Skip to main content
JAMA Network logoLink to JAMA Network
. 2021 Jan 11;181(3):1–9. doi: 10.1001/jamainternmed.2020.5950

Effectiveness of a Nurse-Led Multidisciplinary Intervention vs Usual Care on Advance Care Planning for Vulnerable Older Adults in an Accountable Care Organization

A Randomized Clinical Trial

Jennifer Gabbard 1,2,, Nicholas M Pajewski 2,3, Kathryn E Callahan 1,2, Ajay Dharod 2,4, Kristie L Foley 2,5, Keren Ferris 1,2, Adam Moses 2,6, James Willard 2,3, Jeff D Williamson 1,2
PMCID: PMC7802005  PMID: 33427851

Key Points

Question

Can a nurse navigator–led pathway plus an integrated health care professional–facing electronic health record (EHR) discussion documentation interface increase advance care planning (ACP) documentation among vulnerable older adults compared with usual care?

Findings

In this pragmatic, randomized effectiveness trial of 759 vulnerable older adults from 8 primary care clinics, a nurse navigator–led pathway plus an integrated health care professional–facing EHR interface resulted in higher rates of ACP documentation (42.2% vs 3.7%, P < .001) vs usual care.

Meaning

Use of a nurse navigator–led pathway and the health care professional–facing EHR interface may facilitate greater use of ACP for vulnerable older adults in outpatient primary care settings.


This randomized effectiveness trial evaluates whether a nurse navigator–led advance care planning pathway combined with primary care professional–facing electronic health record interface facilitates use of advance care planning for vulnerable older adults.

Abstract

Importance

Advance care planning (ACP), especially among vulnerable older adults, remains underused in primary care. Additionally, many ACP initiatives fail to integrate directly into the electronic health record (EHR), resulting in infrequent and disorganized documentation.

Objective

To determine whether a nurse navigator–led ACP pathway combined with a health care professional–facing EHR interface improves the occurrence of ACP discussions and their documentation within the EHR.

Design, Setting, and Participants

This was a randomized effectiveness trial using the Zelen design, in which patients are randomized prior to informed consent, with only those randomized to the intervention subsequently approached to provide informed consent. Randomization began November 1, 2018, and follow-up concluded November 1, 2019. The study population included patients 65 years or older with multimorbidity combined with either cognitive or physical impairments, and/or frailty, assessed from 8 primary care practices in North Carolina.

Interventions

Participants were randomized to either a nurse navigator–led ACP pathway (n = 379) or usual care (n = 380).

Main Outcomes and Measures

The primary outcome was documentation of a new ACP discussion within the EHR. Secondary outcomes included the usage of ACP billing codes, designation of a surrogate decision maker, and ACP legal form documentation. Exploratory outcomes included incident health care use.

Results

Among 759 randomized patients (mean age 77.7 years, 455 women [59.9%]), the nurse navigator–led ACP pathway resulted in a higher rate of ACP documentation (42.2% vs 3.7%, P < .001) as compared with usual care. The ACP billing codes were used more frequently for patients randomized to the nurse navigator–led ACP pathway (25.3% vs 1.3%, P < .001). Patients randomized to the nurse navigator–led ACP pathway more frequently designated a surrogate decision maker (64% vs 35%, P < .001) and completed ACP legal forms (24.3% vs 10.0%, P < .001). During follow-up, the incidence of emergency department visits and inpatient hospitalizations was similar between the randomized groups (hazard ratio, 1.17; 95% CI, 0.92-1.50).

Conclusions and Relevance

A nurse navigator–led ACP pathway integrated with a health care professional–facing EHR interface increased the frequency of ACP discussions and their documentation. Additional research will be required to evaluate whether increased EHR documentation leads to improvements in goal-concordant care.

Trial Registration

ClinicalTrials.gov Identifier: NCT03609658

Introduction

Advance care planning (ACP) is increasingly recognized as a crucial step to ensure patients receive goal-concordant medical care.1,2 A number of studies have shown that ACP leads to decreased hospitalization and in-hospital death, decreased health care costs, and increased receipt of goal-concordant care.3,4,5,6,7,8,9,10 However, due to a number of patient and health care professional barriers, ACP discussions continue to be underused, especially within primary care settings.11,12 Fewer than 3% of Medicare beneficiaries are billed for ACP on an annual basis, which is problematic given the dynamic nature of goals and preferences with changing health status.13,14 Recent progress has been made with interventions designed to both promote ACP discussions and facilitate their documentation. Examples include the combination of easy-to-read advance directives with an interactive ACP website (https://prepareforyourcare.org/)15,16 or the combination of a conversational guide with interactive skills-based training evaluated by the Serious Illness Care Program in patients with cancer.17 Despite these advances, patients and their loved ones continue to feel unprepared for ACP discussions, especially in outpatient contexts, and there remains continued need for health care professional–facing tools to help improve documentation of ACP discussions in the electronic health record (EHR) to affect clinical care.13,18,19,20,21

Another limitation of existing ACP interventions is that they are not typically targeted toward vulnerable older adults, that is, those with multimorbidity plus additional impairments in either physical function (eg, mobility) or cognition (eg, dementia), and/or those with frailty. Such patients have a high risk for disability and mortality,22,23,24,25,26,27 and often experience burdensome care that does not meet their health care goals.11,28 Driven by a focus on disease-based treatments, vulnerable older adults often experience lengthy and recurrent hospital stays as well as higher health care cost through the end of life.29,30,31,32 There is a critical need for ACP interventions targeted toward vulnerable older adults to address the lack of preparedness of patients and their loved ones to engage in ACP and produce centralized, structured documentation within the EHR so as to provide a mechanism to support goal-concordant care.33,34,35,36 The objective of this study was to pragmatically determine whether an ACP pathway, combining nurse navigators embedded within a Medicare Accountable Care Organization (ACO) with a health care professional–facing EHR discussion and documentation interface during the Medicare annual wellness visit, improved ACP documentation within the EHR for vulnerable older adults within the outpatient primary care setting.

