People with chronic diseases experience barriers to managing their diseases and accessing available health services. Patient navigator programs are increasingly being used to help people with chronic diseases navigate and access health services.
The objective of this review was to summarize the evidence for patient navigator programs in people with a broad range of chronic diseases, compared to usual care.
We searched MEDLINE, EMBASE, CENTRAL, CINAHL, PsycINFO, and Social Work Abstracts from inception to August 23, 2017. We also searched the reference lists of included articles. We included original reports of randomized controlled trials of patient navigator programs compared to usual care for adult and pediatric patients with any one of a defined set of chronic diseases.
From a total of 14,672 abstracts, 67 unique studies fit our inclusion criteria. Of these, 44 were in cancer, 8 in diabetes, 7 in HIV/AIDS, 4 in cardiovascular disease, 2 in chronic kidney disease, 1 in dementia and 1 in patients with more than one condition. Program characteristics varied considerably. Primary outcomes were most commonly process measures, and 45 of 67 studies reported a statistically significant improvement in the primary outcome.
Our findings indicate that patient navigator programs improve processes of care, although few studies assessed patient experience, clinical outcomes or costs. The inability to definitively outline successful components remains a key uncertainty in the use of patient navigator programs across chronic diseases. Given the increasing popularity of patient navigators, future studies should use a consistent definition for patient navigation and determine which elements of this intervention are most likely to lead to improved outcomes.
Citation: McBrien KA, Ivers N, Barnieh L, Bailey JJ, Lorenzetti DL, Nicholas D, et al. (2018) Patient navigators for people with chronic disease: A systematic review. PLoS ONE 13(2): e0191980. https://doi.org/10.1371/journal.pone.0191980
Editor: Jacobus P. van Wouwe, TNO, NETHERLANDS
Received: June 23, 2017; Accepted: January 14, 2018; Published: February 20, 2018
Copyright: © 2018 McBrien et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: This research was funded by an Alberta Innovates – Health Solutions (AI-HS) team grant to the Interdisciplinary Chronic Disease Collaboration (ICDC), project ID #10007987 (http://albertainnovates.ca/) to Brenda Hemmelgarn and the Canadian Diabetes Association (CA) to Kerry A McBrien. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Chronic diseases, including physical and mental illnesses, are a significant burden to both patients and the health care system. There were an estimated 12.7 million new cases of cancer worldwide in 2008 ; and diabetes prevalence was estimated to be 6.4%, affecting 285 million adults worldwide in 2010 . People with chronic diseases have increased morbidity and consume substantially more health care resources than those without [3, 4]. Adherence to evidence-based recommendations for clinical care is associated with better outcomes and lower resource use for patients with chronic diseases [5–7]. For example, clinical trials show that in patients with diabetes, tight control of blood pressure, use of statins and achieving good glycemic control improves outcomes and lowers costs . Despite widespread dissemination of practice guidelines, many people with chronic diseases do not receive or adhere to recommended care [8–11].
This difficulty in implementing evidence-based care may be due to a combination of patient, provider and system level barriers . Patient level barriers may include lack of awareness of publicly funded programs (including community-based resources), financial constraints, competing priorities (e.g., family and work), personal circumstances, language and culture (i.e., race/ethnicity) ; such barriers could make it challenging to follow even seemingly simple lifestyle recommendations. At the provider level, barriers may include lack of clinical decision support systems to implement recommended care, lack of time and knowledge. System level barriers include the inherent complexity of the health care system and suboptimal access to primary or specialty care.
Patient navigators are trained personnel who help patients overcome modifiable barriers to care and achieve their care goals by providing a tailored approach to addressing individual needs [14–16]. Navigators may be nurses, social workers or lay health workers, including peers. Patient navigator programs were originally established to reduce gaps in timely cancer care among marginalized populations , and are increasingly in use across the United States and Canada within the cancer field . Patient navigation is also currently used for diabetes , smoking cessation [20, 21] and cancer screening . Depending on the targeted barriers, specific tasks may include one or more of: disease education [23, 24], health system education [23, 25, 26], removal of medical system barriers , assistance with insurance coverage , addressing other financial barriers , aiding in care coordination , referral to community resources , and providing emotional support, among others.
Previous reviews of patient navigators have focused on cancer care [16, 28], though patient navigators are increasingly being utilized in other areas [29, 30]. While their popularity is growing, and many are touting their benefits , it is not clear whether patient navigator programs are beneficial across a cross-section of chronic diseases. As there is wide variation in the design and implementation of patient navigator programs in various chronic diseases, a systematic review is needed to summarize the characteristics of programs and their effectiveness. In this systematic review, we assess the effectiveness and attributes of patient navigator services, compared with usual care, on patient-oriented outcomes and processes of care in patients with chronic diseases.
Data sources and searches
We searched MEDLINE, EMBASE, The Cochrane Central Register of Controlled Trials (CENTRAL), CINAHL, PsycINFO, and Social Work Abstracts up to August 23, 2017 with no language or date restrictions. In our MEDLINE search strategy, we included potential synonyms for patient navigator (case management, care coordination, health coach), terms for the set of chronic diseases of interest, and restricted the search to randomized controlled trials (S1 File). The MEDLINE search strategy was peer reviewed via PRESS . We also systematically searched reference lists of included studies and relevant reviews.
We included randomized controlled trials (RCTs) evaluating the effectiveness of a patient navigator program compared to usual care. Study population could be adult or pediatric patients, that either had or were being screened for one of the following chronic diseases, as included in the Statistics Canada Canadian Community Health Survey : asthma, arthritis, hypertension, migraine, COPD/emphysema, diabetes, heart disease, cancer, intestinal/stomach ulcers, stroke, urinary incontinence, inflammatory bowel disorder, dementia, mood disorders, anxiety disorders; with the addition of HIV/AIDS, and chronic kidney disease, which includes transplant recipients and patients on dialysis.
There is currently no standard definition of a patient navigator, thus there is variability between patient navigator programs, as well as overlap with programs under different names . We defined a patient navigator as a person with or without a healthcare-related background that engages with patients on an individual basis to determine barriers to accessing care or following recommended guidelines. The patient navigator also provides information relevant to patients’ specific circumstances to facilitate self-management and access to care. We were flexible in terms of the name of the intervention used by study authors (i.e., patient navigator, community health worker, etc.), as long as program descriptions were consistent with our definition. Studies were excluded if they evaluated programs where patient navigators performed clinical care (i.e., prescribed medication, ordered diagnostic tests, performed physical measurements), or where the role was not formalized (i.e., casual or untrained support).
Two reviewers independently screened all titles and abstracts of retrieved references. Two authors then applied the full set of inclusion and exclusion criteria to all articles chosen for full text review. Reviewers resolved any disagreements by discussion.
