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. 2026 Jul 6;23(3):e70068. doi: 10.1111/jjns.70068

Cancer patient navigation interventions improving timeliness from diagnosis to first treatment in oncology care: A scoping review

Agung Subakti Nuzulullail 1,✉, Halfie Zaqiyah Gusti Puspitasari 1, Dwina Oktavia Deli 1, Ni Made Hari Sugiantini 1, Muhammad Arif Zakyuddin 2, Khudazi Aulawi 3, Sri Hartini 4
PMCID: PMC13457979  PMID: 42410995

Abstract

Background

Cancer is one of the leading causes of death worldwide, with delays in diagnosis and initiation of treatment being a significant problem in cancer care. Cancer Patient Navigation (CPN) interventions were developed to reduce the time from diagnosis to first treatment, but have not yet been systematically integrated.

Aim

To explore the impact of CPN interventions on the time from diagnosis to the start of first therapy among cancer patients.

Methods

This study used a scoping review to examine the literature indexed in PubMed, CINAHL (EBSCOhost), ScienceDirect, Scopus, and the Wiley databases. The inclusion criteria were original articles published between 2010 and 2025 that discussed CPN interventions to reduce waiting time for first therapy. The writing was based on the Arksey and O'Malley framework and followed the PRISMA‐ScR‐2020 guidelines. The data were synthesized narratively using an inductive approach.

Results

Thirteen articles met the inclusion criteria. CPN interventions accelerated services by reducing waiting times from diagnosis to first treatment to <60 days, including biopsy to treatment in 2 days, abnormal imaging to treatment in 38 days, screening‐detected patients in 31 days, and symptomatic patients in 44 days. The interventions provided included early access and patient contact, clinical and multidisciplinary coordination, process management, service scheduling, reduction of system‐related barriers to patient care, communication, continuity of care, patient education, and psychosocial support.

Conclusion

CPN interventions were consistently associated with accelerated initiation of first treatment among patients with cancer. This acceleration was achieved through optimized scheduling, service coordination, and patient support throughout the diagnosis phase.

Keywords: cancer surgery, cancer treatment, chemotherapy, initial treatment, patient navigator, radiotherapy

1. INTRODUCTION

Global cancer incidence continues to rise. The World Health Organization estimates that the number of new cases will exceed 35 million by 2050, an increase of approximately 77% compared to 20 million cases in 2022, along with a high cancer mortality rate of 9.7 million in 2022 (Globocan, 2022). One of the main factors contributing to high cancer mortality is delayed initiation of treatment, which allows tumor progression and metastasis and is associated with poorer clinical outcomes and reduced survival (Haddadi et al., 2024; Hanna et al., 2020). Several studies have shown that a delay in therapy of just four weeks can increase the risk of death by 6%–13% or more, depending on the type of cancer and the modality of therapy, making timely treatment a crucial component in improving the effectiveness of treatment and reducing cancer mortality rates globally (Morrill et al., 2022; Neal et al., 2015; Ungvari et al., 2025).

Several countries have set national waiting‐time targets to ensure timely initiation of cancer treatment in response to evidence that treatment delays adversely affect clinical outcomes. In England, Scotland, and Northern Ireland, cancer treatment is targeted to begin within ≤62 days of referral and ≤31 days of treatment decision, while in Wales, through the Suspected Cancer Pathway, the target is for confirmed diagnosis and treatment to begin within ≤62 days of cancer suspicion (Cancer Research UK, 2025). This policy is supported by evidence that timely treatment plays a crucial role in improving the prognosis and survival of cancer patients (Crosby et al., 2020). Nevertheless, delays in oncology services continue to occur in clinical practice, with the median time to definitive therapy reported to reach 120 days, largely because of systemic barriers during treatment initiation (Gondhowiardjo et al., 2021).

One approach increasingly used to reduce waiting times is care assistance through a Cancer Patient Navigator (CPN) intervention, which may come from various professional backgrounds, including doctors, nurses, social workers, public health workers, and cancer survivors (Wells et al., 2008). CPNs serve as guides for patients, families, and caregivers throughout the entire cancer journey, from prevention and screening to diagnosis, treatment, survival, and end‐of‐life care (Braun et al., 2012). The goal of CPNs is to overcome barriers, improve service coordination, and ensure timely and integrated care (Varanasi et al., 2024).

Cancer patient navigation has been shown to improve access to and coordination of cancer services through interventions such as patient education, care coordination, appointment scheduling assistance, financial and logistical support, and facilitation of communication between patients and healthcare providers. Several models of CPN have been developed and evaluated in oncology care, including socially determined navigation, digital‐based navigation programmes, multicomponent navigation, and navigation with information technology integration (Dessources et al., 2020; Fromm et al., 2025; Gonzalez Donna et al., 2026; Mumukunde et al., 2026). A Previous study suggests that these interventions may reduce delays in initiating cancer therapy and improve the timeliness of oncology care (Umar et al., 2025). Approximately 70% of studies included in a previous review reported shorter time to therapy among patients receiving navigation interventions, although the magnitude of benefit varied according to cancer type, healthcare setting, and intervention intensity (Chen et al., 2024).

