A spreadsheet formula error is a patient-result error

A bench technologist opens the morning STAT chemistry summary and finds a turnaround-time (TAT) dashboard that looks clean: seven rows, a chart, a few flags. Nothing on the screen says whether any formula in that workbook is wrong. That is the problem with a spreadsheet error: it does not look like an error. A dropped parenthesis, a copied formula that shifted one row, or a blank cell treated as a zero produces a number that looks exactly as plausible as a correct one.

This is not a rare event. A synthesis of 14 controlled spreadsheet-development studies covering 967 participants found an average cell error rate of about 3.9%, with individual studies ranging roughly 1% to 17% depending on the task and how an error was defined. A separate field audit of 50 operational spreadsheets found errors affecting roughly 1.8% of formula cells overall, and one workbook in that audit had errors in 28% of its formula cells. A small per-cell error rate is enough to make an entire workbook materially wrong, especially once one erroneous cell is copied down a column.

Laboratory regulation treats this as a real risk, not a theoretical one. Under 42 CFR 493.1291, a CLIA-regulated laboratory must maintain systems, manual or electronic, that ensure patient-specific data and results are transmitted accurately, reliably, and in a timely manner from data entry through the final report, and that includes any calculated result. A spreadsheet that computes a flag, a turnaround time, or a summary statistic that reaches a report or a clinical decision is part of that chain, whether or not anyone thinks of it as 'the analyzer.'

Use a small dataset as a bench technologist would: import it without touching the source, build formulas that are tested before they are trusted, and pick a chart that tells the truth about seven rows of data. Treat every formula that touches a patient result as a test-system component, not a convenience calculation.