Impact of a dedicated consult shift on reducing time to resolution of diagnostic disagreements: A quality improvement initiative

Abstract

BACKGROUND

Delays in resolving diagnostic disagreements within the quantitative imaging analysis core can impede clinical workflow and compromise patient care. To address this, a dedicated consult shift was initiated in 2023. This intervention included real-time monitoring through a dashboard overseen by an imaging specialist who alerted the designated consultant for immediate evaluation and resolution.

AIM

To evaluate the impact of a dedicated consult shift on diagnostic disagreement resolution times and operational efficiency.

METHODS

This retrospective quality improvement study analyzed timestamp data from 1245 cases of diagnostic disagreement spanning the period from 2017 to 2025. Cases were stratified into two groups: Pre-implementation (2017-2022) and post-implementation (2023-2025). The primary metric was time to resolution in days. Both means and medians were compared using an independent samples t-test and a Mann-Whitney U test, respectively.

RESULTS

The average time to resolution significantly decreased from 100.58 days (2017-2022) to 33.05 days (2023-2025) (t = 10.02, P < 0.0001). Additionally, the median time to resolution dropped from 20.90 days to 5.02 days, a statistically significant reduction confirmed by the Mann-Whitney U test (U = 241577, P < 0.0001).

CONCLUSION

The introduction of a dedicated consult shift, supported by real-time dashboard tracking, led to a significant improvement in both average and median resolution times for diagnostic disagreements. This intervention optimized the workflow and reinforced quality assurance processes in a clinical trial imaging setting.

Key Words: Diagnostic disagreements; Quantitative imaging analysis core; Radiology reporting; Clinical trial operations; Workflow optimization; Quality improvement

Core Tip: This study evaluates the implementation of a dedicated consult shift supported by real-time dashboard tracking, which improved resolution times for diagnostic disagreements. After implementation, average and median resolution times improved by more than 67% and 76%, respectively. These post-intervention improvements demonstrate the value of structured quality assurance interventions in enhancing operational efficiency and reducing delays in patient care and clinical trial reporting.

INTRODUCTION

Diagnostic disagreements in radiology reporting pose a significant challenge for clinical trial operations, oncology care, and quality assurance across complex healthcare systems[1,2]. As cancer care has become increasingly evidence-driven and imaging-centric, the workflow for finalizing trial imaging reports must strike a balance between speed, accuracy, and collaborative oversight[3]. The quantitative imaging analysis core (QIAC) at major academic medical centers plays a central role in adjudicating imaging findings for oncology trials; its ability to rapidly resolve diagnostic disagreements is vital to maintaining the integrity and continuity of clinical research as well as informing real-time treatment decisions[4].

Historically, disagreement resolution in radiology departments has required a multi-step administrative process, including asynchronous communication between physicians and quality assurance teams, as well as periodic review cycles[5-7]. Such workflows are inherently subject to delays, especially when complex cases cross multiple subspecialties or involve ambiguous findings. Prolonged time to resolution can hinder trial progression, introduce bias in outcome adjudication, and delay patient management. Recent calls for operational innovation in radiology have highlighted the need for structured process improvements, real-time data tracking, and agile escalation protocols to address these bottlenecks[8].

To address these limitations, the QIAC implemented a dedicated consult shift intervention in 2023. This formal workflow utilized real-time dashboard tracking of open disagreements, immediate case notification, and rapid involvement of an on-call radiology consultant, supported by an imaging specialist who oversaw the dashboard. This systematic escalation model was designed to expedite the evaluation and adjudication process, decrease time to resolution, and foster continuous workflow improvements. By stratifying cases into pre- and post-implementation periods, the present study aims to objectively quantify the impact of the consult shift intervention on time to disagreement resolution and to examine shifts in operational efficiency over time.

This quality improvement study leverages the record of diagnostic disagreements reviewed by QIAC from 2017 to 2025, with robust timestamp analytics enabling calculation of resolution intervals for all finalized cases. This study evaluates the impact of a dedicated consult shift intervention on time to resolution of diagnostic disagreements in a high-volume academic radiology setting. These findings may inform broader workflow optimization, process standardization, and quality monitoring in radiology-driven clinical research.

MATERIALS AND METHODS

Study design and setting

This retrospective quality improvement study was conducted at a comprehensive cancer center, analyzing diagnostic disagreement cases reviewed by the QIAC between January 2017 and May 2025. The QIAC provides centralized imaging review and quality assurance for all cancer clinical trials at the institution. Due to the retrospective nature of this study, unaccounted confounding factors (e.g., staffing changes, case mix, dashboard modifications, or concurrent initiatives) may have influenced outcomes independent of the consult shift.

