Case Study | Pre-Analytical Performance Optimization

5–15%

TAT Improvement

Reduction in tail-end (p95) turnaround time through end-to-end visibility and workflow standardization.

10–20%

Productivity Gain

Increase in samples processed per hour through role clarity, task alignment, and elimination of duplicated effort.

$13.5M+

Peak Annual Value

Structural redesign scenario combining efficiency gains and revenue protection through improved service reliability.

6–8 Wks

Roadmap Timeline

From Discovery to a fully specified, implementation-ready pre-analytical operating model blueprint.

Executive Summary

A Multi-Layered Financial Opportunity Embedded in the Current Operating Model

LTS Global partnered with a premier national diagnostic laboratory network to evaluate pre-analytical performance across the end-to-end sample journey, from collection and transport through depot handling, laboratory intake, and accessioning.

Through the proprietary LTS Impact Framework, the Discovery Phase established that while the network consistently met overall service demands, pre-analytical workflows had evolved through local adaptation rather than standardized, network-wide design. The result was a system characterized by structural inefficiency, not capacity shortage.

The analysis identified a clear, three-layer value opportunity: $25.6M in total addressable workforce capacity value, $2.2M–$4.9M in near-term realisable efficiency gains, and $2.0M–$7.5M in revenue protection and growth, driven by improved turnaround time reliability and referral retention.

Critically, these three value layers are not additive. The consolidated financial impact reflects only incremental, non-overlapping value achievable through structured transformation, ensuring that the identified opportunity is credible, defensible, and investment-grade.

Key quantitative findings from the LTS Global Pre-Analytical Discovery Phase.

Financial Opportunity

Three Layers of Value: From Embedded Capacity to Strategic Growth

The financial opportunity identified in the pre-analytical network spans three interconnected but distinct value dimensions. Understanding this hierarchy is essential to investment decision-making: the layers are not directly additive, and each requires a different implementation mechanism to realize.

Layer 1: Capacity Value

$25.6M

Total addressable workforce capacity value, representing the financial equivalent of improved utilization if all pre-analytical sites operated at internally demonstrated benchmark performance. This is embedded potential, not an immediate cost reduction target, realized progressively through role redesign, task alignment, and hiring avoidance.

Layer 2: Efficiency Gains

$2.2M–$4.9M

Realisable near-term savings achievable through targeted process improvements: eliminating duplication of verification steps, standardizing workflows across comparable environments, resolving role misalignment, and reducing manual workarounds. These represent the practical mechanism through which the broader capacity opportunity is progressively unlocked.

Layer 3: Revenue Protection

$2.0M–$7.5M

Strategic upside driven by improved TAT reliability and service consistency. Pre-analytical performance is a direct driver of clinician confidence and referral behavior. Tail-end delays, inconsistent priority handling, and high rework rates are directly visible at the point of care, and directly influence the organization's ability to retain and grow market share.

Three-layer value hierarchy: Total Addressable Capacity, Realisable Efficiency Gains, and Revenue Protection & Growth. Values are presented as USD.

The Challenge

Systemic Inefficiency at Scale: Five Structural Drivers Across the Pre-Analytical Value Chain

The Discovery Phase established that performance variability and inefficiency across the pre-analytical network were not the result of isolated operational issues or capacity constraints. They were the product of five recurring structural patterns embedded consistently across comparable workflows and intake environments, confirming that the current operating model achieves performance through local adaptation rather than standardized design.

Reactive Control Environments

Excessive duplication of verification steps, including multiple manual data capture checks at intake, secondary verification by additional staff, and technologist re-verification, introduced bottlenecks and increased labor cost without improving quality outcomes. The root cause was process design, not human error.

Process Standardization Gaps

Workflows varied significantly across comparable sites, resulting in uneven performance, unpredictable throughput, and an inability to apply network-wide improvement initiatives. Six defined priority categories, including Hospital, STAT, Urgent, and Routine, were not operationalized within workflows, resulting in uniform processing irrespective of clinical urgency.

Data and Visibility Limitations

The absence of end-to-end timestamp visibility across the sample journey prevented proactive identification and management of delay drivers. The non-conformance logging framework, comprising approximately 228 selectable incident types, diluted analytical signal, limiting root cause identification and corrective action effectiveness.

Workforce Misalignment

Role boundaries were not consistently defined or enforced, resulting in overlapping responsibilities across data capture, specimen reception, and accessioning. Clinical staff, including phlebotomists, were frequently required to perform administrative duties, diverting skilled resources from patient-facing activities and reducing overall productivity.

System and Technology Integration

Core operational and analytical processes required manual extraction of data from systems, manipulation in external tools, and re-documentation across multiple platforms. The majority of sites operated at Level 2–3 on the Pre-Analytical Maturity Scale, partial automation with limited integration, constraining scalability, consistency, and control.

