INTRODUCTION

Colorectal cancer (CRC) remains a leading cause of cancer-related illness and death in the United States. In 2023, an estimated 153,020 new cases of CRC were diagnosed, and approximately 52,550 individuals died from the disease.¹ CRC is the second leading cause of cancer-related deaths nationwide.² In West Virginia, CRC incidence and mortality rates exceed national averages. According to the American Cancer Society’s Colorectal Cancer Facts & Figures 2023–2025, West Virginia has one of the highest CRC mortality rates in the United States, with an age-adjusted rate of 18 per 100,000 population, compared with the national rate of 12.8 per 100,000.^3,4

Early detection of CRC through routine screening is essential for improving treatment outcomes and reducing mortality. Electronic health records (EHRs) are widely used to support screening efforts by facilitating data tracking, generating automated reminders, and streamlining follow-up care. However, their effectiveness in improving screening adherence is often hindered by certain challenges. Logistical barriers, including limited interoperability between EHR platforms, disrupt the exchange of patient information.⁵ Contextual challenges, such as health literacy gaps and disparities in digital access, contribute to lower screening rates, particularly among underserved populations.⁶ Additionally, healthcare system constraints—including staff shortages and competing clinical priorities—often limit the effectiveness of EHR-driven screening reminders and follow-up protocols.⁷

Recent advancements in patient navigation, health information technology (HIT), and team-based care have helped to improve CRC screening rates by addressing barriers in EHR functionality and care coordination. Patient navigation programs are effective strategies for reducing logistical challenges and improving follow-up care through personalized, one-on-one support—especially in underserved populations.⁸ The integration of clinical decision support (CDS) tools within EHRs has also been shown to substantially improve screening adherence.⁹ Patients with related screening needs, such as breast, cervical, and lung cancer screening, have also benefitted from EHR-based alerts and reminders on screening guidelines.^10,11 Recent research confirms that CDS tools—particularly those embedded within EHRs—help to improve process measures, guideline adherence, and quality of care across oncology and primary care settings.^12,13

Effective HIT interventions, including structured protocols and registries embedded in EHRs, support real-time data tracking and facilitate continuous quality improvement through Plan-Do-Study-Act (PDSA) cycles.¹¹ Team-based care models further support CRC screening improvements by integrating patient navigation, FIT (Fecal Immunochemical Test) kit follow-ups, and more optimized EHR tracking into clinical workflows. One clinic reported a 15.8% increase in screening rates after implementing such a comprehensive team-based approach.¹⁴ When effectively integrated into EHR workflows, patient navigation programs significantly improve adherence by addressing patient-specific barriers and ensuring timely follow-ups.¹⁵

Despite these advancements, optimizing EHR use for CRC screening remains a challenge. Many clinics struggle with non-standardized data entry, incomplete tracking of screening outcomes, and underutilized patient recall systems—factors that are essential for timely follow-up care.^16,17 To bridge these gaps, the West Virginia Program to Increase Colorectal Cancer Screening (WVPICCS) developed and implemented a secure, HIPAA-compliant REDCap (Research Electronic Data Capture) system¹⁸ to enhance patient navigation efforts. This system was designed to complement EHRs by facilitating structured data collection, supporting patient tracking, and improving follow-up care. By integrating REDCap into existing workflows, WVPICCS aims to strengthen patient navigation, improve CRC screening adherence, and address longstanding barriers in screening and follow-up processes.

This work contributes to the growing body of evidence around pragmatic strategies for improving CRC screening, particularly in rural and resource-constrained settings. By sharing findings from a real-world implementation effort, we aim to support broader adoption of adaptable tools and workflows that can improve patient tracking, address barriers to care, and strengthen preventive service delivery.

METHODS

To address barriers in CRC screening and follow-up, the WVPICCS team developed a REDCap system to enhance patient navigation in three West Virginia safety-net health systems. REDCap is a secure, web-based software platform designed to support data capture for research and clinical applications. Originally developed by Vanderbilt University, REDCap enables structured data collection, real-time reporting, and secure management of protected health information.¹⁹ The system’s design included structured forms and fields tailored to document essential data across five key areas:

1. Patient Information Form: Captures demographic and risk-related data, including identifiers, insurance status, and CRC risk levels.

2. Screening Colonoscopy Form: Tracks referrals, scheduling, and results of colonoscopies, including dates, types, and abnormal findings.

3. Cologuard Form: Documents ordered and completed at-home screening tests, results, and follow-up actions for positive findings.

4. FIT Form: Monitors FIT dissemination, completion, and results, including follow-up actions.

5. Patient Navigation Form: Addresses barriers to care, capturing logistical, cultural, and insurance-related issues and resolutions.

