Developing a complex intervention to reduce time to presentation with symptoms of lung cancer

Background - Lung cancer is the commonest cause of cancer in Scotland and is usually advanced at diagnosis. Median time between symptom onset and consultation is 14 weeks, so an intervention to prompt earlier presentation could support earlier diagnosis and enable curative treatment in more cases. Aim - To develop and optimise an intervention to reduce the time between onset and first consultation with symptoms that might indicate lung cancer. Design and setting - Iterative development of complex healthcare intervention according to the MRC Framework conducted in Northeast Scotland. Method - The study produced a complex intervention to promote early presentation of lung cancer symptoms. An expert multidisciplinary group developed the first draft of the intervention based on theory and existing evidence. This was refined following focus groups with health professionals and high-risk patients. Results - First draft intervention components included: information communicated persuasively, demonstrations of early consultation and its benefits, behaviour change techniques, and involvement of spouses/partners. Focus groups identified patient engagement, achieving behavioural change, and conflict at the patient–general practice interface as challenges and measures were incorporated to tackle these. Final intervention delivery included a detailed self-help manual and extended consultation with a trained research nurse at which specific action plans were devised. Conclusion -The study has developed an intervention that appeals to patients and health professionals and has theoretical potential for benefit. Now it requires evaluation.

Reducing the time before consulting with symptoms of lung cancer:a randomised controlled trial in primary care

Background: Most individuals with lung cancer have symptoms for several months before presenting to their GP. Earlier consulting may improve survival. Aim: To evaluate whether a theory-based primary care intervention increased timely consulting of individuals with symptoms of lung cancer. Design and setting: Open randomised controlled trial comparing intervention with usual care in two general practices in north-east Scotland. Method: Smokers and ex-smokers aged ≥55 years were randomised to receive a behavioural intervention or usual care. The intervention comprised a single nurse consultation at participants' general practice and a self-help manual. The main outcomes were consultations within target times for individuals with new chest symptoms (≤3 days haemoptysis, ≤3 weeks other symptoms) in the year after the intervention commenced, and intentions about consulting with chest symptoms at 1 and 6 months. Results: Two hundred and twelve participants were randomised and 206 completed the trial. The consultation rate for new chest symptoms in the intervention group was 1.19 (95% confidence interval [CI] = 0.92 to 1.53; P = 0.18) times higher than in the usual-care group and the proportion of consultations within the target time was 1.11 (95% CI = 0.41 to 3.03; P = 0.83) times higher. One month after the intervention commenced, the intervention group reported intending to consult with chest symptoms 31 days (95% CI = 7 to 54; P = 0.012) earlier than the usual care group, and at 6 months this was 25 days (95% CI = 1.5 to 48; P = 0.037) earlier. Conclusion: Behavioural intervention in primary care shortened the time individuals at high risk of lung disease intended to take before consulting with new chest symptoms (the secondary outcome of the study), but increases in consultation rates and the proportions of consultations within target times were not statistically significant. © British Journal of General Practice.

Developing a probabilistic graphical structure from a model of mental-health clinical risk expertise

This paper explores the process of developing a principled approach for translating a model of mental-health risk expertise into a probabilistic graphical structure. The Galatean Risk Screening Tool [1] is a psychological model for mental health risk assessment based on fuzzy sets. This paper details how the knowledge encapsulated in the psychological model was used to develop the structure of the probability graph by exploiting the semantics of the clinical expertise. These semantics are formalised by a detailed specification for an XML structure used to represent the expertise. The component parts were then mapped to equivalent probabilistic graphical structures such as Bayesian Belief Nets and Markov Random Fields to produce a composite chain graph that provides a probabilistic classification of risk expertise to complement the expert clinical judgements. © Springer-Verlag 2010.