Bridging the Anesthesia Digital Data Gap in Low-Middle-Income Countries: Computer Vision-Ready Paper Health Records

Abstract

Abstract Introduction Surgical mortality is the third leading cause of death globally, with mortality rates in Africa double those of high-income countries despite patients being younger and undergoing lower-risk procedures. One of the contributors to poor outcomes in low- and middle-income countries (LMICs) is the lack of digital data, which is essential for quality improvement, audit and feedback systems, and early warning track-and-trigger systems. Due to limited financial resources, paper health records remain the standard in LMICs, making readily accessible digital data an urgent priority. This study builds on our previous work in computer vision-based digitization of smartphone-captured anesthesia records by developing a standardized, computer vision-ready anesthesia paper record. Designed for optimal digitization, this record will align with the Minimum Dataset for Surgical Patients in Africa guidelines. Methods The standardized, computer vision-ready anesthesia paper chart was developed with input from anesthesia experts in LMICs and data scientists. Key adaptations designed to facilitate accurate computer vision digitization included replacing traditional free-text entries with predefined categorical checkboxes, pre-printing the name of commonly used medications, supplementing handwritten medication names with numeric codes, and structuring input fields to improve computer vision digitization accuracy. Prior computer vision software was further iterated to improve digitization accuracy for the new standardized chart. Performance of the updated software running on the new computer vision-ready paper chart was then evaluated by comparing the software output to the human-annotated ground-truth data measuring both detection and interpretation accuracy. Results The training dataset consisted of thirty-three standardized, computer vision-ready anesthesia paper charts completed using synthetic data by a group of ten anesthesia providers. Five charts were reserved for validation, while the test dataset consisted of nine charts that were not used for any training or validation purposes. Updated computer vision software demonstrated high detection accuracy for vital signs: systolic blood pressure (93%), diastolic blood pressure (94%), and heart rate (93%), all physiological indicators (100%), and checkboxes (99%). The mean average error for inferring values from model detections were low: systolic (1.98mmHg), diastolic (1.13mmHg), heart rate (3.8/bpm), oxygen saturation (0.19%), end tidal carbon dioxide (0.65 mmHg), inspired oxygen concentration (2.48%). The accuracy for determining which checkboxes were marked vs. unmarked was 99%. Conclusion This study confirms the feasibility and accuracy of a standardized, computer vision-ready anesthesia chart that can be deployed in LMICs to facilitate digital data access.

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