Data collection

All formal diagnostic disagreements logged in the QIAC database during the study period were extracted. Each case entry included timestamps for disagreement initiation and resolution, the type of disagreement [physician/investigator (PI) or quality assurance (QA)], and the departmental source. Only cases with complete resolution data (both disagreement date/time and resolution date/time) were included in the time-to-resolution analysis. All disagreements used for analysis had documented resolution timestamps. Implementing this filter could have resulted in the exclusion of unresolved or delayed cases. Because reports without documented resolution times were not utilized, the total number of excluded cases could not be assessed.

Intervention

A dedicated consult shift for resolving real-time imaging disagreements was implemented in January 2023. This intervention included a centralized dashboard that tracked all open disagreements. An on-call radiologist (“Consultant”) and imaging specialist facilitated immediate notification, review, and adjudication of newly raised disagreements as part of their routine activities. Comparisons were made between pre- (2017-2022) and post-intervention (2023-2025) periods.

Outcome measures

The primary outcome was time to resolution, defined as the interval (in calendar days) between the disagreement date/time and resolution date/time. Secondary measures included the distribution of disagreement types and departmental patterns.

Statistical analysis

Disagreement cases were stratified into two periods: Pre- and post-intervention. Time-to-resolution was summarized using means, medians, standard deviations, and interquartile ranges. Differences between groups were evaluated by an independent samples t-test (mean) and a Mann-Whitney U test (median) due to non-normal data distribution. Effect size was calculated using Cohen’s d. Statistical significance was set at P < 0.05. Data analyses were performed using SPSS version 22.

Ethical considerations

This quality improvement initiative was conducted in accordance with institutional quality assurance and performance improvement guidelines, and a formal institutional review board review/waiver was obtained as indicated. All data were de-identified and managed in accordance with institutional standards for clinical audit and quality research.

RESULTS

The implementation of the dedicated consult shift intervention resulted in substantial improvements in the efficiency of resolving diagnostic disagreements within the QIAC workflow (Figure 1). Analysis of 1245 resolved cases from 2017 to 2025 revealed that the average time to resolve disagreements decreased dramatically after the intervention, from 100.6 days in the pre-implementation period (2017-2022) to just 33.0 days in the post-implementation era (2023-2025) (Figure 2). Notably, the median time to resolution decreased by 76%, from 20.9 days to 5.0 days, signifying a significant shift toward expedited case review and closure (Table 1).

Figure 1 Diagnostic disagreement workflow before and after the consult shift. QA: Quality assurance.

Figure 2 Comparisons of “days to resolution” between time periods: 2017-2022 (pre-consult shift) and 2023-2025 (post-consult shift).

Table 1 Descriptive statistics.

Metric	2017-2022	2023-2025
Number of cases	601	643
Mean (days)	100.58	33.05
Standard deviation (days)	151.80	67.51
Median (days)	20.90	5.02
Min-max (days)	0.00-576.91	0.00-322.59

Both parametric and non-parametric statistical tests confirmed these findings were highly significant (t-test and Mann-Whitney U, P < 0.0001), and the effect size was moderate, demonstrating genuine operational impact (Table 2). The results suggest that real-time monitoring, structured escalation, and a formal consult workflow were associated with reduced diagnostic delays in complex radiology quality assurance environments. These workflow enhancements may accelerate clinical trial reporting and support rapid and reproducible quality improvement in high-volume academic imaging settings.

Table 2 Median comparison of time to resolution (days).

Metric	2017-2022	2023-2025
Median (days)	20.90	5.02
Statistical test used	Mann-Whitney U
U-statistic	241577.0
P value	2.21 × 10-14

The difference in medians between the two periods is statistically significant (P < 0.0001), further supporting that the consult shift substantially reduced the time to resolution of diagnostic disagreements.

DISCUSSION

This quality improvement initiative demonstrates that structured workflow interventions can significantly accelerate the resolution of diagnostic disagreements in high-volume cancer imaging core facilities. The implementation of a dedicated consult shift model, supported by real-time dashboard monitoring and immediate case escalation protocols, achieved a substantial 67%-76% reduction in time to resolution from a median of 20.9 days in the pre-implementation period (2017-2022) to 5.0 days in the post-implementation era (2023-2025), representing a clinically meaningful and operationally significant improvement.

