An artificially intelligent (or algorithm-enhanced) electronic medical record in orofacial pain. (November 2021)

• Record Type: Journal Article
• Authors: Paulina Vistoso Monreal, Anette; Veas, Nicolas; Clark, Glenn
• Is Part Of: Japanese dental science review. Volume 57(2021)
• Page Start: 242
• Page End: 249
• Publication Date: 2021-11
• Subjects: EMR electronic medical record -- OFP orofacial pain -- ML machine learning -- D1 internal derangement with reduction (DDWR) -- D3 masticatory myalgia and/or cervical myalgia -- D4 arthralgia/capsulitis -- D5 TMJ arthritis; Orofacial pain -- Prediction -- Algorithms -- Modeling -- Machine learning; Dentistry -- Periodicals; Dentistry -- Japan -- Periodicals; 617.6005
• DOI: 10.1016/j.jdsr.2021.11.001
• Languages: English
• ISSNs: 1882-7616

Abstract

Abstract: This review examines how a highly structured data collection system could be used to create data-driven diagnostic classification algorithms. Some preliminary data using this process is provided. The data collection system described is applicable to any clinical domain where the diagnoses being explored are based predominately on clinical history (subjective) and physical examination (objective) information. The system has been piloted and refined using patient encounters collected in a clinic specializing in Orofacial Pain treatment. In summary, whether you believe a branching hybrid check-box based data collection system with built-in algorithms is needed, depends on your individual agenda. If you have no plans for data analysis or publishing about the various phenotypes discovered and you do not need pop-up suggestions for best diagnosis and treatment options, it is easier to use a semi-structured narrative note for your patient encounters. If, however, you want data-driven diagnostic and disease risk algorithms and pop-up best-treatment options, then you need a highly structured data collection system that is compatible with machine learning analysis. Automating the journey from data collection to diagnoses has the potential to improve standards of care by providing faster and reliable predictions.