Methods

Population

This study was approved by the Wake Forest Institutional Review Board. The trial protocol has been published previously, and is available in Supplement 1.37 An automated EHR query was created to identify potentially eligible patients, including patients from 8 primary care practices in the Piedmont area of North Carolina across 5 different counties (4 practices were located in rural counties). Patients were eligible for this study if they were 65 years or older, if they were affiliated with an ACO, and if they had seen their primary care professional within the past 12 months. They were additionally required to have evidence of multimorbidity (Weighted Charlson Comorbidity Index ≥3),38 and an indication of either cognitive or physical impairment, and/or frailty. Cognitive and physical impairments were defined on the basis of diagnosis codes derived from previous encounters and questions from the Medicare annual wellness visit. Frailty was based on an EHR-derived measure (electronic frailty index, eFI) based on the theory of deficit accumulation, with eFI greater than 0.21 taken to indicate frailty.37,39 Patients were excluded if they had moderate to severe hearing loss (due to use of a phone intervention), if they were non-English speaking (not all of the nurse navigators spoke a second language), if no phone number was available, or if they had moderate to severe dementia based on the Short Portable Mental Status Questionnaire (SPMSQ).40,41 Patients on hospice, in a long-term care facility, or who transferred care to a different primary care professional were also excluded from the study. Race and ethnicity data were collected directly from the EHR using fixed categories.

EHR ACP Interface

We created an integrated ACP EHR interface, ACPWise, to allow primary care professionals to document ACP in a standardized manner using structured data elements within the EHR, while also allowing for free-text comments and responses. The ACPWise documentation program was directly integrated into the physician workflow within the EHR, documented ACP in a central location, and served as a conversational guide for health care professionals to ensure up-to-date ACP documentation at the point of care. We also created a telephone version of ACPWise for the nurse navigators. Everything documented by the nurse navigators autopopulated into the primary care professional’s note to help facilitate discussion and documentation. In addition, an ACP order-set with embedded logic was created within the EHR to assist primary care professionals with ACP billing.

Randomization

The goal of the present study was to evaluate the real-world effectiveness of the ACP pathway, which could be evaluated pragmatically through a randomized study embedded within normal health system operations. However, we also wanted to study several aspects of the implementation of the ACP pathway, elements which necessitated informed consent. In order to balance these 2 goals, we used a somewhat uncommon design first proposed by the statistician Marvin Zelen.42,43 In the Zelen design, all participants are randomized prior to informed consent, and then only patients randomized to the intervention are approached for consent, subsequently enrolled, and receive the intervention. The Zelen design permits a pragmatic test of effectiveness, as patients who decline the intervention still factor into overall estimates of effectiveness under an intent-to-treat paradigm, here facilitated by passive outcome follow-up via the EHR performed under an approved waiver of informed consent. Patients were randomized (n = 759) in a 1:1 allocation to either the nurse navigator–led ACP pathway (NN ACP pathway) or usual care, with the randomization stratified by primary care practice.37 Participants randomized to the nurse navigator group were approached for verbal consent by telephone and subsequently enrolled. A copy of this consent was mailed to all enrolled participants in the nurse navigator group.

Intervention: Nurse Navigator–Led ACP Pathway

Previsit ACP Planning

Full details are provided in the study protocol (Supplement 1).37 Briefly, nurse navigators were trained in ACP using Respecting Choices, participated in a 1-hour training session to review the protocol and the telephone version of ACPWise, and observed a short roleplay example of a telephone previsit ACP discussion.44,45 Patients who were randomized to the NN ACP pathway were approached by the nurse navigator via telephone and those who agreed to participate provided verbal consent. The nurse navigator then completed a brief previsit, telephone-based ACP planning discussion with the patient to help prime and engage them in the ACP process. This consisted of the nurse navigator discussing why ACP is important, and then reviewing a script covering health-related goals, things that bring meaning to the patient’s life, preferred location of death, health-related concerns, and naming a surrogate discussion maker. The nurse navigator rated the patient’s level of engagement over the telephone as either precontemplative, contemplative, or action phase.46 They then scheduled the patient for an in-person dyad visit with their surrogate decision maker or loved one and primary care professional in conjunction with their upcoming annual wellness visit. If the patient had recently completed their annual wellness visit, they were scheduled for an independent ACP visit. Nurse navigators used the telephone version of ACPWise to document these discussions and forwarded their note to the patient’s primary care professional. After completion of the ACP telephone visit, patients were mailed an ACP packet which contained additional information about ACP and a copy of the North Carolina Advance Directive.

Dyad ACP Visits During the Annual Wellness Visit

After a patient completed their previsit ACP telephone visit with the nurse navigator, they were scheduled to complete a dyad ACP visit with their primary care professional and, once completed, their primary care professional used the ACPWise documentation program to document and bill for their discussion. Additional topics incorporated into ACPWise that were not covered by the nurse navigators included disease understanding, prognosis, unacceptable states at the end of life related to their goals (eg, not being able to live without being hooked up to machines), reviewing and/or completing an advance directive, and whether to use or avoid 5 treatments: resuscitation, mechanical intubation, artificial feeding, intravenous fluids, and antibiotics. Patients were given the option if desired to opt out of the telephone previsit and only complete an in-person dyad visit or to complete only the telephone previsit. After the visit, patients were asked to complete a survey to assess quality of communication and engagement and primary care professionals were asked to complete a satisfaction survey about their experience.47

Usual Care

Patients who were randomized to usual care (control arm) received usual care, and were not approached by the research team. All primary care professionals had full access to the ACPWise documentation program.

