An average general practitioner (GP) consults with 41 patients each day but some see up to 70 per day and the number of GP consultations and GP workload are increasing. It is not clear why workload is increasing but it may be linked to the increasing numbers of older people, increasing numbers of people with one or more long-term conditions (such as diabetes or heart disease) or both. GP workload is higher when they care for patients who are older, elderly, female or live in more deprived districts. Patients with long-term conditions, particularly those with more than one condition, also tend to consult more frequently and their consultations are generally longer. However it is not clear which is more important for workload: a patients’ long-term conditions or their age, sex or whether they live in a more deprived area.
We want to understand what contributes to the workload of GPs and other staff such as practice nurses. We will investigate the number of patient consultations and their duration in relation to patients’ age, sex, whether they live in a deprived area and whether they have one or more long-term conditions. This will allow us to determine how much each of these factors contributes to GP workload and therefore why workload may be increasing. This information may help understand the need for GPs and help with workforce planning.
This study proposes to conduct an assessment of current clinical staff workload, as well as its predictors.
Currently, NHS England predicts primary care clinical workload using patients’ age and sex group, number of new registrations and deprivation banding (Gardiner and Everard, 2016). However, the workload generated by a patient in primary care is also determined by patients’ clinical characteristics which may not be captured by age, sex and deprivation. By not making use of patient health-related data to predict staff workload, NHS workforce planners may overlook the importance of multimorbidity as a predictor of health service utilisation.
Including a patients’ clinical characteristics in a workload prediction model may be relevant to the understanding of variation in primary care workload (GP and other clinical staff) and future trends in workload. This can help with workforce planning.
Data analysis will be carried out in two stages. Firstly, we will examine the characteristics of a sample of patients who are responsible for a high level of clinical workload, in particular the most common chronic diseases affecting these patients. A subsequent analysis will use stepwise linear regression analysis to determine the predictors of primary care workload. A measure of deprivation will be included in the analysis in order to take health inequalities and need for healthcare into account in the workload formula. NHS England currently uses the Index of Multiple Deprivation (IMD) as a proxy for need in more deprived areas in its workload model. Similarly, IMD will be used in this study as the deprivation variable.
The primary outcome of interest is clinical staff workload (frequency and duration of consultations) which is categorised according to the type of clinical staff (e.g., doctor, nurse, allied health professional).
Tom Marshall - Chief Investigator - University of Birmingham
Lyvia Guerrier de Dumast - Corresponding Applicant - University of Birmingham
Patrick Moore - Collaborator - University of Birmingham
2011 Rural-Urban Classification at LSOA level;Practice Level Index of Multiple Deprivation