Patient Assistance Clinical Trial Pre-screening: Model

Data Characteristics in this Domain

Patient Assistance Program (PAP) clinical trial pre-screening data originates from pharmaceutical companies and Contract Research Organizations (CROs). This includes trial protocols, patient recruitment guidelines, and patient-submitted health information. Data updates typically align with clinical trial progress, such as quarterly or monthly protocol updates, while patient data increases in real-time upon submission.

Document structures often involve PDF-formatted trial protocols, containing both structured and unstructured information like trial objectives and inclusion/exclusion criteria. Patient health information may exist as electronic medical records or questionnaires, including diagnostic results, treatment history, concomitant medications, and genetic testing reports. Common fields and units include diagnosis code (ICD-10), ECOG score (0-5 levels), KPS score (0-100%), pathology report (text description), and imaging results (text description or structured report links).

Constraints Imposed by Data Characteristics on Model Integration and Configuration

The diverse data sources require the model to handle multiple document formats. PDF parsing and OCR capabilities are critical. Uncertain update frequencies mean the model needs to support incremental training or real-time index updates to ensure pre-screening results are based on the latest data.

Extensive unstructured text in trial protocols, such as inclusion/exclusion criteria descriptions, demands high semantic understanding from the model. It must accurately capture key medical concepts and their logical relationships. Mixed structured and unstructured patient health information requires the model to process hybrid data types, for example, extracting numerical metrics like ECOG score or KPS score from free text. Additionally, specialized medical terminology and numerous synonyms necessitate strong medical domain knowledge and terminology mapping capabilities to reduce misjudgments.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size` (Segment Length)	`500–800 characters`	Key information in clinical trial protocols and patient medical records often concentrates in shorter paragraphs. Overly long segments dilute information density.
`Recall count` (Recall Count)	`Top 10–15 items`	The pre-screening process requires comprehensive consideration of multiple inclusion/exclusion criteria. Increasing the recall count helps cover all potentially relevant information.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	Patient assistance pre-screening demands high accuracy. A medium-to-high similarity threshold filters out irrelevant recall results, reducing misjudgments.
`Rerank result count` (Reranked Return Count)	`Top 5 items`	After reranking, the most relevant few pieces of information are usually sufficient to support an initial pre-screening judgment.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large PDF trial protocols or complex medical records can be time-consuming, requiring sufficient timeout duration.
`maxContext`	`8000–16000 tokens`	Combining multiple recalled segments and user queries ensures the large language model has enough contextual understanding for decision-making.

Common Pitfalls

The model response provides only XML or JSON code blocks without rendered charts. This usually indicates incorrect front-end rendering component configuration or a mismatch between the back-end data format and front-end expectations.
After integrating specific models like DeepSeek or Qwen, a Cannot read properties of null error appears. This typically points to incorrect model interface parameter configuration or the model service not starting correctly.
Pre-screening result accuracy fluctuates significantly, leading to eligible patients being missed or ineligible patients being incorrectly included. This often results from insufficient coverage of medical terminology synonyms in the knowledge base or incomplete encoding of inclusion/exclusion criteria logic.

Configuration Verification

Upload multiple clinical trial protocol PDFs containing complex medical terminology and multi-format data. Verify successful parsing and indexing within the PARSE_FILE_TIMEOUT_SECONDS limit.
Use a set of simulated patient data with known inclusion/exclusion outcomes. Run the model for pre-screening and check the consistency between the model's preliminary judgments and the actual results.
Adjust Similarity threshold (Similarity Threshold) and Recall count (Recall Count). Observe changes in the recall rate and accuracy of pre-screening results to find a balance point.

The values provided are common starting points. Measure them against your 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.