In many low- and middle-income countries, healthcare providers continue to rely heavily on paper-based medical records for data collection. This reliance stems from multiple barriers, including the high costs and infrastructure demands associated with implementing and maintaining electronic medical record systems and automated data capture technologies.
Consequently, clinical data documented on paper remains inaccessible in digital form, in contrast to the availability of real-time digital data common in high-income settings. This lack of digitization presents a significant obstacle for clinicians, researchers, and quality improvement initiatives, impeding efforts to leverage data for improving patient care and outcomes.
Working within a larger team, researchers Matthew Beck, Charbel Marche, and Hannah Valenty undertook a capstone project, as a part of their Master's in Data Science curriculum, that created computer vision software for digitizing surgical flowsheets. These challenges were addressed with the creation of the ChartExtractor program, which automates the digitization of paper anesthesia charts.
In their study, the researchers introduced several enhancements to the system through the integration of advanced image processing and machine learning techniques. Unlike previous methods, this work combines thin plate splines and nonrigid point registration to enhance image alignment.
Furthermore, they leveraged multiple clustering techniques to accurately label the axes of intraoperative vital sign charts, making it easier to extract
the corresponding values. Lastly, they used hyperparameter tuning to select smaller, more accurate object detection models, reducing inference time by 54% and deployment size by 183 MB.
These advancements lay the groundwork for the EQUAL Anaesthesia mobile health platform, a low-cost solution designed to support digital data collection and clinical decision-making in resource-constrained healthcare settings.
Researchers: Matthew Beck, Charbel Marche, Hannah Valenty
Sponsors: JRyan Folks
Advisors: Mai Dahshan