Supplementary Material for: Developing and Testing Electronic Health Record-Derived Caries Indices

Caries indices, the basis of epidemiologic caries measures, are not easily obtained in clinical settings. This study’s objective was to design, test, and validate an automated program (Valid Electronic Health Record Dental Caries Indices Calculator Tool [VERDICT]) to calculate caries indices from an electronic health record (EHR). Synthetic use case scenarios and actual patient cases of primary, mixed, and permanent dentition, including decayed, missing, and filled teeth (DMFT/dmft) and tooth surfaces (DMFS/dmfs) were entered into the EHR. VERDICT measures were compared to a previously validated clinical electronic data capture (EDC) system and statistical program to calculate caries indices. Four university clinician-researchers abstracted EHR caries exam data for 45 synthetic use cases into the EDC and post-processed with SAS software creating a gold standard to compare the ­VERDICT-derived caries indices. Then, 2 senior researchers abstracted EHR caries exam data and calculated caries indices for 24 patients, allowing further comparisons to VERDICT indices. Agreement statistics were computed among abstractors, and discrepancies were resolved by consensus. Agreement statistics between the 2 final-phase abstractors and the VERDICT measures showed extremely high concordance: Lin’s concordance coefficients (LCCs) >0.99 for dmfs, dmft, DS, ds, DT, dt, ms, mt, FS, fs, FT, and ft; LCCs >0.95 for DMFS and DMFT; and LCCs of 0.92–0.93 for MS and MT. Caries indices, essential to developing primary health outcome measures for research, can be reliably derived from an EHR using VERDICT. Using these indices will enable population oral health management approaches and inform quality improvement efforts.

龋病指数（Caries indices）作为流行病学龋病测量的核心基础，在临床场景中难以便捷获取。本研究旨在设计、测试并验证一款自动化程序——电子健康记录牙科龋病指数验证计算工具（Valid Electronic Health Record Dental Caries Indices Calculator Tool [VERDICT]，以下简称VERDICT），以从电子健康记录（Electronic Health Record, EHR）中自动计算龋病指数。研究将乳牙列、混合牙列与恒牙列的合成使用场景及实际患者病例（涵盖龋失补牙数（DMFT/dmft）与龋失补牙面数（DMFS/dmfs）相关数据）录入EHR系统。将VERDICT的计算结果与此前经过验证的临床电子数据采集（Electronic Data Capture, EDC）系统及统计程序的龋病指数计算结果进行对比。四名大学临床研究者将45个合成使用场景的EHR龋病检查数据录入EDC系统，并通过SAS软件开展后处理，以此构建金标准，用于与VERDICT得出的龋病指数进行比对。随后，两名资深研究者提取了24名患者的EHR龋病检查数据并计算龋病指数，以进一步与VERDICT的计算结果进行对比。研究计算了不同提取者间的一致性统计量，并通过协商一致解决数据分歧。最终阶段两名提取者与VERDICT计算结果之间的一致性统计结果显示极高的契合度：dmfs、dmft、DS、ds、DT、dt、ms、mt、FS、fs、FT及ft的Lin一致性系数（Lin’s Concordance Coefficients, LCCs）均大于0.99；DMFS与DMFT的LCCs大于0.95；MS与MT的LCCs介于0.92至0.93之间。龋病指数是研发研究中主要健康结局测量指标的关键要素，可通过VERDICT从EHR中可靠获取。利用此类指数可推动人群口腔健康管理策略的实施，并为质量改进工作提供科学参考依据。