Clinical Decision Support for Clinical Trial Pre-screening

Data Characteristics in This Category

Clinical trial pre-screening data primarily originates from Electronic Health Record (EHR) systems, Laboratory Information Management Systems (LIMS), Picture Archiving and Communication Systems (PACS), and registration systems within healthcare institutions. Data update frequencies vary: some data updates in real-time (e.g., vital signs), some periodically (e.g., lab results), and some are recorded in stages (e.g., medical history). Document structures are diverse, including unstructured handwritten doctor's notes, semi-structured examination reports, and structured diagnostic codes. Fields involve medical terminology, units of measurement, and clinical descriptions. For example, drug dosages might use mg/kg or IU, imaging reports describe anatomical locations and lesion characteristics, and diagnostic information follows ICD-10 or SNOMED CT coding standards.

Constraints Imposed by These Characteristics on "Tool Calling and Plugins"

The highly heterogeneous nature of clinical trial pre-screening data requires tool calling and plugins to possess robust data parsing and standardization capabilities. Understanding unstructured text relies on Natural Language Processing (NLP) plugins to extract key patient characteristics and disease states. Standardizing structured data requires predefined mapping rules to unify fields from different sources and with different units. Examples include plugins for converting drug dosage units or mapping ICD-10 codes to disease terms specified in trial protocols. Differences in data update frequency necessitate incremental synchronization and real-time trigger mechanisms for tool calls, ensuring pre-screening results are based on the latest patient information. Furthermore, given sensitive medical data, tool calls must adhere to strict data anonymization and access control policies to ensure compliance. Complex medical logic judgments require plugins to perform multi-condition combinatorial reasoning and even call external knowledge graphs for auxiliary decision-making.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale for Recommendation
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Processing large unstructured medical record files, such as discharge summaries, requires a longer parsing time.
`maxContext`	`8000 tokens`	Ensures that complete patient medical records and trial inclusion/exclusion criteria can be accommodated, preventing information truncation.
`Recall count`	`Top 20 entries`	Increases the likelihood of retrieving relevant medical guidelines, drug interactions, and historical cases from the knowledge base.
`Similarity threshold`	`0.75`	Balances recall and precision, ensuring retrieved information is highly relevant to the clinical context.
`Chunk size`	`500 characters`	Balances semantic integrity and model processing efficiency, preventing loss of context due to long text segmentation.
`Rerank result count`	`Top 5 entries`	Refines the information presented to decision-makers, focusing on the most relevant inclusion/exclusion criteria.

Common Mistakes

External model calls fail to achieve streaming output. This occurs when the streaming option is not correctly enabled in the workflow configuration, or the external model interface itself does not support streaming responses.
Semantic retrieval or full-text search tools return "Connection error." This could be due to incorrect tool service address configuration, network firewall blocking, or the tool's backend service not running properly.
Variables in the workflow are not returned during API calls, appearing as empty placeholders in the return result. This usually happens when variable assignment logic was not executed correctly in a preceding step, or variable names do not match the actual parameters passed.

How to Verify Correct Configuration

Simulate the complete pre-screening process for a typical patient case. Check if tool calls correctly identify and extract all key inclusion/exclusion criteria fields, such as ICD-10, 化验指标, and Medication Records.
Test medical record documents of varying data volume and complexity. Observe if file parsing is successful under the PARSE_FILE_TIMEOUT_SECONDS configuration and record the parsing time.
Invoke the workflow via its API interface. Verify that all expected variables (e.g., 患者ID, 符合条件) are correctly populated in the return result, and check their data types and values against expectations.

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.