Despite these findings, substantial variability remains in the design of CPN interventions, outcome definitions, healthcare contexts, and the magnitude of time reduction reported across studies. Existing reviews have primarily reported the overall effectiveness of CPN programmes without systematically examining how specific intervention components influence timeliness outcomes across different cancer types, healthcare systems, and models of care. The evidence base remains fragmented because of heterogeneity in intervention characteristics and measures of treatment delay, limiting comparisons across studies and reducing the applicability of findings to oncology practice. To the best of the researchers' knowledge, no scoping review has comprehensively mapped the range of CPN interventions alongside the magnitude of reduction in time from diagnosis to initiation of cancer therapy. This scoping review aims to synthesize the available evidence regarding the forms and impact of CPN interventions on the interval from diagnosis to initiation of cancer therapy and to identify gaps requiring further investigation.

2. METHODS

2.1. Study design

This study employed a scoping review design, which aims to explore a specific topic comprehensively (Peterson et al., 2017). The process of compiling this scoping review used a methodological framework based on Arksey and O'Malley (Arksey & O'Malley, 2005) and further refined by (Levac et al., 2010), which includes five main stages, namely: (1) formulating research questions, (2) identifying relevant studies, (3) selecting studies, (4) mapping data, and (5) compiling, summarizing, and reporting findings. This scoping review is registered in the Open Science Framework (OSF) with registration number: [10.17605/OSF.IO/WJEC3]. This review addressed two research questions: (1) what forms of interventions are provided by patient navigators in cancer services, and (2) to what extent do navigation interventions reduce waiting time from diagnosis to initiation of first cancer therapy?

2.2. Search method

A systematic search was conducted in five electronic databases: MEDLINE (via PubMed), Scopus, CINAHL (via EBSCOhost), ScienceDirect, and Wiley Online Library. The main search was conducted between November 25 and December 15, 2025. The search strategy combined controlled vocabulary, including Medical Subject Headings (MeSH), and free‐text keywords to ensure comprehensive identification of relevant literature. Searches were performed in the title, abstract, and keyword fields where applicable. Boolean operators (AND, OR) were used to combine the search terms. The keywords included combinations related to population, outcomes, and healthcare settings, such as “cancer patient*,” “oncology patient*,” “patients with cancer,” “neoplasm*,” “time to treatment,” “diagnosis to treatment,” “treatment delay,” “timeliness of care,” “waiting time,” “hospital*,” “cancer center*,” “oncology clinic*,” and “healthcare setting*.” The search strategy was adapted for each database according to its indexing system and search interface.

Additional hand‐searching and citation searching were also undertaken to enhance the comprehensiveness of the review and minimize the possibility of missing relevant studies. Citation searching was conducted by screening the reference lists of included studies to identify additional relevant articles that may not have been captured through database searching. The complete database‐specific search strategies, including syntax and applied limits, are provided in Supplementary File S2.

2.3. Eligibility criteria

The research questions and eligibility criteria were developed using the PCC framework: Population (P) comprised cancer patients across all age groups and cancer types; Concept (C) focused on CPN interventions aimed at reducing waiting time from diagnosis to first treatment; and Context (C) included hospital settings such as referral hospitals and cancer centers.

Eligible studies were original research articles published in English between 2010 and 2025 that reported outcomes related to waiting time from cancer diagnosis to initiation of first treatment.

Studies were excluded if they were non‐original research (e.g., secondary analyses, protocols, preliminary or pilot studies, books, or book chapters), did not report relevant waiting time outcomes, or did not evaluate CPN‐based interventions. Studies were also excluded if they were assessed as being of low methodological quality based on the Joanna Briggs Institute (JBI) critical appraisal tools, defined as studies scoring <40% of the total JBI checklist score. This cut‐off was applied to ensure inclusion of studies with the minimum acceptable methodological rigor and to reduce the risk of bias in the review.

2.4. Study selection

The article selection process was conducted by three researchers in accordance with the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses extension for Scoping Reviews (PRISMA‐ScR) 2020 guidelines (Page et al., 2021). The selection process was conducted using the Rayyan tool to ensure relevance to the research topic and to systematically identify outputs for reporting (Rožanc & Mernik, 2021). The screening stages included checking for duplicates, selecting participants according to inclusion and exclusion criteria, and assessing suitability for the established PCC framework. After the screening process was completed, all researchers reviewed the full text of each article to assess its eligibility and suitability in answering the research questions. Any differences of opinion among researchers were resolved through discussion until a consensus was reached.

2.5. Quality assessment

Although quality appraisal is not mandatory in scoping review methodology, this process was undertaken to enhance methodological transparency and provide contextual interpretation of the included evidence (Peters et al., 2020; Xue et al., 2024). The results of the quality appraisal were not used as criteria for study exclusion but were considered during data interpretation and synthesis to describe the overall methodological quality and potential risk of bias of the included studies. Methodological quality assessment was conducted independently by three researchers using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist 2020 (McArthur et al., 2025).

The appraisal criteria were selected according to the design of each included study. To improve consistency in evaluating intervention studies, the appraisal process also considered domains commonly addressed in the Cochrane Risk of Bias (RoB) framework and risk‐of‐bias approaches for non‐randomized intervention studies, including selection bias, measurement bias, confounding, completeness of outcome data, and appropriateness of statistical analysis. The JBI checklist itself was not modified; rather, these domains were used to support interpretation of the appraisal findings across different study designs.

