Some patients attend general practice much more frequently than others. It has been estimated that the 10% who attend most frequently make up 30-50% of GP workload. With increasing demand, less money and an aging population who require more health care, we need to manage resources in general practice better.
Little is known about patients who attend very frequently. Understanding why these patients attend often, and if and how they differ from patients who attend less will help us to develop systems and policies to care for them better. Improving their care will not only benefit the patients themselves but will also ease the pressure on general practices.
We will use anonymised electronic data collected nationally from general practices and compare the characteristics of patients who attend frequently with those who go less often. We will be looking particularly at the patients age, gender, ethnicity, underlying health, region and level of deprivation. We will explore the distribution of consultations compared to the practice population and how that has changed over time. We will categorise different groups of frequent attenders, with the ultimate goal of exploring intervention/actions to reduce attendance while meeting their health needs more successfully, thus reducing costs to practices and ensuring a more equitable distribution of consultations. We will separately engage patients and members of the public for their personal experiences and perception of frequent attenders in general practice, their views on the subsequent analysis and how do we best present this work to a wider public audience.
UK Primary Care is facing significant challenges: increasing demand with more complex consultations, as a result of an aging population, and a GP recruitment and retention crisis. We need to understand and adjust our practice to increase system efficiencies and decrease healthcare expenditures, while improving patient experience and safeguarding patient care.
In this challenging context, a small group of patients use a disproportionate amount of GP resources. It has been estimated that the top 10% of attenders make up 30-50% of GP workload. Frequent Attenders (FA) have been found to have different clinical and psychosocial characteristics to Normal Attenders (NA), although UK evidence is limited.
Using CPRD GOLD and Aurum, we aim to understand the prevalence and nature, identifying factors, of frequent attendance to GPs in the UK, to inform methods of addressing unmet needs that may act as drivers for improvement.
First, we will describe the distribution of consultations (by all staff/GPs and by face to face/all consultations) within each practice and explore if that has changed over time and become more inequitable. Cost implications for each practice will also be explored.
Multi-level models will estimate the trends in frequent attendance over time, quantify variability over time and across age, gender, multimorbidity, frailty, practice, region and deprivation. These models will take account of the longitudinal nature of the data and both the patient and practice level factors that might account for the variation and trends. These models will identify which patient and practice characteristics are associated with the number of attendances and FAs.
Clusters within the FA group will be explored, using k-means and hierarchical clustering. Assuming meaningful clusters will be identified, we will attempt to characterise them and link them to specific actions and interventions, aiming to meet patient care needs while reducing the frequency of their consultations.
The primary outcome will be Persistent Frequent Attenders (PFA) defined as those in the top 10% of age and gender-adjusted attendance over three years, collated for each registered patient every quarter within the timeframe (2000-2019). The secondary outcome will be number of GP attendances per patient per financial year over the timeframe, to understand the underlying variation in the number of GP attendances from which the primary outcome is derived.
Evangelos Kontopantelis - Chief Investigator - University of Manchester
Tracey Farragher - Corresponding Applicant - University of Manchester
Aneez Esmail - Collaborator - University of Manchester
Claire Planner - Collaborator - University of Manchester
Luke Munford - Collaborator - University of Manchester
Maria Panagioti - Collaborator - University of Manchester
Qiuyu Lin - Collaborator - University of Manchester
Rosa Parisi - Collaborator - University of Manchester
Evangelos Kontopantelis - Collaborator - University of Manchester
Patient Level Index of Multiple Deprivation;Practice Level Index of Multiple Deprivation