Deployment and Upgrade for DTP Pharmacy Clinical Trial Pre-screening

Data Characteristics

Data for DTP pharmacy clinical trial pre-screening primarily originates from internal Electronic Health Record (EHR) systems, prescription management systems, drug sales records, and laboratory/imaging reports from partner hospitals. This data typically combines unstructured text (e.g., physician diagnoses, patient chief complaints, medication history) and structured data (e.g., diagnostic codes, lab results, medication dosages). Data updates frequently, with new patient visits, prescriptions, and drug sales generating real-time data. Document structures vary, including medical reports in PDF format, prescription slips as images, and structured tables within databases. Fields and units are highly specialized medically; for instance, "HbA1c" represents glycated hemoglobin in %, and "eGFR" represents glomerular filtration rate in mL/min/1.73m².

Constraints on Deployment and Upgrade

The highly sensitive and private nature of DTP pharmacy data mandates that deployment solutions prioritize data security and compliance. On-premise or private cloud deployments are common choices to prevent data leakage. Real-time data updates require the knowledge base to quickly synchronize new data, posing challenges for incremental update mechanisms and index reconstruction efficiency. Mixed document structures and specialized field units necessitate FastGPT's robust multimodal parsing capabilities and custom entity recognition to accurately extract patient disease characteristics, medication status, and laboratory indicators. The large volume of unstructured text requires specific strategies for knowledge base chunking and vector embedding model selection to ensure accurate recall. The complexity of medical terminology increases the difficulty of model training and optimization, demanding higher contextual understanding and specialized terminology processing from the model.

Configuration Settings

Common Pitfalls

• After a knowledge base update, pre-screening results show outdated data. This occurs because incremental indexing did not take effect promptly or an automatic index reconstruction task was not configured.
• Key indicators in some medical reports are not extracted correctly, with error messages showing empty fields. This is due to incomplete custom entity recognition rules that do not cover all formats or aliases.
• After deploying a local model, FastGPT API returns a 401 Unauthorized error. This indicates that the model service's authentication information is not configured correctly or API Key permissions are insufficient.

Verification

• Upload a batch of simulated patient medical records containing various document formats (PDF, images, structured data). Verify that all key fields are accurately extracted and ingested, paying close attention to specialized medical terms and numerical units.
• Perform multiple pre-screening queries against ingested patient data, simulating clinical trial enrollment criteria. Check if the recalled patient list meets expectations and adjust Similarity threshold based on actual business feedback.
• Through the FastGPT management interface, check the knowledge base synchronization status and index building progress. Ensure new data is reflected in pre-screening results in a timely manner, and verify that PARSE_FILE_TIMEOUT_SECONDS is sufficient to process the most complex documents.

The values given are common starting points and should be measured against the reader's own 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.