The success of the QIAC consult shift intervention mirrors findings from other institutions that have implemented similar structured approaches to imaging workflow management and quality assurance. A 2025 study from Oman demonstrated that standardized operating procedures, formal triage systems, and regular interdepartmental case discussions improved overall turnaround time performance from 88% to 95% over a 10-month period, highlighting the critical importance of systematic processes and communication in achieving consistent quality metrics[9].

Similarly, previous groundbreaking work at MD Anderson Cancer Center demonstrated that implementing a web-based QIAC workflow platform reduced the turnaround time for quantitative tumor metric reports from 31.7 hours to 15.9 hours (P = 0.0005), representing a 50% improvement[10]. More recently, innovations such as dual digital alert systems and automatic radiologist rescheduling protocols have further streamlined QIAC report delivery, supporting timely therapeutic decision-making in oncology trials. These findings collectively support the notion that digital infrastructure, coupled with process redesign, drives sustainable operational improvements in radiology quality assurance and demonstrate that incremental refinements to existing successful models can yield additional gains.

The reduction in variability observed in this study is particularly noteworthy and clinically important. The 55.5% decrease in standard deviation (from 151.80 days to 67.47 days) and the 85.3% decrease in interquartile range (from 141.49 days to 20.74 days) indicate that resolution times have become not only faster but also significantly more predictable and consistent. Consistency in resolution times is crucial for clinical trial planning and patient management, enabling more reliable forecasting of report finalization timelines and reducing uncertainty in study workflows. However, persistent outliers extending to 322 days in the post-implementation period indicate that opportunities for further refinement remain.

In addition, adjunct operational models may help reduce delays, and the development of radiology e-consult services has been shown to reduce unnecessary imaging, decrease specialty referrals, and improve access to radiology expertise while simultaneously reducing workflow interruptions for radiologists[11]. These complementary approaches underscore the potential for multifaceted interventions to optimize radiology operations across multiple dimensions.

Limitations of the study

Data quality: The analysis relied on previously recorded operational data from a single center, which may be incomplete or inconsistently documented, potentially leading to issues with data quality and accuracy.

Selection bias: Only cases with complete timing information for both disagreement and resolution were included. This might exclude unresolved or delayed cases and could underestimate the true time to resolution or underrepresent more complex scenarios.

Confounding factors: Due to the study’s multi-year design, unmeasured changes in staffing, technology (e.g., dashboard enhancements), clinical trial complexity, or case mix over time could influence results independent of the consult shift intervention. Differences between pre- and post-implementation periods may be subject to temporal trends or other concurrent quality improvement measures, making attribution solely to the consult shift less certain.

Generalizability: Findings may be specific to the workflows, staffing, and consult shift procedures at our institution and may not be directly applicable to other centers with different structures or processes.

Outcomes and scope: The primary endpoint was operational (time to resolution), and the study did not systematically assess the clinical impact (patient outcomes, downstream care delays, etc.) of disagreement resolution timing. Additionally, the study did not incorporate systematic perceptions or satisfaction surveys from radiologists, referring clinicians, or patients relating to workflow changes and their impact.

Future implications

The principles demonstrated in this work—real-time monitoring, structured escalation, interdisciplinary collaboration, and data-driven process improvement—extend well beyond QIAC operations and can be adapted for use in other high-volume radiology workflows, including emergency radiology departments, teleradiology networks, and community and private practice settings. Future research should investigate whether accelerated disagreement resolution directly translates into improved clinical trial outcomes, reduced protocol deviations, enhanced patient satisfaction, and more cost-effective metrics.

CONCLUSION

The implementation of a dedicated consult shift, supported by real-time dashboard monitoring and rapid escalation protocols, resulted in a significant and sustainable reduction in the time to resolution of diagnostic disagreements within the QIAC radiology workflow. Average and median resolution times improved by more than 67% and 76%, respectively, following the intervention, with robust statistical significance and a clinically meaningful effect size. These process improvements demonstrate the value of structured quality assurance interventions in enhancing operational efficiency and reducing delays in clinical trial reporting. The interpretation of these results is limited by the single-center, retrospective design of the study and the absence of measured subsequent clinical outcomes. Furthermore, continued monitoring and adaptation will help ensure that the gains achieved are sustained and guide further refinements in diagnostic workflow management for multicenter oncology trials and radiology-driven clinical research.