Each appraisal item was assessed using four response options: “Yes,” “No,” “Unclear,” and “Not Applicable.” A score of 1 was assigned for each “Yes” response, whereas “No” and “Unclear” responses were assigned a score of 0. The total score for each study was calculated by summing all applicable items. Study quality was categorized based on percentage scores as follows: >70% indicating high quality, 50%–70% moderate quality, and <50% low quality (Melo et al., 2018). Disagreements during the appraisal process were resolved through discussion with two experts until consensus was achieved among all researchers. All included studies were categorized as having a low risk of bias.

2.6. Data extraction and synthesis

Before data synthesis, researchers extracted research findings using the Update Guidance for Conducting Systematic Scoping Review (Peters et al., 2020) as a guide. Two researchers worked in pairs to extract information, including authors, year, research design, country of research, intervention details, and time of reduction. After data extraction was complete, three researchers synthesized the findings narratively using an inductive approach by reading, organizing, and developing categories (Elo & Kyngäs, 2008). The analysis began with repeated reading of the extracted findings to identify similarities and differences in the types of CPNs, intervention characteristics, treatment programmes, and reported time reductions across studies. The extracted data were then grouped inductively into categories based on similarities in intervention approaches, healthcare professional involvement, and timeliness outcomes.

The researchers subsequently compared findings across studies to identify recurring patterns related to the roles of CPNs, components of navigation interventions, and reductions in waiting time from diagnosis to initiation of cancer therapy. Categories representing similar intervention characteristics were merged and summarized narratively to facilitate the interpretation and synthesis of findings across studies. The authors then engaged in in‐depth discussions to synthesize the extracted data, identify key patterns, and compile summaries for each article to capture the important findings.

All authors independently verified all stages of the analysis to ensure accuracy and consistency. Differences of opinion were addressed through review of the relevant data and systematic discussion until consensus was achieved, thereby supporting the reliability of the findings and ensuring that the review process was conducted rigorously and transparently.

3. RESULTS

3.1. Study selection

A comprehensive literature search yielded 981 records identified through electronic databases and other sources (Figure 1). The primary sources were CINAHL (EBSCOhost) (n = 673; 68.6%), Wiley (n = 197; 20.1%), PubMed (n = 51; 5.2%), ScienceDirect (n = 30; 3.1%), and Scopus (n = 30; 3.1%). Additionally, four additional articles (0.4%) were identified through hand‐searching (n = 2; 0.2%) and citation tracking (n = 2; 0.2%). Before the screening process, 19 duplicate articles were removed, and 132 records were automatically excluded using Rayyan systematic review software based on predefined eligibility criteria screening functions, leaving 830 articles to proceed to the title and abstract screening stage. At this stage, 786 articles were excluded as irrelevant to the research focus. Subsequently, 44 articles were reviewed in full text, whereas nine were not fully accessible.

FIGURE 1.

FIGURE 1

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Prisma flow diagram.

A total of 35 articles were assessed for eligibility; 26 were excluded for not focusing on reducing waiting times (n = 7), for interventions not being appropriate for cancer patient navigation (n = 18), or for inadequate methodological quality (n = 1). Of the other methods, four articles were assessed for eligibility, and all met the inclusion criteria. Thirteen studies met the inclusion criteria and were included in this scoping review: nine from electronic databases, two from hand searches, and two from citation searches.

3.2. Quality appraisal result

Based on the results of the JBI Critical Appraisal, all articles were rated as having good methodological quality and low bias, with scores ranging from 73% to 100%, indicating suitability for further analysis. Imperfections in quality assessment were mainly due to the lack of reporting on confounding factor control strategies, specifically in JBI question number 8, as well as unclear reporting of follow‐up in JBI question number 9, particularly in studies with a retrospective cohort design, where limitations in recording and dependence on medical record data often limit researchers' ability to control confounders and report follow‐up comprehensively (Table 1).

TABLE 1.

Quality Appraisal Result.

Author and year Study design JBI critical appraisal result Interpretation of risk of bias
Muñoz et al. (2018) Retrospective cohort study 10/11 (90%) good quality Low risk
Alsamarai et al. (2013) Retrospective cohort study 9/11 (82%) good quality Low risk
Baliski et al. (2014) Prospective cohort study 9/11 (82%) good quality Low risk
Basu et al. (2013) Retrospective cohort study 10/11 (90%) good quality Low risk
Common et al. (2018) Retrospective cohort study 8/11 (73%) good quality Low risk
McKevitt et al. (2017) Retrospective cohort study 8/11 (73%) good quality Low risk
Gordils‐Perez et al. (2017) Retrospective cohort study 8/11 (73%) good quality Low risk
Kunos et al. (2015) Retrospective cohort study 9/11 (82%) good quality Low risk
Haideri and Moormeier (2011) Retrospective cohort study 10/11 (90%) good quality Low risk
Koh et al. (2011) Prospective cohort study 9/11 (82%) good quality Low risk
McKevitt et al. (2018) Retrospective cohort study 8/11 (73%) good quality Low risk
Enomoto et al. (2019) Retrospective cohort study 11/11 (100%) good quality Low risk
Arora et al. (2023) Retrospective cohort study 8/11 (73%) good quality Low risk
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3.3. Characteristics of participants

Participants in the included studies were adult patients with various types of cancer, most of whom had been newly diagnosed. Participants' ages ranged from 31 to 92 years, corresponding to middle to older age; the gender distribution varied by cancer type. Women dominated Breast and gynecological cancer studies, while lung, gastrointestinal, pancreatic, and esophageal cancer studies involved both male and female participants, with a tendency for male dominance in some studies. Cancer stage at diagnosis ranged from early stage (I‐II) to advanced stage (III‐IV), but not all studies reported the stage in detail. Overall, participant characteristics indicate a heterogeneous cancer patient population that is nevertheless relevant for evaluating the impact of patient navigation interventions in the early phase of the cancer care journey (Table 2).

