Abstract
Objectives: Quality measurement (QM) in primary dental care (PDC) has traditionally relied on manual audit of record cards and administrative claims data. The emergence of data driven QM using data automatically captured from electronic health records and administrative claims may provide the General Dental Practitioner (GDP) with contemporaneous quality data at chairside. This study aims to assess the perceptions of GDPs and patients of automatic QM, how this may impact on care delivery and the incentives and barriers to the use of such systems
Methods: Purposive sampling was used to identify GDPs and patients in PDC. 20 Semi-structured interviews were conducted with participants in the United Kingdom (Dentists n=10, Patients n=10). Interviews were transcribed verbatim, coded using nVivo and analysed using thematic analysis using a constant comparative approach.
Results: Patients and dentists agreed that quality improvement in PDC was very important. Patients wanted to see the quality score of their dentist and compare this with the quality of care delivered by different dentists. For dentists, themes of conducting burdensome audit processes and data collection were noted. The savings in time and effort compared to traditional QM were seen as benefits of potential automatic QM. When exploring barriers for automatic QM, themes of concealed or overt surveillance, remuneration, the impact of patient demographics on potential quality scores, unintended behaviour change of dentists and potential impact on dentist reputation emerged. No concerns with anonymised data being used for QM were raised.
Conclusions: Despite overall positive perceptions of automatic QM, a number of barriers to its use have been identified. Limiting the effects of these barriers must be considered in the introduction of any data driven QM system. Imposing quality measures on dentists by a top down approach risks eroding trust of dentists and stimulating unintended behaviour changes.
Methods: Purposive sampling was used to identify GDPs and patients in PDC. 20 Semi-structured interviews were conducted with participants in the United Kingdom (Dentists n=10, Patients n=10). Interviews were transcribed verbatim, coded using nVivo and analysed using thematic analysis using a constant comparative approach.
Results: Patients and dentists agreed that quality improvement in PDC was very important. Patients wanted to see the quality score of their dentist and compare this with the quality of care delivered by different dentists. For dentists, themes of conducting burdensome audit processes and data collection were noted. The savings in time and effort compared to traditional QM were seen as benefits of potential automatic QM. When exploring barriers for automatic QM, themes of concealed or overt surveillance, remuneration, the impact of patient demographics on potential quality scores, unintended behaviour change of dentists and potential impact on dentist reputation emerged. No concerns with anonymised data being used for QM were raised.
Conclusions: Despite overall positive perceptions of automatic QM, a number of barriers to its use have been identified. Limiting the effects of these barriers must be considered in the introduction of any data driven QM system. Imposing quality measures on dentists by a top down approach risks eroding trust of dentists and stimulating unintended behaviour changes.
Original language | English |
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Publication status | Published - 2020 |
Event | 2020 IADR/AADR/CADR General Session & Exhibition - Washington, United States Duration: 18 Mar 2020 → 21 Mar 2020 |
Conference
Conference | 2020 IADR/AADR/CADR General Session & Exhibition |
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Country/Territory | United States |
City | Washington |
Period | 18/03/20 → 21/03/20 |