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Weissenbacher D, Courtright K, Rawal S, Crane-Droesch A, O'Connor K, Kuhl N, Merlino C, Foxwell A, Haines L, Puhl J, Gonzalez-Hernandez G. Detecting goals of care conversations in clinical notes with active learning. J Biomed Inform 2024; 151:104618. [PMID: 38431151 PMCID: PMC11177878 DOI: 10.1016/j.jbi.2024.104618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 01/22/2024] [Accepted: 02/26/2024] [Indexed: 03/05/2024]
Abstract
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.
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Affiliation(s)
- Davy Weissenbacher
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, West Hollywood, CA, USA.
| | - Katherine Courtright
- Palliative and Advanced Illness Research Center, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Siddharth Rawal
- DBEI, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Andrew Crane-Droesch
- Palliative and Advanced Illness Research Center, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Karen O'Connor
- DBEI, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Nicholas Kuhl
- The Department of Medicine, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Corinne Merlino
- Palliative and Advanced Illness Research Center, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anessa Foxwell
- NewCourtland Center for Transitions and Health, School of Nursing, University of Pennsylvania, Philadelphia, PA, USA
| | - Lindsay Haines
- Hospice & Palliative Care, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Joseph Puhl
- Palliative and Advanced Illness Research Center, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Curtis JR, Lee RY, Brumback LC, Kross EK, Downey L, Torrence J, LeDuc N, Mallon Andrews K, Im J, Heywood J, Brown CE, Sibley J, Lober WB, Cohen T, Weiner BJ, Khandelwal N, Abedini NC, Engelberg RA. Intervention to Promote Communication About Goals of Care for Hospitalized Patients With Serious Illness: A Randomized Clinical Trial. JAMA 2023; 329:2028-2037. [PMID: 37210665 PMCID: PMC10201405 DOI: 10.1001/jama.2023.8812] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 05/05/2023] [Indexed: 05/22/2023]
Abstract
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.
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Affiliation(s)
- J. Randall Curtis
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle
| | - Robert Y. Lee
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle
| | | | - Erin K. Kross
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle
| | - Lois Downey
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle
| | - Janaki Torrence
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle
| | - Nicole LeDuc
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle
| | - Kasey Mallon Andrews
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle
| | - Jennifer Im
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle
- Department of Health Systems and Population Health, University of Washington, Seattle
| | - Joanna Heywood
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle
| | - Crystal E. Brown
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle
| | - James Sibley
- Department of Biobehavioral Nursing and Health Informatics, University of Washington, Seattle
| | - William B. Lober
- Department of Biobehavioral Nursing and Health Informatics, University of Washington, Seattle
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle
| | - Trevor Cohen
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle
| | - Bryan J. Weiner
- Department of Health Systems and Population Health, University of Washington, Seattle
- Department of Global Health, University of Washington, Seattle
| | - Nita Khandelwal
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle
| | - Nauzley C. Abedini
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle
- Division of Gerontology and Geriatric Medicine, Department of Medicine, University of Washington, Seattle
| | - Ruth A. Engelberg
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle
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Fernandes MB, Valizadeh N, Alabsi HS, Quadri SA, Tesh RA, Bucklin AA, Sun H, Jain A, Brenner LN, Ye E, Ge W, Collens SI, Lin S, Das S, Robbins GK, Zafar SF, Mukerji SS, Westover MB. Classification of neurologic outcomes from medical notes using natural language processing. EXPERT SYSTEMS WITH APPLICATIONS 2023; 214:119171. [PMID: 36865787 PMCID: PMC9974159 DOI: 10.1016/j.eswa.2022.119171] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
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.
