Intervention to Promote Communication About Goals of Care for Hospitalized Patients With Serious Illness: A Randomized Clinical Trial.

Importance: Discussions about goals of care are important for high-quality palliative care yet are often lacking for hospitalized older patients with serious illness. Objective: To evaluate a communication-priming intervention to promote goals-of-care discussions between clinicians and hospitalized older patients with serious illness. Design, Setting, and Participants: A 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. Intervention: Physicians 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 Measures: The 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. Results: Of 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 Relevance: Among 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 Registration: ClinicalTrials.gov Identifier: NCT04281784.

Applying human-centered design to adapt the Jumpstart Guide for goals-of-care discussions in persons living with dementia.

BACKGROUND: Goals-of-care discussions (GOCD) are uncommon in persons living with dementia (PLWD) despite the likelihood of eventual loss of decisional capacity in the population. The Jumpstart Guide, an existing serious illness communication priming tool, can improve GOCD in certain populations, but has not previously been adapted for use among PLWD and their caregivers. METHODS: Using human-centered design (HCD), we adapted the Jumpstart Guide for use with PLWD and their caregivers. We conducted qualitative interviews with clinicians and caregivers of PLWD. Six team members conducted qualitative rapid analysis of interviews leading to the development of summary templates and integrative matrices. Four iterations of the Jumpstart Guide led to the final version. RESULTS: Thirteen clinicians and 11 caregivers were interviewed. Interviews provided key insights into the unique barriers PLWD and their caregivers face during GOCD, including discomfort with accepting a dementia diagnosis and concern with using "serious illness" to describe dementia, as is commonly done in palliative care. Clinicians described differences in GOCD with PLWD compared to other serious illnesses, and the challenge of getting patients and families to think about future health states. Interviews led to Jumpstart Guide adaptations in the following domains: (1) format and structure, (2) content, and (3) specific language. Suggested changes included prioritizing naming a decision-maker, changing conversation prompts to improve accessibility and understandability, ensuring the Jumpstart Guide could be used with patients as well as their caregivers, and altering language to avoid references to "serious illness" and "abilities." CONCLUSION: Using HCD yielded valuable insights from clinicians and caregivers about the unique barriers to conducting GOCD among PLWD and their caregivers. These insights were used to adapt the Jumpstart Guide for use with PLWD and their caregivers, which is currently being tested in a pragmatic randomized controlled trial in outpatient clinics.

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

Importance: Many 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. Objective: To 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 Participants: This 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 Measures: Main 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. Results: A 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 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 Relevance: In 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.

Predictors of Documented Goals-of-Care Discussion for Hospitalized Patients With Chronic Illness.

CONTEXT: Goals-of-care discussions are important for patient-centered care among hospitalized patients with serious illness. However, there are little data on the occurrence, predictors, and timing of these discussions. OBJECTIVES: To examine the occurrence, predictors, and timing of electronic health record (EHR)-documented goals-of-care discussions for hospitalized patients. METHODS: This retrospective cohort study used natural language processing (NLP) to examine EHR-documented goals-of-care discussions for adults with chronic life-limiting illness or age ≥80 hospitalized 2015-2019. The primary outcome was NLP-identified documentation of a goals-of-care discussion during the index hospitalization. We used multivariable logistic regression to evaluate associations with baseline characteristics. RESULTS: Of 16,262 consecutive, eligible patients without missing data, 5,918 (36.4%) had a documented goals-of-care discussion during hospitalization; approximately 57% of these discussions occurred within 24 hours of admission. In multivariable analysis, documented goals-of-care discussions were more common for women (OR=1.26, 95%CI 1.18-1.36), older patients (OR=1.04 per year, 95%CI 1.03-1.04), and patients with more comorbidities (OR=1.11 per Deyo-Charlson point, 95%CI 1.10-1.13), cancer (OR=1.88, 95%CI 1.72-2.06), dementia (OR=2.60, 95%CI 2.29-2.94), higher acute illness severity (OR=1.12 per National Early Warning Score point, 95%CI 1.11-1.14), or prior advance care planning documents (OR=1.18, 95%CI 1.08-1.30). Documentation of these discussions was less common for racially or ethnically minoritized patients (OR=0.823, 95%CI 0.75-0.90). CONCLUSION: Among hospitalized patients with serious illness, documented goals-of-care discussions identified by NLP were more common among patients with older age and increased burden of acute or chronic illness, and less common among racially or ethnically minoritized patients. This suggests important disparities in goals-of-care discussions.

Mixed-methods evaluation of three natural language processing modeling approaches for measuring documented goals-of-care discussions in the electronic health record.

