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, treatment, safety, and professional conduct. A 2026 qualitative meta-synthesis in PLOS One brings together evidence from 19 studies and identifies four connected domains of concern. This article explains the study’s findings and shows how AI-enabled qualitative research can help teams organize large collections of complaints, reviews, interviews, and open-ended survey responses without reducing patient experiences to simple categories. The analysis is research-focused and does not provide clinical or diagnostic advice.
Key Takeaways
According to Patient complaints in dentistry: A qualitative meta-synthesis, qualitative analysis of patient complaints identifies four connected concerns: impeded access to care, unsatisfactory communication and attitudes, deficient clinical treatment, and unethical or unprofessional conduct.
The 2026 PLOS One study shows that dental complaints can signal breakdowns in trust, patient agency, relational security, and professional obligations, not only dissatisfaction with a technical outcome.
- The researchers screened 857 studies and included 19 studies in the qualitative meta-synthesis, reported on November 12, 2026.
- Deficient clinical treatment appeared in all 19 included studies, representing 100% of the qualitative evidence base analyzed by Snyman, Postma, and Van Staden.
- Unethical and unprofessional conduct appeared in 17 of 19 studies, or 89%, according to the authors’ meta-summary reported in November 2026.
- The researchers updated their database search on August 26, 2026, across MEDLINE, PsycINFO, PubMed, Scopus, Web of Science, and CINAHL.
- A patient quoted in the study described access failure this way: “you never get through… I tried to make an appointment, phone kept ringing out.”
How the 2026 Dental Complaint Study Worked
The 2026 PLOS One study used qualitative meta-synthesis to interpret meanings across previously published qualitative findings rather than calculate population-level complaint rates. The authors followed the six-step approach of Sandelowski and Barroso, used Hoon’s higher-order case framework, and reported the synthesis according to PRISMA guidance for qualitative evidence synthesis.
The researchers searched six health databases in September 2024 and updated the search on August 26, 2026. After screening 857 records, the researchers retrieved 29 articles for full-text review and included 19 studies.
The analysis used reflexive thematic analysis. According to Snyman, Postma, and Van Staden, one reviewer coded qualitative findings in Atlas.ti 25, while a second reviewer independently reviewed and challenged the coding. The researchers analyzed semantic meanings first and then used latent interpretation to examine deeper relational and ethical concerns.
The evidence came from varied sources, including regulatory complaints, litigation and malpractice claims, and online reviews. The included studies represented settings in the United States, Australia, the United Kingdom, Saudi Arabia, Denmark, France, the Netherlands, Malaysia, Turkey, Finland, Norway, and South Africa.
A patient quoted in the study summarized a communication failure as follows: “the sort of person who got you into the surgery, said ‘Hi’, got the drill out and started almost straightaway.”
Findings Snapshot: Four Themes in Patient Complaint Analysis
| Theme identified by the 2026 study | Evidence in the synthesis | What the complaints may signal |
|---|---|---|
| Impeded access to dental care | The authors identified complaints about availability, responsiveness, inaccessible premises, waiting times, and cost across the included literature. | According to Snyman, Postma, and Van Staden, access complaints can indicate organizational failures and a perception that patients’ needs and time are not valued. |
| Unsatisfactory communication, interactions, and attitudes | The study described complaints about unclear explanations, technical language, poor listening, rudeness, limited empathy, and insufficient discussion of costs. | The authors interpret communication failures as threats to patient agency, respect, and trust. |
| Deficient clinical treatment | This concern appeared in all 19 studies, or 100% of the included evidence base, according to the authors’ November 2026 meta-summary. | The complaints included poor treatment quality, pain, inadequate aftercare, safety concerns, and unmet expectations. |
| Unethical and unprofessional conduct | This concern appeared in 17 of 19 studies, or 89%, according to the 2026 PLOS One study. | The authors linked complaints about consent, accountability, honesty, fairness, negligence, and professional integrity to weakened confidence in the practitioner. |
Implications for Dental Quality and Patient Experience Teams
Dental quality teams should analyze complaints as patient-generated evidence about clinical, relational, organizational, and structural conditions. The 2026 PLOS One authors conclude that complaints may be “signals of broader systemic and relational functioning, ” which means complaint management can support organizational learning rather than only case resolution.
The study’s findings suggest that complaint dashboards should combine frequency with meaning. A count of complaints about waiting times may reveal access and responsiveness problems, while narratives about unclear explanations may reveal weaknesses in informed consent, shared decision-making, or cost transparency.
The 2026 study also shows why cross-source analysis matters. Deficient clinical treatment appeared across online, regulatory, and litigation-related complaint sources, while the absence of access concerns in litigation data may reflect the legal framing of those cases rather than the absence of access problems.