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Affiliation(s)
- Marta B. Fernandes
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States
| | - Navid Valizadeh
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Haitham S. Alabsi
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Syed A. Quadri
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States
| | - Ryan A. Tesh
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States
| | - Abigail A. Bucklin
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States
| | - Haoqi Sun
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States
| | - Aayushee Jain
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States
| | - Laura N. Brenner
- Harvard Medical School, Boston, MA, United States
- Division of Pulmonary and Critical Care Medicine, MGH, Boston, MA, United States
- Division of General Internal Medicine, MGH, Boston, MA, United States
| | - Elissa Ye
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States
| | - Wendong Ge
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States
| | - Sarah I. Collens
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
| | - Stacie Lin
- Harvard Medical School, Boston, MA, United States
| | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Gregory K. Robbins
- Harvard Medical School, Boston, MA, United States
- Division of Infectious Diseases, MGH, Boston, MA, United States
| | - Sahar F. Zafar
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Shibani S. Mukerji
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Vaccine and Immunotherapy Center, Division of Infectious Diseases, MGH, Boston, MA, United States
| | - M. Brandon Westover
- Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States
- McCance Center for Brain Health, MGH, Boston, MA, United States
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4
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Lee RY, Kross EK, Torrence J, Li KS, Sibley J, Cohen T, Lober WB, Engelberg RA, Curtis JR. Assessment of Natural Language Processing of Electronic Health Records to Measure Goals-of-Care Discussions as a Clinical Trial Outcome. JAMA Netw Open 2023; 6:e231204. [PMID: 36862411 PMCID: PMC9982698 DOI: 10.1001/jamanetworkopen.2023.1204] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/03/2023] Open
Abstract
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.
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Affiliation(s)
- Robert Y. Lee
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle
| | - Erin K. Kross
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle
| | - Janaki Torrence
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle
| | - Kevin S. Li
- Division of Biomedical and Health Informatics, Department of Biomedical Informatics and Medical Education, University of Washington, Seattle
| | - James Sibley
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle
- Department of Biobehavioral Nursing and Health Informatics, University of Washington, Seattle
| | - Trevor Cohen
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle
- Division of Biomedical and Health Informatics, Department of Biomedical Informatics and Medical Education, University of Washington, Seattle
| | - William B. Lober
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle
- Division of Biomedical and Health Informatics, Department of Biomedical Informatics and Medical Education, University of Washington, Seattle
- Department of Biobehavioral Nursing and Health Informatics, University of Washington, Seattle
- Department of Global Health, University of Washington, Seattle
| | - Ruth A. Engelberg
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle
| | - J. Randall Curtis
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle
- Department of Biobehavioral Nursing and Health Informatics, University of Washington, Seattle
- Department of Health Systems and Population Health, University of Washington, Seattle
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5
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Uyeda AM, Lee RY, Pollack LR, Paul SR, Downey L, Brumback LC, Engelberg RA, Sibley J, Lober WB, Cohen T, Torrence J, Kross EK, Curtis JR. Predictors of Documented Goals-of-Care Discussion for Hospitalized Patients With Chronic Illness. J Pain Symptom Manage 2023; 65:233-241. [PMID: 36423800 PMCID: PMC9928787 DOI: 10.1016/j.jpainsymman.2022.11.012] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 11/04/2022] [Accepted: 11/13/2022] [Indexed: 11/23/2022]
Abstract
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.
