While recent medical benchmarks for Large Language Models (LLMs) have begun to incorporate clinical temporality, they still overlook a key reality: clinical evidence is encountered incrementally, requiring repeated decisions between reviewing the next available record and committing to a prediction. To address this issue, we introduce [<https://github.com/hwfengcs/INA-Bench>] , a benchmark for Inpatient Note Acquisition that evaluates LLMs’ ability to make acquire-or-predict decisions using sequentially revealed real-world Chinese inpatient records. In this benchmark, each case sequentially reveals up to three note blocks: an admission note, a first-course summary, and a daily-progress summary. After each block, the model under evaluation must either request the next block or predict one or more later documented follow-up actions (laboratory, imaging, pathology, or consultation). Surgery and discharge records are withheld for label construction and boundary checks only. is built from 8,986 de-identified inpatient records collected from two tertiary teaching hospitals, with patient-disjoint and temporal evaluation splits. Representative LLMs exhibit diverse acquire-or-predict behaviors. For example, DeepSeek-chat improves action micro-F1 by +0.102 and +0.074 on the patient-disjoint and temporal splits, respectively, while reading fewer note blocks, though the gains weaken under temporal drift.