Data Characteristics
Clinical trial data for rehabilitation devices primarily originates from Hospital Information Systems (HIS), Electronic Medical Records (EMR), and device sensor logs. Data update frequency varies by trial stage and device type. Updates typically occur daily or weekly after trial initiation, with bulk imports at key milestones. Document structures are diverse, including standardized Case Report Form (CRF) data, unstructured physician diagnostic notes, rehabilitation therapist assessment reports, patient complaints, and device performance parameter reports. Beyond general demographic information, fields include rehabilitation functional scale scores (e.g., FIM, BI), kinematic parameters (e.g., gait analysis data, joint range of motion), physiological signals (e.g., electromyography, heart rate), and device operating status (e.g., usage duration, error codes). Units strictly adhere to medical and engineering standards; for example, angles are in degrees, torque in Newton-meters, and time in seconds or minutes.
Constraints Imposed by Data Characteristics on Multiturn Conversation and Prompts
The diversity and multimodal nature of rehabilitation device clinical trial data impose specific requirements on the accuracy of multiturn conversations and prompt construction. The mix of structured data and unstructured text requires the model to extract key information from different data sources. Prompts must guide the model to identify and integrate this information. High-frequency sensor data updates mean the conversation system needs to process time-series information. Prompt design should consider time windows and trend analysis. The presence of specialized terminology, such as rehabilitation functional scales, requires prompts to effectively activate the model's understanding of specific medical vocabulary, avoiding misunderstanding or overgeneralization. The inclusion of device operating status data necessitates conversations that can link patient physiological indicators with device usage. Prompts should encourage the model to perform cross-domain information correlation analysis; for example, when a patient reports specific discomfort, the system should be able to provide an initial assessment by combining device error codes or usage patterns.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8192 | Accommodates the context length requirements for extensive multiturn conversation history and complex rehabilitation assessment reports. |
Chunk size | 500–800 characters | Balances semantic integrity of text with retrieval efficiency, particularly for physician diagnostic notes and rehabilitation therapist assessment reports. |
Recall count | Top 7 entries | Given the strong correlation within rehabilitation device data, increasing the number of retrieved items helps capture potentially relevant information, such as patient historical rehabilitation plans. |
Similarity threshold | 0.78 | Clinical trial pre-screening demands high information accuracy; appropriately raising the threshold reduces interference from irrelevant information. |
Rerank result count | 5 | Further optimizes the ranking of key information through re-ranking based on retrieval, ensuring the most relevant content is presented first. |
ENABLE_HISTORY_MEMORY | true | Ensures multiturn conversations can effectively utilize historical context, especially for tracking long-term rehabilitation progress. |
Common Pitfalls
- Observation: In multiturn conversations, the model fails to accurately identify the patient's current rehabilitation stage and provides inappropriate advice. Reason: The prompt does not effectively guide the model to combine timestamp information and historical rehabilitation assessment data for stage determination.
- Observation: After a user describes a device malfunction, the system fails to link it to specific error codes in the device logs. Reason: Device log data is not effectively embedded in the knowledge base, or the prompt does not explicitly ask the model to match patient descriptions with log data.
- Observation: The model repeatedly asks for patient basic information or rehabilitation data that has already been provided in the conversation. Reason:
ENABLE_HISTORY_MEMORYis not enabled, ormaxContextis configured too small, preventing the model from effectively remembering multiturn conversation history.
How to Verify Configuration
- Simulate multiple clinical trial pre-screening scenarios to verify if the model can provide appropriate pre-screening judgments at different rehabilitation stages, based on historical conversations and knowledge base content.
- Test conversations containing descriptions of device operational anomalies. Check if the model can accurately link to corresponding device troubleshooting guides in the knowledge base and offer preliminary recommendations.
- Conduct long-duration multiturn conversation tests. Observe if the model can continuously utilize historical information, avoid repetitive questioning, and maintain a coherent understanding of the patient's rehabilitation progress.
The values provided are common starting points. Measure them against specific samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.