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Non-medical prescribing (NMP) is deemed to facilitate better patient care and timely access to medicine. This scoping review protocol is designed to explore and synthesise the evidence on costs and consequences of NMP services provided by non-medical healthcare professionals. This protocol is prepared based on the framework recommended by Arksey and O'Malley and further refined by Levac et al and the Joanna Briggs Institute. MEDLINE, the Cochrane Library, Science Direct, Scopus, PubMed, ISI Web of Science and Google Scholar will be systematically searched. The search strategy will include both peer-reviewed and grey literature written in English from 1999 to 2021. The identified studies will be screened independently by two reviewers for final inclusion. The results will be reported in graphical form and descriptively. The findings of this scoping review will provide valuable insights for researchers and policy-makers to inform policy and practice around NMP.

ObjectivesNon-medical prescribing (NMP) is a key feature of the UK healthcare system that refers to the legal prescribing rights granted to nurses, pharmacists and other non-medical healthcare professionals who have completed an approved training programme. NMP is deemed to facilitate better patient care and timely access to medicine. The aim of this scoping review is to identify, synthesise and report the evidence on the costs, consequences and value for money of NMP provided by non-medical healthcare professionals.DesignScoping reviewData sourcesMEDLINE, Cochrane Library, Scopus, PubMed, ISI Web of Science and Google Scholar were systematically searched from 1999 to 2021.Eligibility criteriaPeer-reviewed and grey literature written in English were included. The research was limited to original studies evaluating economic values only or both consequences and costs of NMP.Data extraction and synthesisThe identified studies were screened independently by two reviewers for final inclusion. The results were reported in tabular form and descriptively.ResultsA total of 420 records were identified. Of these, nine studies evaluating and comparing NMP with patient group discussions, general practitioner-led usual care or services provided by non-prescribing colleagues were included. All studies evaluated the costs and economic values of prescribing services by non-medical prescribers, and eight assessed patient, health or clinical outcomes. Three studies showed pharmacist prescribing was superior in all outcomes and cost saving at a large scale. Others reported similar results in most health and patient outcomes across other non-medical prescribers and control groups. NMP was deemed resource intensive for both providers and other groups of non-medical prescribers (eg, nurses, physiotherapists, podiatrists).ConclusionsThe review demonstrated the need for quality evidence from more rigorous methodological studies examining all relevant costs and consequences to show value for money in NMP and inform the commissioning of NMP for different groups of healthcare professionals.

An important component of multi-criteria decision analysis (MCDA) in the public sector is the elicitation and aggregation of preference data collected via surveys into the relative importance of the criteria for the decision at hand. These aggregated preference data, usually in the form of mean weights on the criteria, are intended to represent the preferences of the relevant population overall. However, random sampling is often not feasible for public-sector MCDA for logistical reasons, including the expense involved in identifying and recruiting participants. Instead, non-random sampling methods such as convenience, purposive or snowball sampling are widely used. Nonetheless, provided the preference data collected are sufficiently ‘cohesive’ in terms of the extent to which the weights of the individuals belonging to the various exogenously defined groups in the sample are similar, non-random sampling can still produce externally valid aggregate preference data. We explain a method for measuring cohesiveness using the Kemeny and Hellinger distance measures, which involve measuring the ‘distance’ of participants’ weights (and the corresponding rankings of the criteria) from each other, within and between the groups respectively. As an illustration, these distance measures are applied to data from a MCDA to rank non-communicable diseases according to their overall burden to society. We conclude that the method is useful for evaluating the external validity of preference data obtained from non-random sampling.