Background: A psychometrically validated instrument to measure patient experience in Chinese public hospitals would be useful and is currently lacking. Our research team developed the Patient-Reported Experience Measure for Care in Chinese Hospitals (PREM-CCH). We aimed to validate this PREM-CCH in the present study.Methods: Data were drawn from a cross-sectional patient survey in 2016. Complete responses from 2,293 outpatients and 1,510 inpatients were included. Separate psychometric evaluation was carried out on outpatient and inpatient PREM-CCHs in terms of exploratory factor analysis, internal consistency, construct validity and criterion validity.Results: The validated outpatient PREM-CCH contained 22 items and five Factors, i.e. Communication and information, Professional competence, Medical costs, Efficiency, and Hospital recommendation. The validated inpatient PREM-CCH contained 19 items and six Factors, i.e. Communication and information, Professional competence, Medical costs, Efficiency, Health outcomes, and Hospital recommendation. The PREM-CCH showed satisfactory internal consistency, construct validity and criterion validity.Conclusions: The PREM-CCH is one of the first validated instruments capturing patient experience of care in the context of Chinese public hospitals. It performed well in the psychometric evaluation. It consists of a basic set of items important to patients that could be applicable to public hospitals in China and actionable to inform quality improvement initiatives.

Background: Urine collection devices (UCD) are being marketed and used in clinical settings to reduce urine sample contamination, despite inadequate supporting evidence. Aim: To determine whether UCDs, compared to standardised instructions for urine sample collection, reduce the proportion of contaminated samples. Design, Setting: Single blind randomised controlled trial in UK Primary care. Method: Women aged ≥ 18 years presenting to with symptoms attributable to urinary tract infection (UTI) were randomised (1:1:1) to use either a Peezy UCD, a Whizaway Midstream UCD, or standardised verbal instructions (SVI) for midstream sample collection. The primary outcome was the proportion of urine samples reported as contaminated by microbiology laboratory analysis. Results: 1264 women (Peezy n=424; Whizaway n=421; SVI n=419) were randomised between 5/10/16 and 20/8/18. 90 women were excluded from the primary analysis due to ineligibility or lack of primary outcome data, leaving 1174 (n=381; n=390; n=403) for intention-to-treat analysis. The proportion of contaminated samples was 26.5% with the Peezy, 28.2% Whizaway, and 29% SVI (relative risk (RR) Peezy v SVI 0.91 [95% Confidence Interval (CI) 0.76 to 1.09] (P = 0.32); Whizaway v SI 0.98 [95% CI 0.97 to 1.20] (P = 0.82)). There were 100 (25.3%) device failures with Peezy and 35 (8.8%) with Whizaway UCDs; the proportion of contaminated samples was similar after device failure samples were excluded. Conclusion: Neither Peezy nor Whizaway UCDs reduced sample contamination when used by women presenting to primary care with suspected UTI. Their use cannot be recommended for this purpose in this setting.

Background: There is little empirical evidence to inform implementation strategies for introducing SARS-CoV-2 point of care (POC) testing into primary care settings. The purpose of this study is to develop a theory-driven understanding of the behavioural determinants underpinning the implementation of SARS-CoV-2 POC testing in primary care. This will allow identification of potential intervention strategies that could encourage successful implementation of testing into routine practice and facilitate face-to-face consultations.Methods: We used a secondary qualitative analysis approach to re-analyse data from a qualitative study that involved interviewing 22 primary care physicians from 21 primary care practices across three regions in England. We followed the three-step method based on the Behaviour Change Wheel to identify barriers/enablers to the implementation of SARS-CoV-2 POC testing and identified behaviour change techniques to inform intervention strategies that targeted the barriers/enablers.Results: We identified 10 barriers and enablers to POC implementation under eight Theoretical Domains Framework (TDF): (1) knowledge; (2) behavioural regulation; (3) reinforcement; (4) skills; (5) environmental context and resources; (6) social influence; (7) professional role and identity; and (8) belief about consequences. Linkages with the Behaviour Change Techniques (BCT) taxonomy enabled the identification of intervention strategies to address the social and contextual factors influencing primary care physician’s willingness and capacity to adopt POC testing.Conclusions: A theory-informed approach identified barriers to the adoption of POC tests in primary care as well as guiding implementation strategies to address these challenges.