OBJECTIVE To create a highly effective algorithm for automatically generating easily confused drug catalogs （ECDC）， as well as to develop a management system for ECDC based on this algorithm， in order to improve the management efficiency of ECDC. METHODS This study， based on Levenshtein distance algorithm， delved deeply into the automatic identification mechanism of easily confused drugs and the screening method for determining similarity thresholds， ultimately leading to the development of an algorithm for automatically generating ECDC. Besides a management system was designed and developed， using SQL Server 2008 R2 Express as the data storage platform and Visual Basic.NET as the programming language. RESULTS The similarity threshold δ played a crucial role in the algorithm for automatically generating ECDC. As the value of δ gradually increased， the total count of easily confused drugs decreased gradually， while the count of drug groups exhibited a pattern of initially increasing and then decreasing. Practically， ECDC could be created using either the generic or varietal names of drugs， with corresponding similarity thresholds of 0.75 and 0.83. Furthermore， ECDC management system had significantly reduced the time required to establish a catalog from about one week to less than one hour， resulting in a substantial enhancement in work efficiency. CONCLUSIONS The algorithm used to automatically generate ECDC is highly efficient and rapid， offering robust technical assistance for the management of easily confused drugs. Implementing the ECDC system can greatly reduce the time cost related to building and maintaining the catalogs， thus significantly improving the efficiency of managing ECDC.

OBJECTIVE To investigate a method for dynamic monitoring of drug consumption （DMDC） based on statistical process control （SPC）， aiming to improve the macro-supervisory capacity in the process of drug utilization. METHODS The lists of key monitoring drug varieties in our hospital were established based on drug cost and relevant national documents. Monthly consumption data of key monitoring drug varieties in the entire hospital， outpatient pharmacy and inpatient pharmacy were taken as monitoring objects，and the DMDC model was established using SPC’s X control chart， moving range control chart， and exponentially weighted moving-average control chart， monitoring from three dimensions： single-month consumption， range variation， and consumption trend. Rosuvastatin， metoprolol and meropenem were taken as examples to demonstrate the monitoring capabilities of the DMDC model. RESULTS Lists of key monitoring drug varieties were established for entire hospital， outpatient pharmacy and inpatient pharmacy， containing 203， 167 and 200 varieties， respectively. After excluding drug varieties that could not be modeled and for which modeling failed， 179， 116 and 172 DMDC models were successfully established for these three drug consumption areas， respectively. During the first four months of 2024， these three groups of model separately warned 54， 32 and 62 drug varieties. The DMDC model successfully monitored the monthly consumption of drugs，such as rosuvastatin throughout the hospital， metoprolol in outpatient pharmacy， and meropenem in inpatient pharmacy. Compared with the previously used floating rate ranking method in our hospital， the application of the DMDC model significantly improved the scope and depth of drug monitoring， with the monitored drug varieties greatly expanded from about 50 to 179， and the monitoring dimensions increased from a single dimension to three. CONCLUSIONS The DMDC model based on SPC is effective and feasible，suitable for monitoring drug varieties with stable monthly consumption.