The Autopsy System: Why Problems Are Discovered Too Late

We have all been there.

A result gets queried, someone tracks down the glitch, files a non-conformance report, and a memo goes out. For a few weeks, everyone is alert and engaged. Then, a month later, another bug slips through the same gap.

That is the loop you get when a quality framework is built around detection instead of prevention. It doesn’t actually stop errors; it just documents them accurately. It’s basically an autopsy system.

This isn’t a skills issue. Laboratory leadership teams are highly experienced and operate within well-established quality frameworks. Instead, the flaw is structural because the system is rigged to react to failure rather than design it out of existence.

Transitioning from a reactive to a proactive culture requires an organizational capability framework such as the Laboratory Management Improvement Program (LMIP), which aligns leadership and accountability to address these structural gaps.

Mario Plebani’s classic iceberg model¹ has been referenced in laboratory medicine for decades, and for good reason. It reframed where the real risk actually lies.

For years, the bulk of improvement investment went into the analytical phase. Laboratories poured money into better automation, tighter QC, and razor-sharp analyzer precision. That work paid off because analytical errors dropped significantly².

Yet analytical errors were never the main problem. Between 60% and 70% of laboratory errors occur in the pre-analytical phase³, specifically during ordering, collection, labeling, and transport. Most of the damage is done before the sample is anywhere near the machine.

That creates an awkward imbalance. The most controlled part of the process carries the lowest error burden, and the least controlled part carries the highest.

When a system is entirely reactive, the operational headache is bad enough, but the clinical cost is harder to absorb. Recent data published in Vascular and Endovascular Review shows that pre-analytical and post-analytical errors introduce a massive diagnostic delay⁴. That is a multi-hour window of paused clinical decision-making just because of a process failure upstream.

At a systemic level, diagnostic error contributes to hundreds of thousands of preventable patient harms annually in the United States alone⁵. For laboratory directors, that isn’t an abstract figure. It shows up as delayed treatment, repeat testing, and immense pressure on a system that is already running close to capacity.

When something goes wrong, the default reflex is usually to address the person closest to the error. They are retrained, reminder notes are sent out, and the assumption is that the problem is behavioral.

Evidence suggests otherwise. Human factors do contribute to laboratory error, but rarely happen in isolation⁶. Integrity lapses and process breakdowns emerge from system design, fragmented workflows, communication gaps, and processes that are easy to do wrong and hard to get right⁷.

When errors are treated as personal failure, the response will always be reactive. If they are treated as system outputs, they become something you can design around.

Laboratories are starting to move the needle. Industry success stories show that moving away from retrospective error tracking toward real-time checking during the collection process intercepts problems before results were compromised, not after⁸.

The IFCC has developed specific quality indicators oriented around earlier detection and prevention⁹. The Joint Commission also frames proactive patient safety systems as a core requirement rather than an optional improvement layer¹⁰.

The tools exist. The harder question is whether they are being adopted at a system level or whether detection is still functioning as the primary control.

At LabVine, this is one of the questions we keep coming back to. We are less interested in how laboratories can find problems faster, and more focused on how they can build systems where fewer issues slip through in the first place.

If you want to look at the practical layout of how to fix this, join us for our upcoming Activation Session. We will be working through the anatomy of delayed discovery and looking at practical approaches to building more proactive laboratory systems. It is well worth the time.

For those ready to implement these changes at scale, explore our capability programs:

LMIP: Build a leadership system that prioritizes prevention over detection.

LTI: Transform your laboratory operations through structured, applied improvement projects.

[1] Plebani, M. (2009). Exploring the iceberg of errors in laboratory medicine. Clinica Chimica Acta.

[2] Plebani, M. (2012). Quality Indicators to Detect Pre-Analytical Errors in Laboratory Testing. Clinical Biochemistry Reviews.

[3] Nordin, N., et al. (2024). Preanalytical Errors in Clinical Laboratory Testing at a Glance: Source and Control Measures. Cureus.

[4] AlSahly, S. N. S., et al. (2024). Evaluating Pre-analytical and Post-analytical Errors in Laboratory Processes and Their Impact on Diagnostic Delay and Patient Safety. Vascular and Endovascular Review.

[5] Newman-Toker, D. E., et al. (2024). Burden of serious harms from diagnostic error in the USA. BMJ Quality & Safety.

[6] Badrick, T. (2023). Identifying human factors as a source of error in laboratory diagnostics. Journal of Laboratory and Precision Medicine.

[7] Cardinal Health. (2023). Specimen Integrity: How Errors Occur.

[8] Melanson, S. (2019). Success Stories and Solutions for Preanalytical Challenges. CLN Stat, Association for Diagnostics & Laboratory Medicine (ADLM).

[9] Sciacovelli, L., et al. (2017). Quality indicators in laboratory medicine: the status of the progress of IFCC Working Group ‘Laboratory Errors and Patient Safety’ project. Clinical Chemistry and Laboratory Medicine.

[10] The Joint Commission. (2026). Patient Safety Systems (PS).