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Affiliation(s)
- Alison M Uyeda
- Department of Medicine, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., R.A.E, J.T., E.K.K., J.R.C.), Seattle, Washington, USA; Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., L.C.B., R.A.E., J.S., W.B.L., T.C., J.T., E.K.K., J.R.C.), Seattle, Washington, USA; Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Harborview Medical Center, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., R.A.E., J.T., E.K.K., J.R.C.), Seattle, Washington, USA
| | - Robert Y Lee
- Department of Medicine, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., R.A.E, J.T., E.K.K., J.R.C.), Seattle, Washington, USA; Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., L.C.B., R.A.E., J.S., W.B.L., T.C., J.T., E.K.K., J.R.C.), Seattle, Washington, USA; Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Harborview Medical Center, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., R.A.E., J.T., E.K.K., J.R.C.), Seattle, Washington, USA
| | - Lauren R Pollack
- Department of Medicine, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., R.A.E, J.T., E.K.K., J.R.C.), Seattle, Washington, USA; Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., L.C.B., R.A.E., J.S., W.B.L., T.C., J.T., E.K.K., J.R.C.), Seattle, Washington, USA; Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Harborview Medical Center, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., R.A.E., J.T., E.K.K., J.R.C.), Seattle, Washington, USA
| | - Sudiptho R Paul
- Department of Medicine, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., R.A.E, J.T., E.K.K., J.R.C.), Seattle, Washington, USA; Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., L.C.B., R.A.E., J.S., W.B.L., T.C., J.T., E.K.K., J.R.C.), Seattle, Washington, USA; Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Harborview Medical Center, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., R.A.E., J.T., E.K.K., J.R.C.), Seattle, Washington, USA
| | - Lois Downey
- Department of Medicine, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., R.A.E, J.T., E.K.K., J.R.C.), Seattle, Washington, USA; Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., L.C.B., R.A.E., J.S., W.B.L., T.C., J.T., E.K.K., J.R.C.), Seattle, Washington, USA; Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Harborview Medical Center, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., R.A.E., J.T., E.K.K., J.R.C.), Seattle, Washington, USA
| | - Lyndia C Brumback
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., L.C.B., R.A.E., J.S., W.B.L., T.C., J.T., E.K.K., J.R.C.), Seattle, Washington, USA; Department of Biostatistics, University of Washington (L.C.B.), Seattle, Washington, USA
| | - Ruth A Engelberg
- Department of Medicine, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., R.A.E, J.T., E.K.K., J.R.C.), Seattle, Washington, USA; Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., L.C.B., R.A.E., J.S., W.B.L., T.C., J.T., E.K.K., J.R.C.), Seattle, Washington, USA; Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Harborview Medical Center, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., R.A.E., J.T., E.K.K., J.R.C.), Seattle, Washington, USA
| | - James Sibley
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., L.C.B., R.A.E., J.S., W.B.L., T.C., J.T., E.K.K., J.R.C.), Seattle, Washington, USA; Department of Biomedical Informatics and Medical Education, University of Washington (J.S., W.B.L., T.C.), Seattle, Washington, USA
| | - William B Lober
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., L.C.B., R.A.E., J.S., W.B.L., T.C., J.T., E.K.K., J.R.C.), Seattle, Washington, USA; Department of Biomedical Informatics and Medical Education, University of Washington (J.S., W.B.L., T.C.), Seattle, Washington, USA; Department of Biobehavioral Nursing and Health Informatics, University of Washington (W.B.L.), Seattle, Washington, USA
| | - Trevor Cohen
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., L.C.B., R.A.E., J.S., W.B.L., T.C., J.T., E.K.K., J.R.C.), Seattle, Washington, USA; Department of Biomedical Informatics and Medical Education, University of Washington (J.S., W.B.L., T.C.), Seattle, Washington, USA
| | - Janaki Torrence
- Department of Medicine, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., R.A.E, J.T., E.K.K., J.R.C.), Seattle, Washington, USA; Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., L.C.B., R.A.E., J.S., W.B.L., T.C., J.T., E.K.K., J.R.C.), Seattle, Washington, USA; Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Harborview Medical Center, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., R.A.E., J.T., E.K.K., J.R.C.), Seattle, Washington, USA
| | - Erin K Kross
- Department of Medicine, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., R.A.E, J.T., E.K.K., J.R.C.), Seattle, Washington, USA; Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., L.C.B., R.A.E., J.S., W.B.L., T.C., J.T., E.K.K., J.R.C.), Seattle, Washington, USA; Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Harborview Medical Center, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., R.A.E., J.T., E.K.K., J.R.C.), Seattle, Washington, USA
| | - J Randall Curtis
- Department of Medicine, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., R.A.E, J.T., E.K.K., J.R.C.), Seattle, Washington, USA; Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., L.C.B., R.A.E., J.S., W.B.L., T.C., J.T., E.K.K., J.R.C.), Seattle, Washington, USA; Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Harborview Medical Center, University of Washington (A.M.U., R.Y.L., L.R.P., S.R.P., L.D., R.A.E., J.T., E.K.K., J.R.C.), Seattle, Washington, USA.
