Using AI to Review Dental Records to Improve Quality Outcomes of Care
Artificial Intelligence (AI) has been rapidly transforming various aspects of our lives, and healthcare is no exception. The field of dentistry, too, is poised to benefit greatly from the integration of AI technologies. I believe one of the most promising uses of this technology is to automate chart review, a process that is currently manual and laborious.
AI has already started making its mark in dentistry through applications such as diagnostic tools and image analysis. There are several companies that have created AI-powered software that can analyze dental radiographs and identify possible tooth decay or periodontal issues. Dentists (and third party payers) can use this to catch a condition that might have been missed, to confirm a diagnosis that isn’t immediately obvious, and to educate and build trust with patients.
Challenges of Manual Chart Review for Quality Outcomes of Care
A chart review is a proactive means of provider quality control. Some standard elements of a patient chart that a reviewer might look for include:
- Completeness of a progress note
- Updated and thorough periodontal charting
- Quality of recent restorations (e.g. closed margins on a crown)
- Calibration of diagnosis (e.g. missed decay)
- Calibration of treatment planning (e.g. too aggressive or too conservative)
The main challenge, of course, is that a clinician reviewer must take the time to manually sort through charts for this information. Additional concerns with this process are human error of the reviewer, potential bias of the reviewer, and a small sample size of reviewed charts.
How AI Can Improve Chart Review and Quality Assessment
AI has the potential to overcome the aforementioned obstacles to chart reviews. By leveraging machine learning algorithms, AI can analyze large volumes of patient data, including images, treatment plans, and clinical notes, in a fraction of the time it takes a human to do so. The process becomes automated and independent from human error and bias. We can also capture far larger samples of charts, even every chart in the practice, for inclusion in the review.
But that is only the beginning. This technology could also help reviewers identify patterns and correlations that may be difficult to detect through manual analysis, leading to more accurate and comprehensive quality assessments. For example, AI can automatically cross-reference patient records with relevant research and guidelines, ensuring that the recommended treatments align with the latest evidence-based practices. Additionally, AI can compare a patient’s treatment plan and outcomes to those of similar patients, providing valuable insights into the effectiveness of the chosen approach. In this way, AI can serve as a powerful tool to support dentists in making data-driven decisions and improving patient care.
Next Steps and Challenges for AI Chart Review
The potential of AI in dental chart review is exciting, however there are still challenges to overcome before it becomes a reality. Data privacy and security are paramount concerns, as the sensitive nature of healthcare information requires strict adherence to regulations such as HIPAA. Dental professionals and technology companies must develop standardized protocols for data collection and sharing. Protected Health Information (PHI) must not become more exposed in the chart review process, nor can it become the property of a third-party company.
We’ll also need to ensure the accuracy and reliability of AI algorithms, as mistakes in analysis could have serious consequences for patient care. Fortunately we’ll still have clinician reviewers at the helm to “review the reviews” and check for accuracy and reliability. The clinician reviewer will not be replaced, rather they will wield this AI tool to multiply their productivity and evolve their levels of chart analysis.
We are on the threshold of a future where AI-driven chart review becomes a standard part of dental practice. We have the tools, we just need the rules on how to use them.
