Document Review With Unsiloed and TypeSafe Jev

说明与用途

将 Unsiloed 与 TypeSafe Jev 结合使用，可检测 OCR 错误并检查药物剂量的合理性，仅将可疑行路由到人工审核。

问题

An extraction pipeline can read a value correctly and still pass bad data downstream; OCR failures can leave incorrect or ambiguous data in extracted fields.

结果

Combining Unsiloed extraction with Jev plausibility checks allows targeted review of only the rows with low confidence or implausible data, improving efficiency and accuracy of medication reconciliation.

工作流

• 1. Extract medication list with Unsiloed using a schema that returns dose, frequency, route, and instructions with confidence scores and citations.
• 2. Assign each row an ID and retain citations for traceability.
• 3. Construct a Jev request with state containing context, careContext, and medications array; include one Noul question per row asking if the dose and frequency are plausible.
• 4. Send the request to Jev (model jev-1.13.0).
• 5. Receive Noul answers (probabilities) and route rows with low plausibility to human review.
• 6. Review original document using citations to verify or correct extraction.

证据

An extraction pipeline can read a value correctly and still pass bad data downstream. A clearly printed extra zero survives excellent optical character recognition (OCR).

来源

Using Unsiloed and TypeSafe’s Jev together helps us check for OCR failures and general mistakes in a document.

来源

We ask one Noul question per row: is this dose and frequency plausible for this medicine? All six rows share one request, with each question evaluated independently.

来源