Tool Calling and Plugins for SMO Clinical Trial Prescreening

Data Characteristics

Site Management Organizations (SMO) use data from Electronic Health Record (EHR) systems, Laboratory Information Management Systems (LIMS), and patient health questionnaires for clinical trial prescreening. This data includes unstructured text (e.g., progress notes, diagnosis reports), semi-structured documents (e.g., PDF examination reports, medical imaging reports), and structured data tables (e.g., lab results, vital signs). Data update frequency varies: some data updates in real-time (e.g., vital signs), some updates per treatment cycle (e.g., outpatient records), and some are imported in batches (e.g., historical medical records). Document structures are complex, containing medical terminology, abbreviations, and specific formats. Fields and units are critical; medical test results often have explicit units (e.g., mg/dL, mmol/L), while diagnoses and symptom descriptions lack standardization and require semantic understanding and normalization.

Constraints on Tool Calling and Plugins

SMO clinical trial prescreening data characteristics impose several constraints on tool calling and plugins. Unstructured text and semi-structured documents require robust text parsing tools for information extraction. Examples include OCR for PDF reports and Natural Language Processing (NLP) to identify diagnoses, medication usage, and key metrics. Varying data update frequencies necessitate incremental processing capabilities to avoid reprocessing historical data. The highly specialized nature of medical terminology and abbreviations means general knowledge bases are insufficient for accurate understanding; custom medical terminology dictionaries or ontologies are required. Field and unit complexity means conditional judgments and numerical comparisons must ensure consistent unit conversion. For example, blood glucose units from different reports must be unified to mmol/L to prevent screening errors. Handling sensitive medical data also demands high data security and privacy protection capabilities from tools.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	SMO documents are often large, multi-page, and contain mixed text and images, requiring longer parsing times.
`Chunk size`	`800–1200 characters`	Medical text has strong contextual relevance. Shorter segments risk semantic loss; longer segments increase recall noise.
`Recall count`	`20 items`	Clinical trial screening criteria are complex, requiring sufficient relevant information from the knowledge base for comprehensive judgment.
`Similarity threshold`	`0.75`	Medical concepts demand high precision. A lower threshold risks introducing irrelevant information, affecting screening accuracy.
`Plugin Timeout`	`300 seconds`	External systems (e.g., EHR interfaces) may have slow responses. Adequate time is needed to prevent timeouts.
`MAX_CONTEXT_TOKENS`	`4096`	Prescreening conditions and patient medical records are often long, requiring a sufficiently large context window for reasoning.

Common Mistakes

HTTP 500 errors when calling external APIs typically occur because the external system's API authentication token is expired or incorrectly formatted, leading to authentication failure.
Screening results show patients clearly not meeting criteria, manifesting as empty key metric fields or abnormal values. This often happens when text parsing tools fail to correctly identify specific layouts or unconventional expressions in medical reports, leading to information extraction failure.
When processing large PDF documents, tasks remain in a "processing" state for extended periods and eventually time out. This usually occurs because PARSE_FILE_TIMEOUT_SECONDS is set too short for complex documents with many pages.

Verification

Upload a comprehensive patient medical record PDF document containing various medical reports (e.g., complete blood count, biochemistry, imaging). Check if the knowledge base correctly parses and extracts key information such as diagnoses, lab results, and medication history. Verify that extracted numerical units are consistent.
Design a query with multiple complex screening criteria, such as "age greater than 60 AND serum creatinine >1.5 mg/dL AND no history of diabetes." Check if tool calling accurately triggers external system queries and returns a list of eligible patients.
Simulate low and high latency scenarios for external system interfaces. Observe if plugin calls consistently return results under different response speeds and if they correctly handle timeouts.

Note: The values provided are common starting points. Measure against specific samples to determine optimal configurations.

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.