Skip to main content
PMC home page
JAMA Network logoLink to JAMA Network
. 2018 Jul 26;4(10):1359–1366. doi: 10.1001/jamaoncol.2018.2446

Effect of a Lay Health Worker Intervention on Goals-of-Care Documentation and on Health Care Use, Costs, and Satisfaction Among Patients With Cancer

A Randomized Clinical Trial

Manali I Patel 1,2,3,4,✉, Vandana Sundaram 5, Manisha Desai 5, Vyjeyanthi S Periyakoil 6,7, James S Kahn 2,6, Jay Bhattacharya 4,8, Steven M Asch 2,6,9, Arnold Milstein 3,6, M Kate Bundorf 4,8
PMCID: PMC6233780  PMID: 30054634

Key Points

Question

Can an outpatient lay health worker intervention improve end-of-life care for patients with cancer?

Findings

In this randomized clinical trial of 213 veterans with stage 3 or 4 or recurrent cancer, a lay health worker intervention significantly increased documentation of patients’ end-of-life care preferences.

Meaning

An outpatient lay health worker program can improve documentation of patients’ goals of care at the end of life.

Abstract

Importance

Although lay health workers (LHWs) improve cancer screening and treatment adherence, evidence on whether they can enhance other aspects of care is limited.

Objective

To determine whether an LHW program can increase documentation of patients’ care preferences after cancer diagnosis.

Design, Setting, and Participants

Randomized clinical trial conducted from August 13, 2013, through February 2, 2015, among 213 patients with stage 3 or 4 or recurrent cancer at the Veterans Affairs Palo Alto Health Care System. Data analysis was by intention to treat and performed from January 15 to August 18, 2017.

Interventions

Six-month program with an LHW trained to assist patients with establishing end-of-life care preferences vs usual care.

Main Outcomes and Measures

The primary outcome was documentation of goals of care. Secondary outcomes were patient satisfaction on the Consumer Assessment of Health Care Providers and Systems “satisfaction with provider” item (on a scale of 0 [worst] to 10 [best possible]), health care use, and costs.

Results

Among the 213 participants randomized and included in the intention-to-treat analysis, the mean (SD) age was 69.3 (9.1) years, 211 (99.1%) were male, and 165 (77.5%) were of non-Hispanic white race/ethnicity. Within 6 months of enrollment, patients randomized to the intervention had greater documentation of goals of care than the control group (97 [92.4%] vs 19 [17.5%.]; P < .001) and larger increases in satisfaction with care on the Consumer Assessment of Health Care Providers and Systems “satisfaction with provider” item (difference-in-difference, 1.53; 95% CI, 0.67-2.41; P < .001). The number of patients who died within 15 months of enrollment did not differ between groups (intervention, 60 of 105 [57.1%] vs control, 60 of 108 [55.6%]; P = .68). In the 30 days before death, patients in the intervention group had greater hospice use (46 [76.7%] vs 29 [48.3%]; P = .002), fewer emergency department visits (mean [SD], 0.05 [0.22] vs 0.60 [0.76]; P < .001), fewer hospitalizations (mean [SD], 0.05 [0.22] vs 0.50 [0.62]; P < .001), and lower costs (median [interquartile range], $1048 [$331-$8522] vs $23 482 [$9708-$55 648]; P < .001) than patients in the control group.

Conclusions and Relevance

Incorporating an LHW into cancer care increases goals-of-care documentation and patient satisfaction and reduces health care use and costs at the end of life.

Trial Registration

ClinicalTrials.gov Identifier: NCT02966509

This randomized clinical trial tests the effect of consultation with a lay health worker on documentation of goals of care and health care use, costs, and satisfaction among patients with advanced-stage or recurrent cancer at a Veterans Affairs medical center.

Introduction

Clinical advances have transformed cancer care; however, the delivery of high-quality end-of-life care remains a considerable challenge.1,2,3 Few patients understand their prognosis,4,5 and many receive care that differs from their documented preferences.1,6,7 These gaps are compounded by rising cancer care costs,8,9 with almost half of total Medicare expenditures in the last year of life spent in patients’ final month.9,10 Communication between patients and physicians regarding prognosis and end-of-life care preferences improves care1,6,11,12 and lowers costs13; however, clinicians have limited time14 and may be reluctant14,15,16 to engage in or document these discussions. Although palliative care clinicians can assist,17 their numbers, and therefore time, are also limited.18 Therefore, other strategies may be needed to ensure that patients understand and communicate their end-of-life care choices to their families and health care providers.14,18,19,20

Internationally, nonclinical, nonprofessional personnel trained in specific skills assist in delivering a variety of services, including end-of-life care.21 These personnel are often called lay health workers (LHWs) but known by more than 60 different names worldwide.22 Lay health workers have been a part of the US health care workforce since the mid-1960s.23 Professional resource shortages and novel payment models have reinvigorated interest in LHW programs,24,25 with a projected 13% increase in LHW employment by 2024.26 Although LHWs aid in cancer screening and treatment adherence,27 they are infrequently integrated into end-of-life care,14,20,28,29,30,31 and few randomized trials have evaluated their effectiveness in this setting.31 We conducted a randomized study among patients with advanced-stage cancer to evaluate the effects of an LHW intervention on documentation of patients’ end-of-life care preferences (goals of care), patient satisfaction, health care use, and total health care expenditures.

