Abstract：Objective To investigate how to mine a large scale dataset of inpatient discharge data based on Access. Methods Inpatient
discharge data in one city from 2002 to 2013 was collected. After the data cleaning and disease recoding, hospitalization measures
were analyzed by year using the form and VBA programming in Access. Hospitalizations of chronic obstructive pulmonary disease
(COPD) were analyzed as an example. Results It took totally 267 s to analyze the whole dataset with almost 6 million records. From
2002 to 2013, the number of hospitalized patients with COPD increased, and the length of stay decreased with the increment of the
charge per stay, while the readmission rate within 30 days had no significant change trend. The hospitalization days and costs of
COPD patients aged over 60 years were higher than those of other age groups. Conclusion It is feasible to analyze the time trends of
hospitalization based on inpatient discharge data during a long period of time. The results can provide valuable information for health
care and hospital authorities on hospital management decisions, epidemiological surveillance and health economics, etc.
王妮，陈婕卿，刘文艳，陈卉. 基于Access的大规模住院病案首页数据挖掘[J]. 中国医疗设备, 2017, 32(10): 126-128.
WANG Ni, CHEN Jieqing, LIU Wenyan, CHEN Hui. Access-Based Data Mining of Large-Scale Database of Hospital Discharge Data. China Medical Devices, 2017, 32(10): 126-128.
