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Study Overview
Objective. To determine the effect of navigation for older cancer patients by lay persons on health care use and Medicare costs.
Design. Observational cohort study using propensity score–matched controls.
Setting and participants. The study was conducted at the University of Alabama at Birmingham Health System Cancer Community Network, which consists of 2 academic and 10 community cancer centers across Alabama, Georgia, Florida, Mississippi and Tennessee. Participants were Medicare beneficiaries who received care at these facilities from 1 Jan 2012 to 31 Dec 2015. The patient population includes Medicare beneficiaries age 65 years or older with primary Medicare Part A and B insurance and a cancer diagnosis from 2012 to 2015, and excludes those with Medicare health maintenance organization coverage. Only patients with 1 quarter of observation prior to receiving patient navigation and those with 2 quarters of observation after initiation of navigation services were included in the sample. Propensity score matching method was used to establish a matched group of patients without patient navigation. Covariates included in the propensity score matching include age at diagnosis, race, sex, cancer acuity, phase of care, comorbidity score, cost of care, treatment with chemotherapy and emergency department (ED) and intensive care unit use at baseline.
Patients were identified to receive patient navigation through review of patient census in the ED and hospital, clinical referral, and self-referral. High-risk patients were prioritized to receive navigation; this included patients with metastatic disease, cancers that have high morbidity, chronic diseases that have high morbidity, or a history of recent acute care utilization.
Navigation program. The patient navigation program was started in 2013 as an innovation project funded by the Center for Medicare and Medicaid Services. Implementation began in March 2013 and all sites started to enroll patients for patient navigation by October 2013. Patient navigation was conducted by lay navigators who were hired from within the community. Navigators were required to have a bachelor’s degree but were not licensed clinicians such as a nurse or social worker. Patient navigators aimed to proactively identify patient needs, connect patient with resources, coordinate patient care, and empower patients to take an active role in their own care. Navigators performed distress screenings that assessed patients’ practical, information, financial, familial, emotional, spiritual and physical concerns. Assistance was given to patients at patients’ request and algorithms were used to guide frequency of contact.
Main outcome measures. The main outcome measure was Medicare costs per beneficiary per quarter; these costs include all amounts paid by Medicare for all care received but exclude Medicare Part D prescription drug costs. Other outcomes included source of costs and resource utilization defined by number of ED visits, hospitalizations, and intensive care unit admissions per quarter. The return on investment was also examined with the investment defined by salaries for the patient navigators, including fringe benefits; each navigator had a mean caseload of 152 patients per quarter. The return on investment was calculated as reduced Medicare costs of navigated patients compared with non-navigated patients multiplied by the number of patients served.
Main results. A total of 6214 matched pairs of navigated and non-navigated patients were included in the analysis. The mean age of the patients was 75 (SD ± 7) and 12% were African American. Medicare costs declined faster among those with navigation compared to the control group by $781 more per quarter per navigated patient. Inpatient and outpatient costs had the largest decline at $294 and $275 respectively. Resource use decreased in the navigated group more so than the non-navigated group, with a 6% more decrease per quarter in ED visits, 8% more decrease in hospitalization, and 11% more decrease in intensive care unit admissions. The return on investment was estimated at 1:10 with a $475,024 reduction in Medicare cost per navigator annually.
Conclusion. Navigation by lay persons among older Medicare beneficiaries with cancer is associated with a decrease in Medicare costs and resource utilization. There is substantial return on investment when considering the salaries of navigator staff against the Medicare savings associated with care navigation. Lay navigation appears to have benefits of reducing health care costs and resource use.
Commentary
The U.S. health care system is often difficult to navigate for older persons, particularly those with complex health care needs. Older adults with chronic disease, including cancer, may utilize care in multiple settings and with multiple providers and teams, making it challenging to organize and obtain needed care. In this study, the authors examined the impact of a patient navigation program on older patients with cancer and found that the use of lay navigation is associated with reduced costs and resource use for the health care system. Prior studies have examined the use of care navigation in complex chronic disease management, such as HIV infection and cancer [1,2] and have often found positive impacts on patient satisfaction, adherence to treatments, and reducing care disparities in vulnerable populations [3]. Care navigation has been performed using clinical staff, such as nursing, but with lay persons as well [4]. Lay persons offer the benefit of reduced costs, and if they are able to perform the care navigation tasks well with training, clinical staff use may not be necessary.