TABLE 2.

Characteristics of participants.

Author and year Age (mean ± SD) Sex Sample Diagnosis Diagnosis status Stage
Muñoz et al. (2018)

CPN = 35–88

Non‐CPN = 31–91

M = 57.5%

F = 42.5%

 Total 120 (intervention n = 60, control n = 60) GI cancer Newly diagnosed Not reported
Alsamarai et al. (2013) 69.0 ± 10.0

M = 96.9%

F = 3.1%

 Total 352 Lung Cancer Newly diagnosed and long‐term patients I = 115, II = 28, III = 92, IV = 117
Baliski et al. (2014) 63.5 ± 13.6 F = 100% Total 197 Breast Cancer Newly diagnosed 0 = 19, I = 93, II = 67, III = 18
Basu et al. (2013) 60.8 ± 13.0 F = 100% Total 176 Breast cancer

Newly diagnosed

Established with surgeon

 0‐III
Common et al. (2018) 69.0 ± 7.9

M = 53.2%

F = 46.8%

Total 133

(Intervention n = 79, control n = 54)

 Lung cancer Newly diagnosed Not reported
McKevitt et al. (2017) Not reported

M = 1%

F = 99%

176

(136 non‐CPN, 40 with CPN)

 Breast cancer Long‐term patient Not reported
Gordils‐Perez et al. (2017)

Gynecology: 60.39 ± 12.0

Hematology: 58.35 ± 14.25

M = 28.6%

F = 71.4%

Total 182

(Intervention group n = 93 gynecology patients and n = 89 hematology patients)

Gynecology: endometrial, then ovarian, cervical.

Hematology: non‐Hodgkin lymphoma, multiple myeloma, acute myeloid leukemia.

 Newly diagnosed Not reported
Kunos et al. (2015) 67–68 years

M = 61.2%

F = 38.8%

Total 460

(navigated n = 97, non‐navigated n = 363)

 Lung Cancer Newly diagnosed IA/B = 158, IIA/B = 60, IIIA = 108, IIB = 54, IV = 80
Haideri and Moormeier (2011) Mean 55.5 F = 100%

Total 322

(Intervention n = 219, control n = 103)

 Breast cancer Newly diagnosed 0 = 13.4%, I = 23.9%, IIA = 25.2%, IIB = 15.2%, IIIA = 8.1%, IIIB = 4.7%, IIIC = 0.6%, IV = 8.7%
Koh et al. (2011) 56.5 ± 12.28 F = 100%

Total 110

(Navigated n = 55, non‐navigated n = 55)

 Breast cancer Newly diagnosed I‐IV
McKevitt et al. (2018) Not reported F = 100% Total 176 Breast cancer Newly diagnosed 0‐IIIA
Enomoto et al. (2019) Mean 66.8

F = 44.3%

M = 55.7%

Total 147

(Intervensi n = 79, kontrol n = 57)

 Pancreatic cancer Newly diagnosed Not reported
Arora et al. (2023) Mean 68 (62–78)

M = 76%

F = 24%

Total 194

(Intervention n = 98, control 96)

 Esophageal cancer Newly diagnosed 0, IA, IB, IC, IIA, IIB, IIIA, IIIB, IVA
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Abbreviations: F, female; M, male; SD, standard deviation.

3.4. Characteristic of the study

A total of 13 articles were further analyzed in this review. All articles used a cohort study design; most were retrospective cohorts (n = 11), while two were prospective cohorts (n = 2). In terms of geographical coverage, most studies were conducted in the United States (n = 8), followed by Canada (n = 5). Detailed characteristics of the included studies are presented in Table 3.

TABLE 3.

Characteristics of included studies and synthesis of results.

Author and year Design Country CPNs Intervention details Duration of intervention First treatment program Time difference Time reduction Statistical significance (p value)
Non‐CPN CPN
Muñoz et al. (2018) Retrospective cohort study USA

  1. Oncologist

  2. ONN

  3. Nutritionist

  4. Social worker

  • Coordinated care delivery across services

  • Early post‐diagnosis patient engagement

  • Regular multidisciplinary team (MDT) meetings (weekly)

  • One‐visit comprehensive evaluation model

  • Structured service timeline and follow‐up schedule

 2 years

  1. Chemotherapy

  2. Radiotherapy

  3. Surgery

  4. Combination of all three

 42.95 days 15.15 days 27.8 days <.001
Alsamarai et al. (2013) Retrospective cohort study USA

  1. Nurse

  2. Oncology specialists

  3. Thoracic surgeon

  4. Pulmonologist

  5. Radiologist

  • Coordinated multidisciplinary care through tumor boards

  • Standardized diagnostic‐to‐treatment pathways

  • Integrated care coordination within a single system (VACHS)

  • Dedicated care coordinator (CCCP coordinator / CCC)

 5 years

  1. Surgery

  2. Chemotherapy

  3. Radiotherapy

 46 days 43 days 3 days .53
Baliski et al. (2014) Prospective cohort study Canada Nurse