Data extraction and quality assessment
Data extraction was done by one reviewer, using standard data extraction forms and verified by a second reviewer. For studies with several trial arms, data were collected across each relevant comparison. Data elements included characteristics of the study, outcomes and results, along with details of the navigator program. Outcome measures of interest fell into one of three broad categories: patient-oriented (mortality, health-related quality of life, and complications of disease, e.g., MI, stroke); surrogate outcomes (e.g., achieving target blood pressure or glycemic control); and process measures, including access to appropriate services, and adherence to recommended clinical actions (e.g., cancer screening). Measures of patient experience and patient satisfaction were also collected. The risk of bias criteria suggested by the Cochrane Effective Practice and Organisation of Care Group (EPOC) were used to assess study quality . Items retained from the tool to assess bias were: random sequence generation, allocation concealment, blinding of outcome assessment, incomplete outcome data, group similarity at baseline and intention-to-treat analysis. Risk of bias in each domain was assessed as high, low or unclear.
Data synthesis and analysis
Though the goal of the systematic review was to provide a quantitative assessment of the effects of an intervention, we found a heterogeneous group of programs, chronic diseases and outcomes, and we therefore used a narrative approach to data synthesis. To assist in assessing effectiveness across this large number of studies, we tabulated the primary outcome of each of the studies, a summary of the result, and whether the changes were statistically significant. We determined the proportion of studies with positive outcome results (primary or secondary) in each outcome category, stratified by chronic disease. We explored the association between program features and a statistically significant improvement using logistic regression.
Given the heterogeneity in outcomes, it was not possible to assess publication bias using a traditional funnel plot. To provide an estimate of publication bias, we divided the studies into quintiles of sample size and compared the proportion of studies reporting a positive statistically significant effect across quintiles. All work aligned with a protocol that was developed and published ahead of the review .
Description of studies
We identified and screened 14,672 potentially relevant abstracts. Seventy-four papers describing 67 unique studies met our inclusion criteria, and were included in the review (Fig 1). Table 1 summarizes the characteristics of the included studies, while Table 2 provides an overview of the individual studies, grouped by disease. The vast majority of studies (90%) were conducted in the United States and sample size varied from 21 to 16,267 participants, with the majority of studies (52%) including between 100 and 500 participants. A summary of the quality assessment is presented in Fig 2 and a detailed assessment by study is presented in the Supplementary Table (S1 Table). Though all studies were RCTs, quality varied, and many studies were lacking information on allocation concealment and blinding of outcome assessment.
Patient navigation has been tested through RCTs more commonly in the context of cancer care (66%; n = 44) than in any other chronic disease. Of the cancer care studies, the majority were in cancer screening where the patient navigator’s focus was on helping the patient complete the screening test. Other chronic diseases where patient navigators have been studied include diabetes (n = 8), HIV (n = 7), cardiovascular disease (n = 4), chronic kidney disease (n = 2), dementia (n = 1) and multiple chronic diseases (n = 1).
Most navigator programs (64%) employed lay persons trained for the role. The primary mode of communication was by phone (90%) and over half were based in primary care or the community (57%). Patient navigators were responsible for a wide variety of activities. The most frequent strategy used by patient navigators to address health system barriers was care facilitation (i.e., making referrals, communicating with providers, coordinating care), followed by appointment scheduling (S2 Table). The most common activities used to address patient barriers included addressing patient attitudes and beliefs, appointment reminders, health literacy support and practical assistance (e.g., assistance with transportation, coordination of dependent care, arrangements for financial help or insurance benefits). Patient navigators most often provided education about the tests and treatments required in the form of discussion with patients. Many patient navigators also provided some form of direct psychosocial support to their patients.
Many studies (n = 26) reported using patient navigators that were culturally aligned, that is, a patient navigator who identified with the patient population in terms of ethnicity or other cultural factors, or included educational materials or communication approaches that were culturally tailored. Frequency of contact between navigators and patients ranged widely from only one contact to ‘as needed’ during the study duration, and duration of navigation varied widely.
We found significant heterogeneity in primary outcomes. With respect to patient-oriented outcomes, one study included death as a primary outcome , two hypoglycaemia [84, 107], and five studies assessed quality of life and/or health status as a primary outcome: two in patients undergoing cancer treatment [41, 108], one in caregivers supporting patients undergoing cancer treatment, one in stroke, and one in patients with multiple chronic diseases. Surrogate outcomes were most often reported in diabetes, where change in A1C levels was reported in seven studies [84, 86–88, 107, 111, 112]; three studies in HIV reported viral load [100, 101, 113] and one study in CKD reported change in estimated glomerular filtration rate (eGFR) . Process outcomes were the most frequently reported primary outcome (n = 50), and they included completion of disease screening and adherence to follow-up procedures. Patient satisfaction or experience was reported as a primary outcome in one study, while hospitalizations and emergency room visits were reported as primary outcomes in three studies [84, 105, 107]. Of the 67 unique studies identified in this review, 45 (67%) reported a statistically significant improvement in one or more primary outcomes. We did not find an association between any program characteristics and the finding of a statistically significant improvement in a primary outcome.
Secondary outcomes were broader in scope, although many were variations of the primary outcome: for example, diagnostic resolution within a specified time period (where the primary outcome was time to diagnostic resolution). Secondary outcomes more frequently included patient-reported outcomes, including physical and mental health status, quality of life, and psychological distress. Other patient-oriented outcomes were reported as secondary outcomes: diagnostic outcomes of cancer screening and follow-up were reported in three studies[114–116], use of acute care was reported in four studies [101, 104, 109, 117], mortality in two [102, 104] and rate of opportunistic infections was reported in one study Costs were considered in six studies [71, 73, 116, 118–120].
Fig 3 depicts the number of studies that included outcomes within each category of interest (either as primary or secondary) and the proportion of these that demonstrated a statistically significant improvement. Studies were more likely to report positive results for process measures, and less so for surrogate markers, health care utilization, or patient-oriented outcomes. No studies found a negative impact from the patient navigator intervention.
With respect to publication bias, the proportion of studies reporting a statistically significant improvement in a primary outcome, across sample size quintiles, was 57%, 56%, 68%, 83%, and 69%.
Healthcare systems worldwide are under tremendous strain to address the needs of patients with chronic diseases. Since improving access to care and adherence to recommended treatment may improve outcomes, there is great interest in navigator programs. Our review of patient navigators for people with chronic diseases identified 54 unique randomized controlled studies. While most studies reported a positive effect of patient navigator programs for their primary outcome, the impact on clinical outcomes remains uncertain. The majority of the outcomes measured in RCTs of patient navigator studies reflect the process of receiving care; few studies assessed patient-oriented outcomes and many studies had short duration of follow-up, with uncertain power to detect an effect on clinical outcomes.
