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

Performance of Predefined Search Patterns for Identifying Documented Goals-of-Care Discussions in Inpatient Electronic Health Records

Objective: To characterize the sensitivity and predictive value of predefined search terms for identifying documented goals-of-care discussions in health records of hospitalized patients with serious illness. Methods: We evaluated the performance of 30 previously published and investigator-defined search terms codified into regular expressions (a type of pattern-based text search) in detecting goals-of-care documentation in a 2974-note corpus of electronic health record notes belonging to 159 inpatients enrolled in a U.S. clinical trial over 2020-2021. Results: Compared to conventional chart abstraction, search terms for "goals of care" and synonyms such as "GOC" had poor sensitivity (range: 29.5-38.3%) and modest positive predictive value (PPV; range: 48.3-61.7%) for identifying notes with goals-of-care documentation. Combinations of search terms demonstrated modest performance (sensitivity 62.0%, PPV 59.4%, F1 0.61) but fell short of more complex natural language processing models. Conclusion: In certain contexts, predefined regular-expression-based search terms may have suboptimal sensitivity and predictive value for identifying documented goals-of-care discussions.

Opportunities and Barriers to Artificial Intelligence Adoption in Palliative/Hospice Care for Underrepresented Groups: A Technology Acceptance Model-Based Review

Underrepresented groups (URGs) in the United States, including African Americans, Latino/Hispanic Americans, Asian Pacific Islanders, and Native Americans, face significant barriers to accessing hospice and palliative care. Factors such as language barriers, cultural perceptions, and mistrust in healthcare systems contribute to the underutilization of these services. Recent advancements in artificial intelligence (AI) offer potential solutions to these challenges by enhancing cultural sensitivity, improving communication, and personalizing care. This article aims to synthesize the literature on AI in palliative/hospice care for URGs through the Technology Acceptance Model (TAM), highlighting current research and application in practice. The scoping review methodology, based on the framework developed by Arksey and O'Malley, was applied to rapidly map the field of AI in palliative and hospice care. A systematic search was conducted in 9 databases to identify studies examining AI applications in hospice and palliative care for URGs. Articles were independently assessed by 2 reviewers and then synthesized via narrative review through the lens of the TAM framework, which focuses on technology acceptance factors such as perceived ease of use and usefulness. Seventeen studies were identified. Findings suggest that AI has the potential to improve decision-making, enhance timely palliative care referrals, and bridge language and cultural gaps. Artificial intelligence tools were found to improve predictive accuracy, support serious illness communication, and assist in addressing language barriers, thus promoting equitable care for URGs. However, barriers such as limited generalizability, biases in data, and challenges in infrastructure were noted, hindering the full adoption of AI in hospice settings. Artificial intelligence has transformative potential to improve hospice care for URGs by enhancing cultural sensitivity, improving communication, and enabling more timely interventions. However, to fully realize its potential, AI solutions must address data biases, infrastructure limitations, and cultural nuances. Future research should prioritize developing culturally competent AI tools that are transparent, explainable, and scalable to ensure equitable access to hospice and palliative care services for all populations.

Improving Clinical Documentation with Artificial Intelligence: A Systematic Review

Clinicians dedicate significant time to clinical documentation, incurring opportunity cost. Artificial Intelligence (AI) tools promise to improve documentation quality and efficiency. This systematic review overviews peer-reviewed AI tools to understand how AI may reduce opportunity cost. PubMed, Embase, Scopus, and Web of Science databases were queried for original, English language research studies published during or before July 2024 that report a new development, application, and validation of an AI tool for improving clinical documentation. 129 studies were extracted from 673 candidate studies. AI tools improve documentation by structuring data, annotating notes, evaluating quality, identifying trends, and detecting errors. Other AI-enabled tools assist clinicians in real-time during office visits, but moderate accuracy precludes broad implementation. While a highly accurate end-to-end AI documentation assistant is not currently reported in peer-reviewed literature, existing techniques such as structuring data offer targeted improvements to clinical documentation workflows.

Improving Clinical Documentation with Artificial Intelligence: A Systematic Review

Clinicians dedicate significant time to clinical documentation, incurring opportunity cost. Artificial Intelligence (AI) tools promise to improve documentation quality and efficiency. This systematic review overviews peer-reviewed AI tools to understand how AI may reduce opportunity cost. PubMed, Embase, Scopus, and Web of Science databases were queried for original, English language research studies published during or before July 2024 that report a new development, application, and validation of an AI tool for improving clinical documentation. 129 studies were extracted from 673 candidate studies. AI tools improve documentation by structuring data, annotating notes, evaluating quality, identifying trends, and detecting errors. Other AI-enabled tools assist clinicians in real-time during office visits, but moderate accuracy precludes broad implementation. While a highly accurate end-to-end AI documentation assistant is not currently reported in peer-reviewed literature, existing techniques such as structuring data offer targeted improvements to clinical documentation workflows.

Detecting goals of care conversations in clinical notes with active learning

OBJECTIVE

Goals of care (GOC) discussions are an increasingly used quality metric in serious illness care and research. Wide variation in documentation practices within the Electronic Health Record (EHR) presents challenges for reliable measurement of GOC discussions. Novel natural language processing approaches are needed to capture GOC discussions documented in real-world samples of seriously ill hospitalized patients' EHR notes, a corpus with a very low event prevalence.

METHODS

To automatically detect sentences documenting GOC discussions outside of dedicated GOC note types, we proposed an ensemble of classifiers aggregating the predictions of rule-based, feature-based, and three transformers-based classifiers. We trained our classifier on 600 manually annotated EHR notes among patients with serious illnesses. Our corpus exhibited an extremely imbalanced ratio between sentences discussing GOC and sentences that do not. This ratio challenges standard supervision methods to train a classifier. Therefore, we trained our classifier with active learning.