Concentration of Quality Failures

Pareto analysis confirmed that approximately 80% of all non-conformance incidents were concentrated in three categories: data capturing errors, billing errors, and specimen rejection. These patterns reflected systemic process control weaknesses rather than isolated human error, and created a chronic rework environment with direct cost implications.

Value Bridge

From Structural Driver to Financial Impact: The Pre-Analytical Value Bridge

The table below links each core structural driver directly to its operational impact, associated financial consequence, and the targeted Roadmap Phase intervention required to unlock the identified value. This bridge ensures that the financial opportunity is grounded in observable operational evidence, not theoretical benchmarks.

Core Driver	Observed Inefficiency	Operational Impact	Financial Implication	Roadmap Intervention
Reactive Control	Excessive duplication of verification steps across intake and reception	Bottlenecks at intake; increased handling time per sample	Elevated labor cost due to redundant administrative effort	Error-proofing, digitization, and process redesign
Process Standardization	Workflow variation across comparable sites; priority categories not operationalized	Inconsistent TAT performance; unpredictable throughput	Inefficiency across comparable environments; missed SLA targets	Standardized operating model with priority-based routing
Data Visibility	Lack of end-to-end timestamp visibility; 228 non-conformance categories	Reactive decision-making; inability to manage tail-end delays	Missed optimization opportunities; sustained rework costs	Digital insight platform and timestamp architecture redesign
Workforce Misalignment	Role overlap; clinical staff performing administrative tasks	Bottlenecks in critical workflow stages; reduced productivity	Reduced samples per FTE; increased cost per test	Workforce model redesign and task allocation framework
System Integration	Manual workarounds; fragmented systems; Level 2–3 maturity	High administrative burden; process bottlenecks; data integrity risk	Hidden operational costs due to system-driven inefficiency	Automation, system integration, and maturity uplift strategy

Discovery Analysis

Quantifying the Drivers: Estimated Annual Savings by Inefficiency Category

The chart below disaggregates the total realisable efficiency opportunity by structural driver. Each estimate is grounded in observed operational variation and validated through site engagement across the network, confirming that the identified patterns are systemic and network-wide rather than site-specific anomalies.

Pre-Analytical Efficiency Breakdown Chart

Estimated annual savings by structural driver (USD). Ranges reflect low and high improvement scenarios based on observed operational variation.

The LTS Impact Approach

Evidence-Based Discovery: The LTS Impact Framework and Pre-Analytical Performance Index

LTS Global deployed the Discovery Phase of the LTS Impact Framework to transition the client from localized performance observation to a structured, network-wide understanding of pre-analytical performance. The framework follows a four-phase methodology, Discovery, Roadmap, Implementation, Sustain, designed to reduce transformation risk at every stage by ensuring that each phase builds directly on the validated outputs of the previous one.

The LTS Impact Framework: four integrated phases from Discovery to sustained pre-analytical performance.

Pre-Analytical Performance Index (PPI)

A Proprietary Scoring Framework for Network-Wide Performance Management

A key output of the Discovery Phase was the development of the Pre-Analytical Performance Index (PPI), a proprietary, multi-dimensional scoring framework designed to consolidate complex performance data into a single, actionable metric for each site across the network.

The PPI evaluates performance across three dimensions, staff productivity, process maturity, and turnaround time, weighted to reflect their relative operational significance. This enables consistent, like-for-like comparison across sites and provides leadership with a structured basis for prioritizing improvement initiatives, validating operating model design decisions, and tracking performance improvement during implementation.

Productivity

20%

Requisitions processed per hour per role, benchmarked against the 85th percentile of internally observed performance. Evaluated at role level to reflect the sequential nature of pre-analytical workflows.

Process Maturity

40%

Assessed through a structured survey evaluating automation, standardization, and system integration across pre-analytical processes. Scored on a four-point scale from Manual (1.0–2.0) to Fully Integrated (3.6–4.0).

Turnaround Time

40%

95th percentile TAT for Urgent and Hospital sample categories, weighted by relative test volume. Focused on clinically critical workflows where timeliness is both measurable and operationally significant.

"The Pre-Analytical Performance Index ensures that performance management is not dependent on individual metrics or local interpretation, but is guided by a standardized, network-wide framework, enabling consistent evaluation, prioritization, and accountability across every site in the network."

LTS Global Methodology Principle

Financial Impact

Scenario-Based ROI: From Conservative Alignment to Structural Redesign

Three improvement scenarios have been defined based on the depth of operational intervention and the associated efficiency improvement range. Across all scenarios, the achievable financial benefit materially exceeds the expected investment required for the Roadmap Phase, establishing a clear and favourable return profile with additional upside potential through full operating model transformation.

Annual value by improvement scenario (USD). Efficiency value reflects process and workforce gains; Revenue Impact reflects referral retention and growth enabled by TAT reliability.

