Dental researchers, healthcare quality teams, and practice leaders need more than complaint counts to understand why patients lose trust. Qualitative analysis of patient complaints reveals the meanings behind dissatisfaction, including concerns about access, communication, clinical treatment, and professional conduct. The November 12, 2026, PLOS One study by Lorraine Snyman, Thomas Corne Postma, and Werdie Van Staden shows how a structured synthesis can turn scattered narratives, regulatory complaints, litigation records, and online reviews into actionable themes. This article explains the study’s findings and refracts them through the lens of AI-enabled qualitative research. Readers will see which themes appeared across the evidence, how researchers conducted the synthesis, and how AI can accelerate coding, comparison, and interpretation while keeping human judgment central.
Key Takeaways
According to the PLOS One qualitative meta-synthesis published on November 12, 2026, patient complaints in dentistry clustered into four connected domains: impeded access, unsatisfactory communication and attitudes, deficient clinical treatment, and unethical or unprofessional conduct.
The study shows that qualitative analysis of patient complaints can reveal threats to trust, patient agency, relational security, and professional obligations that complaint frequency data alone cannot explain.
- Snyman, Postma, and Van Staden screened 857 studies and included 19 studies in their qualitative meta-synthesis in 2026.
- The authors found deficient clinical treatment in 19 of 19 studies, or 100%, and unethical or unprofessional conduct in 17 of 19 studies, or 89%.
- The researchers reported that all four complaint domains appeared together in 11 of the 19 included studies.
- The evidence base included studies from multiple settings and countries, including the United States, Australia, the United Kingdom, Saudi Arabia, Denmark, France, the Netherlands, Malaysia, Turkey, Finland, Norway, and South Africa.
- The study’s search began in September 2024 and was updated on August 26, 2026, showing why research teams need reproducible workflows for continuously changing evidence.
How Qualitative Analysis of Patient Complaints Worked
According to Snyman, Postma, and Van Staden, the study used a six-step qualitative meta-synthesis based on Sandelowski and Barroso, treated the included literature as a higher-order case, and analyzed findings through reflexive thematic analysis.
The researchers searched MEDLINE, PsycINFO, PubMed, Scopus, Web of Science, and CINAHL, then supplemented database searches with Google Scholar and reference-list screening. After screening, 29 studies were retrieved for full-text review and 19 studies met the inclusion criteria.
The authors restricted their synthesis dataset to qualitative findings, including interpreted themes and participant quotations. The researchers explicitly excluded descriptive statistics, numerical summaries, and quantitative complaint categories so that pre-existing numerical frameworks would not determine the inductive coding.
The researchers used semantic coding to capture explicit complaints and latent coding to interpret underlying relational and ethical meanings. For example, scheduling problems, poor availability, and weak responsiveness were interpreted together as the broader theme of impeded access to care.
One patient quoted in the study described access problems as follows: "...give you a number to call...and you never get through... I tried to make an appointment, phone kept ringing out. Tried several times..." The quotation illustrates why qualitative evidence adds context that a complaint count cannot provide.
Dentistry Complaint Findings Snapshot
| Study metric | Value | What the finding means |
|---|---|---|
| Studies identified | 857 studies in searches conducted through August 26, 2026 | The evidence base began broad before qualitative screening narrowed the dataset. |
| Studies included | 19 studies in the 2026 synthesis | The final synthesis focused on formally documented qualitative complaint evidence. |
| Deficient clinical treatment | 19 of 19 studies, 100% | Clinical quality, pain, treatment outcomes, aftercare, and safety were present across the entire included corpus. |
| Unethical and unprofessional conduct | 17 of 19 studies, 89% | Patients also interpreted complaints through the lens of integrity, accountability, consent, and fairness. |
| All four domains together | 11 of 19 studies | Access, communication, treatment, and conduct frequently formed an interconnected experience rather than isolated issues. |
| Initial screening agreement | 97%, Cohen kappa 0.47 | Reviewers showed high percentage agreement during title and abstract screening before discussion. |
| Full-text screening agreement | 93%, Cohen kappa 0.85 | Agreement was strong during full-text screening according to the study’s reported kappa. |
What the Four Complaint Themes Mean for Healthcare Researchers
Access complaints indicate more than scheduling friction
According to the 2026 PLOS One synthesis, impeded access included appointment availability, unanswered telephones, waiting times, inaccessible premises, parking difficulties, and treatment cost.
For healthcare researchers, access narratives should be coded for the underlying experience of responsiveness and whether patients feel that their time, needs, and resources are valued. A patient quoted in the study said, "...wait for the whole day to see a dentist..."
Communication complaints reveal threats to patient agency
According to Snyman and colleagues, patients complained when dentists failed to explain procedures or costs, used excessive technical language, did not listen, or showed limited empathy toward anxiety and special needs.
A patient quoted in the study summarized the problem this way: "A lot of dentists use too many technical terms that the average person on the street doesn’t understand or know anything about." Researchers can therefore analyze communication not only as a topic, but also as a signal of informed consent, respect, and agency.