The variability of patient navigation programs found in the existing literature makes it challenging to make definitive statements about their effectiveness. We propose a consistent definition for a patient navigator, i.e., a person with or without a healthcare-related background that engages with patients on an individual basis to determine barriers to accessing care or following recommended guidelines, and provides information relevant to patients’ specific circumstances to facilitate self-management and access to care. The primary focus is on overcoming barriers, not providing clinical care, and in doing so patient navigators are often a source of social support.
Most other reviews of patient navigation have been restricted to cancer. The reviews by Wells and Paskett identified 33 studies of patient navigator programs in cancer, not restricted to RCTs [16, 28]. Even when examining patient navigation within one chronic disease (i.e., cancer), there was wide variation in intervention design, study design and study quality. In our review, a higher proportion of studies in cancer prevention or management (n = 32/44, 73%) reported a positive statistically significant effect for one or more primary outcomes versus studies in other chronic diseases (n = 13/23, 56%). Ali-Faisal et al published a systematic review and meta-analysis summarizing the effect of patient navigation programs on health care utilization, including adherence to screening and follow-up care, outcomes that our review classified as process outcomes. Programs were required to use the term navigation or a variant in their description to be included in the review. The authors found that patient navigation was effective in increasing screening rates and improving adherence to recommended care; however, the effect on other health outcomes was less convincing and they noted considerable heterogeneity across studies. Our review of patient navigator programs expanded across chronic diseases and despite similar heterogeneity, echoed many of these previous findings and identified similar limitations.
Our study had strengths and limitations. Strengths include the thoroughness of our literature search, and our consideration of a broad group of diseases. A quantitative synthesis may have helped identify factors associated with successful patient navigator interventions, however this was precluded by the heterogeneity in both the intervention design and the outcomes reported. This heterogeneity also made it difficult to make definitive statements about the merit of specific patient navigator activities. We were unable to identify the most important elements of patient navigator programs that were associated with an improvement in the primary outcome. Other potential reasons include incomplete reporting (i.e., some program elements may have been present but not reported) or variation in how the individual features were implemented within programs. Though we were unable to summarize our results quantitatively, a descriptive review of these randomized controlled trials provides a comprehensive summary of navigator programs and outcomes reported. Our review was limited to published reports of randomized controlled trials, and therefore, although we noted a trend toward a lower proportion of small studies reporting statistically significant positive results, we could not rule out publication bias.
Our findings indicate that patient navigator programs improve processes of care, although few studies assessed patient experience, clinical outcomes or costs. The inability to definitively outline successful components remains a key uncertainty in the use of patient navigator programs across chronic diseases. Given the increasing popularity of patient navigator interventions, future studies should use consistent definitions for patient navigator interventions, and in addition to determining which elements of the intervention are most likely to lead to improved outcomes, studies should focus on patient experience and disease-specific clinical outcomes that are important to patients.
We would like to thank Laure Perrier for peer reviewing the MEDLINE search strategy. We would also like to thank Rami Zawi, Elizabeth Kelly, Monica Kidd and Johan Bester for their help with study screening.
- 1. Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. 2010;127(12):2893–917. pmid:21351269.
- 2. Shaw JE, Sicree RA, Zimmet PZ. Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract. 2010;87(1):4–14. pmid:19896746.
- 3. How Do Sicker Canadians with Chronic Disease Rate the Health Care System? Results from the 2011 Commonwealth Fund International Health Policy Survey of Sicker Adults. Toronto: Health Council of Canada, 2011.
- 4. Broemeling AM, Watson DE, Prebtani F. Population patterns of chronic health conditions, co-morbidity and healthcare use in Canada: implications for policy and practice. Healthcare quarterly (Toronto, Ont). 2008;11(3):70–6. Epub 2008/06/10. pmid:18536538.
- 5. Cost effectiveness analysis of improved blood pressure control in hypertensive patients with type 2 diabetes: UKPDS 40. UK Prospective Diabetes Study Group. BMJ. 1998;317(7160):720–6. Epub 1998/09/11. pmid:9732339.
- 6. Cost-effectiveness of intensive glycemic control, intensified hypertension control, and serum cholesterol level reduction for type 2 diabetes. JAMA. 2002;287(19):2542–51. Epub 2002/05/22. doi: joc20021 [pii]. pmid:12020335.
- 7. Gray A, Raikou M, McGuire A, Fenn P, Stevens R, Cull C, et al. Cost effectiveness of an intensive blood glucose control policy in patients with type 2 diabetes: economic analysis alongside randomised controlled trial (UKPDS 41). United Kingdom Prospective Diabetes Study Group. BMJ. 2000;320(7246):1373–8. Epub 2000/05/20. pmid:10818026.
- 8. Manns BJ, Tonelli M, Zhang J, Campbell DJ, Sargious P, Ayyalasomayajula B, et al. Enrolment in primary care networks: impact on outcomes and processes of care for patients with diabetes. CMAJ. 2012;184(2):E144–52. Epub 2011/12/07. pmid:22143232; PubMed Central PMCID: PMC3273535.
- 9. McAlister FA, Majumdar SR, Eurich DT, Johnson JA. The effect of specialist care within the first year on subsequent outcomes in 24,232 adults with new-onset diabetes mellitus: population-based cohort study. Qual Saf Health Care. 2007;16(1):6–11. Epub 2007/02/16. pmid:17301194; PubMed Central PMCID: PMC2464930.
- 10. Shah BR, Hux JE, Austin PC. Diabetes is not treated as a coronary artery disease risk equivalent. Diabetes Care. 2007;30(2):381–3. Epub 2007/01/30. pmid:17259516.
- 11. Toth EL, Majumdar SR, Guirguis LM, Lewanczuk RZ, Lee TK, Johnson JA. Compliance with clinical practice guidelines for type 2 diabetes in rural patients: treatment gaps and opportunities for improvement. Pharmacotherapy. 2003;23(5):659–65. Epub 2003/05/14. pmid:12741441.
- 12. Ferlie EB, Shortell SM. Improving the quality of health care in the United Kingdom and the United States: a framework for change. Milbank Q. 2001;79(2):281–315. Epub 2001/07/07. pmid:11439467.
- 13. Genoff MC, Zaballa A, Gany F, Gonzalez J, Ramirez J, Jewell ST, et al. Navigating Language Barriers: A Systematic Review of Patient Navigators' Impact on Cancer Screening for Limited English Proficient Patients. J Gen Intern Med. 2016;31(4):426–34. pmid:26786875; PubMed Central PMCID: PMCPMC4803699.
- 14. Parker VA, Lemak CH. Navigating patient navigation: crossing health services research and clinical boundaries. Advances in health care management. 2011;11:149–83. Epub 2011/01/01. pmid:22908669.