Primary Outcome

The primary outcome was new documentation of ACP discussions within the EHR after randomization. This was identified through an initial manual review of the EHR by 2 independent reviewers blinded to the randomized assignment. For patients randomized to the NN ACP pathway, quality of ACP discussions was quantified through two measures. First, the quality of end-of-life communication (QOC)47 survey was administered to assess the patient’s perspective of the quality of the ACP discussion. The QOC is a 13-item instrument with 2 subscale scores for general communication skills and communication about end-of-life care.47 Item scores range from 0 (poor) to 10 (perfect). As in previous analyses of the QOC, the ratings ranged from 0 to 11, with 0 imputed for items that were not completed or answered.48 Second, we assessed how many ACP topics captured by the ACPWise documentation program were documented during the telephone and in-person ACP visits. Initially we planned on using a scoring system to measure the quality of discussion for nurse navigators and primary care professionals separately, but we were unable to do this because the nurse navigator notes autopopulated into the primary care professional’s notes.

Secondary Outcomes

Secondary outcomes quantified auxiliary effects of the ACP process. They included use of ACP billing codes (99497, 99498), documentation of a designated surrogate decision maker, and completion and upload of new ACP legal forms (ie, advance directives, living wills, or powers of attorney) within the EHR.

Exploratory Outcomes

Our exploratory outcomes were Medical Orders for Scope of Treatment (MOST) completion rates and health care use. Health care use was obtained by extracting emergency department and inpatient hospitalization encounter information from the EHR. Encounter information was supplemented with admission, discharge, and transfer data from a transitional care network (PatientPing49) to ascertain encounters occurring outside of our health system.

Sample Size and Power

Sample size estimates are fully described in the study protocol (Supplement 1).37 Briefly, using results from a previous randomized trial of ACP strategies, we assumed 44% of patients randomized to the nurse navigator group would consent to participate.16 Power calculations assumed 20% of patients in the nurse navigator group would be ineligible by the time they were contacted for consent, due to death or transition to a nursing home. We assumed that the incidence of documented goals of care in the EHR would be 70% or greater for patients who consented to participate, at most 25% for patients who did not consent to the study, 25% or fewer for patients randomized to usual care, and 10% or fewer for patients who became ineligible prior to being approached for consent. Finally, we assumed a significance level of 0.05 and that loss to follow-up would be 10% over the 1-year follow-up period. These assumptions correspond to assuming that 135 individuals would consent to participate in the nurse navigator intervention, and that the overall rate of documented goals of care discussions in the EHR would be 38% or greater in the nurse navigator group, as compared with 25% or fewer in the usual care group. A total sample size of 765 individuals was estimated to detect such a difference with greater than 80% power.

Statistical Analysis

We used generalized linear mixed models to compare the rate at which ACP discussions were documented within the EHR between the randomized groups, including a random effect for primary care practice. Analyses of secondary and exploratory outcomes (designation of a surrogate decision maker, completion of an Advanced Directive, living will, or power of attorney, completion of a MOST form, and use of ACP billing codes) were similarly based on generalized linear mixed models. Analyses of all-cause mortality were based on Cox proportional hazards regression models with the baseline hazard function stratified by primary care practice.50 Marginal estimates of health care use (emergency department visits or inpatient hospitalizations) were based on the mean cumulative count estimator,51 while randomized group comparisons were based on frailty model extensions of the Cox model as implemented in the R package frailtypack.52 Both approaches accommodate recurrent events as well as the competing risk of death. Within the group of individuals randomized to the nurse navigator intervention, we also compared health care use between individuals who completed either a telephone or in-person ACP visit vs those who did not. We used inverse probability of treatment weights to account for nonrandom completion of an ACP visit,53 with the weights computed using logistic regression including age, sex, race/ethnicity, Charlson Comorbidity Index, and the eFI score as model predictors (we used average treatment effect for the treated weights computed using the PSW R Package).39,54 All analyses were conducted using SAS, version 9.4 (SAS institute), or the R Statistical Computing Environment.55 All hypothesis tests were 2-sided and performed at the α = .05 level of significance.

Results

Study Participants

A total of 765 participants were randomized between November 2018 and April 2019 (Figure 1). A total of 6 patients died prior to randomization, leaving a final population of 759 participants. A total of 146 (49.6%) out of the 294 eligible participants randomized to the nurse navigator group consented to participate and 139 completed the intervention. Overall, the mean (SD) patient age was 77.7 years (7.4 years), with 18.7% participants being 85 years or older. Of all randomized patients, 455 (59.9%) were female and 71 (17.1%) were Black or African American. In the 2 years prior to randomization, patients had a median of 14 outpatient encounters and 71.4% had completed a Medicare annual wellness visit. Based on the eFI, 82.2% were categorized as frail (eFI >0.21), 23.7% had impaired physical function, and 22.0% had impaired cognitive function (Table 1).

Figure 1. Consort Flow Diagram.

Figure 1.

ACP indicates advanced care planning; EHR, electronic health record; QOC, quality of communication, SPSMQ, Short Portable Mental Status Questionnaire.

Table 1. Baseline Characteristics by Randomized Group.