CONTEXT: Documented goals-of-care discussions are an important quality metric for patients with serious illness. Natural language processing (NLP) is a promising approach for identifying goals-of-care discussions in the electronic health record (EHR). OBJECTIVES: To compare three NLP modeling approaches for identifying EHR documentation of goals-of-care discussions and generate hypotheses about differences in performance. METHODS: We conducted a mixed-methods study to evaluate performance and misclassification for three NLP featurization approaches modeled with regularized logistic regression: bag-of-words (BOW), rule-based, and a hybrid approach. From a prospective cohort of 150 patients hospitalized with serious illness over 2018 to 2020, we collected 4391 inpatient EHR notes; 99 (2.3%) contained documented goals-of-care discussions. We used leave-one-out cross-validation to estimate performance by comparing pooled NLP predictions to human abstractors with receiver-operating-characteristic (ROC) and precision-recall (PR) analyses. We qualitatively examined a purposive sample of 70 NLP-misclassified notes using content analysis to identify linguistic features that allowed us to generate hypotheses underpinning misclassification. RESULTS: All three modeling approaches discriminated between notes with and without goals-of-care discussions (AUCROC: BOW, 0.907; rule-based, 0.948; hybrid, 0.965). Precision and recall were only moderate (precision at 70% recall: BOW, 16.2%; rule-based, 50.4%; hybrid, 49.3%; AUCPR: BOW, 0.505; rule-based, 0.579; hybrid, 0.599). Qualitative analysis revealed patterns underlying performance differences between BOW and rule-based approaches. CONCLUSION: NLP holds promise for identifying EHR-documented goals-of-care discussions. However, the rarity of goals-of-care content in EHR data limits performance. Our findings highlight opportunities to optimize NLP modeling approaches, and support further exploration of different NLP approaches to identify goals-of-care discussions.

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

BACKGROUND: Although goals-of-care discussions are important for high-quality palliative care, this communication is often lacking for hospitalized older patients with serious illness. Electronic health records (EHR) provide an opportunity to identify patients who might benefit from these discussions and promote their occurrence, yet prior interventions using the EHR for this purpose are limited. We designed two complementary yet independent randomized trials to examine effectiveness of a communication-priming intervention (Jumpstart) for hospitalized older adults with serious illness. METHODS: We report the protocol for these 2 randomized trials. Trial 1 has two arms, usual care and a clinician-facing Jumpstart, and is a pragmatic trial assessing outcomes with the EHR only (n = 2000). Trial 2 has three arms: usual care, clinician-facing Jumpstart, and clinician- and patient-facing (bi-directional) Jumpstart (n = 600). We hypothesize the clinician-facing Jumpstart will improve outcomes over usual care and the bi-directional Jumpstart will improve outcomes over the clinician-facing Jumpstart and usual care. We use a hybrid effectiveness-implementation design to examine implementation barriers and facilitators. OUTCOMES: For both trials, the primary outcome is EHR documentation of a goals-of-care discussion within 30 days of randomization; additional outcomes include intensity of end-of-life care. Trial 2 also examines patient- or family-reported outcomes assessed by surveys targeting 3-5 days and 4-8 weeks after randomization including quality of goals-of-care communication, receipt of goal-concordant care, and psychological symptoms. CONCLUSIONS: This novel study incorporates two complementary randomized trials and a hybrid effectiveness-implementation approach to improve the quality and value of care for hospitalized older adults with serious illness. CLINICAL TRIALS REGISTRATION: STUDY00007031-A and STUDY00007031-B.

Applying Human-Centered Design to Refinement of the Jumpstart Guide, a Clinician- and Patient-Facing Goals-of-Care Discussion Priming Tool.

CONTEXT: Human-centered design provides a framework to understand the needs of patients and clinicians who are the target of goals-of-care discussion priming tools. Few studies employ human-centered design to develop and refine their tools. OBJECTIVES: To describe how human-centered design can be applied to the development and refinement of clinician- and patient-facing inpatient goals-of-care discussion guides (Jumpstart guides). METHODS: Human-centered design was applied to the development and refinement of the inpatient Jumpstart guides in four phases: (1) discovering problems based on prior pilots, studies, and research team priorities; (2) further defining problems based on stakeholder and expert review of the current guides; (3) designing solutions based on consensus among stakeholders; and (4) validating solutions after research team review of stakeholder comments. RESULTS: Five initial problems were identified by the research team in phase 1. After expert and stakeholder review in phase 2, 30 additional problems were identified related to Jumpstart guide format, structure, and content. In phase 3, stakeholders proposed solutions to these 35 problems and reached consensus on 32 of these. There was disagreement in 3 areas, including how to frame discussions around cardiopulmonary resuscitation and 2 perceived barriers to inpatient goals-of-care discussions. In phase 4, the research team reviewed all stakeholder input and reached final consensus on solutions to all of the identified problems. CONCLUSION: Human-centered design is a useful tool for enhancing communication interventions in serious illness and can easily be integrated in future development and refinement of clinician- and patient-facing interventions to enhance goals-of-care discussions.

Phonomotor Versus Semantic Feature Analysis Treatment for Anomia in 58 Persons With Aphasia: A Randomized Controlled Trial.

