1  Introduction

1.1 Background

Intradepartmental consultations represent a fundamental quality assurance mechanism in surgical pathology, enabling pathologists to seek input from colleagues with specialized expertise when confronted with diagnostically challenging cases. Studies have demonstrated that in-house consultations result in a diagnostic change in approximately 4.3% of cases, with around 1% being clinically significant (Renshaw et al. 2002). Mandatory peer review programs have documented discordance rates of 3.1% across thousands of surgical pathology cases (Solivas and Diwa 2024), while diagnostic error rates in anatomic pathology more broadly range from 1% to 9% depending on specimen type and subspecialty (Peck et al. 2018).

The value of intradepartmental consultation varies substantially by subspecialty. Breast biopsy concordance among pathologists has been reported as low as 75.3%, with atypia diagnoses showing only 48% agreement (Elmore et al. 2015). Soft tissue pathology carries a 25% major diagnostic discordance rate in expert second opinions (Peck et al. 2018), while intraoperative frozen section consultations achieve 99.4% concordance with final diagnoses (Wang, Leng, and Rampisela 2021). Second-opinion pathology review has demonstrated both clinical value — altering management in a significant proportion of cases (Farooq et al. 2021) — and financial implications for health systems (Johnson et al. 2021). These subspecialty-specific differences underscore the importance of understanding consultation patterns at the section level.

The advent of digital pathology has fundamentally transformed how consultations are initiated, tracked, and completed. Whole slide imaging (WSI) has demonstrated 98.3% overall concordance with light microscopy across 25 studies (Azam et al. 2021), and digital telepathology workflows have reduced consultation turnaround time from 86 hours to 35 minutes – a 98% reduction (Haghighi et al. 2021). The COVID-19 pandemic further accelerated digital pathology adoption, with validation studies confirming 100% major diagnostic equivalency for remote digital review (Hanna et al. 2020). Recent multicentre implementation experiences have documented the planning, infrastructure, and change management considerations required for successful integration of digital pathology into clinical laboratory operations (Bruce et al. 2024). Critically, digital systems generate comprehensive, timestamped event logs for every consultation action, providing unprecedented opportunities to analyze these interactions quantitatively (Hanna et al. 2019, 2022).

These analytical opportunities are particularly timely given growing workforce pressures. Over a nine-year period, pathologist workload units rose 23% while case volume decreased 6%, indicating increasing specimen complexity, with blocks per year increasing 20% and report length expanding 19% (Bonert et al. 2021). Activity-based workload measurement systems such as AABACUS have been developed to capture this complexity beyond simple case counts (Cheung et al. 2015), and comparative analyses of workload measurement methodologies have shown that traditional RVU-based systems underestimate workload in subspecialties with high consultation needs (Pantelakos and Agrogiannis 2023). The US experienced a 17.5% decrease in active pathologists from 2007 to 2017 (Metter et al. 2019), creating additional strain on consultation services. Workload maldistribution within departments (Gini coefficients 0.05-0.23) has been associated with significant absences and departures (Bonert et al. 2022).

1.2 Research Gap

Despite the recognized importance of intradepartmental consultations and the growing availability of digital event logs, there remains a paucity of studies that systematically analyze the operational dynamics of consultation workflows in fully digital pathology environments. Most existing literature has focused on diagnostic concordance rates or the clinical value of second opinions, while the operational characteristics — network topology, turnaround time determinants, workload distribution, and temporal patterns — have received comparatively less attention. Furthermore, the few published studies examining consultation workflows have been conducted in hybrid glass/digital laboratories, where the confounding influence of parallel analog processes limits the interpretability of findings. No published study, to our knowledge, has comprehensively characterized the consultation ecosystem of a laboratory operating entirely without a glass-slide pathway, where every consultation event is digitally captured and amenable to quantitative analysis.

1.3 Rationale for Consultation Analysis

The College of American Pathologists (CAP) and the Association of Directors of Anatomic and Surgical Pathology (ADASP) have established evidence-based guidelines positioning intradepartmental consultation as a primary mechanism for interpretive diagnostic error reduction (Nakhleh et al. 2016). Electronic LIS-based tracking systems have enabled systematic documentation and analysis of consultation workflows, replacing paper-based methods and facilitating both quality assurance and workload assessment (Dunbar et al. 2022). The present study builds on this foundation by exploiting the rich event-log data generated in a fully digital environment.