Methods

Study Design and Oversight

We conducted a 2-arm, randomized quality improvement study at the Veterans Affairs Palo Alto Health Care System (VAPAHCS), Palo Alto, California, from August 13, 2013, to February 2, 2015. In 2013, the Stanford University Institutional Review Board and VAPAHCS Research and Development Committee determined that the protocol was quality improvement. In 2016, both boards approved the retrospective evaluation of the program’s effectiveness on patient satisfaction, health care use, and health care expenditures as a clinical trial (the trial protocol is available in Supplement 1). Despite the quality improvement designation, all participants provided written informed consent.

Study Participants

Patients older than 18 years with newly diagnosed stage 3 or 4 solid tumors or those with recurrent disease were eligible to participate. Patients who did not plan to receive oncology care at VAPAHCS were excluded. Participants were randomized in a 1:1 ratio stratified by cancer diagnosis (eTable 1 in Supplement 2) to 1 of 2 strategies: an LHW program integrated with usual care (intervention arm) or usual care alone (control arm). The flow of participants through the study is shown in the Figure and in eFigure 1 in Supplement 2.

Figure. Flow of Participants Through the Study.

Figure.

Open in a new tab

Usual Care

All participants received usual care provided by the oncology team, a social worker who arranged housing and transportation, and a behavioral medicine practitioner who provided mental health counseling.

LHW Intervention

We hired 1 LHW with a bachelor of arts degree who was enrolled in a part-time graduate health education program. We trained the LHW using a curriculum developed by the principal investigator (M.I.P.) and informed by social cognitive theory.32 The training included an 80-hour online skills-based seminar33 and a 4-week observation training with the VAPAHCS palliative care team (eAppendix in Supplement 2). The intervention consisted of a 6-month structured program with the LHW, who assisted patients with advance care planning, including (1) education on goals-of-care principles; (2) establishing care preferences; (3) identifying a surrogate decision maker; (4) filing an advance directive; and (5) encouraging patients to discuss care preferences with providers. The LHW addressed these topics in an initial 30-minute telephone conversation with the patient within 2 weeks after randomization and then reassessed preferences in subsequent 15-minute, twice-monthly conversations by telephone or in person for 6 months after randomization or until patient death, whichever came first. The LHW was employed 20 hours weekly, supervised on-site by a registered nurse, addressed goals of care only, and had no direct interaction with the clinical oncology team. All clinical concerns raised by patients were discussed with the supervising registered nurse.

Blinding

The principal investigator (M.I.P.), oncology clinicians (excluding the supervising registered nurse), and data abstractors were blinded to the participant’s randomization assignment.

Outcomes

The primary outcome—whether an oncology clinician documented patients’ end-of-life care preferences in a clinical note in the electronic health record (EHR) within 6 months of randomization—was intended to measure whether the intervention encouraged patients to discuss goals of care with oncology providers. The objective was to achieve goals-of-care documentation among at least 75% of patients. Prespecified secondary outcomes (defined below) included advance directive documentation, patient satisfaction, health care use, and costs.

Patient Satisfaction Measures

We measured satisfaction with care when patients were randomized into the program (baseline) and again 6 months after randomization using the “satisfaction with provider” item (question 18) of the validated Consumer Assessment of Healthcare Providers and Systems General Survey.13,34 We directed patients to answer this question in regard to their oncology provider on a scale of 0 to 10, where 0 is the worst and 10 is the best possible satisfaction. We assessed satisfaction with decision making within 4 to 6 months of randomization using the validated 6-item Satisfaction With Decision Scale35 on a scale ranging from 0, indicating no satisfaction, to 5, indicating maximum satisfaction. A clinically meaningful difference in the Satisfaction With Decision scale is 0.535 and in the satisfaction with provider scale is 0.9.36 A trained research assistant, blinded to the randomization, administered assessments by telephone.

Health Care Use and Cost Measures

We followed up all patients for 15 months or until death. Based on national survival statistics, we estimated that half of the participants would have died within 15 months.37 We measured health care use and cost outcomes at 6 months after randomization and measured a subset of outcomes again at 15 months after randomization. We collected the use and dates of the following from the EHR: chemotherapy, surgery, and radiotherapy use, VA and non-VA emergency department (ED) use, hospitalizations, inpatient and outpatient palliative care visits, and hospice service use. For ED visits and hospitalizations, we measured whether patients had any use and the frequency of use. We measured chemotherapy, surgery, and radiotherapy use based on any use within the 6-month postrandomization period. We measured all other health care use within the 6- and the 15-month postrandomization periods.

Total health care costs for VA and non-VA care were provided by the VAPAHCS Decision Support System office, VA Allocation Resource Center, and the VAPAHCS Office of Business Analytics and reported for the 6-month period before randomization for each participant (to test whether patients had similar costs of care before study enrollment) and for the 6- and 15-month postrandomization periods. We obtained date of death from the EHR to assess survival, health care use, and total health care costs within 30 days before death38 for the subset of patients who died within 15 months after randomization. We followed up all patients through April 6, 2016, and collected health care use data and total health care costs through December 6, 2016, to account for an 8-month lag time in non–VA facility claims.

