Can online patient reviews be used to assess oncologist competency? RateMD as a cancer care evaluation tool.
Bibliographic record
Abstract
e18656 Background: Medical oncology (MEDONC) requires a combination of skills in collaboration, communication, and professionalism, ultimately delivering technical and clinical knowledge in practice. Standard assessment tools (e.g. written examination, OSCE) are not effective in evaluating competencies beyond technical skills and fail to define the cancer care experience holistically. This explorative, descriptive study aims to identify the potential of unstructured, unsolicited, open access online patient reviews (OPRs) as a tool to assess physician competency. Methods: University-affiliated MEDONCs in Ontario (Canada) were selected. All OPRs were identified on RateMD using every name permutation; physician names and institutional affiliations were removed from comments. A descriptive analysis of the cohort was completed. The CanMEDS Framework, defining physician standards, was used with its hierarchy of roles, concepts, and competencies. Two reviewers, a communication studies researcher and a healthcare professional, independently assessed comments and identified common themes. Competency-level assessments were evaluated using kappa with linear weights. Results: 473 OPRs were identified for 49 MEDONCs (71% male, 29% female). Of these, 23% were written by care providers. Competencies defining roles of Medical Expert, Communicator, and Professional were most prevalent (64%, 38%, and 27% respectively). Agreement levels were high in all roles (wK = 0.71 - 1.00). Themes identified were similar in positive and negative evaluations. Most commonly discussed positive themes were knowledge translation and compassionate interpersonal skills. Most common negative themes centered on lack of humility, compassion, and communication skills. 38% of comments were marked helpful, indicating engagement with other OPRs as a key characteristic of rating tools. In addition to the physician in question, 21% of OPRs reported on healthcare delivery by staff. Conclusions: OPRs emphasize experiential competencies related to interpersonal skills and suggest an alternative format to evaluating such aspects of MEDONC competencies.[Table: see text]
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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 | 0.049 | 0.171 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".