Characteristic No. (%)
Nurse navigator (n = 379) Usual care (n = 380)
Age, mean (SD), y 77.7 (7.5) 77.7 (7.4)
Age, y
65-<75 149 (39.3) 156 (41.1)
75-<85 161 (42.5) 151 (39.7)
≥85 69 (18.2) 73 (19.2)
Female sex 226 (59.6) 229 (60.3)
Race/ethnicity
White 300 (79.2) 317 (83.4)
Black or African American 71 (18.7) 59 (15.5)
Other 8 (2.1) 4 (1.1)
No. of outpatient encounters in prior 2 y, median (IQR) 14 (10-19.5) 14 (10-19)
Medicare annual wellness visit in prior 2 y 278 (73.4) 264 (69.5)
Weighted Charlson Comorbidity Index, median (IQR)a 4 (3-5) 4 (3-5)
eFI, median (IQR)b 0.25 (0.22-0.29) 0.25 (0.22-0.29)
eFI >0.21 311 (82.1) 313 (82.4)
Diagnosis code for impaired physical function 95 (25.1) 85 (22.4)
Diagnosis code for impaired cognitive function 91 (24.0) 76 (20.0)
Charlson comorbidities
Myocardial infarction 52 (13.7) 46 (12.1)
Chronic heart failure 94 (24.8) 95 (25.0)
Peripheral vascular disease 96 (25.3) 113 (29.7)
Cerebrovascular disease 126 (33.2) 118 (31.1)
Dementia 36 (9.5) 31 (8.2)
Pulmonary disease 184 (48.5) 172 (45.3)
Mild liver disease 15 (4.0) 21 (5.5)
Diabetes without complications 158 (41.7) 157 (41.3)
Diabetes with complications 189 (49.9) 198 (52.1)
Renal disease 209 (55.1) 203 (53.4)
Malignant tumor 101 (26.6) 103 (27.1)
Metastatic disease 13 (3.4) 5 (1.3)

Abbreviations: eFI, electronic frailty index; IQR, interquartile range.

a

Scores range from 0 to 37 with higher scores indicating greater comorbidity.

b

Scores range from 0 to 1 with higher scores indicating greater frailty.

Table 2 summarizes ACP outcomes by randomization group. The primary outcome of documented ACP within the EHR occurred in 160 patients randomized to the nurse navigator group (42.2%) as compared with 14 (3.7%) in the usual care group (P < .001). There were similarly large increases for naming a surrogate decision maker; having an advanced directive, living will, or power of attorney; and completing a MOST form (all P < .001). Use of billing codes for ACP visits occurred in 96 (25.3%) of 379 patients randomized to the nurse navigator group, as compared with 5 (1.3%) of 380 patients in the usual care group (P < .001).

Table 2. Advanced Care Planning Outcomes by Randomized Group.

Outcomes within the EHR No. (%) Odds ratio (95% CI) P value
Nurse navigator (n = 379) Usual care (n = 380)
Documented ACP/goals of care 160 (42.2) 14 (3.7) 20.7 (11.6-36.9) <.001
Named surrogate decision maker 241 (63.6) 132 (34.7) 3.3 (2.5-4.5) <.001
Advance directive/living will/power of attorney 92 (24.3) 38 (10) 3.0 (2.0-4.5) <.001
Medical scope of treatment form 39 (10.3) 4 (1.1) 12.2 (4.2-34.9) <.001
Use of advance care planning billing codes 96 (25.3) 5 (1.3) 28.3 (11.4-70.7) <.001

Abbreviations: ACP, advanced care planning; EHR, electronic health record. Odds ratio based on generalized linear mixed model with random effect for primary care practice.

In terms of the quality of ACP discussions, 87 participants (85% response rate) in the nurse navigator group completed the QOC survey. Average ratings for the general communication subscale were very high (mean [SD] 10.2 [1.8]), whereas scores on the communication about the end-of-life subscale were somewhat lower (mean [SD] 7.9 [3.1]). Table 3 summarizes which ACP components were discussed and documented with the EHR, computed for participants in the nurse navigator group who completed a telephone visit with a nurse navigator and/or an in-person visit with their primary care professional. In general, the nurse navigators tended to discuss the majority of specified ACP components during the telephone visit, with 74 (55.2%) covering all 7 components. The majority of in-person ACP visits included a discussion of disease understanding (90.6%), prognosis (86.5%), and factors that would lead to a focus on comfort rather than longevity (51.0%). Other frequently addressed components were the discussion and completion of an advance directive (37.8%) or MOST form (47.8%).

Table 3. Quality of Completed Telephone and In-Person Advanced Care Planning Visits.

ACP components discussed and documented within the electronic health record ACP visit, No. (%)
Completed (n = 96) Completed telephone only (n = 37) Completed in-person only (n = 6)
Telephone In-person
Surrogate decision maker named 87 (98.9) 2 (2.0)a 44 (100) 5 (83.3)
Component discussed
Health-related goals 77 (85.6) 11 (12.2)b 43 (97.7) 6 (100)
What brings meaning to patient's life 73 (81.1) 19 (21.1)b 44 (100) 5 (83.3)
What would be important should health worsen 90 (100) 4 (4.4)b 44 (100) 6 (100)
Preferred location at the end of life 90 (100) 9 (10.0)b 44 (100) 5 (100)
Health-related worries 90 (100) 14 (15.6)b 44 (100) 4 (100)
Level of engagement
Contemplative phase 4 (4.4) 5 (5.6) 3 (6.8) 1 (16.7)
Action phase 79 (87.8) 4 (4.4) 38 (86.4) 5 (83.3)
Did not answer 7 (7.8) 81 (90.0) 3 (6.8) 0 )
Disease understanding discussed 82 (91.1)c 5 (83.3)c
Prognosis discussed 79 (87.8)c 4 (66.7)c
Unacceptable states at the end of life 43 (47.8)c 6 (100)c
Advance directive
Discussed only 38 (42.2)c 3 (50.0)c
Completed 34 (37.8)c 4 (66.7)c
Medical scope of treatment
Discussed only 20 (22.2)c 2 (33.3)c
Completed 43 (47.8)c 4 (100)c

Abbreviation: ACP, advanced care planning.

a

If not discussed and documented during the telephone visit.

b

Additional ACP documentation beyond what was documented during the telephone visit.

c

These questions were not asked during the telephone previsit ACP visits and were only asked during the in-person ACP visit with their primary care professional.

