Improving communication about goals of care for hospitalized patients with serious illness: Study protocol for two complementary randomized trials

Curtis, J. Randall; Lee, Robert Y.; Brumback, Lyndia C.; Kross, Erin K.; Downey, Lois; Torrence, Janaki; Heywood, Joanna; Leduc, Nicole; Andrews, Kasey Mallon; Im, Jennifer; Weiner, Bryan J.; Abedini, Nauzley C.; Engelberg, Ruth A.

doi:10.1016/j.cct.2022.106879

Cited by 7 publications

(18 citation statements)

References 90 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Methodssupporting

confidence: 53%

“…This diagnostic study was conducted to inform selection of an outcome measurement strategy for a pragmatic randomized clinical trial of a communication-priming intervention for hospitalized patients (Project to Improve Communication About Serious Illness-Hospital Study: Pragmatic Trial 1 [PICSI-H Trial 1]). 32,33 All procedures for this study and the parent clinical trial were approved by the University of Washington institutional review board. Patients were enrolled under an institutional review board-approved waiver of informed consent and Health Insurance Portability and Accountability Act authorization due to minimal risk of the intervention, which was designed to promote best practices.…”

Section: Methodsmentioning

confidence: 99%

“…EHR indicates electronic health record; GOC, goals of care; PICSI-H Trial 1, Project to Improve Communication About Serious Illness—Hospital Study: Pragmatic Trial (Trial 1); and PICSI-P, Project to Improve Communication About Serious Illness—Pilot Study …”

Section: Methodsmentioning

confidence: 99%

“…7,14 In this study, we used deep-learning NLP to measure the primary outcome of EHR-documented goals-of-care discussions in a large pragmatic trial of a communication-priming intervention for hospitalized patients. 32,33 We evaluated the sensitivity, specificity, and predictive values of our deeplearning NLP model through receiver operating characteristic (ROC) curves and precision-recall analyses and examined the effect of NLP-related misclassification on study power. We concluded by examining the performance, feasibility, and power implications of an NLP-screened human abstraction 34 approach to measuring the primary outcome of the parent trial.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Assessment of Natural Language Processing of Electronic Health Records to Measure Goals-of-Care Discussions as a Clinical Trial Outcome

et al. 2023

Self Cite

View full text Add to dashboard Cite

ImportanceMany clinical trial outcomes are documented in free-text electronic health records (EHRs), making manual data collection costly and infeasible at scale. Natural language processing (NLP) is a promising approach for measuring such outcomes efficiently, but ignoring NLP-related misclassification may lead to underpowered studies.ObjectiveTo evaluate the performance, feasibility, and power implications of using NLP to measure the primary outcome of EHR-documented goals-of-care discussions in a pragmatic randomized clinical trial of a communication intervention.Design, Setting, and ParticipantsThis diagnostic study compared the performance, feasibility, and power implications of measuring EHR-documented goals-of-care discussions using 3 approaches: (1) deep-learning NLP, (2) NLP-screened human abstraction (manual verification of NLP-positive records), and (3) conventional manual abstraction. The study included hospitalized patients aged 55 years or older with serious illness enrolled between April 23, 2020, and March 26, 2021, in a pragmatic randomized clinical trial of a communication intervention in a multihospital US academic health system.Main Outcomes and MeasuresMain outcomes were natural language processing performance characteristics, human abstractor-hours, and misclassification-adjusted statistical power of methods of measuring clinician-documented goals-of-care discussions. Performance of NLP was evaluated with receiver operating characteristic (ROC) curves and precision-recall (PR) analyses and examined the effects of misclassification on power using mathematical substitution and Monte Carlo simulation.ResultsA total of 2512 trial participants (mean [SD] age, 71.7 [10.8] years; 1456 [58%] female) amassed 44 324 clinical notes during 30-day follow-up. In a validation sample of 159 participants, deep-learning NLP trained on a separate training data set from identified patients with documented goals-of-care discussions with moderate accuracy (maximal F1 score, 0.82; area under the ROC curve, 0.924; area under the PR curve, 0.879). Manual abstraction of the outcome from the trial data set would require an estimated 2000 abstractor-hours and would power the trial to detect a risk difference of 5.4% (assuming 33.5% control-arm prevalence, 80% power, and 2-sided α = .05). Measuring the outcome by NLP alone would power the trial to detect a risk difference of 7.6%. Measuring the outcome by NLP-screened human abstraction would require 34.3 abstractor-hours to achieve estimated sensitivity of 92.6% and would power the trial to detect a risk difference of 5.7%. Monte Carlo simulations corroborated misclassification-adjusted power calculations.Conclusions and RelevanceIn this diagnostic study, deep-learning NLP and NLP-screened human abstraction had favorable characteristics for measuring an EHR outcome at scale. Adjusted power calculations accurately quantified power loss from NLP-related misclassification, suggesting that incorporation of this approach into the design of studies using NLP would be beneficial.

show abstract

Section: Methodssupporting

confidence: 53%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Assessment of Natural Language Processing of Electronic Health Records to Measure Goals-of-Care Discussions as a Clinical Trial Outcome

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…12 To address this challenge, guidance emerged to promote ACP for PWD. 3,6,7,[13][14][15][16] However, documented ACP among PWD remains low 17 for reasons incompletely understood. 18 Further, how to optimize ACP with PWDs is insufficiently characterized and existing recommendations for conducting ACP in persons with early-stage dementia are sparse and based on small studies with limited evidence.…”

Section: Introductionmentioning

confidence: 99%

US clinicians' perspectives on advance care planning for persons with dementia: A qualitative study

