Dental Quality Improvement

Sometimes obtaining data is not the problem. The scale of information available can be as much of an issue as a lack of data. Modern dental systems generate data on such a scale that it can be difficult to separate the wheat from the chaff. Knowing your aims and how you want to use the information is at least as important a consideration as data availability.

When deciding what you need, it is worth thinking about what information you need for different activities. Quality activities fall into four main, overlapping, groups:

• Quality Assurance
• Quality Planning
• Quality Control
• Quality Improvement

Quality Assurance – the sum of all the actions taken to assure quality – results from the work to plan, control, and improve quality. (For more on this, see our book, Quality Improvement and Audit in Dentistry.)

Joo Wan James Kim and colleagues set out to assure the work of a dental oncology service in Toronto, Canada. Their service undertakes dental examinations and any required dental treatment for patients before the start of cancer therapy. Both timing and quality of care are important to them and to their patients.

They don’t describe the internal processes they use, but the team uses a quality control process to identify improvement targets, and then applies standard quality improvement approaches to resolve the problem.

As noted above, the team started by identifying their aims, which were:

• Ensure efficient clinic flow
• Prevent potential oral complications associated with cancer therapy
• Identify barriers to improve patient access to dental care

From this, they looked for relevant standards they could apply to their work. Where standards did not exist, they agreed their own and used them as checks on their current performance. This is similar to the work undertaken at a baseline audit where current metrics are compared to agreed targets.

They decided to focus on data on:

• Clinic Volume Data – number of treatments and overall activity
• Cancer Demographic Data – cancer mix
• Clinic Flow Data – waiting times, and time to completion of treatment
• Dental Treatment Data – type and number of dental treatments provided

The volume, flow and treatment data have a clear relation to the aims. The cancer type data was used to justify funding, and to track any sudden changes that might indicate changing demand, or changes in referral patterns.

Lengthening waits for treatment resulted from staffing problems, and the service used their data to argue for, and receive, an increase in staff numbers. Information on time for treatment completion was also fed back to other parts of the service, so that the dental component could be incorporated into the overall treatment pathway.

By focusing their improvement work on capacity and flow, the service succeeded in reducing time to treatment completion from about 4.5 days to two days over a four year period.

This is a good example of a service deciding which data requires their attention and then using it to drive improvement. Whether on a small or large scale, the same considerations are relevant in all dental services.

Reference

Kim, Joo Wan James, Shahad Joudah, Terry Michaelson, Walter G. Maxymiw, Christopher M. K. L. Yao, Ezra Hahn, Andrew Hope, et al. 2026. “Quality Assurance and Improvement in Dental Oncology.” Supportive Care in Cancer 34 (9). https://doi.org/10.1007/s00520-026-11077-z.