Improving data quality from routine clinical appointments - Development of a minimum dataset for traumatic dental injuries in children and adolescents

Background/Aims It is currently difficult to evaluate the success or not of treatment for dental injuries due to poor recording of diagnostic and treatment codes in clinical dentistry. A minimum dataset comprises a standardised minimum set of outcomes along with a specified outcome measurement instrument, to allow aggregated use of data from routine clinical care appointments. This study aimed to determine which outcomes should be included in a minimum dataset for traumatic dental injuries (TDI). Materials and Methods This is a three-stage sequential, mixed-methods study, using evidence-based best practice for dataset development. Normalisation process theory informed the development of the study protocols. In Stage 1, semi-structured interviews with patients and their parent or guardian were undertaken to identify outcomes of importance to patients. In Stage 2, an online Delphi survey was undertaken to identify outcomes of importance to clinicians. In Stage 3, a National Consensus Meeting was undertaken involving patient representatives, clinicians and other stakeholders, to agree which outcomes should be included in the minimum dataset. Results Stage 1: Eleven participants were recruited, five children and six parents. Two key themes emerged from the analysis—communication and aesthetics. In Stage 2, 34 dentists were recruited, and 32 completed both rounds of the survey (97% retention). Most outcomes were deemed by participants to be of ‘critical importance’, with three outcomes deemed ‘important’ and none to be ‘of limited importance’. In Stage 3, 15 participants took part in the consensus meeting. Participants agreed that the dataset should comprise a list of clinician-important outcomes (pulp healing, periodontal healing, discolouration, tooth loss) and a list of patient-important outcomes (communication, aesthetics, pain, quality of life). Conclusion A Minimum Dataset for TDI has been developed using a robust and transparent methodology.