Purpose The ultimate goal of anomia treatment should be to achieve gains in exemplars trained in the therapy session, as well as generalization to untrained exemplars and contexts. The purpose of this study was to test the efficacy of phonomotor treatment, a treatment focusing on enhancement of phonological sequence knowledge, against semantic feature analysis (SFA), a lexical-semantic therapy that focuses on enhancement of semantic knowledge and is well known and commonly used to treat anomia in aphasia. Method In a between-groups randomized controlled trial, 58 persons with aphasia characterized by anomia and phonological dysfunction were randomized to receive 56-60 hr of intensively delivered treatment over 6 weeks with testing pretreatment, posttreatment, and 3 months posttreatment termination. Results There was no significant between-groups difference on the primary outcome measure (untrained nouns phonologically and semantically unrelated to each treatment) at 3 months posttreatment. Significant within-group immediately posttreatment acquisition effects for confrontation naming and response latency were observed for both groups. Treatment-specific generalization effects for confrontation naming were observed for both groups immediately and 3 months posttreatment; a significant decrease in response latency was observed at both time points for the SFA group only. Finally, significant within-group differences on the Comprehensive Aphasia Test-Disability Questionnaire (Swinburn, Porter, & Howard, 2004) were observed both immediately and 3 months posttreatment for the SFA group, and significant within-group differences on the Functional Outcome Questionnaire (Glueckauf et al., 2003) were found for both treatment groups 3 months posttreatment. Discussion Our results are consistent with those of prior studies that have shown that SFA treatment and phonomotor treatment generalize to untrained words that share features (semantic or phonological sequence, respectively) with the training set. However, they show that there is no significant generalization to untrained words that do not share semantic features or phonological sequence features.

Effects of Phonomotor Treatment on discourse production.

BACKGROUND: Aphasia is an acquired language disorder that makes it difficult for people to produce and comprehend language, with every person with aphasia (PWA) demonstrating difficulty accessing and selecting words (anomia). While aphasia treatments typically focus on a single aspect of language, such as word retrieval, the ultimate goal of aphasia therapy is to improve communication, which is best seen at the level of discourse. AIMS: This retrospective study investigated the effects of one effective anomia therapy, Phonomotor Treatment, on discourse production. METHODS & PROCEDURES: Twenty-six PWA participated in 60 hours of Phonomotor Treatment, which focuses on building a person's ability to recognise, produce, and manipulate phonemes in progressively longer non-word and real-word contexts. Language samples were collected prior to, immediately after, and three months after the treatment program. Percent Correct Information Units (CIUs) and CIUs per minute were calculated. OUTCOMES & RESULTS: Overall, PWA showed significantly improved CIUs per minute, relative to baseline, immediately after treatment and three months later, as well as significantly improved percent CIUs, relative to baseline, three months following treatment. CONCLUSIONS: Phonomotor Treatment, which focuses on phonological processing, can lead to widespread improvement throughout the language system, including to the functionally critical level of discourse production.

The Influence of Phonomotor Treatment on Word Retrieval: Insights From Naming Errors.

Purpose An increasing number of anomia treatment studies have coupled traditional word retrieval accuracy outcome measures with more fine-grained analysis of word retrieval errors to allow for more comprehensive measurement of treatment-induced changes in word retrieval. The aim of this study was to examine changes in picture naming errors after phonomotor treatment. Method Twenty-eight individuals with aphasia received 60 hr of phonomotor treatment, an intensive, phoneme-based therapy for anomia. Confrontation naming was assessed pretreatment, immediately posttreatment, and 3 months posttreatment for trained and untrained nouns. Responses were scored for accuracy and coded for error type, and error proportions of each error type (e.g., semantic, phonological, omission) were compared: pre- versus posttreatment and pretreatment versus 3 months posttreatment. Results The group of treatment participants improved in whole-word naming accuracy on trained items and maintained their improvement. Treatment effects also generalized to untrained nouns at the maintenance testing phase. Additionally, participants demonstrated a decrease in proportions of omission and description errors on trained items immediately posttreatment. Conclusions Along with generalized improved whole-word naming accuracy, results of the error analysis suggest that a global (i.e., both lexical-semantic and phonological) change in lexical knowledge underlies the observed changes in confrontation naming accuracy following phonomotor treatment.

Addressing Communicative Participation in Treatment Planning for Adults: A Survey of U.S. Speech-Language Pathologists.

PURPOSE: In speech-language pathology, there is increasing attention on participation-focused interventions to optimize participation in valued life roles. The purpose of this study was to investigate how speech-language pathologists (SLPs) in the United States address life participation in therapy programs, as well as their opinions regarding barriers and facilitators to participation-focused intervention. METHOD: An online questionnaire presented case scenarios for aphasia, dysarthria, and laryngectomy to 66 SLPs who have worked with adults. SLPs were asked to write goals and describe therapy activities for the scenarios. The final section of the questionnaire was an open-ended question regarding barriers and facilitators to participation-focused intervention. RESULTS: Many SLPs addressed participation in their rationales for therapy; 50% of goals had a participation-focused rationale. However, the goals, activities, and outcomes measures typically focused more on impairment and skill performance. Only 8% of goals specifically referenced participation. Although many SLPs stated that participation-focused intervention is important, they identified many barriers to implementation including time and productivity constraints, limits of clinical settings, and documentation challenges. CONCLUSIONS: There is potential for gaps between SLPs' participation-focused rationale for therapy and activities or outcomes measures that often do not include participation elements. SLPs are interested in participation-focused treatment resources.