  • Rapid imaging to surgery coordination

  • Continuous care communication and follow‐up

  • Single‐facility diagnostic and examination pathway

 2 years Definitive surgery (lumpectomy and mastectomy) 59 48 11 days .717
Basu et al. (2013) Retrospective cohort study USA ONN

  • Multidisciplinary coordination for rapid scheduling of care

  • Active monitoring of treatment timelines and schedules

 Not reported Not reported 11 days 9 days 2 days .002
Common et al. (2018) Retrospective cohort study Canada

  1. ONN

  2. Radiologist

  3. Physician

  • Nurse navigator–initiated patient contact

  • Case review within ≤7 days

  • Coordination of diagnostic examinations and specialist consultations

  • Ongoing communication with primary care providers (PCP)

  • Rapid referral to specialist therapy services

 1 years First thoracic surgery or first day of chemotherapy or radiation

Biopsy to treatment: 41 days

Abnormal imaging to treatment: 118 days

Biopsy to treatment: 39 days

Abnormal imaging to treatment: 80 days

Biopsy to treatment: 2 days

Abnormal imaging to treatment: 38 days

 .000
McKevitt et al. (2017) Retrospective cohort study Canada Nurse

  • Integrated coordination through RABC system

  • Integrated patient evaluation process

  • Rapid referral pathways

  • Active scheduling of diagnostic and treatment services

 Not reported Surgery Not reported Not reported ≤60 days since diagnosis .004
Gordils‐Perez et al. (2017) Retrospective cohort study USA ONN

  • Routine patient follow‐up

  • Facilitation of interprofessional team communication

  • Identification and resolution of patient barriers to care

  • Connection to immediate treatment programs

 10 months Not reported

Gynecology: 18.1 days.

Hematology: 14.1 days

Gynecology: 7.1 days.

Hematology: 11.1 days

Gynecology: 11 days.

Hematology: 3 days

Gynecology: .29

Hematology: .002
Kunos et al. (2015) Retrospective cohort study USA Nurse

  • Face‐to‐face support for newly diagnosed cancer patients

  • Facilitation of interprofessional communication

  • Identification of clinical trial eligibility

  • Identification and removal of barriers to treatment

  • Linkage to supportive care services

  • Coordination with referring physicians

  • Patient education on disease and treatment options

  • Orientation to the cancer institute care system

 6 months

  1. Surgery

  2. Chemotherapy

  3. Radiotherapy

 45 days 19 days 26 days <.001
Haideri and Moormeier (2011) Retrospective cohort study USA Not reported

  • Scheduling of diagnostic and clinical examinations

  • Patient accompaniment during clinical visits

  • Advocacy to address care barriers, including financial assistance

  • Provision of information and educational support

  • Liaison and coordination with other healthcare professionals

 4 years Surgery 42 days 33 days 9 days .008
Koh et al. (2011) Prospective cohort study USA ONNs

  • Identification of patient‐related barriers to care

  • Provision of direct active patient support

  • Continuous patient follow‐up

  • Coordination of multidisciplinary services

 6 months Breast‐conserving surgery 30 days 26.2 days 3.8 days .112, Cohen's d = 0.366
McKevitt et al. (2018) Retrospective cohort study Canada

  1. Surgeons

  2. Radiologist

  3. Anatomical pathologist

  4. Nurse

  5. Medical oncologist

  6. Radiation oncologist

  • Coordination of integrated clinical and radiological assessments within a single facility (RABC system)

  • Acceleration of diagnostic processes

  • Expedited referral to surgical services

 2 months Definitive surgery (breast‐conserving surgery or mastectomy)

Cancer patients detected through screening: 78 days

Cancer patients with symptoms: 126 days

Cancer patients detected through screening: 47 days

Cancer patients with symptoms: 82 days

Cancer patients detected through screening: 31 days

Cancer patients with symptoms: 44 days

.025

<.024
Enomoto et al. (2019) Retrospective cohort study USA ONNs and RN

  • Early patient contact beginning at initial visit

  • Coordination of logistical needs throughout care

  • Scheduling of appointments and treatment services

  • Coordination of referrals and healthcare resources

  • Provision of ongoing support throughout therapy

 2 years

  1. Chemotherapy

  2. Surgery

  3. Radiation

 46 days 26 days 20 days .005
Arora et al. (2023) Retrospective cohort study Canada ONNs

  • Patient contact within 2 business days of referral

  • Initial clinical triage assessment

  • Expedited referral processing

  • Coordination of diagnostic and therapeutic services across specialties

  • Ongoing patient education and support until initiation of therapy

 4 years First treatment: chemoradiation or definitive surgery 60 days 51 days 9 days .02
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Abbreviations: CCC, Cancer Care Coordinator; CCCP, Centralized Cancer Care Program; PCP, primary care provider; RABC, Rapid Access Breast Clinic; VACHS, Veterans Affairs Connecticut Healthcare System.

3.5. Main findings

The findings obtained from this scoping review were then synthesized and grouped into four main domains (Figure 2).

FIGURE 2.

FIGURE 2

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CPNs, CPN interventions, treatment initiation, and time reduction from diagnosis to treatment.

