An evidence-informed Delphi study of ambulatory care sensitive conditions in China: a policy tool to assess primary care performance.

Background: Ambulatory care sensitive conditions (ACSCs) serve as a critical indicator for assessing healthcare system performance globally. However, China lacks a standardized ACSCs list adapted to its unique healthcare context and evolving medical needs. Methods: This study employed a modified Delphi method combined with evidence-based medicine. First, we systematically reviewed international ACSCs lists and their development methodologies to identify potential diseases. Next, we evaluated the evidence of these potential conditions within China's healthcare system. Finally, a two-round Delphi survey was performed to finalize a consensus-based ACSCs list for China. Findings: The finalized ACSCs list comprises 14 conditions, categorized into: five core conditions (chronic obstructive pulmonary disease [COPD], bronchial asthma, hypertension, chronic kidney disease [CKD], and diabetes mellitus) and nine general conditions (bronchiectasis, chronic heart failure, atrial fibrillation, chronic hepatitis B, tuberculosis, iron-deficiency anemia, primary osteoporosis, gastroenteritis, and influenza). Based on the prevailing classification framework in academia, the list includes 12 chronic ACSCs, one acute ACSC, and one infectious ACSC. Compared to most international lists (typically covering about 20 ACSCs), China's ACSCs list prioritizes diagnostic specificity over breadth, ensuring practical applicability in China's current healthcare setting. Interpretation: This study developed the first evidence-based ACSCs list tailored to China, providing a tool for healthcare performance evaluation and policy development. Future studies should validate its real-world applicability and implement mechanisms for dynamic updates. Funding: This work is supported by National Science and Technology Major Project of China (Grant No. 2024ZD0523902), National Natural Science Foundation of China (Grant No. 72374149), and Institutional Research Fund from Sichuan University (Grant No. 2023SCUH0025).