Data Characteristics
Patient Assistance Programs (PAPs) generate data for clinical trial pre-screening from various sources. These include patient-submitted personal information, medical records, genetic test reports, medication history, and patient complaints. Data often exists as unstructured text (e.g., doctor's notes, patient progress descriptions) and semi-structured data (e.g., specific fields exported from electronic medical record systems). Data update frequency varies; medical records might update after each follow-up, while genetic test reports are typically one-time data. Document structures are diverse and lack uniform standards. They may contain medical terminology, abbreviations, and different units for test results (e.g., some biomarkers in ng/mL or pmol/L, blood count indicators in g/dL or mmol/L).
Constraints Imposed by These Characteristics on Tool Calling and Plugins
Diverse and non-standardized data sources require robust data extraction and parsing capabilities for tool calling. The tools must identify key information from various document formats. Uncertain update frequencies mean tools must consider data timeliness during calls to avoid using outdated information for decisions. Complex document structures and inconsistent fields and units challenge the semantic understanding and unit conversion capabilities of plugins. This requires specialized medical knowledge graphs or conversion rules. For example, pre-screening might involve comparing specific biomarker levels across different patients. Inconsistent units would lead to incorrect comparisons. Tool calling must handle fuzzy matching and medical term ambiguity to accurately identify whether a patient meets trial inclusion criteria for disease stage or co-morbidities.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 4000–8000 tokens | Accommodates the length of patient medical records, ensuring complete context understanding. |
PARSE_FILE_TIMEOUT_SECONDS | 180–300 seconds | Handles parsing of large or complex medical record files, preventing timeouts. |
Chunk size (Segment Length) | 800–1200 characters | Balances semantic completeness with recall efficiency, ensuring coherence of medical text. |
Recall count (Recall Count) | 15–25 items | Increases coverage of relevant medical record snippets, capturing potential enrollment information. |
Similarity threshold (Similarity Threshold) | 0.78–0.85 | Precisely matches medical terms and symptom descriptions, reducing misjudgment rates. |
Rerank result count (Reranked Return Count) | 8–12 items | Further optimizes sorting after initial screening, highlighting the most relevant inclusion criteria. |
Common Pitfalls
- Tool calling fails to identify critical diagnostic descriptions in medical records, leading to patients being incorrectly excluded from pre-screening. This happens due to a lack of pre-trained models or dictionaries for medical terminology, resulting in inaccurate semantic understanding.
- Specific biomarker values returned by a plugin cannot be directly compared with inclusion criteria, with logs showing unit mismatch errors. This occurs when unit conversion rules are not configured or the plugin fails to correctly extract values and units from unstructured text.
- When processing large volumes of patient data, tool calling frequently encounters API rate limit errors (HTTP 429). This is due to improper configuration of concurrent requests or a lack of effective request queue management, leading to excessive access frequency to external tools or databases within a short period.
How to Verify Configuration
- Select a set of typical patient medical records known to meet and not meet trial inclusion criteria. Use tool calling for pre-screening and verify if the output matches expectations.
- During tool calling, check if the plugin correctly extracts and converts numerical fields with multiple units in medical records to a unified unit, and effectively compares them with inclusion criteria.
- Monitor system logs for any timeout or rate limit errors from external tool APIs when processing batch patient data, and evaluate the stability of tool calling.
- Randomly sample a portion of pre-screening results. Manually review patient medical records against inclusion criteria to assess the accuracy and recall rate of tool calling and plugins. Adjust parameters like
Similarity threshold(Similarity Threshold) based on feedback.
The values provided are common starting points and should be measured 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.