3.6. Cancer patient navigators

This review found that various health professions, predominantly nurses, perform CPNs, particularly Oncology Nurse Navigators (ONNs) and Registered Nurses (RNs), who serve as the primary coordinators of patient support from diagnosis through initial cancer treatment (Table 3). (Arora et al., 2023; Baliski et al., 2014; Basu et al., 2013; Enomoto et al., 2019; Gordils‐Perez et al., 2017; Koh et al., 2011; McKevitt et al., 2017). In addition to nurses, several studies involved oncologists and other specialists such as surgeons, radiologists, pulmonologists, and pathologists, either individually or in multidisciplinary teams (Alsamarai et al., 2013; McKevitt et al., 2018; Muñoz et al., 2018). Supporting professions such as nutritionists and social workers have also been reported to contribute to meeting patients' holistic needs (Muñoz et al., 2018). This indicates that the cancer patient navigation model is flexible yet consistently focuses on improving coordination of oncology services.

3.7. CPN interventions

CPN intervention accelerates initiation of cancer therapy by strengthening care coordination and reducing service fragmentation (Table 3). Most studies report that CPNs, typically oncology nurses, make early contact with patients after referral or diagnosis to conduct triage, education, and needs assessment, a process reinforced through multidisciplinary coordination (Baliski et al., 2014; Basu et al., 2013; Muñoz et al., 2018). In addition, CPNs facilitate the scheduling and monitoring of diagnostic tests and consultations, maintain continuity of care, and address patients' logistical, financial, and psychosocial barriers, thereby contributing to a reduction in the time from diagnosis to surgery, chemotherapy, or radiotherapy (Arora et al., 2023; Enomoto et al., 2019; Koh et al., 2011; McKevitt et al., 2017).

3.8. First treatment program

Navigators play an important role in facilitating cancer patients' access to initial therapy after diagnosis (Table 3, Figure 2). The initial therapy accessed varies according to the type and stage of cancer, including surgery, chemotherapy, and radiotherapy, either as a single therapy or in combination (Alsamarai et al., 2013; Enomoto et al., 2019; Kunos et al., 2015; Muñoz et al., 2018). Several studies report definitive surgery, such as lumpectomy and mastectomy, as the most common initial therapy facilitated through patient navigation (Baliski et al., 2014; Haideri & Moormeier, 2011; Koh et al., 2011; McKevitt et al., 2017; McKevitt et al., 2018), while other studies indicate chemotherapy or radiotherapy as initial therapy, particularly for lung and esophageal cancer (Arora et al., 2023; Common et al., 2018; Enomoto et al., 2019).

3.9. Reduction in time from diagnosis to treatment

Overall, the reviewed studies show that cancer patients with CPN support have a shorter time from diagnosis to first treatment compared to patients without CPN (Table 3), with the magnitude of reduction varying across clinical contexts and cancer types. The most significant reduction in time was reported by Muñoz et al. (2018), with a decrease from 42.95 to 15.15 days (a difference of 27.8 days), as well as by Kunos et al. (2015) and Enomoto et al. (2019), with reductions of 26 and 20 days, respectively. Moderate reductions were reported by Baliski et al. (2014) at 11 days, Haideri and Moormeier (2011) at 9 days, and Arora et al. (2023) at 5 days. McKevitt et al. (2018) reported a significant reduction in time to screening for patients with symptoms (31 days) and for patients with symptoms (44 days). While some studies reported minor time differences (2–4 days), all findings consistently indicated accelerated initiation of cancer therapy among patients receiving CPN intervention.

4. DISCUSSION

This scoping review synthesized evidence from 13 cohort studies evaluating the impact of CPN on the timeliness of cancer care, particularly the interval from diagnosis to first treatment initiation. Overall, the available evidence suggests that CPN interventions were associated with significant reductions in waiting time from diagnosis to initial treatment in several included studies, although the magnitude of reduction varied across intervention models, healthcare systems, and clinical contexts. Across the included studies, CPN was associated with improvements in the coordination and continuity of care, particularly through early patient contact, multidisciplinary clinical coordination, process management, scheduling support, reduction of system‐related barriers, communication enhancement, patient education, and psychosocial support. Four main findings emerged from this review, including the healthcare professionals involved in CPN, the types and characteristics of CPN interventions, the initial treatment programs, and the reduction in waiting time from diagnosis to treatment initiation associated with CPN implementation.

4.1. Cancer patient navigators

The findings of this scoping review indicate that healthcare professionals deliver CPN interventions from diverse professional backgrounds and collaborate. CPN plays an important role in accompanying patients and families and in identifying and overcoming barriers to care from diagnosis through end‐of‐life care (Chen et al., 2024). The role of navigator is most often performed by an ONN, who acts as the primary care coordinator, connecting patients with the services they need and ensuring continuity of the diagnosis‐therapy flow (Doerfler‐Evans, 2016). Conceptually, CPN is a multidisciplinary approach that involves doctors, nurses, social workers, and other support staff, with the shared goal of reducing barriers to care and helping patients navigate complex cancer services (Doerfler‐Evans, 2016; Varanasi et al., 2024). Importantly, this review positions CPN not merely as a supportive coordination role, but as a system‐level intervention that modifies the temporal structure of cancer care pathways by reducing discontinuities between diagnostic completion and treatment initiation.

In the field, doctors play a role in establishing diagnoses, determining stages, formulating treatment plans, and coordinating multidisciplinary care so that treatment can begin immediately (Karthi et al., 2025). Nurses coordinate care flow, scheduling, and referrals, and provide education and emotional support, while also helping to overcome systemic, social, and administrative barriers that could delay therapy (Haroen et al., 2025; Karam et al., 2021). In addition, nutritionists contribute through nutritional assessment and intervention to support patient readiness for therapy (Tinkilic et al., 2025). Social workers play a role in psychosocial support, social and financial problem‐solving, and in facilitating access to resources such as transportation and accommodation (Arajärvi et al., 2025). Collectively, these roles operate as a distributed coordination network in which the navigator functions as the integrative node, reducing fragmentation across institutional boundaries that typically generate avoidable delays in the diagnostic‐to‐treatment interval (Wijekulasuriya et al., 2025).

