A plausibility range, sometimes called a critical range or biological limit, defines the absolute minimum and maximum values that are biologically possible for a specific laboratory test. Unlike reference ranges, which indicate what is normal, plausibility ranges indicate what is physically possible in a living human being. A glucose value of 5,000 mg/dL or a hemoglobin of -3 g/dL are not just abnormal — they are biologically impossible and indicate a data error rather than a clinical finding.
Plausibility ranges serve as a critical quality control mechanism in lab data processing. When digitizing lab reports through OCR, the system may occasionally misread digits, confuse decimal points, or concatenate values from adjacent columns. A misread hemoglobin of 145 g/dL (instead of 14.5) would pass many validation checks — it is a valid number with a valid unit — but it far exceeds the plausibility range for hemoglobin (typically 3-25 g/dL). Flagging this value as implausible triggers a review process that catches the error before it enters a clinical system.
Implementing plausibility ranges requires maintaining a comprehensive database of biologically possible ranges for each laboratory test. These ranges must account for extreme clinical scenarios — a glucose of 800 mg/dL is extraordinarily high but can occur in diabetic ketoacidosis, while a sodium of 110 mEq/L is dangerously low but physiologically possible. The ranges are typically set wider than any clinically observed value to avoid false positives, with multipliers based on the reference range serving as practical guidelines.
In a lab report digitization pipeline, plausibility checking works alongside other validation steps. After a value is extracted by OCR and parsed by the NLP system, it is checked against the plausibility range for its corresponding LOINC code. Values outside the plausibility range are flagged for manual review or corrected through alternative extraction methods. This multi-layered validation approach significantly improves the accuracy and safety of automated lab data extraction.