This study uses alternative methods to randomization to generate a balanced non-navigated control group for comparison to determine the impact of care navigation. The limitation is that the propensity score method used may balance potential confounders that are specified but unmeasured confounders were not accounted for in the analysis [5]. Another limitation is that the comparison group may not be concurrent, because non-navigated patients prior to the initiation of the navigation program were included in the control group. As care delivery often changes over time, non-concurrent comparison may introduce bias in the study. Nonetheless, the effect that is found on costs and resource utilization appear to be a strong one and is consistent with prior studies on the effects of care navigation.
Although this study spanned several clinical settings across a large geographic area, it is unclear if the program will offer similar benefits at other institutions. Additional studies that examine the impact of lay navigation on other patient outcomes such as satisfaction will be useful, as will studying the model at other institutions and in other settings to examine whether the program’s effects can be generalized.
Applications for Clinical Practice
In general, evidence suggests that patient navigation is an effective intervention for use in health care. Clinicians should consider assigning team members to help their patients with cancer navigate the health care system.
—William W. Hung, MD, MPH
1. Shacham E, López JD, Brown TM, et al. Enhancing adherence to care in the HIV care continuum: the Barrier Elimination and Care Navigation (BEACON) project evaluation. AIDS Behav 2017.
2. 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;100:436–48.
3. Steinberg ML, Fremont A, Khan DC, et al. Lay patient navigator program implementation for equal access to cancer care and clinical trials: essential steps and initial challenges. Cancer 2006;107:2669–77.
4. Kim K, Choi JS, Choi E, et al. Effects of community-based health worker interventions to improve chronic disease management and care among vulnerable populations: a systematic review. Am J Public Health 2016;106:e3–e28.
5. Austin PC, Grootendorst P, Anderson GM. A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study. Stat Med 2007;26:734–53.
Study Overview
Objective. To determine the effect of navigation for older cancer patients by lay persons on health care use and Medicare costs.
Design. Observational cohort study using propensity score–matched controls.
Setting and participants. The study was conducted at the University of Alabama at Birmingham Health System Cancer Community Network, which consists of 2 academic and 10 community cancer centers across Alabama, Georgia, Florida, Mississippi and Tennessee. Participants were Medicare beneficiaries who received care at these facilities from 1 Jan 2012 to 31 Dec 2015. The patient population includes Medicare beneficiaries age 65 years or older with primary Medicare Part A and B insurance and a cancer diagnosis from 2012 to 2015, and excludes those with Medicare health maintenance organization coverage. Only patients with 1 quarter of observation prior to receiving patient navigation and those with 2 quarters of observation after initiation of navigation services were included in the sample. Propensity score matching method was used to establish a matched group of patients without patient navigation. Covariates included in the propensity score matching include age at diagnosis, race, sex, cancer acuity, phase of care, comorbidity score, cost of care, treatment with chemotherapy and emergency department (ED) and intensive care unit use at baseline.
Patients were identified to receive patient navigation through review of patient census in the ED and hospital, clinical referral, and self-referral. High-risk patients were prioritized to receive navigation; this included patients with metastatic disease, cancers that have high morbidity, chronic diseases that have high morbidity, or a history of recent acute care utilization.
Navigation program. The patient navigation program was started in 2013 as an innovation project funded by the Center for Medicare and Medicaid Services. Implementation began in March 2013 and all sites started to enroll patients for patient navigation by October 2013. Patient navigation was conducted by lay navigators who were hired from within the community. Navigators were required to have a bachelor’s degree but were not licensed clinicians such as a nurse or social worker. Patient navigators aimed to proactively identify patient needs, connect patient with resources, coordinate patient care, and empower patients to take an active role in their own care. Navigators performed distress screenings that assessed patients’ practical, information, financial, familial, emotional, spiritual and physical concerns. Assistance was given to patients at patients’ request and algorithms were used to guide frequency of contact.
Main outcome measures. The main outcome measure was Medicare costs per beneficiary per quarter; these costs include all amounts paid by Medicare for all care received but exclude Medicare Part D prescription drug costs. Other outcomes included source of costs and resource utilization defined by number of ED visits, hospitalizations, and intensive care unit admissions per quarter. The return on investment was also examined with the investment defined by salaries for the patient navigators, including fringe benefits; each navigator had a mean caseload of 152 patients per quarter. The return on investment was calculated as reduced Medicare costs of navigated patients compared with non-navigated patients multiplied by the number of patients served.
Main results. A total of 6214 matched pairs of navigated and non-navigated patients were included in the analysis. The mean age of the patients was 75 (SD ± 7) and 12% were African American. Medicare costs declined faster among those with navigation compared to the control group by $781 more per quarter per navigated patient. Inpatient and outpatient costs had the largest decline at $294 and $275 respectively. Resource use decreased in the navigated group more so than the non-navigated group, with a 6% more decrease per quarter in ED visits, 8% more decrease in hospitalization, and 11% more decrease in intensive care unit admissions. The return on investment was estimated at 1:10 with a $475,024 reduction in Medicare cost per navigator annually.