Demographic and Clinical Characteristics

We collected the following additional data from the EHR: age, sex, cancer diagnosis and stage, new diagnosis or recurrent cancer, 1-way travel distance from patient residence to VAPAHCS, and advance directive documentation. Patients self-reported their race/ethnicity and marital status.

Statistical Analysis

We calculated that a sample of 105 patients in the program would provide 80% power and a 2-sided α level of .05 to detect an increase in goals of care documentation from 57%, based on previously reported data among patients with cancer in another VA facility,39 to 75% within 6 months after randomization. We analyzed the program’s effect on primary and secondary outcomes on an intention-to-treat basis after adjusting all analyses for anatomic site of cancer diagnosis. We used logistic regression to compare differences between the 2 groups for dichotomous outcomes (goals-of-care documentation, advance-directive documentation, any palliative care, any hospice, any chemotherapy, any radiotherapy, any surgery, any ED visit, and any hospitalization). We compared ED visits and hospitalizations per patient by using exact Poisson regression models, with an offset term for length of follow-up. We compared total health care costs by using a generalized linear model with a gamma link-log function to account for skewed data, with an offset term for length of follow-up. To analyze satisfaction, we used generalized linear regression to compare the change in satisfaction with care from baseline between study arms by using a repeated-measures analysis of variance model. Patient satisfaction observations that were missing owing to death or nonresponse were dropped from the patient satisfaction analysis. We compared survival by using Kaplan-Meier methods and risk of death by using Cox proportional hazards regression models. We conducted all significance testing at a 2-sided P value of .05 and performed all statistical analyses from January 15 to August 18, 2017, with SAS, version 9.3 (SAS Institute Inc).

Results

A total of 213 patients were enrolled. The mean (SD) age of participants was 69.3 (9.1) years, and 211 (99.1%) were male. Of this cohort, 165 (77.5%) reported being of non-Hispanic white race/ethnicity, 11 (5.2%) were non-Hispanic black, 7 (3.3%) were Asian/Pacific Islander, 4 (1.9%) were Hispanic, 4 (1.9%) were Native American/Native Alaskan, and 9 (4.2%) were of another race/ethnicity. One hundred five participants were randomized to the intervention and 108 to the control arm (eFigure 1 in Supplement 2). Three patients died after randomization but before enrollment, 2 patients were randomized but could not be contacted, and 5 patients withdrew from the study at the 6-month follow-up visit. All 213 were included in the intention-to-treat analysis for health care use and cost outcomes. There were no statistically significant differences between the groups in demographic or clinical characteristics, satisfaction with health care, or total health care costs in the 6-month period before randomization (eTable 1 in Supplement 2).

Goals of Care and Advance Directive

Ninety-seven patients (92.4%) in the intervention arm had their goals of care documented in the EHR within 6 months after randomization, exceeding the program’s objective of 75%. Patients in the intervention arm had higher rates, within both 6 and 15 months of randomization, of documented goals of care (6 months, 97 of 105 [92.4%] vs 19 of 108 [17.6%]; P < .001; 15 months, 98 of 105 [93.3%] vs 26 of 108 [24.1%]; P < .001) and advance directives (6 months, 71 of 105 [67.6%] vs 28 of 108 [25.9%]; P < .001; 15 months, 78 of 105 [74.3%] vs 36 of 108 [33.3%]; P < .001) compared with patients in the control arm (Table 1).

Table 1. Advance Care Planning and Satisfaction With Decision Making and Carea.

Variable Usual Care Intervention Difference Between Usual Care and Intervention (95% CI)
Advance care planning, No. (%)b
Goals of care documented
6 mo 19 (17.6) 97 (92.4) 78 (62-81)
15 mo 26 (24.1) 98 (93.3) 72 (65-76)
Advance directive documented
6 mo 28 (25.9) 71 (67.6) 43 (30-55)
15 mo 36 (33.3) 78 (74.3) 42 (31-56)
Satisfaction with decision assessment, mean (SD), scorec
“I am satisfied that I am adequately informed about the issues important to my decision” 4.16 (0.93) 4.71 (0.72) 0.56 (0.29-0.82)
“The decision was the best decision for me personally” 4.17 (0.99) 4.70 (0.66) 0.53 (0.26-0.80)
“I am satisfied that my decision was consistent with my personal values” 4.13 (1.08) 4.64 (0.75) 0.51 (0.21-0.80)
“I expect to successfully carry out the decision I made” 3.91 (1.26) 4.66 (0.73) 0.75 (0.43-1.08)
“I am satisfied that this was my decision to make” 4.07 (1.06) 4.71 (0.60) 0.64 (0.37-0.91)
“I am satisfied with my decision” 4.15 (1.02) 4.73 (0.61) 0.58 (0.32-0.85)
Assessment of satisfaction with care, mean (SD), scored 7.83 (2.36) 9.16 (1.44) 1.33 (0.62-2.04)
Difference in satisfaction from baseline, mean (SD)e −0.99 (2.40) 0.54 (2.41) 1.53 (0.67-2.41)
Open in a new tab
a