Clinical complaints combine outcomes, pain, and aftercare
According to the study’s results, every included study contained complaints about deficient clinical treatment, including poor treatment quality, repeated visits, pain, inadequate treatment planning, weak aftercare, and safety concerns.
A patient quoted in the study said, "After the surgery, [the provider] simply let me walk out of the room right away without giving me any directions about how to prevent possible infections or tell me to make a revisit appointment." The finding supports analyzing the full care journey rather than coding only the final treatment outcome.
Conduct complaints expose ethical and trust failures
According to Snyman, Postma, and Van Staden, unethical or unprofessional conduct included inadequate informed consent, dishonesty, poor accountability, unequal treatment, fraud, medication errors, wrong-site procedures, and failures to recognize complications.
The authors interpreted these patterns as potential breakdowns in the fiduciary patient-practitioner relationship. Healthcare quality teams can use this interpretation to connect individual complaints with organizational learning, professional education, transparency, and accountability.
How AI-Enabled Qualitative Research Helps Analyze Dental Complaints
Problem: Complaint narratives are dispersed across many sources
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, papers, reports, news, and other documents.
For a dental quality team, Evidano can bring complaint reports, interview transcripts, patient surveys, and online review exports into one analysis workspace. Researchers can then compare themes across complaint sources instead of treating each source as a separate evidence silo. Explore the Evidano qualitative research features.
Problem: Manual thematic coding slows synthesis
The 2026 PLOS One authors used reflexive thematic analysis to move from surface-level complaints to latent relational and ethical meanings. Evidano can support this workflow with thematic, content, frequency, and cross-segment analyses.
Researchers can use AI-generated themes as an organizing aid, inspect supporting excerpts, refine code definitions, and retain human control over interpretive decisions. AI can accelerate the first pass, but the study’s emphasis on reflexivity means researchers should validate themes against the original patient language and study context.
Problem: Teams need to compare patient groups and settings
According to the PLOS One synthesis, complaint themes varied by source, including online complaints, regulatory complaints, litigation, and malpractice-related claims. Evidano’s cross-segment analysis can help teams compare themes by clinic, service type, patient group, complaint channel, or time period.
A cross-segment view can show whether access concerns are concentrated in public services, whether communication concerns differ by age or anxiety status, or whether treatment and conduct concerns recur across locations. These comparisons are research outputs that require careful sampling and interpretation, not automatic proof of causation.
Problem: Sensitive patient data requires responsible handling
Dental complaints can contain health information, names, and details about adverse experiences. Evidano provides transcription with PII redaction, encrypted data handling, and a policy that customer data is never used to train third-party models, as described by Evidano.
Research teams should still obtain appropriate approvals, minimize identifiable data, define access controls, and follow applicable privacy and research-ethics requirements. AI-assisted analysis does not replace ethics review or professional accountability.
FAQ: Qualitative Analysis of Patient Complaints
What are the main themes in dental patient complaints?
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The main themes are impeded access to dental care, unsatisfactory communication and interactions, deficient clinical treatment, and unethical or unprofessional conduct.
According to the November 12, 2026, PLOS One study, these four domains were identified across 19 qualitative studies and often overlapped.
How many studies did the dental complaints meta-synthesis include?
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The meta-synthesis included 19 studies from an initial search of 857 studies.
According to Snyman, Postma, and Van Staden, the review included evidence from varied countries, healthcare settings, and complaint sources.
Why is qualitative analysis useful for patient complaints?
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Qualitative analysis explains what patient complaints mean, not only how often complaints occur.
According to the PLOS One authors, patient narratives revealed threats to trust, agency, relational security, and professional obligations that quantitative complaint categories capture poorly.
Can AI analyze dental complaints without replacing researchers?
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AI can accelerate document ingestion, coding, thematic synthesis, and cross-segment comparison while researchers retain responsibility for interpretation and validation.
Evidano supports AI-assisted qualitative analysis, but healthcare teams should review source excerpts, assess bias, protect patient privacy, and make final analytic judgments themselves.
What should researchers code in dental complaint data?
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Researchers should code the explicit issue, the care setting, the complaint source, the patient’s stated experience, and the underlying relational or ethical concern.
The 2026 PLOS One study demonstrates the value of combining semantic codes, such as long waits, with latent interpretations, such as perceived disregard for patient needs.
Conclusion and Next Steps
The 2026 PLOS One meta-synthesis shows that dental complaints are multidimensional signals about access, communication, clinical care, professional conduct, and trust. The study’s 19 included papers and four recurring domains demonstrate why qualitative analysis of patient complaints should complement complaint counts and regulatory metrics. AI-enabled tools can help healthcare researchers organize large evidence collections, compare segments, and identify candidate themes more efficiently, while human researchers remain responsible for context, ethics, and interpretation. Teams that want to analyze dental interviews, complaint documents, and open-ended patient feedback can Try Evidano for free.
Topics
- qualitative analysis of patient complaints
- dental patient feedback analysis
- AI qualitative research healthcare
- thematic analysis of complaints
- patient experience research
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