ABSTRACT Objectives Public dental organizations require effective and evidence‐based support for clinical and business decision‐making. A pilot business intelligence system has been proposed for a public dental service in New South Wales, Australia, to aid with this decision‐making. To inform the design and development of this system, we aim to identify the key performance indicators and report features perceived to be the most valuable to the dental service decision‐makers. Method A self‐administered survey employing best–worst scaling with balanced incomplete block designs was sent to all eligible decision‐makers in a Local Health District public dental service in New South Wales, Australia. Participants ranked seven areas of information and five report features. Quantitative rankings were complemented with free‐text comments. Results Nine of the possible 12 participants (75%) completed the survey. The results reported “Patient demand” as the most important area of information for inclusion in the business intelligence system, with “patient demographics” ranked the lowest. For report features, the most important features were reported to be “readability” and “level of detail”. All participants reported that they were expected to use computerized reports as part of their role, with four (44%) participants reporting accessing computerized reports more than 100 times in their previous 30 working days. Conclusion The novel pilot study applies best–worst scaling for prioritizing key performance indicators among public dental decision‐makers at a Local Health District within New South Wales and underscores the established role of computerized reports in decision‐making processes. These findings will directly guide the design and implementation of a pilot business intelligence reporting system, aiming to enhance data accessibility and accelerate evidence‐based decisions within a public dental service to ultimately enhance service efficiency. Future evaluation will be needed to assess the adoption rates of this system and its impact on operational efficiency and patient care outcomes.
Walters et al. (Fri,) studied this question.