Tjia

D’Arcangelo

Carlston

et al. 2022

J American Geriatrics Society

View full text Add to dashboard Cite

Background Although advance care planning (ACP) for persons with dementia (PWD) can promote patient‐centered care by aligning future healthcare with patient values, few PWD have documented ACPs for reasons incompletely understood. The objective of this paper is to characterize the perceived value of, barriers to, and successful strategies for completing ACP for PWD as reported by frontline clinicians. Methods Qualitative study using semi‐structured interviews (August 2018–December 2019) with clinicians (physicians, nurse practitioners, nurses, social workers) at 11 US health systems. Interviews asked clinicians about their approaches to ACP with PWDs, including how ACP was initiated, what was discussed, how carepartners were involved, how decision‐making was approached, and how decision‐making capacity was assessed. Results Of 75 participating generalist and specialty clinicians from across the United States, 61% reported conducting ACP with PWD, of whom 19% conducted ACP as early as possible with PWD. Three themes emerged: value of early ACP preserves PWD's autonomy in cases of differing PWD carepartner values, acute medical crises, and clinician paternalism; barriers to ACP with PWD including the dynamic and subjective assessment of patient decision‐making capacity, inconsistent awareness of cognitive impairment by clinicians, and the need to balance patient and family carepartner involvement; and strategies to support ACP include clarifying clinicians' roles in ACP, standardizing clinicians' approach to PWD and their carepartners, and making time for ACP and decision‐making assessments that allow PWD and carepartner involvement regardless of the patients' capacity. Conclusions Clinicians found early ACP for PWD valuable in promoting patient‐centered care among an at‐risk population. In sharing their perspectives on conducting ACP for PWD, clinicians described challenges that are amenable to changes in training, workflow, and material support for clinician time. Clinical practices need sustainable scheduling and financial support models.

show abstract

Intervention to Promote Communication About Goals of Care for Hospitalized Patients With Serious Illness

Curtis

Lee

Brumback

et al. 2023

JAMA

Self Cite

View full text Add to dashboard Cite

ImportanceDiscussions about goals of care are important for high-quality palliative care yet are often lacking for hospitalized older patients with serious illness.ObjectiveTo evaluate a communication-priming intervention to promote goals-of-care discussions between clinicians and hospitalized older patients with serious illness.Design, Setting, and ParticipantsA pragmatic, randomized clinical trial of a clinician-facing communication-priming intervention vs usual care was conducted at 3 US hospitals within 1 health care system, including a university, county, and community hospital. Eligible hospitalized patients were aged 55 years or older with any of the chronic illnesses used by the Dartmouth Atlas project to study end-of-life care or were aged 80 years or older. Patients with documented goals-of-care discussions or a palliative care consultation between hospital admission and eligibility screening were excluded. Randomization occurred between April 2020 and March 2021 and was stratified by study site and history of dementia.InterventionPhysicians and advance practice clinicians who were treating the patients randomized to the intervention received a 1-page, patient-specific intervention (Jumpstart Guide) to prompt and guide goals-of-care discussions.Main Outcomes and MeasuresThe primary outcome was the proportion of patients with electronic health record–documented goals-of-care discussions within 30 days. There was also an evaluation of whether the effect of the intervention varied by age, sex, history of dementia, minoritized race or ethnicity, or study site.ResultsOf 3918 patients screened, 2512 were enrolled (mean age, 71.7 [SD, 10.8] years and 42% were women) and randomized (1255 to the intervention group and 1257 to the usual care group). The patients were American Indian or Alaska Native (1.8%), Asian (12%), Black (13%), Hispanic (6%), Native Hawaiian or Pacific Islander (0.5%), non-Hispanic (93%), and White (70%). The proportion of patients with electronic health record–documented goals-of-care discussions within 30 days was 34.5% (433 of 1255 patients) in the intervention group vs 30.4% (382 of 1257 patients) in the usual care group (hospital- and dementia-adjusted difference, 4.1% [95% CI, 0.4% to 7.8%]). The analyses of the treatment effect modifiers suggested that the intervention had a larger effect size among patients with minoritized race or ethnicity. Among 803 patients with minoritized race or ethnicity, the hospital- and dementia-adjusted proportion with goals-of-care discussions was 10.2% (95% CI, 4.0% to 16.5%) higher in the intervention group than in the usual care group. Among 1641 non-Hispanic White patients, the adjusted proportion with goals-of-care discussions was 1.6% (95% CI, −3.0% to 6.2%) higher in the intervention group than in the usual care group. There was no evidence of differential treatment effects of the intervention on the primary outcome by age, sex, history of dementia, or study site.Conclusions and RelevanceAmong hospitalized older adults with serious illness, a pragmatic clinician-facing communication-priming intervention significantly improved documentation of goals-of-care discussions in the electronic health record, with a greater effect size in racially or ethnically minoritized patients.Trial RegistrationClinicalTrials.gov Identifier: NCT04281784

show abstract

Improving communication about goals of care for hospitalized patients with serious illness: Study protocol for two complementary randomized trials

Cited by 7 publications

References 90 publications

Assessment of Natural Language Processing of Electronic Health Records to Measure Goals-of-Care Discussions as a Clinical Trial Outcome

Assessment of Natural Language Processing of Electronic Health Records to Measure Goals-of-Care Discussions as a Clinical Trial Outcome

US clinicians' perspectives on advance care planning for persons with dementia: A qualitative study

Intervention to Promote Communication About Goals of Care for Hospitalized Patients With Serious Illness

Contact Info

Product

Resources

About