- 15. Pedersen A, Hack TF. Pilots of oncology health care: a concept analysis of the patient navigator role. Oncol Nurs Forum. 2010;37(1):55–60. pmid:20044339
- 16. Wells KJ, Battaglia TA, Dudley DJ, Garcia R, Greene A, Calhoun E, et al. Patient navigation: state of the art or is it science? Cancer. 2008;113(8):1999–2010. Epub 2008/09/10. pmid:18780320; PubMed Central PMCID: PMCPMC2679696.
- 17. Freeman HP. The history, principles, and future of patient navigation: commentary. Seminars in oncology nursing. 2013;29(2):72–5. Epub 2013/05/09. pmid:23651676.
- 18. Walkinshaw E. Patient navigators becoming the norm in Canada. CMAJ. 2011;183(15):E1109–10. Epub 2011/09/21. pmid:21930738; PubMed Central PMCID: PMCPMC3193138.
- 19. Thom DH, Ghorob A, Hessler D, De Vore D, Chen E, Bodenheimer TA. Impact of peer health coaching on glycemic control in low-income patients with diabetes: a randomized controlled trial. Ann Fam Med. 2013;11(2):137–44. pmid:23508600; PubMed Central PMCID: PMCPMC3601392.
- 20. Lasser KE, Kenst KS, Quintiliani LM, Wiener RS, Murillo J, Pbert L, et al. Patient navigation to promote smoking cessation among low-income primary care patients: a pilot randomized controlled trial. J Ethn Subst Abuse. 2013;12(4):374–90. pmid:24215228; PubMed Central PMCID: PMCPMC3827692.
- 21. Quintiliani LM, Russinova ZL, Bloch PP, Truong V, Xuan Z, Pbert L, et al. Patient navigation and financial incentives to promote smoking cessation in an underserved primary care population: A randomized controlled trial protocol. Contemp Clin Trials. 2015;45(Pt B):449–57. pmid:26362691.
- 22. Enard KR, Nevarez L, Hernandez M, Hovick SR, Moguel MR, Hajek RA, et al. Patient navigation to increase colorectal cancer screening among Latino Medicare enrollees: a randomized controlled trial. Cancer Causes Control. 2015;26(9):1351–9. pmid:26109462.
- 23. Fischer SM, Sauaia A, Kutner JS. Patient navigation: a culturally competent strategy to address disparities in palliative care. Journal of palliative medicine. 2007;10(5):1023–8. Epub 2007/11/08. pmid:17985954.
- 24. Shlay JC, Barber B, Mickiewicz T, Maravi M, Drisko J, Estacio R, et al. Reducing cardiovascular disease risk using patient navigators, Denver, Colorado, 2007–2009. Preventing chronic disease. 2011;8(6):A143. Epub 2011/10/19. pmid:22005636; PubMed Central PMCID: PMCPMC3221582.
- 25. Goff SL, Pekow PS, White KO, Lagu T, Mazor KM, Lindenauer PK. IDEAS for a healthy baby—reducing disparities in use of publicly reported quality data: study protocol for a randomized controlled trial. Trials. 2013;14:244. Epub 2013/08/08. pmid:23919671; PubMed Central PMCID: PMCPMC3751013.
- 26. Scott LB, Gravely S, Sexton TR, Brzostek S, Brown DL. Examining the Effect of a Patient Navigation Intervention on Outpatient Cardiac Rehabilitation Awareness and Enrollment. Journal of cardiopulmonary rehabilitation and prevention. 2013;33(5):281–91. Epub 2013/07/05. pmid:23823904; PubMed Central PMCID: PMCPMC3759655.
- 27. Darnell JS. Navigators and assisters: two case management roles for social workers in the Affordable Care Act. Health & social work. 2013;38(2):123–6. Epub 2013/07/20. pmid:23865289.
- 28. Paskett ED, Harrop JP, Wells KJ. Patient navigation: an update on the state of the science. CA: a cancer journal for clinicians. 2011;61(4):237–49. Epub 2011/06/11. pmid:21659419; PubMed Central PMCID: PMCPMC3623288.
- 29. Monza K, Harris D, Shaw C. The Role of the Nurse Navigator in the Management of the Heart Failure Patient. Critical care nursing clinics of North America. 2015;27(4):537–49. Epub 2015/11/17. pmid:26567497.
- 30. Loskutova NY, Tsai AG, Fisher EB, LaCruz DM, Cherrington AL, Harrington TM, et al. Patient Navigators Connecting Patients to Community Resources to Improve Diabetes Outcomes. Journal of the American Board of Family Medicine: JABFM. 2016;29(1):78–89. Epub 2016/01/16. pmid:26769880.
- 31. Gardner E. A Personal Compass: What Patient Navigation Can Do for You. US News 2015 Sept 16, 2015.
- 32. PRESS Peer Review of Electronic Search Strategies: 2015 Guideline Explanation and Elaboration (PRESS E&E). Ottawa: 2016 January 2016. Report No.
- 33. Canadian Community Health Survey 2012: Statistics Canada; 2012 [January 15, 2013]. Available from: http://www23.statcan.gc.ca/imdb/p3Instr.pl?Function=getInstrumentList&Item_Id=119788&UL=1V&.
- 34. Dohan D, Schrag D. Using navigators to improve care of underserved patients: current practices and approaches. Cancer. 2005;104(4):848–55. pmid:16010658.
- 35. Kelly E, Ivers N, Zawi R, Barnieh L, Manns B, Lorenzetti DL, et al. Patient navigators for people with chronic disease: protocol for a systematic review and meta-analysis. Systematic Reviews. 2015;4. pmid:25874724.
- 36. Fiscella K, Whitley E, Hendren S, Raich P, Humiston S, Winters P, et al. Patient navigation for breast and colorectal cancer treatment: a randomized trial. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2012;21(10):1673–81. Epub 2012/10/10. pmid:23045542; PubMed Central PMCID: PMCPMC3724524.
- 37. Hendren S, Griggs JJ, Epstein R, Humiston S, Jean-Pierre P, Winters P, et al. Randomized controlled trial of patient navigation for newly diagnosed cancer patients: effects on quality of life. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2012;21(10):1682–90. Epub 2012/10/10. pmid:23045543; PubMed Central PMCID: PMCPMC3468902.
- 38. Ell K, Vourlekis B, Xie B, Nedjat-Haiem FR, Lee PJ, Muderspach L, et al. Cancer treatment adherence among low-income women with breast or gynecologic cancer: a randomized controlled trial of patient navigation. Cancer. 2009;115(19):4606–15. Epub 2009/06/25. pmid:19551881; PubMed Central PMCID: PMCPMC2749894.
- 39. White VM, Macvean ML, Grogan S, D'Este C, Akkerman D, Ieropoli S, et al. Can a tailored telephone intervention delivered by volunteers reduce the supportive care needs, anxiety and depression of people with colorectal cancer? A randomised controlled trial. Psycho-oncology. 2012;21(10):1053–62. Epub 2011/07/20. pmid:21769989.