Systematic analysis of intradepartmental consultations addresses several interconnected needs:

  1. Quality Assurance: Tracking consultation patterns helps identify cases requiring multiple expert opinions and ensures appropriate peer review. Standardized consultation processes vary between academic and community settings, with 92% of academic and 90% of community pathologists also requesting undocumented informal consultations (Goebel, Ettler, and Walsh 2018).

  2. Workflow Optimization: Analysis of turnaround times can reveal bottlenecks and opportunities for improving response efficiency. Digital pathology implementation has been shown to reduce surgical case turnaround time by one day and decrease archive retrieval requests by 93% (Hanna et al. 2019).

  3. Resource Allocation: Understanding who consults whom and at what volume may help optimize staffing and subspecialty coverage. Current workload measurement approaches vary widely — wRVU productivity differs 4- to 7-fold between subspecialties (Parkash et al. 2018), and no standardized workload measurement tool exists for pathologists (Hanna et al. 2024).

  4. Training and Development: Consultation patterns can inform mentoring relationships and highlight educational needs within a department.

  5. Network Dynamics: Collaboration networks reveal informal subspecialty expertise and communication structures. Perceived expertise is the primary factor in selecting consultation partners, but interpersonal relationships and office proximity also significantly influence informal consultation choices (Goebel, Ettler, and Walsh 2018). Social network analysis methods have been increasingly applied to study professional advice and performance among healthcare providers, with systematic reviews identifying key gaps in the application of these methods to clinical practice settings (Sabot et al. 2017).

1.4 Study Objectives and Research Questions

This study aims to comprehensively characterize the operational dynamics of intradepartmental consultations in a fully digital pathology laboratory. The specific research questions are organized around six domains:

1.4.1 Network Structure

  • Who consults whom within the department, and are there identifiable expert hubs?
  • Do subspecialty clusters or communities emerge from the consultation network?
  • How has the consultation network topology evolved over time?

1.4.2 Efficiency and Turnaround Time

  • What is the distribution of consultation turnaround times, and what factors are associated with prolonged response?
  • How does turnaround time vary by consultant, time of day, and day of week?
  • Are institutional performance targets being met?

1.4.3 Temporal Patterns

  • How has consultation volume changed over time, and are there seasonal, weekly, or daily cycles?
  • Is there an association between time of consultation initiation and response time?

1.4.4 Workload Distribution

  • How equitably is consultation workload distributed among pathologists?
  • What is the balance between requesting and providing consultations for each pathologist?

1.4.5 Case Complexity

  • What proportion of cases require multiple consultations, and are such cases associated with longer resolution times?
  • Are there sequential consultation chains suggesting diagnostic escalation?

1.4.6 Quality Metrics

  • What percentage of consultations are completed within target timeframes, and how do these metrics trend over time?
  • Can outlier cases requiring exceptional response times be identified and characterized?

1.5 Study Scope and Analytical Framework

This study analyzed intradepartmental consultations in a fully digital pathology laboratory using five complementary data sources: consultation initiation logs, consultation completion logs, consultant assignment records, section-level (biopsy workstation) case volumes, and pathologist workload and employment data. The multi-source integration approach, similar to that of Dunbar et al. (Dunbar et al. 2022) who processed 3,049 intradepartmental consultations through an LIS-based tracking module, was designed to maximize data completeness and validity.

The analytical framework encompasses the following dimensions:

  • Descriptive statistics and data quality assessment
  • Network analysis and visualization of consultation flows
  • Statistical hypothesis testing for temporal and responder-level effects
  • Temporal trend analysis using time series methods
  • Section-level consultation rate analysis
  • Per-pathologist consultation rate trends
  • Predictive modeling for turnaround time and volume forecasting
  • Quality metrics and statistical process control

1.6 Expected Contributions

This analysis is intended to provide:

  1. Actionable insights for laboratory management to optimize consultation workflows
  2. Evidence-based recommendations for improving turnaround times and operational efficiency
  3. Characterization of network structure and identification of key consultants within the department
  4. Section-level consultation rates identifying which biopsy sections generate the most consultations and how rates vary by subspecialty
  5. Per-pathologist consultation trends tracking how individual consultation behavior changes over time
  6. Predictive models to forecast consultation demand and anticipate response time delays
  7. Quality metrics aligned with CAP/ADASP quality guidelines (Nakhleh et al. 2016) to support ongoing process monitoring

By systematically analyzing consultation data from a fully digital environment, this study seeks to contribute to the growing evidence base on pathology workflow optimization — addressing a critical need given increasing case complexity (Bonert et al. 2021) and a declining pathologist workforce (Metter et al. 2019). The findings may also inform the design of consultation tracking systems at other institutions transitioning to digital pathology.