Over a median follow-up time of 304 days, there were 31 deaths in the nurse navigator group and 34 deaths in the usual care group (hazard ratio [HR], 0.92; 95% CI, 0.56-1.49; P = .71). Figure 2 displays the incidence of emergency department visits and/or inpatient hospitalizations during follow-up. During follow-up, there were 659 emergency department visits and/or inpatient hospitalizations; of those, 167 (25.3%) occurred at facilities outside the Wake Forest Baptist Health Network. At 1 year, the mean cumulative count estimate per 100 individuals was 101.4 events (95% CI, 83.8-120.8) for the nurse navigator group and 97.6 events (95% CI, 79.1-118.5) for the usual care group, with no significant between-group differences (HR, 1.17; 95% CI, 0.92-1.50; P = .20). In exploratory analyses, we examined the incidence of these visits within populations of patients randomized to the nurse navigator group, comparing patients who completed a telephone or in-person ACP visit vs those who did not (eFigure in Supplement 2). At 1 year, the mean cumulative count estimate was 72.0 (95% CI, 51.3-94.7) per 100 individuals for patients who completed a telephone or in-person ACP visit, and 119.2 (95% CI, 94.0-144.5) per 100 individuals for patients who did not complete an ACP visit (HR, 0.59; 95% CI, 0.42-0.83; P = .003).

Figure 2. Incidence of Emergency Department Visits and Inpatient Hospitalizations by Randomized Group.

Figure 2.

Discussion

A nurse navigator–led ACP pathway combined with a health care professional-facing EHR ACP interface was effective in improving ACP documentation within the EHR in an outpatient population of vulnerable older adults. The observed increase in this trial of ACP documentation within the EHR (42%) is clinically meaningful and encouraging, since documented ACP leads to greater congruence between proxies and patients in terms of end-of-life preferences,56 a higher percentage of patients receiving their desired care at the end of their life,8,57 and a reduction in unwanted care.58

Prior studies have highlighted that barriers to ACP for primary care professionals are related to uncertainty regarding when to discuss ACP, insufficient time, limited understanding of how to properly discuss ACP, and an inability to bill for ACP.59,60 This study shows that some progress in combating these barriers can be made by expanding the team that guides ACP (nurse navigators), linking this team-based process to the Medicare annual wellness visit, and creating an EHR documentation interface to facilitate the workflow. Additionally, educating health care professionals about the ACP billing codes and creating order-sets within the EHR facilitates reimbursement. Even though ACP billing codes have been in existence since 2016, we found that many primary care professionals were not familiar with their use. Nurse navigators performing previsit planning with patients optimizes time spent with the primary care professional during the visit; the present study showed that primary care professionals required additional documentation of goals and values beyond those already noted by the nurse navigator in fewer than 20% of visits. As a result, primary care professionals were able to focus on disease understanding, prognostic awareness, unacceptable patient states of being, and ACP form completion within the EHR. In addition, 26% of participants preferred to only discuss ACP with the nurse navigator and were not interested in further discussing ACP with their primary care professional.

While the NN led ACP pathway did increase the completion of ACP forms, only 37% were completed during the initial in-person ACP visit; the remaining 63% were later scanned into the EHR. Health care professionals cited time limitations and lack of a notary or witness as the most significant barriers to completion of forms during the initial ACP visit. Another barrier to completion was the necessity of scanning forms into the EHR; for example, about 9% of the MOST forms that were completed were not scanned into the EHR, which highlights the need for universal electronic advance directives and MOST/Provider Orders for Life-Sustaining Treatment (POLST) forms.61 We did not observe differences between the randomized groups in emergency department visits or inpatient hospitalizations. Given the short follow-up period of this trial, and the fact that many patients did not agree to the intervention, this is not unexpected. It is also unclear to what extent improvements in goal-concordant care would necessarily lead to decreases for these types of health care encounters. Further research with longer follow-up will be required to examine how improvements in ACP documentation eventually impact use, especially at the end of life.

Strengths and Limitations

The multiple strengths of this trial include its pragmatic design; automated identification of eligible patients from the EHR; integration of ACP documentation into the EHR to facilitate ACP discussions and enable centralized documentation; and supplementation of the ascertainment of health care use using admission, discharge, and transfer data to overcome the long delays associated with administrative claims. In addition, the use of nurse navigators embedded within an ACO, and not paid research nurses, is both a strength and limitation. On one hand, this demonstrates that the intervention can be integrated into existing clinical workflows without additional resources. However, implementation will naturally be more difficult in settings without existing nurse navigators or with other resource limitations. In addition, given the pragmatic design, we were limited by the depth of survey information we could collect from patients, with no contact with patients randomized to usual care. Generalizability may also be limited because participants were recruited from a single health system, all were within an ACO population, patients who were non-English speaking or residing within a long-term care faculty were excluded, and the majority of randomized patients were White. Finally, we could not assess the longitudinal effect of ACP discussions on care delivery, on the quality of medical decision making, nor on cost due to the short duration of this study.

Conclusions

A nurse navigator led ACP pathway integrated with a health care professional–facing EHR interface substantially increases ACP discussion and documentation within the EHR. This trial suggests a promising new approach to ACP in the outpatient primary care setting and a potentially scalable approach to ACP for vulnerable older adults.

Supplement 1.

Trial Protocol

Supplement 2.

eFigure. Incidence of Emergency Department Visits and Inpatient Hospitalizations

Supplement 3.