- 40. Percac-Lima S, Cronin PR, Ryan DP, Chabner BA, Daly EA, Kimball AB. Patient navigation based on predictive modeling decreases no-show rates in cancer care. Cancer. 2015;121(10):1662–70. Epub 2015/01/15. pmid:25585595.
- 41. Giese-Davis J, Bliss-Isberg C, Wittenberg L, White J, Star P, Zhong L, et al. Peer-counseling for women newly diagnosed with breast cancer: A randomized community/research collaboration trial. Cancer. 2016;122(15):2408–17. pmid:27198057.
- 42. Shaw Joanne, Young Jane, Butow Phyllis, et al. Improving psychosocial outcomes for caregivers of people with poor prognosis gastrointestinal cancers: a randomized controlled trial (Family Connect). Supportive Care in Cancer. 2016;24(2):585–95. pmid:26111955. Language: English. Entry Date: 20160804. Revision Date: 20170601. Publication Type: journal article. Journal Subset: Biomedical.
- 43. Ferrante JM, Chen PH, Kim S. The effect of patient navigation on time to diagnosis, anxiety, and satisfaction in urban minority women with abnormal mammograms: a randomized controlled trial. Journal of urban health: bulletin of the New York Academy of Medicine. 2008;85(1):114–24. Epub 2007/10/02. pmid:17906931; PubMed Central PMCID: PMCPMC2430139.
- 44. Ell K, Vourlekis B, Lee PJ, Xie B. Patient navigation and case management following an abnormal mammogram: a randomized clinical trial. Preventive medicine. 2007;44(1):26–33. Epub 2006/09/12. pmid:16962652.
- 45. Crump SR, Shipp MP, McCray GG, Morris SJ, Okoli JA, Caplan LS, et al. Abnormal mammogram follow-up: do community lay health advocates make a difference? Health promotion practice. 2008;9(2):140–8. Epub 2008/03/15. pmid:18340089.
- 46. Maxwell AE, Jo AM, Crespi CM, Sudan M, Bastani R. Peer navigation improves diagnostic follow-up after breast cancer screening among Korean American women: results of a randomized trial. Cancer Causes Control. 2010;21(11):1931–40. Epub 2010/08/03. pmid:20676928; PubMed Central PMCID: PMCPMC2959157.
- 47. Bastani R, Mojica CM, Berman BA, Ganz PA. Low-income women with abnormal breast findings: results of a randomized trial to increase rates of diagnostic resolution. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2010;19(8):1927–36. Epub 2010/07/22. pmid:20647406.
- 48. Lerman C, Hanjani P, Caputo C, Miller S, Delmoor E, Nolte S, et al. Telephone counseling improves adherence to colposcopy among lower-income minority women. Journal of clinical oncology: official journal of the American Society of Clinical Oncology. 1992;10(2):330–3. Epub 1992/02/01. pmid:1732434.
- 49. Miller SM, Siejak KK, Schroeder CM, Lerman C, Hernandez E, Helm CW. Enhancing adherence following abnormal Pap smears among low-income minority women: a preventive telephone counseling strategy. Journal of the National Cancer Institute. 1997;89(10):703–8. Epub 1997/05/21. pmid:9168185.
- 50. Engelstad LP, Stewart S, Otero-Sabogal R, Leung MS, Davis PI, Pasick RJ. The effectiveness of a community outreach intervention to improve follow-up among underserved women at highest risk for cervical cancer. Preventive medicine. 2005;41(3–4):741–8. Epub 2005/08/30. pmid:16125761.
- 51. Lee JH, Fulp W, Wells KJ, Meade CD, Calcano E, Roetzheim R. Patient navigation and time to diagnostic resolution: results for a cluster randomized trial evaluating the efficacy of patient navigation among patients with breast cancer screening abnormalities, Tampa, FL. PloS one. 2013;8(9):e74542. Epub 2013/09/26. pmid:24066145; PubMed Central PMCID: PMCPMC3774725.
- 52. Lee JH, Fulp W, Wells KJ, Meade CD, Calcano E, Roetzheim R. Effect of patient navigation on time to diagnostic resolution among patients with colorectal cancer-related abnormalities. Journal of cancer education: the official journal of the American Association for Cancer Education. 2014;29(1):144–50. Epub 2013/10/12. pmid:24113902; PubMed Central PMCID: PMCPMC3945676.
- 53. Wells KJ, Lee JH, Calcano ER, Meade CD, Rivera M, Fulp WJ, et al. A cluster randomized trial evaluating the efficacy of patient navigation in improving quality of diagnostic care for patients with breast or colorectal cancer abnormalities. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2012;21(10):1664–72. Epub 2012/10/10. pmid:23045541; PubMed Central PMCID: PMCPMC3511588.
- 54. Raich PC, Whitley EM, Thorland W, Valverde P, Fairclough D. Patient navigation improves cancer diagnostic resolution: an individually randomized clinical trial in an underserved population. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2012;21(10):1629–38. Epub 2012/10/10. pmid:23045537; PubMed Central PMCID: PMCPMC4053249.
- 55. Paskett ED, Katz ML, Post DM, Pennell ML, Young GS, Seiber EE, et al. The Ohio Patient Navigation Research Program: does the American Cancer Society patient navigation model improve time to resolution in patients with abnormal screening tests? Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2012;21(10):1620–8. Epub 2012/10/10. pmid:23045536; PubMed Central PMCID: PMCPMC3785236.
- 56. Weber BE, Reilly BM. Enhancing mammography use in the inner city. A randomized trial of intensive case management. Archives of internal medicine. 1997;157(20):2345–9. Epub 1997/11/15. pmid:9361575.
- 57. West DS, Greene P, Pulley L, Kratt P, Gore S, Weiss H, et al. Stepped-care, community clinic interventions to promote mammography use among low-income rural African American women. Health education & behavior: the official publication of the Society for Public Health Education. 2004;31(4 Suppl):29S–44S. Epub 2004/08/07. pmid:15296690.
- 58. Paskett E, Tatum C, Rushing J, Michielutte R, Bell R, Long Foley K, et al. Randomized trial of an intervention to improve mammography utilization among a triracial rural population of women. Journal of the National Cancer Institute. 2006;98(17):1226–37. Epub 2006/09/07. pmid:16954475; PubMed Central PMCID: PMCPMC4450352.
- 59. Rahm AK, Sukhanova A, Ellis J, Mouchawar J. Increasing utilization of cancer genetic counseling services using a patient navigator model. Journal of genetic counseling. 2007;16(2):171–7. Epub 2007/02/06. pmid:17277995.
