Real-time database drawn from an electronic health record for a thoracic surgery unit: high-quality clinical data saving time and human resources†

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The aim of the present study was to verify whether the implementation of an electronic health record (EHR) in our thoracic surgery unit allows creation of a high-quality clinical database saving time and costs.


Before August 2011, multiple individuals compiled the on-paper documents/records and a single data manager inputted selected data into the database (traditional database, tDB). Since the adoption of an EHR in August 2011, multiple individuals have been responsible for compiling the EHR, which automatically generates a real-time database (EHR-based database, eDB), without the need for a data manager. During the initial period of implementation of the EHR, periodic meetings were held with all physicians involved in the use of the EHR in order to monitor and standardize the data registration process. Data quality of the first 100 anatomical lung resections recorded in the eDB was assessed by measuring the total number of missing values (MVs: existing non-reported value) and inaccurate values (wrong data) occurring in 95 core variables. The average MV of the eDB was compared with the one occurring in the same variables of the last 100 records registered in the tDB.


A learning curve was constructed by plotting the number of MVs in the electronic database and tDB with the patients arranged by the date of registration.


The tDB and eDB had similar MVs (0.74 vs 1, P = 0.13). The learning curve showed an initial phase including about 35 records, where MV in the eDB was higher than that in the tDB (1.9 vs 0.74, P = 0.03), and a subsequent phase, where the MV was similar in the two databases (0.7 vs 0.74, P = 0.6). The inaccuracy rate of these two phases in the eDB was stable (0.5 vs 0.3, P = 0.3). Using EHR saved an average of 9 min per patient, totalling 15 h saved for obtaining a dataset of 100 patients with respect to the tDB.


The implementation of EHR allowed streamlining the process of clinical data recording. It saved time and human resource costs, without compromising the quality of data.

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