Data Sharing Statement

References

  • 1.Sudore RL, Heyland DK, Lum HD, et al. Outcomes that define successful advance care planning: a Delphi panel consensus. J Pain Symptom Manage. 2018;55(2):245-255.e8. doi: 10.1016/j.jpainsymman.2017.08.025 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Sanders JJ, Curtis JR, Tulsky JA; Achieving Goal-Concordant Care . Achieving goal-concordant care: a conceptual model and approach to measuring serious illness communication and its impact. J Palliat Med. 2018;21(S2):S17-S27. doi: 10.1089/jpm.2017.0459 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Bond WF, Kim M, Franciskovich CM, et al. Advance care planning in an accountable care organization is associated with increased advanced directive documentation and decreased costs. J Palliat Med. 2018;21(4):489-502. doi: 10.1089/jpm.2017.0566 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Bischoff KE, Sudore R, Miao Y, Boscardin WJ, Smith AK. Advance care planning and the quality of end-of-life care in older adults. J Am Geriatr Soc. 2013;61(2):209-214. doi: 10.1111/jgs.12105 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Mehta A, Kelley AS. Advance care planning codes—getting paid for quality care. JAMA Intern Med. 2019;179(6):830-831. doi: 10.1001/jamainternmed.2018.8105 [DOI] [PubMed] [Google Scholar]
  • 6.Brinkman-Stoppelenburg A, Rietjens JA, van der Heide A. The effects of advance care planning on end-of-life care: a systematic review. Palliat Med. 2014;28(8):1000-1025. doi: 10.1177/0269216314526272 [DOI] [PubMed] [Google Scholar]
  • 7.Houben CHM, Spruit MA, Groenen MTJ, Wouters EFM, Janssen DJA. Efficacy of advance care planning: a systematic review and meta-analysis. J Am Med Dir Assoc. 2014;15(7):477-489. doi: 10.1016/j.jamda.2014.01.008 [DOI] [PubMed] [Google Scholar]
  • 8.Silveira MJ, Kim SY, Langa KM. Advance directives and outcomes of surrogate decision making before death. N Engl J Med. 2010;362(13):1211-1218. doi: 10.1056/NEJMsa0907901 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Hammes BJ, Rooney BL, Gundrum JD. A comparative, retrospective, observational study of the prevalence, availability, and specificity of advance care plans in a county that implemented an advance care planning microsystem. J Am Geriatr Soc. 2010;58(7):1249-1255. doi: 10.1111/j.1532-5415.2010.02956.x [DOI] [PubMed] [Google Scholar]
  • 10.Sinuff T, Dodek P, You JJ, et al. Improving end-of-life communication and decision making: the development of a conceptual framework and quality indicators. J Pain Symptom Manage. 2015;49(6):1070-1080. doi: 10.1016/j.jpainsymman.2014.12.007 [DOI] [PubMed] [Google Scholar]
  • 11.Jimenez G, Tan WS, Virk AK, Low CK, Car J, Ho AHY. Overview of systematic reviews of advance care planning: summary of evidence and global lessons. J Pain Symptom Manage. 2018;56(3):436-459.e25. doi: 10.1016/j.jpainsymman.2018.05.016 [DOI] [PubMed] [Google Scholar]
  • 12.Risk J, Mohammadi L, Rhee J, Walters L, Ward PR. Barriers, enablers and initiatives for uptake of advance care planning in general practice: a systematic review and critical interpretive synthesis. BMJ Open. 2019;9(9):e030275. doi: 10.1136/bmjopen-2019-030275 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Belanger E, Loomer L, Teno JM, Mitchell SL, Adhikari D, Gozalo PL. Early utilization patterns of the new Medicare procedure codes for advance care planning. JAMA Intern Med. 2019;179(6):829-830. doi: 10.1001/jamainternmed.2018.8615 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Pelland K, Morphis B, Harris D, Gardner R. Assessment of first-year use of Medicare’s advance care planning billing codes. JAMA Intern Med. 2019;179(6):827-829. doi: 10.1001/jamainternmed.2018.8107 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Sudore RL, Schillinger D, Katen MT, et al. Engaging diverse English- and Spanish-speaking older adults in advance care planning: the PREPARE randomized clinical trial. JAMA Intern Med. 2018;178(12):1616-1625. doi: 10.1001/jamainternmed.2018.4657 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Sudore RL, Boscardin J, Feuz MA, McMahan RD, Katen MT, Barnes DE. Effect of the PREPARE website vs an easy-to-read advance directive on advance care planning documentation and engagement among veterans: a randomized clinical trial. JAMA Intern Med. 2017;177(8):1102-1109. doi: 10.1001/jamainternmed.2017.1607 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Bernacki R, Paladino J, Neville BA, et al. Effect of the Serious Illness Care Program in outpatient oncology: a cluster randomized clinical trial. JAMA Intern Med. 2019;179(6):751-759. doi: 10.1001/jamainternmed.2019.0077 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Dillon E, Chuang J, Gupta A, et al. Provider perspectives on advance care planning documentation in the electronic health record: the experience of primary care providers and specialists using advance health-care directives and physician orders for life-sustaining treatment. Am J Hosp Palliat Care. 2017;34(10):918-924. doi: 10.1177/1049909117693578 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Tai-Seale M, Yang Y, Dillon E, et al. Community-based palliative care and advance care planning documentation: evidence from a multispecialty group. J Am Geriatr Soc. 2018;66(2):327-332. doi: 10.1111/jgs.15145 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.You JJ, Downar J, Fowler RA, et al. ; Canadian Researchers at the End of Life Network . Barriers to goals of care discussions with seriously ill hospitalized patients and their families: a multicenter survey of clinicians. JAMA Intern Med. 2015;175(4):549-556. doi: 10.1001/jamainternmed.2014.7732 [DOI] [PubMed] [Google Scholar]