For all variables, 2-sided P < .001.

b

The P values were estimated using logistic regression models after adjustment for cancer site.

c

Satisfaction with decision was assessed with the use of the 6-item Satisfaction With Decision Scale35; scores range from 0 to 5, with higher scores indicating better satisfaction. In all, 77 patients (71.3%) in the control arm and 80 (76.2%) in the intervention arm completed this survey between 4 and 6 months after study enrollment; 25 patients in the control arm and 22 in the intervention arm had died at the time of this assessment. The P values were estimated using generalized linear regression models after adjustment for cancer site.

d

Satisfaction with care was assessed with the use of the Consumer Assessment of Healthcare Providers and Systems–General Survey34 question 18, which measured rating of health provider; scores range from 0 to 10, with higher scores indicating higher ratings. In all, 60 patients (55.6%) in the control arm and 58 (55.2%) in the intervention arm completed this survey at 6 months after study enrollment; 36 patients in the control arm and 36 in the intervention arm had died at the time of this assessment. The P value was estimated using generalized linear regression models after adjustment for cancer site.

e

The P value was estimated using a repeated-measures analysis of variance model after adjustment for cancer site.

Patient Satisfaction

Patients in the intervention arm were more satisfied with their decision making and care than patients in the control arm (Table 1). Compared with the control arm, patients in the intervention arm had higher scores across all domains of satisfaction with decision and reported greater satisfaction with their oncology provider (mean [SD] score, 9.16 [1.44] vs 7.83 [2.36]; P < .001). The change in mean satisfaction with the oncology provider from baseline to 6 months after randomization between the groups was significantly different (difference-in-difference, 1.53; 95% CI, 0.67-2.41; P < .001).

Health Care Use and Total Health Care Costs

Rates of intensive cancer treatments (chemotherapy, radiotherapy, and surgery) within 6 months of randomization were similar between the groups. Patients in the intervention arm were more likely to have used hospice services within both 6 and 15 months of randomization (6 months, 37 of 105 [35.2%] vs 20 of 108 [18.5%]; P = .006; 15 months, 47 of 105 [44.8%] vs 30 of 108 [27.8%]; P = .009) (eTable 2 in Supplement 2) than patients in the control arm. The use of palliative care did not differ between the groups. Although higher rates of ED and hospitalization use among the control group emerged by 15 months after randomization, the differences were not statistically significant. In the 15-month period after randomization, total health care costs were lower among patients in the intervention arm compared with the control arm, but the difference was not statistically significant (median [interquartile range], $86 025 [$63 255-133 256] vs $111 958 [$75 803-171 025]; P = .08).

Health Care Use and Costs at the End of Life

Mortality rates (intervention group, 60 of 105 patients [57.1%] vs control group, 60 of 108 [55.6%]; P = .68) (eFigure 2 in Supplement 2) and characteristics of those who died within 15 months of enrollment (eTable 3 in Supplement 2) did not differ between the groups. Health care use at the end of life differed between patients in the intervention and control groups (Table 2). Among the patients who died, those receiving the intervention had fewer ED visits (mean [SD] ED visits per patient, 0.05 [0.22] vs 0.60 [0.76]; P < .001) and fewer hospitalizations (mean [SD] hospitalizations per patient, 0.05 [0.22] vs 0.5 [0.62]; P < .001), were more likely to receive hospice care (46 [76.7%] vs 29 [48.3%]; P = .002), and had lower total health care costs within 30 days of death (median [interquartile range], $1048 [$331-$8522] vs $23 482 [$9708-$55 648]; P < .001) than patients in the control arm.

Table 2. Use of Health Care Within 30 Days of Deatha.

Variable Usual Care (n = 60) Intervention (n = 60) P Value
Emergency department use
Any use, No. (%)b 27 (45.0) 3 (5.0) <.001
No. of visits, mean (SD)c 0.60 (0.76) 0.05 (0.22) <.001
Hospitalization
Any use, No. (%)b 26 (43.3) 3 (5.0) <.001
No. of admissions, mean (SD)c 0.50 (0.62) 0.05 (0.22) <.001
Hospice services received, No. (%)d 29 (48.3) 46 (76.7) .002
Total health care costs, median (IQR), $e 23 482 (9708-55 648) 1048 (331-8522) <.001
Open in a new tab

Abbreviation: IQR, interquartile range.

a

Calculations are based on the sample of the 60 patients in the control arm and the 60 patients in the intervention arm who died within 15 months of study enrollment. All P values are 2-sided.

b

The P value was estimated using the χ2 test.

c

The P value was estimated using the exact Poisson regression model after adjustment for cancer site and offset for duration of follow-up.

d

The P value was estimated using logistic regression model after adjustment for cancer site.

e

The P value was estimated using a generalized linear model with gamma link-log function to account for skewed cost data after adjustment for cancer site and offset for length of follow-up.

Discussion

This single-center, randomized study within the VA demonstrates that integrating an LHW into usual cancer care is an effective strategy for increasing goals-of-care documentation among patients with stage 3 or 4 cancer or recurrent cancer. The intervention resulted in greater patient satisfaction with care and decision making, higher rates of hospice use, lower acute care use, and lower total health care expenditures at the end of life without adversely affecting survival.

