Developing and Testing Electronic Health Record-Derived Caries Indices
| Autor: | Shwetha V. Kumar, Ram Vaderhobli, Joshua B. Even, Larry Jenson, Stuart A. Gansky, Elizabeth Mertz, Joel M. White, William Santo, Suhasini Bangar, Elena Blaga, Ryan Brandon, Aubri Kottek, Trey Guy, Joanna Mullins |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Male
medicine.medical_specialty Caries detection Dental informatics/bioinformatics Electronic data capture Epidemiology diagnosis Concordance Population Oral health Dental Caries Dental informatics Article 03 medical and health sciences Automation 0302 clinical medicine prevention Electronic health record Clinical Research Primary health Statistics medicine Dentition Permanent Electronic Health Records Humans 030212 general & internal medicine Dental/Oral and Craniofacial Disease education General Dentistry education.field_of_study business.industry DMF Index 030206 dentistry Gold standard (test) bioinformatics Dentition Permanent Infectious Diseases Outcomes research Dentistry Caries detection/diagnosis/prevention Female Electronic dental records business Algorithms |
| Zdroj: | Caries Res Caries research, vol 53, iss 6 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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