Project: "Data Gap": Challenge for Trauma Quality Improvement Strategies
Organization: Fundación Trauma
Status: delivered
Introduction and goal
Implementing trauma quality improvement programs requires high-quality data to identify opportunities for improvement. This research aims to evaluate the quality of data essential to identify opportunities or improvements in the care processes registered in the hospitals that are part of the Province of Buenos Aires Trauma Network in 2018.
Materials and methods
A retrospective observational study analyzing the quality of 48 fields pertaining 4,489 cases with trauma medical history entered in the trauma registry (TR) or undación trauma (FT) in 13 hospitals in 2018. The data were distributed and analyzed in 6 categories: trauma event; prehospital care; patient’s data; hospital admission and care process; vital signs, comorbidities and injuries; and trauma indexes and scores.
Results
When analyzing the 48 fields, an average completeness of 64% was found. Of the 13 surveyed hospitals, the one with the best data quality had a completeness average of 92%, and the one with the lowest quality had an average of 58%. Considering the average o completeness, the categories are distributed in the following order: (1) patient´s data, 97.5%; (2) indexes and scores, 71.2%; (3) trauma event, 68.6%; (4) vital signs, comorbidities, and injuries, 66.5%; (5) hospital admission and care process, 60%; and (6) prehospital care, 38.6%.
Conclusions
Complete data distribution presents a wide variation between the analyzed categories, with the patient’s data and the injuries at one end and the procedures and complications at the other. In general terms, we can say that even though there is information available to work on implementing and monitoring quality improvement strategies, the quality of the information or the development of scores and improvement strategies presents a challenge. In this sense, it is necessary to have specific strategies to improve the quality of information in medical records.
Additional resources
Methodologies and tools deployed during this project
SQL
RStudio
Excel