Data Characteristics in This Category
SMO (Site Management Organization) clinical trial pre-screening data originates from Electronic Health Record (EHR) systems, Laboratory Information Management Systems (LIMS), and Case Report Forms (CRF) at various research centers (hospitals). This data typically includes patient demographics, diagnostic records, medical history, medication details, laboratory and imaging results (e.g., complete blood count, liver and kidney function, imaging reports), and vital signs. Data update frequency varies by source; EHR and LIMS data often update in real-time or daily, while CRF data is entered periodically according to visit schedules. Document formats are diverse, including unstructured medical text (e.g., physician's handwritten progress notes, discharge summaries), semi-structured PDF lab reports, and structured CRF electronic tables. Fields and units are highly specialized medically; for example, blood pressure units are mmHg, blood glucose units are mmol/L or mg/dL, and numerous medical acronyms and synonyms exist.
Constraints Imposed by These Characteristics on "Workflow Orchestration"
The highly heterogeneous nature of SMO pre-screening data requires robust data cleaning and standardization capabilities in workflow orchestration. Unstructured medical text necessitates integrating Natural Language Processing (NLP) modules into the workflow to extract key information such as disease diagnoses, medication dosages, and adverse events. Parsing semi-structured PDF reports requires configuring specific document parsing nodes to accurately identify test results and reference ranges. Inconsistent data update frequencies demand that workflows support both scheduled and event-driven triggers; for example, daily automatic synchronization from EHR or immediate initiation of the pre-screening process when LIMS reports update. The diversity of medical terminology means that rule engines or knowledge graph query nodes within the workflow must handle synonyms and medical concept mapping to accurately match inclusion and exclusion criteria. Furthermore, the complexity of clinical trial protocols implies that workflows must support multi-branch logical judgments and conditional jumps to accommodate screening requirements for different patient characteristics and trial phases.
Configuration Settings
| Configuration Item | Suggested Value | Rationale for This Value |
|---|---|---|
maxContext | 2000 characters | Accommodates the length of unstructured medical records, ensuring key information is fully captured. |
chunk_size | 500 characters | Balances semantic completeness of text with subsequent processing efficiency, avoiding overly long or short segments. |
similarity_threshold | 0.85 | In medical term matching, prevents omissions due to synonyms or minor differences. |
rerank_top_n | top 10 | Ensures sufficient candidate results for fine-grained judgment after initial retrieval. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles parsing duration for large PDF lab reports or multi-page progress notes, preventing timeouts. |
conditional_branch_mode | AND logic | Clinical trial inclusion/exclusion criteria typically require multiple conditions to be met simultaneously, ensuring screening rigor. |
Three Common Mistakes
- Key fields (e.g.,
diagnosis,medication) are empty in logs. This occurs when the NLP model is not optimized for specific medical terms or abbreviations, leading to information extraction failures. - Workflow execution times out or stalls, indicated by a
504 Gateway Timeoutstatus code. This often happens when document parsing nodes process large PDF reports or image-based text without sufficient processing time or resource limits configured. - The number of screening results is significantly low. This may be due to setting an excessively high
similarity_threshold, which prevents effective matching of eligible patient records when dealing with medical term synonyms.
How to Confirm Correct Configuration
- Select a simulated patient dataset containing various data types (structured, semi-structured, unstructured), run the workflow, and verify that the output of each node matches expectations.
- Check workflow logs to confirm that all key information extraction nodes (e.g., disease diagnosis, medication dosage) successfully return non-empty values.
- Use a set of patient data known to meet inclusion/exclusion criteria and another set known not to meet them. Run the workflow separately for each set and verify the accuracy of the screening results.
- Monitor workflow execution time to ensure that overall execution time is within an acceptable range when processing typical data volumes, for example,
processing time < 30 seconds.
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.