- Code complaints by both surface topic and underlying concern, such as waiting time linked to perceived disregard or unclear explanations linked to reduced agency.
- Compare complaint themes across public and private practices, online reviews, regulatory records, litigation claims, and patient surveys.
- Preserve representative patient language because direct quotations can reveal emotional, ethical, and relational meanings that numerical categories miss.
- Use findings to inform communication training, informed consent processes, aftercare protocols, referral pathways, and service-access improvements.
- Treat AI-generated themes as research support that requires human review, especially when complaints involve safety, ethics, discrimination, or potential clinical negligence.
How Evidano Helps With AI-Enabled Qualitative Complaint Analysis
Problem: Complaint data is scattered across documents and spreadsheets
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze patient complaints, interviews, open-ended surveys, reviews, and research documents.
Teams can bring complaint narratives and survey responses into one analysis workflow, then use thematic, content, frequency, and cross-segment analyses to compare recurring concerns. Researchers can learn more about the platform’s relevant capabilities on the Evidano features page.
Problem: Researchers need themes without losing patient language
Evidano can organize qualitative material into themes, codes, and subcodes while allowing researchers to inspect the underlying text. Visualizations such as hierarchical codes and co-occurrence networks can help teams examine how access, communication, treatment quality, and professional conduct appear together.
Researchers should validate AI-generated interpretations against the original complaint text and retain quotations only in accordance with consent, privacy, and governance requirements.
Problem: Different patient groups may experience different failures
Evidano supports cross-segment analysis, allowing teams to compare themes by setting, patient group, complaint source, treatment type, or demographic segment when those variables are available and ethically appropriate.
Cross-segment analysis can extend the approach used in the 2026 study by testing whether the four domains vary across public and private settings, online reviews, regulatory complaints, and litigation-related records.
Problem: Healthcare complaint data requires careful handling
Evidano provides encrypted data handling and states that customer data is never used to train third-party models. Healthcare researchers should still apply institutional privacy rules, remove unnecessary identifiers, and obtain the required approvals before uploading sensitive complaint data.
For additional information about data handling, review Evidano’s data security information.
FAQ: Qualitative Analysis of Patient Complaints
What are the four main themes in qualitative analysis of dental patient complaints?
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The four main themes are impeded access to dental care, unsatisfactory communication and attitudes, deficient clinical treatment, and unethical or unprofessional conduct.
The four themes come from the 2026 PLOS One qualitative meta-synthesis by Snyman, Postma, and Van Staden, which included 19 studies.
How many studies were included in the 2026 qualitative dental complaint review?
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The 2026 review included 19 studies after screening 857 studies and reviewing 29 full-text articles.
The authors searched six health databases and updated the search on August 26, 2026.
Which dental complaint theme appeared most often in the study?
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Deficient clinical treatment appeared most often, occurring in all 19 included studies, or 100% of the evidence base.
Unethical and unprofessional conduct was the next most common theme, appearing in 17 of 19 studies, or 89%, according to the authors’ meta-summary.
Why is qualitative analysis useful for understanding patient complaints?
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Qualitative analysis explains what complaints mean to patients, including how patients experience trust, respect, agency, safety, and professional accountability.
The 2026 PLOS One study states that quantitative complaint counts provide limited insight into the meaning patients express in their complaints.
Can AI analyze dental complaints and patient reviews?
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AI can help researchers organize, code, summarize, and compare dental complaints and patient reviews, but human researchers must validate the interpretation.
An AI-enabled workflow such as Evidano can support thematic, frequency, content, and cross-segment analysis while keeping the original qualitative evidence available for review.
What should researchers do with sensitive patient complaint data?
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Researchers should apply consent, privacy, ethics, access-control, and data-retention requirements before analyzing sensitive patient complaint data.
Researchers should also remove unnecessary personally identifiable information and verify that any AI platform uses appropriate security and data-training practices.
Conclusion and Next Steps
The 2026 PLOS One meta-synthesis shows that dental complaints are multidimensional evidence about access, communication, clinical care, professional conduct, and trust. The study’s 19-study evidence base demonstrates why qualitative analysis can reveal meanings that complaint counts alone cannot capture.
AI-enabled qualitative research can help dental organizations process large volumes of complaints, preserve patient language, compare segments, and identify recurring patterns faster. Human review remains essential for ethical interpretation, privacy protection, and decisions involving patient safety or professional accountability.
Teams that want to turn patient narratives into structured quality insight can Try Evidano for free.
Topics
- qualitative analysis of patient complaints
- dental patient complaints analysis
- AI thematic analysis healthcare
- qualitative meta-synthesis dentistry
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