Although new initiatives encourage clinicians to discuss and document their patients’ goals of care,40 less than half of the patients in the United States with terminal illnesses have their end-of-life wishes documented.41 We provide evidence that an LHW trained to assist with end-of-life care integrated into usual cancer care can effectively address this deficiency. Our intervention, comprising up to 6 months of engagement with an LHW by telephone or in person, increased documentation of goals of care 5-fold and advance directives nearly 3-fold and nearly doubled hospice use within 6 months of enrollment. The intervention also substantively changed end-of-life care, with a nearly 6-fold reduction in ED and hospital use, nearly 2-fold increase in hospice use, and a 95% reduction in total health care spending in the final month of life among patients who died within 15 months of enrollment, supporting the long-lasting effect of the intervention. Although our study does not explain why the LHW was effective, it is possible that the LHW conveyed information about goals of care and advance directives in a way that was more easily accepted by patients and their families42,43 and that the LHW had more time than medical professionals to encourage patients to frequently assess their end-of-life care choices and proactively communicate their preferences to their clinical teams. The improved documentation of patients’ end-of-life care preferences, such as do not resuscitate and do not intubate, may have also prompted oncology providers to discuss hospice earlier. Despite the significant increase in hospice use, the intervention did not increase palliative care use, possibly reflecting the limited accessibility of outpatient palliative care services in this setting.

We found that reductions in cost associated with the intervention occurred primarily at the end of life. Although we targeted patients who had a high likelihood of death, approximately one-third of patients died within 6 months and slightly more than half of patients died within 15 months of enrollment. Correspondingly, the reduction in total health care costs associated with the intervention increased between 6 and 15 months as more patients reached the end of life, although the difference between the intervention and control arms was not significant at either point. Our small sample size may have limited the ability to see differences in costs at 15 months after randomization because the study was not powered to detect cost differences. It is possible, however, that as more patients reach the end of life, the potential for having had their end-of-life care preferences documented increases; thus, the intervention generates even greater cost reductions. Our large and statistically significant reductions in ED use and hospitalizations within 30 days of death are consistent with this interpretation. Future research should include a longer follow-up period to determine whether these results persist for patients with longer survival.

Our study differed in important ways from earlier studies. First, the LHW in our study assisted patients only with goals of care. In other studies, either clinicians44,45 or lay personnel28 were also involved in other aspects of care, including care coordination and symptom management. Furthermore, a previous intervention that used lay personnel directly embedded the intervention in oncology care and provided the intervention for a longer duration, whereas ours lasted 6 months and did not require the LHW to interact with oncology clinicians.28 Finally, our intervention, which was intensely and narrowly focused on advance care planning, achieved substantively larger cost reductions.46 Although we did not conduct a formal cost-benefit analysis, it is likely the reduction in health care spending exceeded the intervention’s implementation costs. Our intention-to-treat analysis estimates that the intervention reduced total health care costs by approximately $31 660 per patient in the intervention resulting in an overall savings of $3 324 300 ($31 660 multiplied by 105 patients) during the 15-month study. The total costs associated with the intervention implementation, including training and labor costs, were $20 368. Thus, the net savings associated with the program were approximately $3 303 932, equivalent to a 20% reduction in total health care spending. (eAppendix in Supplement 2).

Limitations

The study had some limitations. First, the study was limited to 1 VA institution and a single LHW for a predominantly older, non-Hispanic white male population, potentially limiting the generalizability of our findings to members of racial/ethnic minority groups and younger patients. Veterans receive less aggressive end-of-life care than nonveterans,47,48 likely owing to the availability of palliative care and hospice services49 and neutral financial incentives.48,50 These features may become more common in other health care systems as value-based reimbursement initiatives become more widespread.51,52 Although we expect more room for improvement in other systems, understanding the potential of the LHW program outside the VA will require further investigation. Second, because the study was conducted at the patient level, there could be contamination: the oncologists’ practice patterns may have changed given that patients in the intervention prompted them to discuss goals of care. The low rates of goals-of-care documentation among patients in the control arm, however, suggest that this is unlikely to be the case. Finally, we did not have a sufficient sample size to analyze differences across cancer stages or diagnoses.

Conclusions

Our results demonstrate that an LHW, when integrated into cancer care, can improve patient satisfaction and reduce health care use and costs. Given recent trends toward reimbursement models that reward high-value care delivery, LHWs may represent one solution, through greater discussion and documentation of care preferences, to more broadly address patients’ preferences and mitigate unwanted, burdensome, and costly care at the end of life. Although our intervention may be a promising approach to improving end-of-life care delivery for patients with cancer, further research is needed to assess the generalizability of this approach for patients in other settings and with other near-terminal, serious illnesses.

Supplement 1.

