What AI Reveals: Qualitative Analysis of Dental Complaints

According to Patient complaints in dentistry: A qualitative meta-synthesis, qualitative evidence shows that dental complaints are signals of threats to trust, patient agency, relational security, and professional obligations, not merely isolated reports of dissatisfaction.

The study published on November 12, 2026, synthesized 19 qualitative studies and identified four connected domains: impeded access to dental care, unsatisfactory communication and attitudes, deficient clinical treatment, and unethical or unprofessional conduct.

• The researchers screened 857 studies and included 19 studies after full-text assessment in the 2026 meta-synthesis.
• Deficient clinical treatment appeared in all 19 included studies, representing 100% of the qualitative evidence base analyzed by the authors.
• Unethical and unprofessional conduct appeared in 17 of 19 studies, or 89%, according to the authors' meta-summary published in November 2026.
• The source study found that all four complaint domains appeared together in 11 of the 19 included studies.
• AI-enabled thematic analysis can help health researchers organize large complaint corpora, compare segments, and trace themes back to supporting excerpts, while human researchers remain responsible for interpretation and ethics.

The 2026 PLOS One study used qualitative meta-synthesis to integrate findings from previously published qualitative research rather than calculating population-level complaint rates.

According to Snyman, Postma, and Van Staden, the review searched MEDLINE, PsycINFO, PubMed, Scopus, Web of Science, and CINAHL in September 2024 and updated the search on August 26, 2026. The researchers used the six-step approach of Sandelowski and Barroso, Hoon's higher-order case framework, reflexive thematic analysis, and PRISMA reporting.

The authors coded qualitative findings, including participant quotations and interpreted themes, while excluding descriptive statistics and quantitative complaint categories from their inductive coding. Two reviewers compared interpretations and refined themes through discussion.

The method matters for AI-enabled qualitative research because automated systems can support document ingestion, coding, retrieval, and comparison, but the source study demonstrates why theme boundaries require contextual judgment. For example, a complaint about a poor treatment outcome could indicate deficient treatment or unethical conduct depending on whether the central concern was clinical quality, concealment, refusal of care, or accountability.

One patient quoted in the source study described access failure in concrete terms: "give you a number to call…and you never get through… I tried to make an appointment, phone kept ringing out." The quotation shows why complaint analysis must preserve patient language instead of reducing every complaint to a generic category.

Dental quality teams should treat complaints as patient-generated evidence about how care systems function, not only as incidents to close.

The 2026 PLOS One authors interpret access complaints as possible signals of organizational responsiveness problems, communication complaints as possible failures in person-centered care and informed consent, and conduct complaints as possible disruptions in professional accountability.

A patient quoted in the study summarized the communication problem this way: "the sort of person who got you into the surgery, said ‘Hi’, got the drill out and started almost straightaway." For dental leaders, this type of narrative can point toward training needs in explanation, active listening, consent, and expectation management.

The source study also found that the four domains appeared across varied complaint sources, including online reviews, regulatory complaints, and litigation or malpractice-related claims. Cross-source comparison can help teams distinguish themes that recur across contexts from concerns that are specific to a complaint mechanism.

AI-enabled qualitative research can support this work by grouping complaints into hierarchical themes, comparing public and regulatory feedback, identifying co-occurring concepts such as cost and access, and retrieving representative excerpts for review. AI outputs should support quality improvement decisions rather than replace clinical, ethical, or patient-safety judgment.

Because the source concerns healthcare experiences, researchers should use anonymization, secure data handling, and non-diagnostic interpretation. Complaint analysis can inform service improvement, but it cannot establish whether an individual clinician committed negligence.

Qualitative analysis of dental complaints shows that patient dissatisfaction often reflects connected failures in access, communication, clinical treatment, and professional conduct.

The 2026 PLOS One meta-synthesis demonstrates the value of moving beyond complaint counts to interpret the meanings patients attach to care experiences.

AI-enabled qualitative research can make that process more efficient by helping teams synthesize large collections of complaints, compare segments, and retain evidence trails for human review.

Dental researchers and quality teams can begin by consolidating complaint documents and open-ended feedback, defining an auditable coding framework, and reviewing AI-assisted themes against original patient excerpts. Try Evidano for free to analyze qualitative feedback with AI.