Conclusion. Navigation by lay persons among older Medicare beneficiaries with cancer is associated with a decrease in Medicare costs and resource utilization. There is substantial return on investment when considering the salaries of navigator staff against the Medicare savings associated with care navigation. Lay navigation appears to have benefits of reducing health care costs and resource use.
Commentary
The U.S. health care system is often difficult to navigate for older persons, particularly those with complex health care needs. Older adults with chronic disease, including cancer, may utilize care in multiple settings and with multiple providers and teams, making it challenging to organize and obtain needed care. In this study, the authors examined the impact of a patient navigation program on older patients with cancer and found that the use of lay navigation is associated with reduced costs and resource use for the health care system. Prior studies have examined the use of care navigation in complex chronic disease management, such as HIV infection and cancer [1,2] and have often found positive impacts on patient satisfaction, adherence to treatments, and reducing care disparities in vulnerable populations [3]. Care navigation has been performed using clinical staff, such as nursing, but with lay persons as well [4]. Lay persons offer the benefit of reduced costs, and if they are able to perform the care navigation tasks well with training, clinical staff use may not be necessary.
This study uses alternative methods to randomization to generate a balanced non-navigated control group for comparison to determine the impact of care navigation. The limitation is that the propensity score method used may balance potential confounders that are specified but unmeasured confounders were not accounted for in the analysis [5]. Another limitation is that the comparison group may not be concurrent, because non-navigated patients prior to the initiation of the navigation program were included in the control group. As care delivery often changes over time, non-concurrent comparison may introduce bias in the study. Nonetheless, the effect that is found on costs and resource utilization appear to be a strong one and is consistent with prior studies on the effects of care navigation.
Although this study spanned several clinical settings across a large geographic area, it is unclear if the program will offer similar benefits at other institutions. Additional studies that examine the impact of lay navigation on other patient outcomes such as satisfaction will be useful, as will studying the model at other institutions and in other settings to examine whether the program’s effects can be generalized.
Applications for Clinical Practice
In general, evidence suggests that patient navigation is an effective intervention for use in health care. Clinicians should consider assigning team members to help their patients with cancer navigate the health care system.
—William W. Hung, MD, MPH
Study Overview
Objective. To determine the effect of navigation for older cancer patients by lay persons on health care use and Medicare costs.
Design. Observational cohort study using propensity score–matched controls.
Setting and participants. The study was conducted at the University of Alabama at Birmingham Health System Cancer Community Network, which consists of 2 academic and 10 community cancer centers across Alabama, Georgia, Florida, Mississippi and Tennessee. Participants were Medicare beneficiaries who received care at these facilities from 1 Jan 2012 to 31 Dec 2015. The patient population includes Medicare beneficiaries age 65 years or older with primary Medicare Part A and B insurance and a cancer diagnosis from 2012 to 2015, and excludes those with Medicare health maintenance organization coverage. Only patients with 1 quarter of observation prior to receiving patient navigation and those with 2 quarters of observation after initiation of navigation services were included in the sample. Propensity score matching method was used to establish a matched group of patients without patient navigation. Covariates included in the propensity score matching include age at diagnosis, race, sex, cancer acuity, phase of care, comorbidity score, cost of care, treatment with chemotherapy and emergency department (ED) and intensive care unit use at baseline.
Patients were identified to receive patient navigation through review of patient census in the ED and hospital, clinical referral, and self-referral. High-risk patients were prioritized to receive navigation; this included patients with metastatic disease, cancers that have high morbidity, chronic diseases that have high morbidity, or a history of recent acute care utilization.
Navigation program. The patient navigation program was started in 2013 as an innovation project funded by the Center for Medicare and Medicaid Services. Implementation began in March 2013 and all sites started to enroll patients for patient navigation by October 2013. Patient navigation was conducted by lay navigators who were hired from within the community. Navigators were required to have a bachelor’s degree but were not licensed clinicians such as a nurse or social worker. Patient navigators aimed to proactively identify patient needs, connect patient with resources, coordinate patient care, and empower patients to take an active role in their own care. Navigators performed distress screenings that assessed patients’ practical, information, financial, familial, emotional, spiritual and physical concerns. Assistance was given to patients at patients’ request and algorithms were used to guide frequency of contact.