Additionally, custom EHR reports were developed to monitor CRC screening orders and completions. Microsoft Access queries linked REDCap tables and EHR-derived reports using a shared patient identifier (medical record number), allowing navigation status, barriers, and screening outcomes to be viewed in a combined patient-level format for reporting and quality improvement. This process enabled navigation teams to identify patients needing follow-ups and prioritize care.

Across participating clinics, CRC screening eligibility was determined using age-based criteria (typically ages 45 to 75) and screening history documented in the EHR. However, the EHR systems in use did not offer reliable built-in tools for tracking navigation activity or documenting barriers to care. Prior to REDCap implementation, clinics relied on external spreadsheets, manual notes, or ad hoc methods to monitor screening follow-up. With the introduction of REDCap, each site used a combination of custom EHR reports and manual review to identify patients due for screening. These patients were then entered into REDCap, which served as a centralized tracking platform. Navigation was prioritized for patients with known barriers (eg, prior missed appointments, insurance lapses, or transportation needs) or those who had not responded to previous reminders. REDCap allowed navigation teams to consistently document encounters, monitor screening progress, and support patients across the continuum of screening and follow-up.

Patient navigation in this project was carried out by trained clinic-based staff, including nurses and care coordinators, depending on the site. Once eligible patients were identified through EHR reports or manual review, navigation staff reached out directly—typically by phone or in-person during scheduled visits. Patients were asked about potential barriers to CRC screening (eg, transportation, understanding of screening options, or insurance coverage). These navigation encounters were documented in REDCap using standardized forms, which supported consistent data capture across sites. Navigators used the collected information to provide individualized support—rescheduling missed appointments, clarifying test procedures, and connecting patients to transportation or financial aid resources. REDCap served as a supplemental tracking tool within the clinical workflow, enabling navigation teams to prioritize outreach, document barrier resolution, and monitor progress across screening steps. Although staffing and implementation varied by site, all sites integrated navigation into regular patient care with typically one to three points of contact over several weeks. Although this required navigation staff to document information in REDCap in addition to their usual EHR notes, this additional step was accepted as necessary for achieving consistent tracking of navigation activity, patient barriers, and outcomes within this quality improvement initiative.

Data analyses were exploratory in nature and conducted post hoc. Descriptive statistics were used to summarize patient characteristics, navigation uptake, and screening completion. Bivariate associations between navigation status and CRC screening were evaluated using chi-square tests (Pearson and likelihood ratio) and Fisher’s exact test, as appropriate. Given the non-randomized sample and potential selection bias, both two-tailed and one-tailed Fisher’s exact tests were performed. One-tailed tests were only interpreted in contexts where prior literature and clinical rationale supported an expected direction of effect. Odds ratios and 95% confidence intervals were calculated to assess the strength and precision of associations. Statistical significance was evaluated using a threshold of α = 0.05.

RESULTS

A total of 4,654 patients were identified for CRC screening, of whom 2,222 (47.7%) had completed screening. Of these, 893 patients were tracked more closely for navigation-related outcomes based on clinical workflows. Within this subgroup, 109 (12.2%) received patient navigation services. Navigation uptake varied by health system, ranging from 9.2% to 17.7%. Among the 109 patients who received navigation, 38.5% (n = 42) had at least one documented barrier to care (Table 1). Among the 42 patients with documented barriers to care, the most frequently reported barriers included needs for additional health education related to CRC screening (59.5%) and insurance-related challenges (52.4%). Other commonly documented barriers included logistical challenges (28.6%), psycho/social barriers (28.6%), language-related barriers (19.0%), and cultural or spiritual barriers (11.9%).

Patients who received navigation had higher CRC screening rates than those who did not (39.5% vs. 31.0%). Since the sample was not randomized, chi-square tests were used to assess the relationship between navigation and screening completion (Pearson χ² p = 0.0763; Likelihood Ratio p = 0.0808). Although statistical significance was not reached at the α = 0.05 level, patients who received navigation had 1.45 times higher odds of completing screening (95% CI: 0.96–2.19). To account for potential selection bias, Fisher’s exact test was also conducted. The two-tailed test (p = 0.0803) confirmed no significant difference overall, but the right-tailed test (p = 0.0497) indicated that patients without navigation were significantly less likely to complete screening. While these results suggest a positive trend favoring navigation, findings should be interpreted with caution due to sample selection limitations.

A subgroup analysis examined the 109 patients who received navigation, of whom 42 had documented barriers. Within this subset, 38.1% (n = 16) completed screening, compared with 31.0% (n = 243/784) among patients without navigation. Although this difference was not statistically significant (Pearson χ² p = 0.3339; Likelihood Ratio p = 0.3417; Fisher’s exact p = 0.3931), the trend suggests that navigation may help patients overcome screening barriers (Table 2).

