics and the overlap with the profession of data science is a key consideration. Douglas Fridsma, the former president and COE of the AMIA, discussed how such similarities may be operationally defined via the development of core competencies.21 There may be some overlap in areas of analytical and computational skills in healthcare data science as seen in a recent content analysis of healthcare data scientists job postings22 and informatics. Although data science may involve a deeper dive into analytical methods and processes, informatics spans a wider remit and can be involved with decision-making, communication and implementation of data analysis and health systems. The boundaries between such professions may be blurred at times, but as Fridsma points out, core competencies can help to distinguish professions as well as define how they overlap.
Another point raised was the importance of topics such as AI and the subdomain of machine learning (ML). Both AI and ML are mentioned prominently in the Topol review23 and are receiving serious consideration and funding for their application to healthcare. AI is not a new concept and has been around since the 1950s.24 Many algorithms are referred to as a black box because the exact workings of certain algorithms (eg, neural networks) can be complex and unpredictable.24 Despite their current popularity, it is unclear whether AI and ML will continue to hold the same prominent position in future. The framework therefore incorporates AI and ML into the broader category of ‘Analytical Methodologies and Applications’ rather than a section in its own right. Many statistical methods are still widely used in medical contexts, and ‘statistical learning’, which is focused on prediction as well as supervised and unsupervised modelling, spans the domains of machine learning and statistics.25
One of the challenges going forward will be the implementation of the framework. This will include tasks such as developing operational definitions for the competencies concerning their related knowledge, skills, attitudes and behaviours as well as defining how such competence will be measured for individuals.15 The CCF provides the initial foundation for defining the requirements to be a clinical informatician in the UK and provides a starting point for professional development in this fast-moving area of healthcare.
As the interviews highlight, careful consideration is required when linking the framework to developmental stages and/or academic levels. Various stage models of professional development exist26–28 and mostly originate from the field of cognitive psychology. These focus on moving through various developmental stages (eg, novice, advanced beginner, competent, proficient and expert29). Stage models are often based on attributes including attitudes, skills and underpinning knowledge which are supplemented with additional skills and knowledge from the workplace.30
Criticism of such models suggest that some aspects of professional skill development, such as the skill being developed in terms of understanding and practice, may be concealed as the primary focus is on the developmental stages themselves.30
More recent work considers models that account for multiple dimensions such as progression of skills over time and embodied understanding of practice.30 This reflects the difference between skill progression over time that is possible without such understanding of practice which differentiates experienced and expert practitioners. Other models consider extra dimensions, such as functional and foundational competency domains and stages of professional development.31
It can also be difficult to ascertain exactly which skills, knowledge and attitudes differentiate between being an expert or not. Given that there is a connection between the individual and their characteristics and their professional practice,30 applying a staged model can be complex, especially when considering applying this to the various clinical groups that form the clinical informatics community with their underlying cultures and contexts. This may have to be applied in slightly different ways to account for these variations in context.
The FCI have developed a working group that seeks to address such issues, including the mapping of the framework to academic courses, accreditation using the framework and other details around its validation and implementation. Progress of the working group is reported publicly online via the FCI website giving the option for more end users and stakeholders to comment and feedback on the implementation as well as future revisions.
Limitations
Interviews were conducted at the start of the coronavirus pandemic which may have impacted on recruitment levels. It is possible that not all perspectives of all professional groups relevant to clinical informatics were represented. We were however able to engage with a range of participants that represented different roles, professional backgrounds and experience levels. A further limitation is that the work is based in the UK and therefore may not be fully generalisable outside of the UK without adaptions for other contexts. Finally, the survey only received 87 responses, which given the size of the informatics community in the UK would not indicate a large take-up. This was partly due to the project’s limited time frame as well as the start of the COVID-19 pandemic, which meant many potential respondents prioritised pandemic-related work.