4.2. CPN interventions

This study focuses on interventions implemented by navigators from diagnosis through initiation of the first cancer therapy. Cancer patient care is complex and requires patient involvement in various stages of service, ranging from diagnostic examinations and consultations to therapy referrals (Dasat et al., 2024). In this context, CPNs act as care coordinators who facilitate service integration through service coordination, information provision, and patient symptom management, thereby making the care flow more structured and continuous (Kerr et al., 2021).

CPN bridges patients and families in various phases of cancer care, from screening, diagnosis, and therapy to survivorship and palliative care, to prevent service fragmentation (Braun et al., 2012; McMullen, 2013). During the diagnostic phase, until the initiation of initial treatment, the CPN conducts triage and needs assessment, coordinates diagnostic examinations, and facilitates cross‐specialty consultations to prevent service delays (Chen et al., 2024). In addition, CPNs identify and address administrative, logistical, and financial barriers, maintain ongoing communication between service providers, and provide education and psychosocial support to patients and families during the critical period from diagnosis to initiation of treatment (Badal et al., 2025).

These interventions directly contribute to reducing the time from diagnosis to treatment by shortening the gaps between services, optimizing scheduling, and preventing administrative delays and duplicate tests. Evidence indicates that approximately 70% of studies report a significant improvement in the time to therapy initiation among patients receiving navigation interventions, underscoring the role of navigation in accelerating access to services and care coordination (Chen et al., 2024). The reduction in time is primarily achieved through three interlinked mechanisms: (1) compression of inter‐step waiting time between diagnostic milestones and treatment scheduling, (2) active resolution of non‐clinical bottlenecks (administrative, logistical, financial), and (3) continuous synchronization of multidisciplinary workflows that are otherwise sequential and fragmented (Venchiarutti et al., 2023).

4.3. Initial treatment program

The type of initial therapy administered varies depending on the patient's clinical condition, disease stage, cancer type, and multidisciplinary team recommendations, including surgery, chemotherapy, radiotherapy, or a combination of several modalities, reflecting differences in cancer type, disease stage, and patient clinical condition (Heudel et al., 2024). This variation confirms that the decision regarding initial therapy is individualized and influenced by each patient's clinical complexity. In addition to clinical factors, the effectiveness of healthcare coordination plays an important role in determining the timeliness of initial therapy (Weaver & Jacobsen, 2018). However, within the conceptual framework of this review, clinical variability primarily determines the therapeutic pathway, whereas the timeliness of pathway execution is determined by system coordination efficiency mediated by CPN (Aspland et al., 2021).

CPN intervention helps ensure that treatment decisions can be implemented immediately by coordinating the scheduling of surgery, chemotherapy, and radiotherapy, as well as facilitating referrals and communication among specialists (Campbell et al., 2020; Dobrea, 2019; Rezaei et al., 2025). Ongoing support and education by CPNs also increase patient readiness to undergo the selected therapy, so that variations in clinical approaches are accompanied by timeliness and continuity of care (Chen et al., 2024; Freund, 2017). In this model, CPN functions as a temporal alignment mechanism that converts treatment decisions into executable clinical actions by synchronizing interdependent service components, particularly in multimodal treatment pathways where delays are most frequently generated at interdepartmental transition points.

In addition, CPN interventions play a role in accelerating the transition from clinical decision‐making to the implementation of therapy by reducing the interval between the establishment of a treatment plan and the initiation of therapy. Through active coordination, CPNs ensure patient readiness, including the completion of diagnostic tests, administrative preparations, and physical and psychological readiness before therapy begins. Studies show that patient navigation interventions are significantly associated with faster treatment initiation through improved service coordination and the reduction of systemic barriers in the cancer care pathway (Lubuzo et al., 2022; Tomasone et al., 2016). This role becomes increasingly important in cases involving multimodal therapy that requires collaboration among various specialists and involves complex care pathways, where care coordination has been shown to improve the efficiency of transitions between care settings and accelerate access to definitive treatment (Chen et al., 2024; Haroen et al., 2025). Thus, although the initial type of therapy may vary, the involvement of the CPN ensures that each treatment plan can be implemented in a timelier and coordinated manner. This indicates that CPN not only contributes to the decision‐making process but also directly influences the time from diagnosis to the initiation of the first cancer treatment (Rezaei et al., 2025; Tomasone et al., 2016).

4.4. Reduction in time from diagnosis to treatment

This scoping review shows that CPN support consistently reduces the waiting time for cancer patients from diagnosis to first treatment. The average reduction in time was 16 days; in the non‐CPN group, the longest time was 126 days in symptomatic patients, and in the CPN group, the shortest time was 2 days with ONN support (Basu et al., 2013; Common et al., 2018). The time from diagnosis to first treatment is an important indicator of service quality, as delays in diagnosis and treatment affect disease progression, treatment options, and survival among cancer patients (Devbhandari et al., 2008).