- 60. Ahmed NU, Haber G, Semenya KA, Hargreaves MK. Randomized controlled trial of mammography intervention in insured very low-income women. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2010;19(7):1790–8. Epub 2010/07/01. pmid:20587669.
- 61. Phillips CE, Rothstein JD, Beaver K, Sherman BJ, Freund KM, Battaglia TA. Patient navigation to increase mammography screening among inner city women. J Gen Intern Med. 2011;26(2):123–9. Epub 2010/10/12. pmid:20931294; PubMed Central PMCID: PMCPMC3019333.
- 62. Marshall JK, Mbah OM, Ford JG, Phelan-Emrick D, Ahmed S, Bone L, et al. Effect of Patient Navigation on Breast Cancer Screening Among African American Medicare Beneficiaries: A Randomized Controlled Trial. J Gen Intern Med. 2016;31(1):68–76. Epub 2015/08/12. pmid:26259762; PubMed Central PMCID: PMCPMC4700012.
- 63. Taylor VM, Hislop TG, Jackson JC, Tu SP, Yasui Y, Schwartz SM, et al. A randomized controlled trial of interventions to promote cervical cancer screening among Chinese women in North America. Journal of the National Cancer Institute. 2002;94(9):670–7. Epub 2002/05/02. pmid:11983755; PubMed Central PMCID: PMCPMC1592333.
- 64. Taylor VM, Jackson JC, Yasui Y, Nguyen TT, Woodall E, Acorda E, et al. Evaluation of a cervical cancer control intervention using lay health workers for Vietnamese American women. American journal of public health. 2010;100(10):1924–9. Epub 2010/08/21. pmid:20724673; PubMed Central PMCID: PMCPMC2936992.
- 65. Jandorf L, Gutierrez Y, Lopez J, Christie J, Itzkowitz SH. Use of a patient navigator to increase colorectal cancer screening in an urban neighborhood health clinic. Journal of urban health: bulletin of the New York Academy of Medicine. 2005;82(2):216–24. Epub 2005/05/13. pmid:15888638; PubMed Central PMCID: PMCPMC3456577.
- 66. Basch CE, Wolf RL, Brouse CH, Shmukler C, Neugut A, DeCarlo LT, et al. Telephone outreach to increase colorectal cancer screening in an urban minority population. American journal of public health. 2006;96(12):2246–53. Epub 2006/11/02. pmid:17077394; PubMed Central PMCID: PMCPMC1698159.
- 67. Percac-Lima S, Grant RW, Green AR, Ashburner JM, Gamba G, Oo S, et al. A culturally tailored navigator program for colorectal cancer screening in a community health center: a randomized, controlled trial. J Gen Intern Med. 2009;24(2):211–7. Epub 2008/12/11. pmid:19067085; PubMed Central PMCID: PMCPMC2628981.
- 68. Christie J, Itzkowitz S, Lihau-Nkanza I, Castillo A, Redd W, Jandorf L. A randomized controlled trial using patient navigation to increase colonoscopy screening among low-income minorities. Journal of the National Medical Association. 2008;100(3):278–84. Epub 2008/04/09. pmid:18390020.
- 69. Lasser KE, Murillo J, Lisboa S, Casimir AN, Valley-Shah L, Emmons KM, et al. Colorectal cancer screening among ethnically diverse, low-income patients: a randomized controlled trial. Archives of internal medicine. 2011;171(10):906–12. Epub 2011/05/25. pmid:21606094.
- 70. Coronado GD, Golovaty I, Longton G, Levy L, Jimenez R. Effectiveness of a clinic-based colorectal cancer screening promotion program for underserved Hispanics. Cancer. 2011;117(8):1745–54. Epub 2011/04/08. pmid:21472722.
- 71. Green BB, Wang CY, Anderson ML, Chubak J, Meenan RT, Vernon SW, et al. An automated intervention with stepped increases in support to increase uptake of colorectal cancer screening: a randomized trial. Ann Intern Med. 2013;158(5 Pt 1):301–11. Epub 03/06. pmid:23460053.
- 72. Lairson DR, Dicarlo M, Deshmuk AA, Fagan HB, Sifri R, Katurakes N, et al. Cost-effectiveness of a standard intervention versus a navigated intervention on colorectal cancer screening use in primary care. Cancer. 2014;120(7):1042–9. Epub 2014/01/18. pmid:24435411; PubMed Central PMCID: PMCPMC3961516.
- 73. Myers RE, Bittner-Fagan H, Daskalakis C, Sifri R, Vernon SW, Cocroft J, et al. A randomized controlled trial of a tailored navigation and a standard intervention in colorectal cancer screening. Cancer Epidemiol Biomarkers Prev. 2013;22(1):109–17. Epub 2012/11/03. PubMed Central PMCID: PMCPMC5537598. pmid:23118143
- 74. Myers RE, Sifri R, Daskalakis C, DiCarlo M, Geethakumari PR, Cocroft J, et al. Increasing colon cancer screening in primary care among African Americans. Journal of the National Cancer Institute. 2014;106(12). Epub 2014/12/08. PubMed PMID: 25174031; PubMed Central PMCID: PMCPMC4817126.
- 75. Ritvo PG, Myers RE, Paszat LF, Tinmouth JM, McColeman J, Mitchell B, et al. Personal navigation increases colorectal cancer screening uptake. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2015;24(3):506–11. Epub 2014/11/08. pmid:25378365.
- 76. Greenspan M, Shawron K, Avery E, Barnes LL, Olinger T, Li H, et al. Patient directed navigation does not improve adherence to colonoscopy or bowel preparation quality: A randomized controlled trial. Gastroenterology. 2016;150(4 SUPPL. 1):S757. PubMed PMID: CN-01160715 NEW.
- 77. Cole H, Thompson HS, White M, Browne R, Trinh-Shevrin C, Braithwaite S, et al. Community-Based, Preclinical Patient Navigation for Colorectal Cancer Screening Among Older Black Men Recruited From Barbershops: The MISTER B Trial. American Journal of Public Health. 2017;107(9):1433–40. pmid:28727540.
- 78. Guillaume E, Dejardin O, Bouvier V, et al. Patient navigation to reduce social inequalities in colorectal cancer screening participation: A cluster randomized controlled trial. Preventive Medicine. 2017;16:16. PubMed PMID: 28823681.
- 79. DeGroff A, Schroy P C, Morrissey rd, et al. Patient Navigation for Colonoscopy Completion: Results of an RCT. American Journal of Preventive Medicine. 2017;53(3):363–72. pmid:28676254.
- 80. Dietrich AJ, Tobin JN, Cassells A, Robinson CM, Greene MA, Sox CH, et al. Telephone care management to improve cancer screening among low-income women: a randomized, controlled trial. Annals of internal medicine. 2006;144(8):563–71. Epub 2006/04/19. pmid:16618953; PubMed Central PMCID: PMCPMC3841972.
