Objective: To explore how men and their partners utilise social support in the first 12 months following a localised prostate cancer diagnosis. Design: A longitudinal qualitative design.Methods: Eighteen couples were recruited from two outpatient clinics following a localised prostate cancer diagnosis. Participants took part in semi-structured interviews at three time-points following diagnosis. Data were analysed using thematic analysis.

Introduction: Emergency admission risk prediction models are increasingly used to identify patients, typically with one or more chronic conditions, for proactive management in primary care to avoid admissions, save costs and improve patient experience.Aim: To identify and review the published evidence on the costs, effects and implementation of emergency admission risk prediction models in primary care for patients with, or at risk of, chronic conditions. Methods:We shall search for studies of healthcare interventions using routine data-generated emergency admission risk models. We shall report: the effects on emergency admissions and health costs; clinician and patient views; and implementation findings.

Purpose The purpose of this paper is to present an evaluation of a collaborative commissioning approach to improve quality and experience and reduce cost within integrated health and social care. Design/methodology/approach A multi-method approach is used involving qualitative interviews, documentary analysis and non-participant observation. Findings The findings suggest that the approach provides a suitable framework for the collaborative commissioning of integrated health and social care services. Research limitations/implications Further research is now needed to provide a definitive evaluation of its value outside of Wales. Practical implications With the significant scrutiny on health systems, the approach demonstrates effectiveness in securing quality improvements, achievement of recognised care standards and patient outcomes, while providing scope for financial gains and a goal for stakeholders to engage in effective communication. Originality/value This research presents an innovative method for collaborative commissioning and reveals activities that appear to contribute to more effective commissioning processes.

BackgroundThe majority of colorectal cancers (CRCs) are detected after symptomatic presentation to primary care. Given the shared symptoms of CRC and benign disorders it is challenging to manage this risk of missed diagnosis. Colonoscopy resources cannot keep pace with increasing demand. There is a pressing need for access to simple triage tools in primary care to help prioritise patients for referral.AimTo evaluate the performance of a novel spectroscopy-based CRC blood test in primary care.Design & settingMixed methods pilot study of test performance and GP focus group discussions.MethodUrgent suspected cancer patients were recruited for the Raman spectroscopy (RS) test coupled to machine learning classification (‘Raman-CRC’) to identify CRC within the referred population. Qualitative focus group work evaluated the acceptability of the test in primary care by thematic analysis of focus group theorising.Results532 patients age over 50 referred on the USC pathway were recruited from 27 GP practices. Twenty nine patients (5%) were diagnosed with CRC. Raman-CRC identified CRC with sensitivity 95.7%, specificity 69.3% with Area Under Curve (AUC) of 0.80 as compared to colonoscopy as reference test (248 patients). Stage I/II cancers were detected with 78.6% sensitivity. Focus group themes underlined the convenience of a blood test for the patient and the test’s value as a risk assessment tool in primary care.ConclusionsOur findings support this novel, non-invasive blood-based method to prioritise those patients most likely to have CRC. Raman-CRC may accelerate access to diagnosis with potential to improve cancer outcomes.

Suspected colorectal cancer (CRC) referrals based on non-specific symptoms currently lead to large numbers of patients being referred for invasive investigations and poor yield in cancer detection. Secondary care diagnostics, particularly endoscopy, struggle to meet the ever-increasing demand and patients face lengthy waits from the point of referral. Here we propose a blood test utilising high-throughput Raman spectroscopy and machine learning as an accurate triage tool. We present results from the first mixed methods clinical validation study of its kind, evaluating the ability of the test to perform in its target population of primary care patients, and its acceptability to those administering and receiving the test. The test was able to accurately rule out cancer with a negative predictive value of 98.0%. This performance could reduce the number of invasive diagnostic procedures in the cohort by at least 47%. Collectively, our findings promote a novel, non-invasive solution to triage CRC referrals with potential to reduce patient anxiety, accelerate access to treatment and improve outcomes.