Trial Protocol

Click here for additional data file. (648.8KB, pdf)
Supplement 2.

eTable 1. Baseline Characteristics of the Study Participants

eTable 2. Health Care Utilization and Total Costs of Care

eTable 3. Characteristics of the Pilot Participants Who Died Within 15 Months

eFigure 1. Assessment for Eligibility, Randomization and Follow-up

eFigure 2. Survival Within 15 Months Post-Randomization by Group

eFigure 3. Distribution of Total Costs in the 30 Days Prior to Death Among Patients Who Died Within 15 Months of Study Enrollment

eAppendix. Lay Health Worker Program Cost and Interactions

Click here for additional data file. (81.4KB, pdf)

References

  • 1.Mack JW, Weeks JC, Wright AA, Block SD, Prigerson HG. End-of-life discussions, goal attainment, and distress at the end of life: predictors and outcomes of receipt of care consistent with preferences. J Clin Oncol. 2010;28(7):1203-1208. doi: 10.1200/JCO.2009.25.4672 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Wachterman MW, Pilver C, Smith D, Ersek M, Lipsitz SR, Keating NL. Quality of end-of-life care provided to patients with different serious illnesses. JAMA Intern Med. 2016;176(8):1095-1102. doi: 10.1001/jamainternmed.2016.1200 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Morden NE, Chang CH, Jacobson JO, et al. . End-of-life care for Medicare beneficiaries with cancer is highly intensive overall and varies widely. Health Aff (Millwood). 2012;31(4):786-796. doi: 10.1377/hlthaff.2011.0650 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Weeks JC, Catalano PJ, Cronin A, et al. . Patients’ expectations about effects of chemotherapy for advanced cancer. N Engl J Med. 2012;367(17):1616-1625. doi: 10.1056/NEJMoa1204410 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Mack JW, Walling A, Dy S, et al. . Patient beliefs that chemotherapy may be curative and care received at the end of life among patients with metastatic lung and colorectal cancer. Cancer. 2015;121(11):1891-1897. doi: 10.1002/cncr.29250 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Mack JW, Cronin A, Keating NL, et al. . Associations between end-of-life discussion characteristics and care received near death: a prospective cohort study. J Clin Oncol. 2012;30(35):4387-4395. doi: 10.1200/JCO.2012.43.6055 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Teno JM, Fisher ES, Hamel MB, Coppola K, Dawson NV. Medical care inconsistent with patients’ treatment goals: association with 1-year Medicare resource use and survival. J Am Geriatr Soc. 2002;50(3):496-500. doi: 10.1046/j.1532-5415.2002.50116.x [DOI] [PubMed] [Google Scholar]
  • 8.Chastek B, Harley C, Kallich J, Newcomer L, Paoli CJ, Teitelbaum AH. Health care costs for patients with cancer at the end of life. J Oncol Pract. 2012;8(6):75s-80s. doi: 10.1200/JOP.2011.000469 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Brooks GA, Li L, Uno H, Hassett MJ, Landon BE, Schrag D. Acute hospital care is the chief driver of regional spending variation in Medicare patients with advanced cancer. Health Aff (Millwood). 2014;33(10):1793-1800. doi: 10.1377/hlthaff.2014.0280 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Hogan C, Lunney J, Gabel J, Lynn J. Medicare beneficiaries’ costs of care in the last year of life. Health Aff (Millwood). 2001;20(4):188-195. doi: 10.1377/hlthaff.20.4.188 [DOI] [PubMed] [Google Scholar]
  • 11.Wright AA, Zhang B, Ray A, et al. . Associations between end-of-life discussions, patient mental health, medical care near death, and caregiver bereavement adjustment. JAMA. 2008;300(14):1665-1673. doi: 10.1001/jama.300.14.1665 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Teno JM, Gruneir A, Schwartz Z, Nanda A, Wetle T. Association between advance directives and quality of end-of-life care: a national study. J Am Geriatr Soc. 2007;55(2):189-194. doi: 10.1111/j.1532-5415.2007.01045.x [DOI] [PubMed] [Google Scholar]
  • 13.Zhang B, Wright AA, Huskamp HA, et al. . Health care costs in the last week of life: associations with end-of-life conversations. Arch Intern Med. 2009;169(5):480-488. doi: 10.1001/archinternmed.2008.587 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Patel MI, Periyakoil VS, Moore D, Nevedal A, Coker TR. Delivering end-of-life cancer care: perspectives of providers. Am J Hosp Palliat Care. 2017;35(3):497-504. doi: 10.1177/1049909117719879 [DOI] [PubMed] [Google Scholar]
  • 15.Lamont EB, Christakis NA. Prognostic disclosure to patients with cancer near the end of life. Ann Intern Med. 2001;134(12):1096-1105. doi: 10.7326/0003-4819-134-12-200106190-00009 [DOI] [PubMed] [Google Scholar]
  • 16.Mack JW, Cronin A, Taback N, et al. . End-of-life care discussions among patients with advanced cancer: a cohort study. Ann Intern Med. 2012;156(3):204-210. doi: 10.7326/0003-4819-156-3-201202070-00008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Temel JS, Greer JA, Muzikansky A, et al. . Early palliative care for patients with metastatic non-small-cell lung cancer. N Engl J Med. 2010;363(8):733-742. doi: 10.1056/NEJMoa1000678 [DOI] [PubMed] [Google Scholar]
