Abstract
Objectives: Global, Covid-driven restrictions around face-to-face interviews for healthcare student selection have forced admissions staff to rapidly adopt adapted online systems before supporting evidence is available. We have developed, what we believe is, the first automated interview grounded in Multiple Mini-Interview (MMI) methodology. This study aimed to explore test re-test reliability, acceptability, and usability of the system.
Design, setting and participants: Multi-method feasibility study in Physician Associate (PA) programmes from two UK and one US university during 2019 - 2020.
Primary, secondary outcomes: Feasibility measures (test-retest reliability acceptability and usability) were assessed using intra-class correlation (ICC), descriptive statistics, thematic and content analysis.
Methods: Volunteers took (T1), then repeated (T2), the automated MMI, with a seven-day interval (+/- 2) then completed an evaluation questionnaire. Admissions staff participated in focus group discussions.
Results: Sixty-two students and seven admission staff participated; 34 students and four staff from UK and 28 students and three staff from US universities.
Good-excellent test-retest reliability was observed with T1 and T2 ICC between 0.62-0.81 (p<0.001) when assessed by individual total scores (range 80.6-119), station total scores 0.6-0.91, p<0.005, individual site (all ICC≥ 0.76 p<0.001) and mean test retest across sites 0.82 p<0.001 (95% CI 0.7-0.9).
Admissions staff reported potential to reduce resource costs and bias through a more objective screening tool for pre-selection or to replace some MMI stations in a ‘hybrid model’. Maintaining human interaction through ‘touch points’ was considered essential.
Users positively evaluated the system, stating it was intuitive with an accessible interface. Concepts chosen for dynamic probing needed to be appropriately tailored.
Conclusion: These preliminary findings suggest that the system is reliable, generating consistent scores for candidates and is acceptable to end-users provided human touchpoints are maintained. Thus, there is evidence for the potential of such an automated system to augment healthcare student selection.
Strengths and limitations of this study
• The underpinning iterative theoretical approach enabled a responsive, dynamic design and development process for a new technology with no known precedent.
• The conceptual leap from face-to-face or videoconference facilitated MMIs to a fully automated interview and assessment system may present barriers to stakeholders irrespective of the technology and its’ features.
• The multi-method design provided for a diverse set of insights which have been essential to informing the progression of the technology.
• We were unable to assess for potential differential performance within sub-groups, as would require a larger sample size.