- 81. Braun KL, Thomas WL Jr., Domingo JL, Allison AL, Ponce A, Haunani Kamakana P, et al. Reducing cancer screening disparities in medicare beneficiaries through cancer patient navigation. Journal of the American Geriatrics Society. 2015;63(2):365–70. Epub 2015/02/03. pmid:25640884; PubMed Central PMCID: PMCPMC4850231.
- 82. Percac-Lima S, Ashburner JM, Zai AH, Chang Y, Oo SA, Guimaraes E, et al. Patient Navigation for Comprehensive Cancer Screening in High-Risk Patients Using a Population-Based Health Information Technology System: A Randomized Clinical Trial. JAMA Internal Medicine. 2016;176(7):930–7. pmid:27273602.
- 83. Corkery E, Palmer C, Foley ME, Schechter CB, Frisher L, Roman SH. Effect of a bicultural community health worker on completion of diabetes education in a Hispanic population. Diabetes Care. 1997;20(3):254–7. Epub 1997/03/01. pmid:9051367.
- 84. Laffel LM, Brackett J, Ho J, Anderson BJ. Changing the process of diabetes care improves metabolic outcomes and reduces hospitalizations. Qual Manag Health Care. 1998;6(4):53–62. Epub 05/29. pmid:10339045.
- 85. Svoren BM, Butler D, Levine BS, Anderson BJ, Laffel LM. Reducing acute adverse outcomes in youths with type 1 diabetes: a randomized, controlled trial. Pediatrics. 2003;112(4):914–22. Epub 2003/10/03. pmid:14523186.
- 86. Gary TL, Batts-Turner M, Bone LR, Yeh HC, Wang NY, Hill-Briggs F, et al. A randomized controlled trial of the effects of nurse case manager and community health worker team interventions in urban African-Americans with type 2 diabetes. Control Clin Trials. 2004;25(1):53–66. Epub 2004/02/26. pmid:14980748.
- 87. Spencer MS, Rosland AM, Kieffer EC, Sinco BR, Valerio M, Palmisano G, et al. Effectiveness of a community health worker intervention among African American and Latino adults with type 2 diabetes: a randomized controlled trial. American journal of public health. 2011;101(12):2253–60. Epub 2011/06/18. pmid:21680932; PubMed Central PMCID: PMCPMC3222418.
- 88. Prezio EA, Cheng D, Balasubramanian BA, Shuval K, Kendzor DE, Culica D. Community Diabetes Education (CoDE) for uninsured Mexican Americans: A randomized controlled trial of a culturally tailored diabetes education and management program led by a community health worker. Diabetes Res Clin Pract. 2013;100(1):19–28. pmid:23453178. Language: English. Entry Date: In Process. Revision Date: 20130503. Publication Type: journal article. Journal Subset: Biomedical.
- 89. Carrasquillo O, Alonzo Y, Lebron C, Ferras N, Reyes-Arrechea E, Kenya S. A randomized trial of a community health worker led intervention to improve diabetes intermediate outcomes among latino patients with poorly controlled diabetes. Journal of General Internal Medicine. 2014;29:S13.
- 90. Thom DH, Hessler D, Willard-Grace R, Bodenheimer T, Najmabadi A, Araujo C, et al. Does health coaching change patients' trust in their primary care provider? Patient education and counseling. 2014;96(1):135–8. Epub 2014/04/30. pmid:24776175.
- 91. Thom DH, Hessler D, Willard-Grace R, DeVore D, Prado C, Bodenheimer T, et al. Health coaching by medical assistants improves patients' chronic care experience. The American journal of managed care. 2015;21(10):685–91. Epub 2015/12/04. pmid:26633093.
- 92. Willard-Grace R, Chen EH, Hessler D, DeVore D, Prado C, Bodenheimer T, et al. Health coaching by medical assistants to improve control of diabetes, hypertension, and hyperlipidemia in low-income patients: a randomized controlled trial. Ann Fam Med. 2015;13(2):130–8. Epub 2015/03/11. pmid:25755034; PubMed Central PMCID: PMCPMC4369595.
- 93. Scott LB, Gravely S, Sexton TR, Brzostek S, Brown DL. Effect of patient navigation on enrollment in cardiac rehabilitation. JAMA Internal Medicine. 2013;173(3):244–6. pmid:23247823
- 94. Dennis M, O'Rourke S, Slattery J, Staniforth T, Warlow C. Evaluation of a stroke family care worker: results of a randomised controlled trial. BMJ. 1997;314(7087):1071–6; discussion 6–7. Epub 1997/04/12. pmid:9133884; PubMed Central PMCID: PMCPMC2126479.
- 95. Ali-Faisal SF, Benz Scott L, Johnston L, Grace SL. Cardiac rehabilitation referral and enrolment across an academic health sciences centre with eReferral and peer navigation: a randomised controlled pilot trial. BMJ Open. 2016;6(3):e010214. Epub 2016/03/24. pmid:27000785; PubMed Central PMCID: PMCPMC4809077.
- 96. Gardner LI, Metsch LR, Anderson-Mahoney P, Loughlin AM, del Rio C, Strathdee S, et al. Efficacy of a brief case management intervention to link recently diagnosed HIV-infected persons to care. AIDS (London, England). 2005;19(4):423–31. Epub 2005/03/08. pmid:15750396.
- 97. Wohl AR, Garland WH, Valencia R, Squires K, Witt MD, Kovacs A, et al. A randomized trial of directly administered antiretroviral therapy and adherence case management intervention. Clinical infectious diseases: an official publication of the Infectious Diseases Society of America. 2006;42(11):1619–27. Epub 2006/05/03. pmid:16652320.
- 98. Wohl DA, Scheyett A, Golin CE, White B, Matuszewski J, Bowling M, et al. Intensive case management before and after prison release is no more effective than comprehensive pre-release discharge planning in linking HIV-infected prisoners to care: a randomized trial. AIDS and behavior. 2011;15(2):356–64. Epub 2010/11/03. pmid:21042930; PubMed Central PMCID: PMCPMC3532052.
- 99. Metsch LR, Pereyra M, Messinger S, Jeanty Y, Parish C, Valverde E, et al. Effects of a Brief Case Management Intervention Linking People With HIV to Oral Health Care: Project SMILE. American journal of public health. 2015;105(1):77–84. Epub 2014/05/17. pmid:24832421; PubMed Central PMCID: PMCPMC4265910.
- 100. Metsch L, Feaster D, Gooden L, Matheson T, Stitzer M, Das M, et al. Effect of Patient Navigation With or Without Financial Incentives on Viral Suppression Among Hospitalized Patients With HIV Infection and Substance Use: A Randomized Clinical Trial. JAMA. 2016;316(2):156–70. pmid:27404184.
