Development of a tool for generating comprehensible, individualized and digitalized outpatient letter
Bibliographic record
Abstract
Abstract Background Patients often remember little from a medical consultation or a conversation with a doctor. A comprehensible, individual patient letter summarizing the consultation can support, as a German project which successfully implemented a digital tool to generate such a letter in inpatient care showed. Based on this, our aim is to develop a tool for letters in outpatient care in Switzerland, to support understanding of the consultation and increase patient’s health literacy (HL). Methods The development of the tool was conducted participatively with targeted health professionals (HP) and patients with a chronic disease to 1) identify specific patient groups and situations, 2) confirm eligibility and receive concrete input regarding structure and content, and 3) to ensure the technical practicality and usability by the target groups. Concretely, several expert and patient interviews, a focus group and consultation observations were performed. Further workshops and usability tests are planned. Results We first defined 8 criteria to identify situations suitable for the development of the letter (e.g. high information need) and interviewed 5 experts on selected topics. Based on this, chronic back pain was chosen for a deeper feasibility analysis: A focus group interview (n = 4 HP) and 5 telephone interviews with patients confirmed the eligibility and provided information on the structure and content of the letter. Observations of 5 consultations provided further insights. Then, a first concept for the letter’s content was developed and is currently being finalized. It consists of: 1) summary; 2) background; 3) diagnosis; 4) treatment; and 5) next steps and includes infoboxes and a glossary. Conclusions The tool for patient letters can be adapted to other diseases (e.g. chronic back pain) and settings (outpatient care). In a next phase, the letter will be piloted in an outpatient clinic and evaluated with HP and patients. The effect on HL will also be assessed. Key messages • Comprehensible, individualized, and digitalized patient letters can help patients to deal with health information and take better decisions for their health and well-being. • The developed patient letter and the corresponding tool should be easily adaptable for other diseases and settings.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".