  • 18.Lupu D; American Academy of Hospice and Palliative Medicine Workforce Task Force . Estimate of current hospice and palliative medicine physician workforce shortage. J Pain Symptom Manage. 2010;40(6):899-911. doi: 10.1016/j.jpainsymman.2010.07.004 [DOI] [PubMed] [Google Scholar]
  • 19.Institute of Medicine Ensuring Quality Cancer Care Through the Oncology Workforce: Sustaining Care in the 21st Century: Workshop Summary. Washington, DC: National Academies Press; 2009. [PubMed] [Google Scholar]
  • 20.Patel MI, Periyakoil VS, Blayney DW, et al. . Redesigning cancer care delivery: views from patients and caregivers. J Oncol Pract. 2017;13(4):e291-e302. doi: 10.1200/JOP.2016.017327 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Chaturvedi SK, Chandra PS. Palliative care in India. Support Care Cancer. 1998;6(2):81-84. doi: 10.1007/s005200050139 [DOI] [PubMed] [Google Scholar]
  • 22.Lehmann U, Sanders D. Community Health Workers: What Do We Know About Them? The State of the Evidence on Programmes, Activities, Costs and Impact on Health Outcomes of Using Community Health Workers. Geneva, Switzerland: World Health Organization; 2007. http://www.who.int/hrh/documents/community_health_workers.pdf. Accessed January 15, 2018.
  • 23.Kangovi S, Grande D, Trinh-Shevrin C. From rhetoric to reality—community health workers in post-reform U.S. health care. N Engl J Med. 2015;372(24):2277-2279. doi: 10.1056/NEJMp1502569 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Final Rule: Medicaid and Children’s Health Insurance Program: essential health benefits in alternative benefit plans, eligibility notices: fair hearing and appeal processes and premiums and cost-sharing; exchanges: eligibility and enrollment. Fed Regist. 2013;78(135):42160-42322. To be codified at 42 CFR §440.130. [PubMed]
  • 25.The Millennium Development Goals Will Not Be Attained Without New Research Addressing Health System Constraints to Delivering Effective Interventions: Report of the Task Force on Health Systems Research. Published March 2005. http://www.who.int/rpc/summit/en/Task_Force_on_Health_Systems_Research.pdf. Accessed January 18, 2018.
  • 26.Bureau of Labor Statistics, US Department of Labor Health educators and community health workers. Occupational Outlook Handbook, 2016-2017 Edition. https://www.bls.gov/ooh/community-and-social-service/health-educators.htm. Modified April 13, 2018. Accessed February 14, 2018.
  • 27.Community Health Worker National Workforce Study: An Annotated Bibliography. US Department of Health and Human Services, Health Resources and Services Administration, Bureau of Health Professions. https://bhw.hrsa.gov/sites/default/files/bhw/nchwa/projections/communityhealthworkforcebibliography.pdf. Published March 2007. Accessed May 6, 2017.
  • 28.Rocque GB, Partridge EE, Pisu M, et al. . The Patient Care Connect Program: transforming health care through lay navigation. J Oncol Pract. 2016;12(6):e633-e642. doi: 10.1200/JOP.2015.008896 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Patel MI, Moore D, Milstein A. Redesigning advanced cancer care delivery: three ways to create higher value cancer care. J Oncol Pract. 2015;11(4):280-284. doi: 10.1200/JOP.2014.001065 [DOI] [PubMed] [Google Scholar]
  • 30.Shippee ND, Shippee TP, Mobley PD, Fernstrom KM, Britt HR. Effect of a whole-person model of care on patient experience in patients with complex chronic illness in late life. Am J Hosp Palliat Care. 2018;35(1):104-109. doi: 10.1177/1049909117690710 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Fischer SM, Cervantes L, Fink RM, Kutner JS. Apoyo con Cariño: a pilot randomized controlled trial of a patient navigator intervention to improve palliative care outcomes for Latinos with serious illness. J Pain Symptom Manage. 2015;49(4):657-665. doi: 10.1016/j.jpainsymman.2014.08.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Bandura A. Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ: Prentice Hall; 1986. [Google Scholar]
  • 33.iSAGE mini-fellowship overview. https://aging.stanford.edu/isage-mini-fellowship-overview/. Accessed January 15, 2018.
  • 34.Consumer Assessment of Healthcare Providers and Systems (CAHPS). Agency for Healthcare Research and Quality. https://www.ahrq.gov/cahps/index.html. Accessed July 27, 2018.
  • 35.Holmes-Rovner M, Kroll J, Schmitt N, et al. . Patient satisfaction with health care decisions: the satisfaction with decision scale. Med Decis Making. 1996;16(1):58-64. doi: 10.1177/0272989X9601600114 [DOI] [PubMed] [Google Scholar]
