The aim is to use CPRD to identify a measure of workload and then identify the effect of workload on the health outcomes; namely, the probability of referral. We focus on London to minimise the confounding effect of geographical differences in the access to health care and underlying health conditions among patients. We will look at cross-sectional variation of workload across practices and within each practice.
Our research will consist of the following steps:
1. Constructing a measure for daily workload at practice level. This is the average patient consultation to GP ratio.
2. Identifying the fluctuations of the daily workload during the year within a practice.
3. We use the unexpected fluctuations in the workload pressure through unexpected absence of a GP to identify an exogeneous and random variation in the workload pressure. Absence is measured if the doctor was working in the practice in a 2-week window period but has zero consultation in a given day
4. Then we will examine whether there is any association between this exogeneous variation in the workload and the choice of diagnostic inputs. We will focus on a linear probability regression for the referral to specialist, but we will also include referral to test labs.
5. The analysis will be expanded to explain how the workload of other staffs, namely practice nurses varies and how it helps reducing the workload of GPs.
The analysis will be done using 2 stage least square analysis for two years.
GP daily workload, nurse daily workload, practice average workload, referral probability by the urgency of referral (2 week wait, urgent, soon, routine, dated), referral to test laboratories.
Toby Watt - Chief Investigator - The Health Foundation
Andrew Campbell - Corresponding Applicant - The Health Foundation
Hanifa Pilvar - Collaborator - Queen Mary University of London