7. Data & Digital
Data, Digital and Oral Health Information
The New Zealand Dental Association position is:
That New Zealand requires surveillance and health information systems to provide timely and relevant feedback on oral health to decision-makers.
That efficient and effective integrated health information systems which include oral health, are required to improve clinical care for dental and oral health patients and to inform oral health planning, management and policymaking across the life course.
That integration of electronic patient records for oral health with wider health information systems, including medical and pharmacological records, and across public and private providers of health care, can facilitate both improvements to people-centered care and population-level health monitoring.
That strong regulation of data protection and confidentiality, including clinical governance, is required.
That artificial intelligence methodologies have the potential to improve individual patient care, population oral health monitoring, and policy and programme development. However, transparency and clinical governance protocols are necessary to ensure the accuracy and veracity of system responses.
Population oral health information and health surveillance systems in New Zealand are currently a patchwork from a variety of sources. Limited surveillance information is provided on 5-year-old and Year 8 child oral health status and access to care in publicly-funded child and adolescent programmes. While strong time series data are available in these datasets, there is increasing concern that, as service coverage declines, it has diminishing accuracy.
Dentistry and other oral health workforces are highly regulated under the Health Practitioners Competence Assurance Act (2003). As a result, periodic supply-side information on the oral health workforce is available through Dental Council workforce reports.
However, very limited other population-level information on adult oral health, services, workforce, demand and risk factors is available. This has led to assumptions that are not necessarily correct, and difficulties in planning and monitoring oral health. A recent example is emerging information by researchers at the Universities of Canterbury and Otago, who are investigating water supply mapping and community water fluoridation coverage. This research has revealed concerning periods of less-than-optimal supply of fluoridated water and an overestimation of the coverage with community water fluoridation for Māori.
Looking ahead, digital transformation, powered by interoperable data and secure platforms, is reshaping healthcare and many aspects of daily life.
The use of shared data will enable targeted interventions that may improve both individual and community health. As the industry evolves, new roles and functions will emerge.
Oral health professions encourage the integration of electronic patient records for oral health with wider health information, including medical and pharmacological records, and the sharing of data across public and private providers of health care. Greater connectivity can facilitate both improvements to people-centred care and population-level health monitoring. As systems develop, strong regulation of data protection and confidentiality will be required.
Artificial Intelligence (AI) methodologies, including both generative and predictive AI, are being increasingly applied to optimise health and health service delivery. AI has the potential to lower barriers for timely and equitable access to oral healthcare, increase oral health awareness, support clinical decision making and increase treatment compliance. Properly trained and deployed, with appropriate and strong clinical governance systems, predictive AI can facilitate improved health outcomes in the community at the patient, practice, and population health levels. Without appropriate transparency and governance, there are multiple potential consequences of misuse, including adverse clinical, financial, and/or reputational outcomes, data privacy and security breaches, misuse of time and resources, unintended inequities, and loss of trust in healthcare professionals and organisations.
At present, generative AI tools using deep learning algorithms to identify patterns in large datasets can produce convincing and apparently authoritative content, but often without the ability for verification or validation, and with unclear governance protocols. If used in clinical or population-health decision-making without verification of the accuracy and veracity of the responses, there is substantial potential to cause harm to people and patients.