Do navigators' estimates of navigation intensity predict navigation time for cancer care?

Derivation of a Unique, Algorithm-Based Approach to Cancer Patient Navigator Workload Management

PURPOSE

Cancer patient navigators (CPNs) can decrease the time from diagnosis to treatment, but workloads vary widely, which may lead to burnout and less optimal navigation. Current practice for patient distribution among CPNs at our institution approximates random distribution. A literature search did not uncover previous reports of an automated algorithm to distribute patients to CPNs. We sought to develop an automated algorithm to fairly distribute new patients among CPNs specializing in the same cancer type(s) and assess its performance through simulation on a retrospective data set.

METHODS

Using a 3-year data set, a proxy for CPN work was identified and multiple models were developed to predict the upcoming week's workload for each patient. An XGBoost-based predictor was retained on the basis of its superior performance. A distribution model was developed to fairly distribute new patients among CPNs within a specialty on the basis of predicted work needed. The predicted work included the week's predicted workload from a CPN's existing patients plus that of newly distributed patients to the CPN. Resulting workload unfairness was compared between predictor-informed and random distribution.

RESULTS

Predictor-informed distribution significantly outperformed random distribution for equalizing weekly workloads across CPNs within a specialty.

CONCLUSION

This derivation work demonstrates the feasibility of an automated model to distribute new patients more fairly than random assignment (with unfairness assessed using a workload proxy). Improved workload management may help reduce CPN burnout and improve navigation assistance for patients with cancer.

Patient barriers to cancer clinical trial participation and navigator activities to assist

Clinical research is vital to the discovery of new cancer treatments that can enhance health and prolong life for cancer patients, but breakthroughs in cancer treatment are limited by challenges recruiting patients into cancer clinical trials (CT). Only 3-5% of cancer patients in the United States participate in a cancer CT and there are disparities in CT participation by age, race and gender. Strategies such as patient navigation, which is designed to provide patients with education and practical support, may help to overcome challenges of CT recruitment. The current study evaluated an intervention in which lay navigators were utilized to provide patient education and practical support for helping patients overcome barriers to CT participation and related clinical care. A patient barrier checklist was utilized to record patient barriers to CT participation and care, actions taken by navigators to assist patients with these barriers, and whether or not these barriers could be overcome. Forty patients received patient navigation services. The most common barriers faced by navigated patients were fear (n=9), issues communicating with medical personnel (n=9), insurance issues (n=8), transportation difficulties (n=6) and perceptions about providers and treatment (n=4). The most common activities undertaken by navigators were making referrals and contacts on behalf of patients (e.g., support services, family, clinicians; n=25). Navigators also made arrangement for transportation, financial, medication and equipment services for patients (n=11) and proactively navigated patients (n=8). Barriers that were not overcome for two or more patients included insurance issues, lack of temporary housing resources for patients in treatment and assistance with household bills. The wide array of patient barriers to CT participation and navigator assistance documented in this study supports the CT navigator role in facilitating quality care.

Nurse Case Manager: Measurement of Care Coordination Activities and Quality and Resource Use Outcomes When Caring for the Complex Patient With Hematologic Cancer

BACKGROUND

The lack of coordination of care for complex patients in the hematology setting has prompted nurse case managers (NCMs) to coordinate that care.

OBJECTIVES

This article aimed to identify the frequency of NCM care coordination activities and quality and resource use outcomes in the complex care of patients in the hematology setting.

METHODS

NCM aggregate data from complex outpatients with hematologic cancer were retrieved from electronic health records at a comprehensive cancer center in the midwestern United States. Total volume of activities and outcomes were calculated as frequency and percentage.

FINDINGS

Care coordination activities included communicating; monitoring, following up, and responding to change; and creating a proactive plan of care. Quality outcomes included improving continuity of care and change in health behavior, and resource use outcomes most documented were patient healthcare cost savings.

Social Network Structures of Breast Cancer Patients and the Contributing Role of Patient Navigators

BACKGROUND

Minority women in the U.S. continue to experience inferior breast cancer outcomes compared with white women, in part due to delays in care delivery. Emerging cancer care delivery models like patient navigation focus on social barriers, but evidence demonstrating how these models increase social capital is lacking. This pilot study describes the social networks of newly diagnosed breast cancer patients and explores the contributing role of patient navigators.

MATERIALS AND METHODS

Twenty-five women completed a one hour interview about their social networks related to cancer care support. Network metrics identified important structural attributes and influential individuals. Bivariate associations between network metrics, type of network, and whether the network included a navigator were measured. Secondary analyses explored associations between network structures and clinical outcomes.

RESULTS

We identified three types of networks: kin-based, role and/or affect-based, or heterogeneous. Network metrics did not vary significantly by network type. There was a low prevalence of navigators included in the support networks (25%). Network density scores were significantly higher in those networks without a navigator. Network metrics were not predictive of clinical outcomes in multivariate models.

CONCLUSION

Patient navigators were not frequently included in support networks, but provided distinctive types of support. If navigators can identify patients with poorly integrated (less dense) social networks, or who have unmet tangible support needs, the intensity of navigation services could be tailored. Services and systems that address gaps and variations in patient social networks should be explored for their potential to reduce cancer health disparities.

IMPLICATIONS FOR PRACTICE

This study used a new method to identify the breadth and strength of social support following a diagnosis of breast cancer, especially examining the role of patient navigators in providing support. While navigators were only included in one quarter of patient support networks, they did provide essential supports to some individuals. Health care providers and systems need to better understand the contributions of social supports both within and outside of health care to design and tailor interventions that seek to reduce health care disparities and improve cancer outcomes.

Costs and outcomes evaluation of patient navigation after abnormal cancer screening: evidence from the Patient Navigation Research Program

BACKGROUND

Navigators can facilitate timely access to cancer services, but to the authors' knowledge there are little data available regarding their economic impact.

METHODS

The authors conducted a cost-consequence analysis of navigation versus usual care among 10,521 individuals with abnormal breast, cervical, colorectal, or prostate cancer screening results who enrolled in the Patient Navigation Research Program study from January 1, 2006 to March 31, 2010. Navigation costs included diagnostic evaluation, patient and staff time, materials, and overhead. Consequences or outcomes were time to diagnostic resolution and probability of resolution. Differences in costs and outcomes were evaluated using multilevel, mixed-effects regression modeling adjusting for age, race/ethnicity, language, marital status, insurance status, cancer, and site clustering.

RESULTS

The majority of individuals were members of a minority (70.7%) and uninsured or publically insured (72.7%). Diagnostic resolution was higher for navigation versus usual care at 180 days (56.2% vs 53.8%; P = .008) and 270 days (70.0% vs 68.2%; P < .001). Although there were no differences in the average number of days to resolution between the 2 groups (110 days vs 109 days; P = .63), the probability of ever having diagnostic resolution was higher for the navigation group versus the usual-care group (84.5% vs 79.6%; P < .001). The added cost of navigation versus usual care was $275 per patient (95% confidence interval, $260-$290; P < .001). There was no significant difference in stage distribution among the 12.4% of patients in the navigation group vs 11% of the usual-care patients diagnosed with cancer.

CONCLUSIONS

Navigation adds costs and modestly increases the probability of diagnostic resolution among patients with abnormal screening test results. Navigation is only likely to be cost-effective if improved resolution translates into an earlier cancer stage at the time of diagnosis.