Population growth, ageing populations and increases in multimorbidity are placing growing pressure on primary healthcare services around the world. At scale, effective public health initiatives are seen as a ‘prevention is better than cure’ solution to the long-term mitigation of these challenges. Health authorities offering wellbeing referral services are one method by which these challenges are being practically addressed. Despite such services presenting both primary care practitioners and their patients' additional support, they can be underutilised, often due to a lack of awareness or misrepresentation in a domain where evidence-based medicine is de facto. To effectively measure and optimise their communication initiatives, health authorities face a need for structured tracking and analysis across services often outside of their control.
This study reviews the way in which GP referral data can be related to professional medical communication practices to better inform strategic communication decisions. A cross-discipline, innovative approach is proposed for the measurement of communication activities in a public health environment. Through means of case-study research, a chronology of archival public health communication interventions was captured. The effectiveness of the interventions was quantified by utilising causal inference analysis of the trend in the number of referrals that follow targeted communication activities.
The method proposed offered a flexible and relatively accessible solution to a complex problem. However, the results themselves proved inconclusive for the particular case study - this was, in part, attributed to nonspecific historic tracking of intervention delivery. Supporting analysis identified seasonality trends as affecting both GP and non GP referral rates, suggesting greater importance of patient participation in decision making as part of the referral process. Recommendations are proposed for the delivery, supervision and analysis of communication interventions at an organisational level in a primary healthcare setting.
Data analysis written in python and submitted supporting a thesis in part fulfilment of the requirements for the degree of Master of Research.
The original data analyised by the following functions remains owned by Public Health Dorset and is therefore not included in the deployent. It is only available upon their explicit instruction.
Functions developed to specifically assist processing of Live Well Dorset wellbeing service data ready for further analysis.
Analysis of the Live Well Dorset referral data distribution density, GP / non GP disparity, and time seris trends components as part of a wider analysis of the effect of professional healthcare communication on GP referral rates.
Analysis of the Live Well Dorset referral data time series for potential causal indicators as the result of communication activities.
Analysis of the Live Well Dorset referral data demographic distributions as part of a wider analysis of the effect of professional healthcare communication on GP referral rates.