Assessing the quality of primary healthcare in seven Chinese provinces with unannounced standardised patients: protocol of a cross-sectional survey. Issue 2 (13th February 2019) - Xu, Dong Roman; Hu, Mengyao; He, Wenjun; Liao, Jing; Cai, Yiyuan; Sylvia, Sean; Hanson, Kara; Chen, Yaolong; Pan, Jay; Zhou, Zhongliang; Zhang, Nan; Tang, Chengxiang; et al.
Abstract : Introduction: Primary healthcare (PHC) serves as the cornerstone for the attainment of universal health coverage (UHC). Efforts to promote UHC should focus on the expansion of access and on healthcare quality. However, robust quality evidence has remained scarce in China. Common quality assessment methods such as chart abstraction, patient rating and clinical vignette use indirect information that may not represent real practice. This study will send standardised patients (SP or healthy person trained to consistently simulate the medical history, physical symptoms and emotional characteristics of a real patient) unannounced to PHC providers to collect quality information and represent real practice. Methods and analysis: 1981 SP–clinician visits will be made to a random sample of PHC providers across seven provinces in China. SP cases will be developed for 10 tracer conditions in PHC. Each case will include a standard script for the SP to use and a quality checklist that the SP will complete after the clinical visit to indicate diagnostic and treatment activities performed by the clinician. Patient-centredness will be assessed according to the Patient Perception of Patient-Centeredness Rating Scale by the SP. SP cases and the checklist will be developed through a standard protocol and assessed for content, face and criterion validity, and test–retest and inter-rater reliability before its full use. Various descriptive analyses will be performed for the survey results, such as a tabulation of quality scores across geographies and provider types. Ethics and dissemination: This study has been reviewed and approved by the Institutional Review Board of the School of Public Health of Sun Yat-sen University (#SYSU 2017-011). Results will be actively disseminated through print and social media, and SP tools will be made available for other researchers.