- 101. Giordano TP, Cully J, Amico K, R, Davila J, A, Kallen M, A, Hartman C, et al. A Randomized Trial to Test a Peer Mentor Intervention to Improve Outcomes in Persons Hospitalized With HIV Infection. Clinical Infectious Diseases. 2016;63(5):678–86. pmid:27217266.
- 102. Bassett IV, Coleman SM, Giddy J, Bogart LM, Chaisson CE, Ross D, et al. Sizanani: A Randomized Trial of Health System Navigators to Improve Linkage to HIV and TB Care in South Africa. Journal of Acquired Immune Deficiency Syndromes: JAIDS. 2016;73(2):154–60. pmid:27632145.
- 103. Sullivan C, Leon JB, Sayre SS, Marbury M, Ivers M, Pencak JA, et al. Impact of navigators on completion of steps in the kidney transplant process: a randomized, controlled trial. Clinical journal of the American Society of Nephrology: CJASN. 2012;7(10):1639–45. Epub 2012/07/17. pmid:22798540; PubMed Central PMCID: PMCPMC3463214.
- 104. Navaneethan SD, Jolly SE, Schold JD, Arrigain S, Nakhoul G, Konig V, et al. Pragmatic Randomized, Controlled Trial of Patient Navigators and Enhanced Personal Health Records in CKD. Clinical Journal of The American Society of Nephrology: CJASN. 2017;04:04. PubMed PMID: 28778854.
- 105. Amjad H, Wong SK, Roth DL, Huang J, Willink A, Black BS, et al. Health Services Utilization in Older Adults with Dementia Receiving Care Coordination: The MIND at Home Trial. Health Services Research. 2017;12:12. PubMed PMID: 28083879.
- 106. Kneipp SM, Kairalla JA, Lutz BJ, Pereira D, Hall AG, Flocks J, et al. Public health nursing case management for women receiving temporary assistance for needy families: a randomized controlled trial using community-based participatory research. American journal of public health. 2011;101(9):1759–68. Epub 2011/07/23. pmid:21778474; PubMed Central PMCID: PMCPMC3154225.
- 107. Svoren BM, Butler D, Levine BS, Anderson BJ, Laffel LMB. Reducing acute adverse outcomes in youths with type 1 diabetes: A randomized, controlled trial. Pediatrics. 2003;112(4):914–22. pmid:14523186.
- 108. White VM, Macvean ML, Grogan S, Este CD, Akkerman D, Ieropoli S, et al. Can a tailored telephone intervention delivered by volunteers reduce the supportive care needs, anxiety and depression of people with colorectal cancer? A randomised controlled trial. Psychooncology. 2011;21(10):1053–62. Epub 07/20. pmid:21769989.
- 109. Shaw JM, Young JM, Butow PN, Badgery-Parker T, Durcinoska I, Harrison J, et al. Improving psychosocial outcomes for caregivers of people with poor prognosis gastrointestinal cancers: a randomized controlled trial (Family Connect). Supportive Care in Cancer. 2016;24(2):585–95. pmid:26111955. Language: English. Entry Date: 20160804. Revision Date: 20170601. Publication Type: journal article. Journal Subset: Biomedical.
- 110. Kneipp SM, Kairalla JA, Lutz BJ, Pereira D, Hall AG, Flocks J, et al. Public health nursing case management for women receiving Temporary Assistance for Needy Families: A randomized controlled trial using community-based participatory research. [References]. American Journal of Public Health. 2011;(9):1759–68. pmid:21778474
- 111. Thom D, Ghorob A, Hessler D, De Vore D, Chen E, Bodenheimer T. Impact of peer health coaching on glycemic control in low-income patients with diabetes: a randomized controlled trial. Ann Fam Med. 2013;11(2):137–44. pmid:23508600. Language: English. Entry Date: 20130920. Revision Date: 20130920. Publication Type: journal article.
- 112. Carrasquillo O, Alonzo Y, Lebron C, Ferras N, Reyes-Arrechea E, Li H, et al. A randomized trial of a community health worker led intervention to improve diabetes intermediate outcomes among latinos patients with poorly controlled diabetes. Journal of general internal medicine. 2014;29(23). PubMed PMID: CN-01063665 NEW.
- 113. Wohl AR, Garland WH, Valencia R, Squires K, Witt MD, Kovacs A, et al. A randomized trial of directly administered antiretroviral therapy and adherence case management intervention. Clinical Infectious Diseases. 2006;42(11):1619–27. pmid:16652320.
- 114. Lasser KE, Murillo J, Lisboa S, Casimir AN, Shah LV, Emmons KM, et al. Colorectal cancer screening among ethnically diverse, low-income patients: A randomized controlled trial. Archives of Internal Medicine. 2011;171(10):906–12. pmid:21606094.
- 115. Percac-Lima S, Grant RW, Green AR, Ashburner JM, Gamba G, Oo S, et al. A culturally tailored navigator program for colorectal cancer screening in a community health center: a randomized, controlled trial. Journal of General Internal Medicine. 2009;24(2):211–7. pmid:19067085.
- 116. Weber BE, Reilly BM. Enhancing mammography use in the inner city. A randomized trial of intensive case management. Archives of Internal Medicine. 1997;157(20):2345–9. pmid:9361575.
- 117. Wohl DA, Scheyett A, Golin CE, White B, Matuszewski J, Bowling M, et al. Intensive Case Management Before and After Prison Release is No More Effective Than Comprehensive Pre-Release Discharge Planning in Linking HIV-Infected Prisoners to Care: A Randomized Trial. AIDS and behavior. 2011;15(2):356–64. pmid:21042930
- 118. Paskett E, Tatum C, Rushing J, Michielutte R, Bell R, Foley KL, et al. Randomized trial of an intervention to improve mammography utilization among a triracial rural population of women. J Natl Cancer Inst. 2006;98(17):1226–37. Epub 09/07. pmid:16954475.
- 119. Rahm AK, Sukhanova A, Ellis J, Mouchawar J. Increasing utilization of cancer genetic counseling services using a patient navigator model. Journal of Genetic Counseling. 2007;16(2):171–7. pmid:17277995
- 120. Gardner LI, Metsch LR, Anderson-Mahoney P, Loughlin AM, Rio CD, Strathdee S, et al. Efficacy of a brief case management intervention to link recently diagnosed HIV-infected persons to care. Aids. 2005;19(4):423–31. pmid:15750396.
- 121. Ali-Faisal SF, Colella TJ, Medina-Jaudes N, Benz Scott L. The effectiveness of patient navigation to improve healthcare utilization outcomes: A meta-analysis of randomized controlled trials. Patient Educ Couns. 2017;100(3):436–48. Epub 2016/10/25. pmid:27771161.