  • 36.Jha AK, Orav EJ, Zheng J, Epstein AM. Patients’ perception of hospital care in the United States. N Engl J Med. 2008;359(18):1921-1931. doi: 10.1056/NEJMsa0804116 [DOI] [PubMed] [Google Scholar]
  • 37.National Cancer Institute Surveillance, Epidemiology, and End Results Program. SEER cancer statistics review 1975-2014: Table 1.5: age-adjusted SEER incidence and U.S. death rates and 5-year relative survival (percent) by primary cancer site, sex, and time period. https://seer.cancer.gov/archive/csr/1975_2014/results_merged/topic_survival.pdf. Accessed June 28, 2017.
  • 38.Earle CC, Park ER, Lai B, Weeks JC, Ayanian JZ, Block S. Identifying potential indicators of the quality of end-of-life cancer care from administrative data. J Clin Oncol. 2003;21(6):1133-1138. doi: 10.1200/JCO.2003.03.059 [DOI] [PubMed] [Google Scholar]
  • 39.Ahluwalia SC, Chuang FL, Antonio AL, Malin JL, Lorenz KA, Walling AM. Documentation and discussion of preferences for care among patients with advanced cancer. J Oncol Pract. 2011;7(6):361-366. doi: 10.1200/JOP.2011.000364 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Advance Care Planning Baltimore, MD: Centers for Medicare & Medicaid Services, Dept of Health & Human Services; August 2016. Publication ICN 909289. https://www.cms.gov/Outreach-and-Education/Medicare-Learning-Network-MLN/MLNProducts/Downloads/AdvanceCarePlanning.pdf. Accessed January 2, 2017.
  • 41.Butler M, Ratner E, McCreedy E, Shippee N, Kane RL. Decision aids for advance care planning: an overview of the state of the science. Ann Intern Med. 2014;161(6):408-418. doi: 10.7326/M14-0644 [DOI] [PubMed] [Google Scholar]
  • 42.Kangovi S, Long JA, Emanuel E. Community health workers combat readmission. Arch Intern Med. 2012;172(22):1756-1757. doi: 10.1001/2013.jamainternmed.82 [DOI] [PubMed] [Google Scholar]
  • 43.Gorman A. Community health workers reach some patients that doctors can’t. https://www.npr.org/sections/health-shots/2015/10/29/452653733/community-health-workers-can-reach-some-patients-that-doctors-cant. Published October 29, 2015. Accessed August 1, 2017.
  • 44.Bakitas M, Stevens M, Ahles T, et al. ; Project Enable Co-Investigators . Project ENABLE: a palliative care demonstration project for advanced cancer patients in three settings. J Palliat Med. 2004;7(2):363-372. doi: 10.1089/109662104773709530 [DOI] [PubMed] [Google Scholar]
  • 45.Waters TM, Webster JA, Stevens LA, et al. . Community oncology medical homes: physician-driven change to improve patient care and reduce costs. J Oncol Pract. 2015;11(6):462-467. doi: 10.1200/JOP.2015.005256 [DOI] [PubMed] [Google Scholar]
  • 46.Colligan EM, Ewald E, Keating NL, et al. . Two innovative cancer care programs have potential to reduce utilization and spending. Med Care. 2017;55(10):873-878. doi: 10.1097/MLR.0000000000000795 [DOI] [PubMed] [Google Scholar]
  • 47.Duffy SA, Ronis D, Fowler K, Schim SM, Jackson FC. Differences in veterans’ and nonveterans’ end-of-life preferences: a pilot study. J Palliat Med. 2006;9(5):1099-1105. doi: 10.1089/jpm.2006.9.1099 [DOI] [PubMed] [Google Scholar]
  • 48.Keating NL, Landrum MB, Lamont EB, Earle CC, Bozeman SR, McNeil BJ. End-of-life care for older cancer patients in the Veterans Health Administration versus the private sector. Cancer. 2010;116(15):3732-3739. doi: 10.1002/cncr.25077 [DOI] [PubMed] [Google Scholar]
  • 49.Frahm KA, Barnett SD, Brown LM. Trends in hospice utilization across age among the veteran population. Am J Hosp Palliat Care. 2011;28(6):424-428. doi: 10.1177/1049909110397655 [DOI] [PubMed] [Google Scholar]
  • 50.Kizer KW, Demakis JG, Feussner JR. Reinventing VA health care: systematizing quality improvement and quality innovation. Med Care. 2000;38(6)(suppl 1):I7-I16. [PubMed] [Google Scholar]
  • 51.Oncology care model. Centers for Medicare & Medicaid Services. Published July 27, 2017. Updated June 12, 2018. https://innovation.cms.gov/initiatives/oncology-care. Accessed August 11, 2017.
  • 52.Quality Payment Program. Centers for Medicare and Medicaid Services. https://qpp.cms.gov/. Accessed August 10, 2017.

Associated Data

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

Supplementary Materials

Supplement 1.

Trial Protocol

Click here for additional data file. (648.8KB, pdf)
Supplement 2.

eTable 1. Baseline Characteristics of the Study Participants

eTable 2. Health Care Utilization and Total Costs of Care

eTable 3. Characteristics of the Pilot Participants Who Died Within 15 Months

eFigure 1. Assessment for Eligibility, Randomization and Follow-up

eFigure 2. Survival Within 15 Months Post-Randomization by Group

eFigure 3. Distribution of Total Costs in the 30 Days Prior to Death Among Patients Who Died Within 15 Months of Study Enrollment

eAppendix. Lay Health Worker Program Cost and Interactions

Click here for additional data file. (81.4KB, pdf)

Articles from JAMA Oncology are provided here courtesy of American Medical Association

RESOURCES