RESULTS

Using active learning, we reduced the annotation cost to fine-tune our ensemble by 70% while improving its performance in our test set of 176 EHR notes, with 0.557 F1-score for sentence classification and 0.629 for note classification.

CONCLUSION

When classifying notes, with a true positive rate of 72% (13/18) and false positive rate of 8% (13/158), our performance may be sufficient for deploying our classifier in the EHR to facilitate bedside clinicians' access to GOC conversations documented outside of dedicated notes types, without overburdening clinicians with false positives. Improvements are needed before using it to enrich trial populations or as an outcome measure.

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.

Classification of neurologic outcomes from medical notes using natural language processing

Neurologic disability level at hospital discharge is an important outcome in many clinical research studies. Outside of clinical trials, neurologic outcomes must typically be extracted by labor intensive manual review of clinical notes in the electronic health record (EHR). To overcome this challenge, we set out to develop a natural language processing (NLP) approach that automatically reads clinical notes to determine neurologic outcomes, to make it possible to conduct larger scale neurologic outcomes studies. We obtained 7314 notes from 3632 patients hospitalized at two large Boston hospitals between January 2012 and June 2020, including discharge summaries (3485), occupational therapy (1472) and physical therapy (2357) notes. Fourteen clinical experts reviewed notes to assign scores on the Glasgow Outcome Scale (GOS) with 4 classes, namely 'good recovery', 'moderate disability', 'severe disability', and 'death' and on the Modified Rankin Scale (mRS), with 7 classes, namely 'no symptoms', 'no significant disability', 'slight disability', 'moderate disability', 'moderately severe disability', 'severe disability', and 'death'. For 428 patients' notes, 2 experts scored the cases generating interrater reliability estimates for GOS and mRS. After preprocessing and extracting features from the notes, we trained a multiclass logistic regression model using LASSO regularization and 5-fold cross validation for hyperparameter tuning. The model performed well on the test set, achieving a micro average area under the receiver operating characteristic and F-score of 0.94 (95% CI 0.93-0.95) and 0.77 (0.75-0.80) for GOS, and 0.90 (0.89-0.91) and 0.59 (0.57-0.62) for mRS, respectively. Our work demonstrates that an NLP algorithm can accurately assign neurologic outcomes based on free text clinical notes. This algorithm increases the scale of research on neurological outcomes that is possible with EHR data.

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.

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 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 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.

Development of a keyword library for capturing PRO-CTCAE-focused "symptom talk" in oncology conversations

Objectives

As computational methods for detecting symptoms can help us better attend to patient suffering, the objectives of this study were to develop and evaluate the performance of a natural language processing keyword library for detecting symptom talk, and to describe symptom communication within our dataset to generate insights for future model building.

Materials and Methods

This was a secondary analysis of 121 transcribed outpatient oncology conversations from the Communication in Oncologist-Patient Encounters trial. Through an iterative process of identifying symptom expressions via inductive and deductive techniques, we generated a library of keywords relevant to the Patient-Reported Outcome version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) framework from 90 conversations, and tested the library on 31 additional transcripts. To contextualize symptom expressions and the nature of misclassifications, we qualitatively analyzed 450 mislabeled and properly labeled symptom-positive turns.

Results

The final library, comprising 1320 terms, identified symptom talk among conversation turns with an F1 of 0.82 against a PRO-CTCAE-focused gold standard, and an F1 of 0.61 against a broad gold standard. Qualitative observations suggest that physical symptoms are more easily detected than psychological symptoms (eg, anxiety), and ambiguity persists throughout symptom communication.

Discussion

This rudimentary keyword library captures most PRO-CTCAE-focused symptom talk, but the ambiguity of symptom speech limits the utility of rule-based methods alone, and limits to generalizability must be considered.

Conclusion

Our findings highlight opportunities for more advanced computational models to detect symptom expressions from transcribed clinical conversations. Future improvements in speech-to-text could enable real-time detection at scale.

The use of natural language processing in palliative care research: A scoping review

BACKGROUND

Natural language processing has been increasingly used in palliative care research over the last 5 years for its versatility and accuracy.

AIM

To evaluate and characterize natural language processing use in palliative care research, including the most commonly used natural language processing software and computational methods, data sources, trends in natural language processing use over time, and palliative care topics addressed.

DESIGN

A scoping review using the framework by Arksey and O'Malley and the updated recommendations proposed by Levac et al. was conducted.

SOURCES

PubMed, Web of Science, Embase, Scopus, and IEEE Xplore databases were searched for palliative care studies that utilized natural language processing tools. Data on study characteristics and natural language processing instruments used were collected and relevant palliative care topics were identified.

RESULTS

197 relevant references were identified. Of these, 82 were included after full-text review. Studies were published in 48 different journals from 2007 to 2022. The average sample size was 21,541 (median 435). Thirty-two different natural language processing software and 33 machine-learning methods were identified. Nine main sources for data processing and 15 main palliative care topics across the included studies were identified. The most frequent topic was mortality and prognosis prediction. We also identified a trend where natural language processing was frequently used in analyzing clinical serious illness conversations extracted from audio recordings.

CONCLUSIONS

We found 82 papers on palliative care using natural language processing methods for a wide-range of topics and sources of data that could expand the use of this methodology. We encourage researchers to consider incorporating this cutting-edge research methodology in future studies to improve published palliative care data.

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.