Increasing the ethnic diversity of senior leadership within the English National Health Service: using an artificial intelligence approach to evaluate inclusive recruitment strategies in hospital settings.

BACKGROUND

The English National Health Service (NHS) strives for a fair, diverse, and inclusive workplace, but Black and Minority Ethnic (BME) representation in senior leadership roles remains limited. To address this, a large multi-hospital acute NHS Trust introduced an inclusive recruitment programme, requiring ethnically and gender diverse interview panels and a letter to the Chief Executive Officer (CEO) explaining hiring manager's candidate choice. This generated large amount of valuable structured and free-text data, but manual analysis to derive actionable insights is challenging, limiting efforts to evaluate and improve such equality, diversity, and inclusion (EDI) recruitment initiatives.

METHODS

Using this routinely collected recruitment data from the programme between September 2021 to January 2024, we used natural language processing artificial intelligence techniques, triangulated with secondary data analysis, to evaluate the programme's effectiveness in increasing the number of BME appointees to senior leadership roles. Multivariate logistic regression identified recruitment factors that influence the odds of BME candidates applying, being shortlisted or offered a role compared to white candidates. Topic and sentiment analysis revealed thematic trends and tone of candidate assessments, stratified by hiring manager and candidate characteristics. Normalised average interview scores were also compared by job grades and candidate characteristics.

RESULTS

The requirement for hiring managers to write a letter to the CEO explaining recruitment decisions raised the odds of a BME candidate being offered a role by 1.7 times [95% CI 1.2-2.3] compared to white candidates. However, white candidates still had higher overall odds of being offered senior roles. BME candidates scored lower in interviews, with BME women twice as likely (p < 0.05) to receive negative assessments compared to white women.

CONCLUSIONS

The Letter to the CEO component of the inclusive recruitment programme increased BME representation in senior leadership roles, but inequities still persist in the recruitment process, reflecting national NHS recruitment trends. While the initiative marks progress, further strategies are needed to ensure equitable recruitment, career development, and retention. Artificial intelligence tools, such as natural language processing, provide effective methods to evaluate and enhance EDI recruitment initiatives by analysing routinely collected recruitment data to identify areas for improvement and establish best practices.