Beyond medical errors: exploring the interpersonal dynamics in physician-patient relationships linked to medico-legal complaints
Bibliographic record
Abstract
BACKGROUND: Previous research suggests that medico-legal complaints often arise from various factors influencing patient dissatisfaction, including medical errors, physician-patient relationships, communication, trust, informed consent, perceived quality of care, and continuity of care. However, these findings are not typically derived from actual patients' cases. This study aims to identify factors impacting the interpersonal dynamics between physicians and patients using real patient cases to understand how patients perceive doctor-patient relational problems that can lead to dissatisfaction and subsequent medico-legal complaints. METHODS: We conducted a retrospective study using data from closed medical regulatory authority complaint cases from the Canadian Medical Protective Association (CMPA) between January 1, 2015, and December 31, 2020. The study population included patients who experienced sepsis and survived, with complaints written by the patients themselves. A multi-stage standardized thematic analysis using Braun and Clarke's approach was employed. Two researchers independently coded the files to ensure the reliability of the identified codes and themes. RESULTS: Thematic analysis of 50 patient cases revealed four broad themes: (1) Ethics in physician's work, (2) Quality of care, (3) Communication, and (4) Healthcare system/policy impacting patient satisfaction. Key sub-themes included confidentiality, honesty, patient involvement, perceived negligence, perceived lack of concern, active engagement and empathy, transparency and clarity, informed consent, respect and demeanor, lack of resources, long wait times, and insufficient time with physicians. CONCLUSIONS: This study identifies and categorizes various factors impacting relational issues between physicians and patients, aiming to increase patient satisfaction and reduce medico-legal cases. Improving physicians' skills in areas such as communication, ethical practices, and patient involvement, as well as addressing systemic problems like long wait times, can enhance the quality of care and reduce medico-legal complaints. Additional training in communication and other skills may help promote stronger relationships between physicians and patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".