Enhancing access to specialist appointments in tertiary healthcare in Shanghai, China: a structured reservation pathway using digital health technologies
Authors: Chen Minjie , Zhao Xiaojing , Zheng Tao , Zhang Binyuan , Zhao Xuji , Shao Weijun , Li Li , Fan Yiling , Dong Enhong 
Published: December 2024

ObjectiveThe aim of this study is to develop, implement the precise reservation path (PRP) and investigate its prediction function for scheduling shunting patients for specialist appointment registration in Shanghai, China.DesignThe PRP system was built on the hospital’s existing information system, integrated with WeChat (WeCom) for user convenience. The outcome analysis employed a mixed-methods approach, integrating quantitative analysis with statistical and machine learning techniques, including multivariate logistic regression, random forest (RF) and artificial neural network (ANN) analysis.SettingThis study was conducted at Renji Hospital, a premier general tertiary care institution in Shanghai, China, where the innovative PRP system was implemented. The programme was designed to efficiently connect patients requiring specialised care with the appropriate medical specialists.ParticipantsThe PRP encompassed both voluntary specialists at Renji Hospital, as well as patients seeking outpatient specialist services.Primary outcome measuresThe pass rates of patient for specialist applications.Secondary outcome measuresClinical department, specialists’ and patients’ characteristics influencing specialist review result.ResultsFrom a data set of 58 271 applicants across 26 departments between 1 December 2020 and 30 November 2022, we noted an overall pass rate of 34.8%. The departments of urology, breast surgery and thoracic surgery, along with five others, accounted for 86.65% of applications. Pass rates varied significantly, and demographic distributions of applicants across departments revealed distinct patient profiles, with preferences evident for age and gender. We developed an RF model based on pass rates from 26 specialised departments. The RF model, with 92.31% accuracy, identified age as the primary predictor of pass rates, underscoring its impact on specialist review outcomes. Focus on patient demographics, we conducted univariate and multivariate logistic regression analyses on the 58 271 patient data set to explore the relationship between demographic factors and review outcomes. Key findings from logistic regression included significant associations with gender, age and specialist title. Results indicated that older patients were more likely to be approved in specialist reviews, while middle-aged patients had lower pass rates. The generalised linear model, enhanced with specialist and clinical department variables, showed superior predictive accuracy (67.86–68.26%) and model fit over the previous logistic model. An ANN model also identified specialist and clinical department as the most influential, achieving comparable accuracy (67.72–68.28%).ConclusionsThe PRP programme demonstrates the potential of digital innovation in enhancing the hierarchical medical system. The study’s findings also underscore the value of the PRP programme in healthcare systems for optimising resource allocation, particularly for ageing populations. The programme’s design and implementation offer a scalable model for other healthcare institutions seeking to enhance their appointment systems and specialist engagement through digital innovation.