Modelling workforce skill-mix: how can dental professionals meet the needs and demands of older people in England?

Abstract

Background: There is an urgent need to consider the skill-mix of the dental team to meet the oral health needs and demands of the population in general, and older people in particular. As people live longer and retain their teeth there will be a progressive change in both the volume and type of dental care required, and the demand for care. Operational research modelling provides the opportunity to examine and test future scenarios for National Health Service (NHS) care.

Aim: The aim of this research was to explore the required skill-mix of the dental team to meet future need and demand of older people in England to 2028 utilising operational research methods and to examine a range of future scenarios.

Method: A three-stage computer model was developed to consider demand for dental care, workforce supply and skill-mix. First, the demand model combined population demography and a marker of oral health with attendance and treatment rates based on NHS activity data. Monte Carlo simulation was used to give an indication of the uncertainty surrounding this projected demand. Second, projections on workforce supply and other assumptions relating to clinical hours, NHS commitment and workforce whole time equivalents (WTEs) were analysed to produce a range of estimates for the current and future workforce. Third, staff skill-mix competencies were examined and the data fed into an optimisation model. Linear programming was used to give the optimal workforce makeup and predictions for workforce requirements. Five future scenarios were run from 'no skill-mix' through to 'maximum skill-mix' in the dental team, and the outputs compared.

Results: The results indicate that by 2028 there will be an increase in demand for care among older people of over 80% to almost 8.8 million hours; however, Monte Carlo simulation suggests considerable uncertainty surrounding the demand model outputs with demand deviating from the average in terms of treatment hours by as much as 22%. Modelling a healthcare system with 'no skill-mix' resulted in the lowest volume of clinical staff equivalents (dentists: 8,668) providing care for older people, whereas maximum skill-mix involved more staff (clinical staff = 10,337, of whom 2,623 were dentists, 4,180 hygienist/therapists and 3,534 clinical dental technicians) if all care is provided at the relevant level of competence.

Conclusion: The model suggests that with widening skill-mix, dental care professionals can play a major role in building dental care capacity for older people in future. The implications for health policy, professional bodies and dental teamworking are discussed.