  • 21.Joseph-Williams N, Elwyn G, Edwards A. Knowledge is not power for patients: a systematic review and thematic synthesis of patient-reported barriers and facilitators to shared decision making. Patient Educ Couns. 2014;94(3):291-309. doi: 10.1016/j.pec.2013.10.031 [DOI] [PubMed] [Google Scholar]
  • 22.Gómez-Batiste X, Martínez-Muñoz M, Blay C, et al. Prevalence and characteristics of patients with advanced chronic conditions in need of palliative care in the general population: a cross-sectional study. Palliat Med. 2014;28(4):302-311. doi: 10.1177/0269216313518266 [DOI] [PubMed] [Google Scholar]
  • 23.Quiñones AR, Markwardt S, Botoseneanu A. Multimorbidity combinations and disability in older adults. J Gerontol A Biol Sci Med Sci. 2016;71(6):823-830. doi: 10.1093/gerona/glw035 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Kogan AC, Wilber K, Mosqueda L. Person-centered care for older adults with chronic conditions and functional impairment: a systematic literature review. J Am Geriatr Soc. 2016;64(1):e1-e7. doi: 10.1111/jgs.13873 [DOI] [PubMed] [Google Scholar]
  • 25.Ryan A, Wallace E, O’Hara P, Smith SM. Multimorbidity and functional decline in community-dwelling adults: a systematic review. Health Qual Life Outcomes. 2015;13:168. doi: 10.1186/s12955-015-0355-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Connors MH, Sachdev PS, Kochan NA, Xu J, Draper B, Brodaty H. Cognition and mortality in older people: the Sydney Memory and Ageing Study. Age Ageing. 2015;44(6):1049-1054. doi: 10.1093/ageing/afv139 [DOI] [PubMed] [Google Scholar]
  • 27.Bunn F, Burn AM, Goodman C, et al. Comorbidity and dementia: a scoping review of the literature. BMC Med. 2014;12:192. doi: 10.1186/s12916-014-0192-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Block BL, Jeon SY, Sudore RL, Matthay MA, Boscardin WJ, Smith AK. Patterns and trends in advance care planning among older adults who received intensive care at the end of life. JAMA Intern Med. 2020;180(5):786-789. doi: 10.1001/jamainternmed.2019.7535 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Campbell SE, Seymour DG, Primrose WR; ACMEPLUS Project . A systematic literature review of factors affecting outcome in older medical patients admitted to hospital. Age Ageing. 2004;33(2):110-115. doi: 10.1093/ageing/afh036 [DOI] [PubMed] [Google Scholar]
  • 30.Hagerty RG, Butow PN, Ellis PM, et al. Communicating with realism and hope: incurable cancer patients’ views on the disclosure of prognosis. J Clin Oncol. 2005;23(6):1278-1288. doi: 10.1200/JCO.2005.11.138 [DOI] [PubMed] [Google Scholar]
  • 31.Herrin J, Harris KG, Kenward K, Hines S, Joshi MS, Frosch DL. Patient and family engagement: a survey of US hospital practices. BMJ Qual Saf. 2016;25(3):182-189. doi: 10.1136/bmjqs-2015-004006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Wagner E, Patrick DL, Khandelwal N, et al. The influence of multimorbidity on health care utilization at the end of life for patients with chronic conditions. J Palliat Med. 2019;22(10):1260-1265. doi: 10.1089/jpm.2018.0349 [DOI] [PubMed] [Google Scholar]
  • 33.Street RL Jr, Makoul G, Arora NK, Epstein RM. How does communication heal? pathways linking clinician–patient communication to health outcomes. Patient Educ Couns. 2009;74(3):295-301. doi: 10.1016/j.pec.2008.11.015 [DOI] [PubMed] [Google Scholar]
  • 34.Sanders JJ, Curtis JR, Tulsky JA. Achieving goal-concordant care: a conceptual model and approach to measuring serious illness communication and its impact. J Palliat Med. 2018;21(S2):S17-S27. doi: 10.1089/jpm.2017.0459 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Haines L, Rahman OK, Sanders JJ, Johnson K, Kelley A. Factors that impact family perception of goal-concordant care at the end of life. J Palliat Med. 2019;22(8):927-932. doi: 10.1089/jpm.2018.0508 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Lyles CR, Altschuler A, Chawla N, et al. User-centered design of a tablet waiting room tool for complex patients to prioritize discussion topics for primary care visits. JMIR Mhealth Uhealth. 2016;4(3):e108. doi: 10.2196/mhealth.6187 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Gabbard J, Pajewski NM, Callahan KE, et al. Advance care planning for vulnerable older adults within an Accountable Care Organization: study protocol for the IMPACT randomised controlled trial. BMJ Open. 2019;9(12):e032732. doi: 10.1136/bmjopen-2019-032732 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-383. doi: 10.1016/0021-9681(87)90171-8 [DOI] [PubMed] [Google Scholar]
  • 39.Pajewski NM, Lenoir K, Wells BJ, Williamson JD, Callahan KE. Frailty screening using the electronic health record within a Medicare Accountable Care Organization. J Gerontol A Biol Sci Med Sci. 2019;74(11):1771-1777. doi: 10.1093/gerona/glz017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Erkinjuntti T, Sulkava R, Wikström J, Autio L. Short Portable Mental Status Questionnaire as a screening test for dementia and delirium among the elderly. J Am Geriatr Soc. 1987;35(5):412-416. doi: 10.1111/j.1532-5415.1987.tb04662.x [DOI] [PubMed] [Google Scholar]
