Bridging the Anesthesia Digital Data Gap in Low-Middle-Income Countries: Computer Vision-Ready Paper Health Records
Rattachement africain : us, Rwanda, Kenya, Afrique du Sud. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
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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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Bridging the Anesthesia Digital Data Gap in Low-Middle-Income Countries: Computer Vision-Ready Paper Health Records
- Date Crossref
- 12/02/2025
- Éditeur
- openRxiv
- Type
- posted-content
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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University of Virginia pays non établi dans la noticeUniversité ou école supérieure
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University of Kigali University of Kigali, Rwanda (code pays fourni par la source)Université ou école supérieure
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Centre Hospitalier Universitaire de Kigali Kigali, Rwanda (code pays fourni par la source)Établissement de santé
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Kijabe Hospital Kenya (code pays fourni par la source)Établissement de santé
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University of Cape Town University of Cape Town, Afrique du Sud (code pays fourni par la source)Université ou école supérieure
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Community Connections pays non établi dans la noticeOrganisation à but non lucratif
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Sefako Makgatho Health Sciences University Afrique du Sud (code pays fourni par la source)Université ou école supérieure
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University of Rwanda and University Teaching Hospital Kigali, Rwanda (pays nommé en fin d’affiliation)Université ou école supérieure
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Anesthesia Connections pays non établi dans la noticeInstitution
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Sefago Makgatho health Sciences University pays non établi dans la noticeUniversité ou école supérieure
University of Virginia, University of Kigali (University of Kigali, Rwanda) et Centre Hospitalier Universitaire de Kigali (Kigali, Rwanda), avec 7 autres affiliations. Pays d’affiliation : Rwanda, Kenya, Afrique du Sud.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.