Overall, while statistical significance was not reached in most analyses, patients receiving navigation consistently demonstrated higher CRC screening rates than those who did not. These findings highlight a potential clinical benefit of navigation services and underscore the need for further research with larger sample sizes to confirm these trends.

DISCUSSION

The REDCap system developed by the WVPICCS program highlights the value of structured data in supporting patient navigation and improving CRC screening adherence. By facilitating a patient-centered approach—addressing barriers to care and enabling targeted follow-up—the system helps streamline screening processes. In addition to these direct outcomes, this work illustrates the broader value of data-driven quality improvement in primary care. Navigation efforts not only address immediate barriers to CRC screening but also reinforce the healthcare system by fostering stronger collaboration among clinics, community resources, and patients.⁸ This approach is particularly important in underserved and rural areas like West Virginia, where disparities in preventive screening remain a critical concern.

While REDCap is not a replacement for EHR functionality, its use in this initiative was designed to complement existing systems and address specific gaps in tracking and follow-up for CRC screening. Many rural and safety-net health systems lack consistent or fully optimized EHR functionality for preventive care workflows. In this context, REDCap provided a flexible, low-cost, and secure solution to capture navigation activity and track patient care. While REDCap does require technical support, it enabled centralized coordination and supported data-driven decision-making where EHR tools alone were insufficient. Future efforts could focus on sustaining this progress by building EHR capacity and gradually integrating the core elements of navigation tracking into EHR workflows.

REDCap has emerged as a practical tool in primary care for supporting both patient navigation and screening workflows. Its user-friendly, web-based interface and built-in data validation mechanisms facilitate accurate and efficient data capture—capabilities that are critical when tracking patients through complex clinical processes. REDCap has been effectively used to collect screening-related data in real-world settings, such as opioid risk assessments and other clinical self-assessments, helping to streamline provider workflows and improve coordination of care.²⁰ In addition, recent research suggests that REDCap aligns well with the preferences of both clinical staff and patients, reinforcing its value as a flexible platform for enhancing data-driven navigation and screening interventions.²¹ Our work aligns with these efforts by illustrating how REDCap can be adapted for patient navigation documentation and barrier resolution in safety-net settings, offering a pragmatic model for health systems that face EHR limitations or resource constraints.

The integration of REDCap enhances the ability of navigation teams to track patients and deliver tailored interventions. By enabling navigation teams to identify at-risk patients, direct resources effectively, and track progress over time, this effort illustrates how data systems with specialized tracking and patient registry functionality can bridge the gap between data collection and effective care delivery. Integrating these capabilities into EHRs will help to sustain navigation initiatives and spur further progress in preventive care.

This study also has direct implications for the health information profession. Health information professionals play an important role in selecting, managing, and optimizing data systems that support clinical decision-making and patient engagement. The integration of REDCap into clinical workflows demonstrates how adaptable data platforms can be used to augment traditional EHR systems—particularly in settings where EHR limitations hinder comprehensive data capture and tracking. By designing structured forms, ensuring data integrity, and supporting quality improvement reporting, health information specialists contribute to improving population health outcomes. These findings reinforce the importance of cross-disciplinary collaboration and underscore the role of health information professionals in advancing quality improvement.

This study has several limitations. First, the sample was not randomized, and navigation assignment varied across sites based on available staffing, workflows, and patient circumstances. Second, while positive trends were observed, the analyses were exploratory in nature and did not include multivariable modeling to account for potential confounding factors. Third, documentation of navigation encounters was limited to what was recorded in REDCap; informal interactions may not have been consistently captured. Fourth, implementation varied across health systems, reflecting local resources and priorities, which may affect the consistency and scalability of the model. Finally, these findings reflect the experience of three West Virginia safety-net health systems and may not generalize to settings with more advanced EHR-integrated navigation tools or differing patient populations. Future efforts should build on these findings through larger studies, more standardized implementation, and expanded evaluation of EHR-integrated approaches.

CONCLUSIONS

Patient navigation is a vital component of CRC screening initiatives, enabling more personalized support and greater adherence to recommended screening. The REDCap system developed by WVPICCS provides a replicable framework for addressing barriers, enhancing care coordination, and improving screening rates. This effort also contributes to broader quality improvement by reducing disparities in access to care. The success of WVPICCS underscores the clinical relevance of data-driven navigation strategies and their capacity for scalability in primary care settings. Refining and expanding these data systems will be pivotal in overcoming persistent challenges in CRC early detection, ultimately leading to better health outcomes and reduced CRC burden.

Disclosures

The authors have nothing to disclose.

Funding

This work was supported by the US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion (Grant/Contract No.: 5 NU58DP006768-02-00).