Optimizing hematology workflows without compromising quality

Small changes to analyzer rules and smarter use of routine CBC parameters can significantly reduce unnecessary smear reviews while maintaining clinically meaningful detection

15 Jul 2026
Lucy Lawrence Image
Lucy Lawrence
Science Editor

Modern hematology laboratories are under increasing pressure to deliver fast, accurate results while managing rising test volumes and ongoing staffing constraints. At the same time, missing clinically significant abnormalities is not an option. To balance these demands, hematology analyzers are typically configured for high sensitivity, generating a large number of flags that trigger manual peripheral blood smear review.

While this approach reduces the risk of missed findings, it also creates a significant workload reviewing smears that often do not yield clinically relevant information. The challenge is not simply reducing the number of manual reviews, but identifying which ones genuinely add diagnostic value.

Dr. Olga Pozdnyakova, MD, Ph.D., Director of the Hematopathology Division at Penn Medicine, explains that a more targeted approach is possible. Drawing on experience in both hematopathology and high-volume laboratory settings, her work focuses on optimizing analyzer settings and introducing laboratory specific decision rules that support hematology workflow efficiency without compromising clinical confidence.

Sensitivity alone is not enough

One of the main drivers of unnecessary smear reviews is how analyzers are configured. "The key factor is that the hematology analyzers that trigger manual reviews are set to be very sensitive, but not very specific," Pozdnyakova explains.

This is intentional. Manufacturers design analyzers to prioritize detection and minimize the risk of missing abnormalities. However, laboratories often continue to use default settings long after implementation, even when workflows and patient populations differ.

The result is a high false positive rate and a growing number of manual reviews that do not add clinical value. Each unnecessary review takes time, delays reporting, and reduces capacity for cases that require expert attention.

Tailoring manual review criteria to laboratory need

There is no single benchmark for the ideal manual smear review rate. As Pozdnyakova notes, “there is no standard manual review rate.” Laboratories vary widely in the patients they serve. A community laboratory processing routine samples will have very different requirements from a tertiary center managing complex hematology and oncology cases.

Applying a single standard operating procedure across all settings often leads to conservative rules that increase workload without improving patient care. Instead, laboratories should develop decision rules that reflect their specific clinical priorities and patient populations.

Defining what matters clinically

Improving efficiency does not mean lowering standards. It starts with defining which findings are critical to detect. "You need to know what you want to find," Pozdnyakova explains. "What is the key finding you do not want to miss?"

Clinical priorities should guide how review criteria are set. In some specialized settings, this may include reviewing all samples regardless of analyzer flags. In others, more targeted approaches can safely reduce workload while maintaining detection of clinically significant abnormalities.

Addressing high impact workflow triggers

At Penn Medicine, platelet-related flags were a major contributor to unnecessary manual review. Although designed to detect giant platelets and platelet clumping, these flags were highly sensitive but not specific. In practice, most smears triggered by these flags did not contain clinically meaningful findings.

Turning platelet flagging off entirely was not an ideal solution. Instead, the team looked for a more effective way to identify cases that truly required review.

Using MPV to refine decision making

The solution came from a routine parameter already included in every complete blood count, mean platelet volume, or MPV.

MPV reflects platelet size and is validated as part of standard CBC testing on the DxH 900 Hematology Analyzer from Beckman Coulter Diagnostics. By combining MPV with platelet count thresholds, the laboratory created a more targeted decision rule.

Validation studies showed clear differences in MPV between patients with normal platelet counts and those with thrombocytopenia. A platelet threshold of 120,000 per microliter, combined with an MPV of 11.5 fL or higher, provided a practical approach to identifying clinically relevant cases, with both sensitivity and specificity around 80 percent.

This approach allowed the laboratory to move beyond single analyzer flags, using multiple data points to improve both clinical relevance and workflow efficiency.

Measurable impact on smear review optimization

The changes delivered clear operational benefits. At Penn Medicine, around 10 unnecessary smear reviews were eliminated per day in a single laboratory, with broader system level reductions reaching more than 300 smears per day.

These improvements freed up technologist time, allowing teams to focus on more complex cases and other critical tasks. For laboratories managing increasing workloads with limited resources, this represents a meaningful gain in capacity.

A collaborative approach to optimization

Optimizing hematology workflows requires more than adjusting analyzer settings. It depends on a clear understanding of both the technology and the clinical context in which it operates.

"It is really collaborative work between the medical director, supervisors, and technologists," Pozdnyakova says.

Successful implementation requires validation, training, and alignment across the laboratory. What works in one setting may not translate directly to another, so changes must be tailored and evaluated carefully.

Ultimately, the goal is not to review fewer smears for the sake of efficiency, but to focus expertise where it has the greatest clinical impact. As testing volumes continue to rise, combining refined analyzer rules with laboratory specific decision making offers a practical route to improved efficiency and high-quality patient care.


Lucy Lawrence, Science Editor at SelectScience, spoke with Dr. Olga Pozdnyakova, MD, Ph.D., Director of the Hematopathology Division at Penn Medicine.

Frequently asked questions

How did Penn Medicine optimize hematology smear review workflows using MPV and platelet count on the DxH 900 Hematology Analyzer?

By combining mean platelet volume (MPV) with platelet count thresholds on the DxH 900 Hematology Analyzer from Beckman Coulter Diagnostics, Penn Medicine created a targeted decision rule. A platelet count below 120,000/µL plus MPV ≥11.5 fL identified clinically relevant thrombocytopenia cases with about 80% sensitivity and specificity, reducing unnecessary manual peripheral blood smear reviews.

What role did Dr. Olga Pozdnyakova and Penn Medicine play in improving hematology analyzer flagging and manual smear review rates?

Dr. Olga Pozdnyakova, Director of the Hematopathology Division at Penn Medicine, led efforts to refine hematology analyzer settings and implement laboratory-specific decision rules. By focusing on clinically meaningful findings and adjusting platelet-related flagging using MPV and platelet count, her team reduced false positives, optimized smear review rates, and improved workflow efficiency without compromising diagnostic confidence.

How did refining platelet-related decision rules impact manual smear review workload across Penn Medicine laboratories?

At Penn Medicine, platelet-related flags were a major source of unnecessary manual smear reviews. After implementing MPV- and platelet count–based decision rules, a single laboratory eliminated about 10 unnecessary smear reviews per day. Across the broader health system, the optimization reduced more than 300 smears per day, freeing technologists to focus on complex hematology and oncology cases.

Links

Tags

HematologyIn Haematology / Hematology, complete blood cell counts (or full blood counts) are obtained using automated blood count analyzers to enumerate blood cell types.  Hematology also encompasses haemostasis and coagulation, thrombophilia and hemophilia, plasma viscosity and ESR analysis, hemoglobinopathies, cell morphology and haematinic measurement.Clinical decision making