  • 41.Castanho TC, Amorim L, Zihl J, Palha JA, Sousa N, Santos NC. Telephone-based screening tools for mild cognitive impairment and dementia in aging studies: a review of validated instruments. Front Aging Neurosci. 2014;6:16. doi: 10.3389/fnagi.2014.00016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Zelen M. A new design for randomized clinical trials. N Engl J Med. 1979;300(22):1242-1245. doi: 10.1056/NEJM197905313002203 [DOI] [PubMed] [Google Scholar]
  • 43.Adamson J, Cockayne S, Puffer S, Torgerson DJ. Review of randomised trials using the post-randomised consent (Zelen’s) design. Contemp Clin Trials. 2006;27(4):305-319. doi: 10.1016/j.cct.2005.11.003 [DOI] [PubMed] [Google Scholar]
  • 44.Moorman SM, Carr D, Kirchhoff KT, Hammes BJ. An assessment of social diffusion in the Respecting Choices advance care planning program. Death Stud. 2012;36(4):301-322. doi: 10.1080/07481187.2011.584016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Rietjens JA, Korfage IJ, Dunleavy L, et al. Advance care planning—a multi-centre cluster randomised clinical trial: the research protocol of the ACTION study. BMC Cancer. 2016;16:264. doi: 10.1186/s12885-016-2298-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Prochaska JO, Velicer WF. The transtheoretical model of health behavior change. Am J Health Promot. 1997;12(1):38-48. doi: 10.4278/0890-1171-12.1.38 [DOI] [PubMed] [Google Scholar]
  • 47.Engelberg R, Downey L, Curtis JR. Psychometric characteristics of a quality of communication questionnaire assessing communication about end-of-life care. J Palliat Med. 2006;9(5):1086-1098. doi: 10.1089/jpm.2006.9.1086 [DOI] [PubMed] [Google Scholar]
  • 48.Curtis JR, Downey L, Back AL, et al. Effect of a patient and clinician communication-priming intervention on patient-reported goals-of-care discussions between patients with serious illness and clinicians: a randomized clinical trial. JAMA Intern Med. 2018;178(7):930-940. doi: 10.1001/jamainternmed.2018.2317 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Patient Ping. Accountable care organizations and provider organizations. https://patientping.com/who-we-help/acos-pos/
  • 50.Glidden DV, Vittinghoff E. Modelling clustered survival data from multicentre clinical trials. Stat Med. 2004;23(3):369-388. doi: 10.1002/sim.1599 [DOI] [PubMed] [Google Scholar]
  • 51.Dong H, Robison LL, Leisenring WM, Martin LJ, Armstrong GT, Yasui Y. Estimating the burden of recurrent events in the presence of competing risks: the method of mean cumulative count. Am J Epidemiol. 2015;181(7):532-540. doi: 10.1093/aje/kwu289 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Rondeau V, Mazroui Y, Gonzalez JR. frailtypack: an R package for the analysis of correlated survival data with frailty models using penalized likelihood estimation or parametrical estimation. Journal of Statistical Software. 2012;47(4):28. doi:10.18637/jss.v047.i04 doi: 10.18637/jss.v047.i04 [DOI] [Google Scholar]
  • 53.Seaman SR, White IR. Review of inverse probability weighting for dealing with missing data. Stat Methods Med Res. 2013;22(3):278-295. doi: 10.1177/0962280210395740 [DOI] [PubMed] [Google Scholar]
  • 54.Mao H, Li L. PSW: Propensity Score Weighting Methods for Dichotomous Treatments. 2018, R package version 1.1-3. https://cran.r-project.org/web/packages/PSW/index.html
  • 55.R Core Team . R: a language and environment for statistical computing. R Foundation for Statistical Computing. 2020. http://www.r-project.org
  • 56.Schwartz CE, Wheeler HB, Hammes B, et al. ; UMass End-of-Life Working Group . Early intervention in planning end-of-life care with ambulatory geriatric patients: results of a pilot trial. Arch Intern Med. 2002;162(14):1611-1618. doi: 10.1001/archinte.162.14.1611 [DOI] [PubMed] [Google Scholar]
  • 57.Detering KM, Hancock AD, Reade MC, Silvester W. The impact of advance care planning on end of life care in elderly patients: randomised controlled trial. BMJ. 2010;340:c1345. doi: 10.1136/bmj.c1345 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Oo NM, Scott IA, Maggacis R, Rajakaruna N. Assessing concordance between patient preferences in advance care plans and in-hospital care. Aust Health Rev. 2019;43(4):425-431. doi: 10.1071/AH18011 [DOI] [PubMed] [Google Scholar]
  • 59.De Vleminck A, Houttekier D, Pardon K, et al. Barriers and facilitators for general practitioners to engage in advance care planning: a systematic review. Scand J Prim Health Care. 2013;31(4):215-226. doi: 10.3109/02813432.2013.854590 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Howard M, Bernard C, Klein D, et al. Barriers to and enablers of advance care planning with patients in primary care: Survey of health care providers. Can Fam Physician. 2018;64(4):e190-e198. [PMC free article] [PubMed] [Google Scholar]
  • 61.Zive DM, Cook J, Yang C, Sibell D, Tolle SW, Lieberman M. Implementation of a novel electronic health record-embedded physician orders for life-sustaining treatment system. J Med Syst. 2016;40(11):245. doi: 10.1007/s10916-016-0605-3 [DOI] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

Supplement 1.

Trial Protocol

Supplement 2.

eFigure. Incidence of Emergency Department Visits and Inpatient Hospitalizations

Supplement 3.

Data Sharing Statement


Articles from JAMA Internal Medicine are provided here courtesy of American Medical Association

RESOURCES