Scenario	Efficiency Improvement	Efficiency Value	Revenue Impact	Total Annual Value	Strategic Interpretation
Conservative	5–10%	$2.2M–$3.8M	Minimal	$2.2M–$3.8M	High-confidence, achievable through low-disruption interventions including process standardization and initial workforce alignment.
Moderate	10–20%	$3.4M–$5.4M	$1.3M–$2.7M	$4.7M–$8.1M	Balanced impact through targeted operational improvements and selective structural changes, including workflow redesign and workforce optimization.
Structural	+20%	$4.7M–$6.7M	$2.7M–$7.5M	$7.4M–$13.5M	High-impact transformation enabled by end-to-end operating model redesign, system enablement, and full workforce optimization.
Total Pre-Analytical Value Range		$2.2M–$13.5M annually, representing a combination of capacity-equivalent workforce savings, operational efficiency improvements, and revenue protection. Not inclusive of total project value.

Operational Insight Platform

From Data Availability to Decision Capability

A critical outcome of the Discovery Phase was the development of an Operational Insight Platform, a suite of integrated analytical dashboards consolidating pre-analytical data into a unified decision-support environment. While the client already generated significant volumes of operational data across transport, depot operations, and laboratory intake processes, the Discovery Phase confirmed that data availability alone does not enable effective performance management.

The platform provides leadership with network-wide visibility of TAT performance against defined targets, staff productivity benchmarks across sites, demand patterns and test volume trends, and structural variation that is not visible through isolated reporting. Critically, the platform extends beyond the Discovery Phase; it serves as the primary mechanism for measuring the impact of transformation initiatives during implementation and sustaining performance improvements over time.

Traditional Reporting vs. Operational Insight Platform

Traditional Reporting	Operational Insight Platform
Shows what happened	Explains why it happened
Static, retrospective	Dynamic, decision-oriented
Site-level visibility	Network-level performance management
Limited actionability	Directly supports operational decisions

Platform Capability Across the LTS Impact Phases

Discovery: Integrated baseline view of network performance, the analytical foundation for all findings presented in this report.

Roadmap: Scenario modeling and operating model validation, ensuring design decisions are grounded in network-specific data.

Implementation: Real-time tracking of performance improvement against defined targets; early identification of sites requiring intervention.

Sustain: Ongoing performance management, detection of emerging gaps, and continuous improvement capability.

The Path Forward

From Insight to Execution: The Pre-Analytical Roadmap Phase

The Discovery Phase confirmed that the client possessed the necessary data and visibility to understand performance. The constraint was structural: the absence of a consistent, network-wide mechanism to translate pre-analytical insights into executed and sustained improvement at scale. The Roadmap Phase addresses this gap by converting analytical insight into a fully specified, implementation-ready pre-analytical operating model.

Standardized Pre-Analytical Operating Model

A defined, network-wide operating model aligned to throughput requirements, eliminating workflow variation, standardizing priority-based routing, and reducing duplicated and non-value-adding activities across all site categories.

Workforce and Capacity Alignment

Intake capacity models aligned to workload demand and peak volumes, with clearly defined roles, task allocation frameworks, and workforce productivity models, resolving the role overlap and administrative burden identified during Discovery.

TAT Performance Management Framework

Defined TAT targets by priority category, end-to-end timestamp architecture, structured delay management and escalation protocols, and tail-end (p95) management mechanisms, enabling proactive rather than reactive performance management.

Quality and Non-Conformance Framework

Rationalization of the non-conformance logging structure from 228 to a manageable set of actionable categories, integration of root cause and corrective action tracking, and development of quality performance dashboards, transforming the non-conformance system from a descriptive tool into a structured quality management capability.

Digital Enablement and System Integration

System integration requirements, automation of quality control mechanisms, reduction of manual workarounds, and a maturity uplift strategy, transitioning the network from a fragmented, manually supported environment to a standardized, system-enabled pre-analytical model.

Governance and Value Realization Framework

Performance monitoring mechanisms, structured planning cycles, escalation protocols, and a quantified, trackable value realization pathway, ensuring that the opportunity identified during Discovery is translated into measurable, sustainable outcomes across the network.

Conclusion

Systemic Insight. Credible Value. Executable Transformation.

Through the LTS Impact Framework, LTS Global provided this leading diagnostic network with the first integrated, evidence-based view of pre-analytical performance across the entire sample journey. By identifying systemic structural inefficiencies, rather than isolated operational issues, and quantifying their financial impact across three distinct value layers, LTS Global delivered a credible, investment-grade case for transformation.

The pre-analytical optimization opportunity is not theoretical. It is grounded in operational evidence validated through both network-wide data analysis and direct site engagement. The identified value, up to $13.5M annually in the structural redesign scenario, is achievable through a structured, phased approach that prioritizes high-impact, low-disruption interventions first, building organisational momentum before progressing to more complex operating model changes.

The Roadmap Phase is the critical next step: translating analytical insight into a defined operating model, a prioritized implementation plan, and a governance framework capable of sustaining performance improvement at network scale. LTS Global remains a committed partner across every phase of this journey.

Unlocking $13.5M+ in Annual Pre-Analytical Value for a National Laboratory Network