Main outcome measures. The main outcome measure was Medicare costs per beneficiary per quarter; these costs include all amounts paid by Medicare for all care received but exclude Medicare Part D prescription drug costs. Other outcomes included source of costs and resource utilization defined by number of ED visits, hospitalizations, and intensive care unit admissions per quarter. The return on investment was also examined with the investment defined by salaries for the patient navigators, including fringe benefits; each navigator had a mean caseload of 152 patients per quarter. The return on investment was calculated as reduced Medicare costs of navigated patients compared with non-navigated patients multiplied by the number of patients served.
Main results. A total of 6214 matched pairs of navigated and non-navigated patients were included in the analysis. The mean age of the patients was 75 (SD ± 7) and 12% were African American. Medicare costs declined faster among those with navigation compared to the control group by $781 more per quarter per navigated patient. Inpatient and outpatient costs had the largest decline at $294 and $275 respectively. Resource use decreased in the navigated group more so than the non-navigated group, with a 6% more decrease per quarter in ED visits, 8% more decrease in hospitalization, and 11% more decrease in intensive care unit admissions. The return on investment was estimated at 1:10 with a $475,024 reduction in Medicare cost per navigator annually.
Conclusion. Navigation by lay persons among older Medicare beneficiaries with cancer is associated with a decrease in Medicare costs and resource utilization. There is substantial return on investment when considering the salaries of navigator staff against the Medicare savings associated with care navigation. Lay navigation appears to have benefits of reducing health care costs and resource use.
Commentary
The U.S. health care system is often difficult to navigate for older persons, particularly those with complex health care needs. Older adults with chronic disease, including cancer, may utilize care in multiple settings and with multiple providers and teams, making it challenging to organize and obtain needed care. In this study, the authors examined the impact of a patient navigation program on older patients with cancer and found that the use of lay navigation is associated with reduced costs and resource use for the health care system. Prior studies have examined the use of care navigation in complex chronic disease management, such as HIV infection and cancer [1,2] and have often found positive impacts on patient satisfaction, adherence to treatments, and reducing care disparities in vulnerable populations [3]. Care navigation has been performed using clinical staff, such as nursing, but with lay persons as well [4]. Lay persons offer the benefit of reduced costs, and if they are able to perform the care navigation tasks well with training, clinical staff use may not be necessary.
This study uses alternative methods to randomization to generate a balanced non-navigated control group for comparison to determine the impact of care navigation. The limitation is that the propensity score method used may balance potential confounders that are specified but unmeasured confounders were not accounted for in the analysis [5]. Another limitation is that the comparison group may not be concurrent, because non-navigated patients prior to the initiation of the navigation program were included in the control group. As care delivery often changes over time, non-concurrent comparison may introduce bias in the study. Nonetheless, the effect that is found on costs and resource utilization appear to be a strong one and is consistent with prior studies on the effects of care navigation.
Although this study spanned several clinical settings across a large geographic area, it is unclear if the program will offer similar benefits at other institutions. Additional studies that examine the impact of lay navigation on other patient outcomes such as satisfaction will be useful, as will studying the model at other institutions and in other settings to examine whether the program’s effects can be generalized.
Applications for Clinical Practice
In general, evidence suggests that patient navigation is an effective intervention for use in health care. Clinicians should consider assigning team members to help their patients with cancer navigate the health care system.
—William W. Hung, MD, MPH
1. Shacham E, López JD, Brown TM, et al. Enhancing adherence to care in the HIV care continuum: the Barrier Elimination and Care Navigation (BEACON) project evaluation. AIDS Behav 2017.
2. 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;100:436–48.
3. Steinberg ML, Fremont A, Khan DC, et al. Lay patient navigator program implementation for equal access to cancer care and clinical trials: essential steps and initial challenges. Cancer 2006;107:2669–77.
4. Kim K, Choi JS, Choi E, et al. Effects of community-based health worker interventions to improve chronic disease management and care among vulnerable populations: a systematic review. Am J Public Health 2016;106:e3–e28.
5. Austin PC, Grootendorst P, Anderson GM. A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study. Stat Med 2007;26:734–53.
1. Shacham E, López JD, Brown TM, et al. Enhancing adherence to care in the HIV care continuum: the Barrier Elimination and Care Navigation (BEACON) project evaluation. AIDS Behav 2017.
2. 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;100:436–48.
3. Steinberg ML, Fremont A, Khan DC, et al. Lay patient navigator program implementation for equal access to cancer care and clinical trials: essential steps and initial challenges. Cancer 2006;107:2669–77.
4. Kim K, Choi JS, Choi E, et al. Effects of community-based health worker interventions to improve chronic disease management and care among vulnerable populations: a systematic review. Am J Public Health 2016;106:e3–e28.
5. Austin PC, Grootendorst P, Anderson GM. A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study. Stat Med 2007;26:734–53.