Hybrid storage solutions have emerged as potent strategies to alleviate the data storage bottlenecks prevalent in blockchain systems. These solutions harness off-chain Storage Services Providers (SP) in conjunction with Authenticated Data Structures (ADS) to ensure data integrity and accuracy. Despite these advancements, the reliance on centralized SPs raises concerns about query correctness, as the integrity of query results depends on the SPs' trustworthiness. Although ADS can verify the integrity of individual data points, they fall short of preventing SPs from omitting valid results. In this paper, we delineate the fundamental distinctions between data retrieval in blockchains and traditional database systems. Drawing upon these insights, we introduce the BPI framework, which employs a suite of validation models that ascertain the inclusion of all valid content in retrieval outcomes, with low overhead. We further present ''Articulated Search'', a query pattern specifically tailored for blockchain environments, which not only enhances retrieval efficiency but also substantially reduces costs during data user updates. Extensive experimental evaluations demonstrate that the BPI framework achieves outstanding scalability and performance in keyword searches within blockchain environments, surpassing EthMB+ and state-of-the-art search databases commonly used in mainstream hybrid storage blockchains (HSB). Notably, the Articulated Search pattern improves query performance by over three orders of magnitude, highlighting its potential as a transformative approach to blockchain query optimization.

Early recognition and treatment of shock are vital for critically ill patients. Timely shock treatment depends on the rapid identification of patients with potential shock, which is challenging in the busy working environment of ICU. Hypotension events are typically clinical signs of shock, which can be detected by monitoring patients’ blood pressure (BP). The monitor issues a sound alarm as soon as BP measured is below a preconfigured threshold. However, nurses have to filter out the valid hypotension events and report to the responsible intensivists. This process is error-prone due to nurses’ and intensivists’ workload and alert fatigue. Rule-based early warning systems have the potential to detect all hypotension events and filter out invalid hypotension events. Consequently, they lead to a lower workload and better performance. Nevertheless, the accuracy of the rule-based approach remains a question, especially considering the complex nature of intensive care. In order to validate the accuracy and understand the limitations of the rule-based approach, we carried out a case study in a Chinese hospital. A ruled-based system, named Shock Alert Assistant (SAA), had been developed and tested. This system screens the real-time BP of all patients every 5 minutes and sends alert messages to intensivists. In a one-month testing phase, SAA produced 619 shock early warnings for 82 patients. The SAA’s performance was compared against 126 nurses’ notes. Clinical experts checked the inconsistency of SAA and nurses’ notes and made a golden standard. Regarding SAA, the sensitivity is 87.6%, whereas the specificity is 10.7%. Among all the false-positive alerts, 52.7% are pure low systolic blood pressure without clinical meaning, which does not lead to shock. By analyzing the results, in addition to systolic blood pressure (SBP), mean arterial pressure (MAP) and medication order related to BP control should also be used to determine hypotension events.