This reduction in time resulted from CPN intervention targeting critical points that often cause delays, including faster scheduling of examinations and consultations, active monitoring of patient progress, and coordination across services (Mansell et al., 2011; Nurseline, 2025). In addition, CPN helps overcome administrative, communication, and logistical barriers from the early stages of diagnosis, improves patient compliance with treatment schedules, and standardizes service flows so that the transition to first‐line therapy becomes more efficient (Badal et al., 2025; Janssen et al., 2022; Valverde et al., 2022).

The UK National Health Service (NHS) has set a target of first‐line therapy within ≤31 days of diagnosis, as delays of more than 4 weeks increase the risk of death (NHS, 2024). However, treatment delays persist; for example, in Indonesia, the median time from diagnosis to treatment is approximately 120 days, attributable to delays in diagnosis and the time spent waiting for supporting examinations (Gondhowiardjo et al., 2021). This gap underscores that CPN is a relevant systemic strategy to accelerate the cancer care pathway and bring practice closer to international timeliness standards (Watanabe et al., 2023).

Overall, the findings of this review support a conceptual model in which CPN reduces time from diagnosis to treatment through three hierarchical layers: (i) structural integration of fragmented services, (ii) procedural acceleration of diagnostic‐to‐treatment transitions, and (iii) elimination of non‐clinical delays that accumulate across care interfaces. In this framework, CPN does not alter clinical decision‐making itself but functions as a temporal coordination system that compresses the interval between diagnosis and treatment initiation. For nursing practice, these findings underscore the role of nurses, particularly oncology navigators, and the importance of CPNs in identifying and addressing barriers that contribute to delays in first‐line cancer therapy.

5. STRENGTHS AND LIMITATIONS

The main strength of this scoping review lies in its ability to map CPN interventions in reducing time from diagnosis to first treatment, highlighting their role in improving timeliness and coordination of oncology services. However, all included studies originated from the United States and Canada, which limits generalizability to other healthcare systems with different socioeconomic and policy contexts.

Several limitations should be acknowledged. The search strategy combined intervention‐related terms with outcome terms on treatment timeliness, which restricted inclusion to studies reporting both CPN interventions and waiting time outcomes. As a result, the mapping of intervention characteristics is limited to this subset and does not fully capture all forms of CPN interventions. In addition, clinical heterogeneity exists between solid tumors and hematologic malignancies; while longer waiting times may be acceptable for solid tumors, hematologic malignancies require urgent treatment initiation, meaning shorter waiting times reflect clinical necessity rather than CPN effectiveness. This heterogeneity may influence interpretation, and subgroup analysis by cancer type is recommended in future research.

6. CONCLUSION

This scoping review shows that CPN interventions play a significant role in reducing the time from diagnosis to initiation of first therapy in patients with cancer by improving service coordination, enhancing continuity of care, and reducing systemic and patient barriers. The involvement of multidisciplinary health personnel and the variety of initial therapies reflect the complexity of cancer management and the need for an integrated approach from the diagnosis phase. These findings confirm CPN as a relevant intervention to improve cancer care flow and increase service timeliness, particularly in health systems facing challenges related to delayed diagnosis and treatment.

AUTHOR CONTRIBUTIONS

Agung Subakti Nuzulullail contributed to conceptualization, development of the methodology, article screening, data extraction, quality appraisal of the included studies, drafting the original manuscript, and project supervision and administration. Halfie Zaqiyah Gusti Puspitasari contributed to article screening, data extraction, and manuscript review and editing. Dwina Oktavia Deli contributed to article screening and narrative synthesis of the findings. Ni Made Hari Sugiantini contributed to the quality appraisal of the included studies and narrative synthesis of the findings. Muhammad Arif Zakyuddin contributed to the quality appraisal of the included studies and narrative synthesis of the findings. Khudazi Aulawi and Sri Hartini contributed to supervision, manuscript review, and editing. All authors read and approved the final version of the manuscript.

CONFLICT OF INTEREST STATEMENT

The authors declare that there is no conflict of interest.

Supporting information

Data S1. Supporting Information.

S1. PCC Framework.

S2. Keyword in each databases.

S3. Summary of penilaian kritis terhadap risiko bias dilakukan menggunakan JBI Critical Appraisal Tools for Cohort Study (2020).

S4. Preferred Reporting Items for Systematic reviews and Meta‐Analyses extension for Scoping Reviews (PRISMA‐ScR) Checklist.

JJNS-23-e70068-s001.docx (2.4MB, docx)

ACKNOWLEDGMENTS

The author would like to thank Universitas Gadjah Mada for its support in the literature search process, particularly for providing access to various international databases, which enabled the author to enrich and expand the data used in this scoping review. The author would also like to express his appreciation to the Lembaga Pengelola Dana Pendidikan (LPDP) and the Ministry of Finance of the Republic of Indonesia for their financial support in conducting this study and for facilitating its publication.

DATA AVAILABILITY STATEMENT

The data that supports the findings of this study are available in the Supplementary Material of this article.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data S1. Supporting Information.

S1. PCC Framework.

S2. Keyword in each databases.

S3. Summary of penilaian kritis terhadap risiko bias dilakukan menggunakan JBI Critical Appraisal Tools for Cohort Study (2020).

S4. Preferred Reporting Items for Systematic reviews and Meta‐Analyses extension for Scoping Reviews (PRISMA‐ScR) Checklist.

JJNS-23-e70068-s001.docx (2.4MB, docx)

Data Availability Statement

The data that supports the findings of this study are available in the Supplementary Material of this article.

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