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Durieux BN, Zverev SR, Tarbi EC, Kwok A, Sciacca K, Pollak KI, Tulsky JA, Lindvall C. Development of a keyword library for capturing PRO-CTCAE-focused "symptom talk" in oncology conversations. JAMIA Open 2023; 6:ooad009. [PMID: 36789287 PMCID: PMC9912707 DOI: 10.1093/jamiaopen/ooad009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 01/18/2023] [Accepted: 02/02/2023] [Indexed: 02/12/2023] Open
Abstract
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.
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Affiliation(s)
- Brigitte N Durieux
- Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Samuel R Zverev
- Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, Massachusetts, USA,NYU School of Medicine, New York University, New York, New York, USA
| | - Elise C Tarbi
- Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, Massachusetts, USA,Department of Nursing, University of Vermont, Burlington, Vermont, USA
| | - Anne Kwok
- Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Kate Sciacca
- Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, Massachusetts, USA,Department of Palliative Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Kathryn I Pollak
- Department of Population Health Sciences, Duke University School of Medicine, Duke University, Durham, North Carolina, USA,Cancer Prevention and Control Program, Duke Cancer Institute, Duke University, Durham, North Carolina, USA
| | - James A Tulsky
- Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, Massachusetts, USA,Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Charlotta Lindvall
- Corresponding Author: Charlotta Lindvall, MD, PhD, Department of Psychosocial Oncology & Palliative Care, Dana-Farber Cancer Institute, 450 Brookline Ave, LW670, Boston, MA 02215, USA;
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7
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Sarmet M, Kabani A, Coelho L, Dos Reis SS, Zeredo JL, Mehta AK. The use of natural language processing in palliative care research: A scoping review. Palliat Med 2023; 37:275-290. [PMID: 36495082 DOI: 10.1177/02692163221141969] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
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.
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Affiliation(s)
- Max Sarmet
- Tertiary Referral Center of Neuromuscular Diseases, Hospital de Apoio de Brasília, Brazil.,Graduate Department of Health Science and Technology, University of Brasília, Brazil
| | - Aamna Kabani
- Johns Hopkins University, School of Medicine, USA
| | - Luis Coelho
- Center of Innovation in Engineering and Industrial Technology, Polytechnic of Porto - School of Engineering (ISEP), Portugal
| | - Sara Seabra Dos Reis
- Center of Innovation in Engineering and Industrial Technology, Polytechnic of Porto - School of Engineering (ISEP), Portugal
| | - Jorge L Zeredo
- Graduate Department of Health Science and Technology, University of Brasília, Brazil
| | - Ambereen K Mehta
- Palliative Care Program, Division of General Internal Medicine, Johns Hopkins Bayview Medical Center, Johns Hopkins University, School of Medicine, USA
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8
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Curtis JR, Lee RY, Brumback LC, Kross EK, Downey L, Torrence J, Heywood J, LeDuc N, Mallon Andrews K, Im J, Weiner BJ, Khandelwal N, Abedini NC, Engelberg RA. Improving communication about goals of care for hospitalized patients with serious illness: Study protocol for two complementary randomized trials. Contemp Clin Trials 2022; 120:106879. [PMID: 35963531 PMCID: PMC10042145 DOI: 10.1016/j.cct.2022.106879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 07/26/2022] [Accepted: 08/06/2022] [Indexed: 11/03/2022]
Abstract
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.
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Affiliation(s)
- J Randall Curtis
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle, WA, United States of America; Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA, United States of America.
| | - Robert Y Lee
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle, WA, United States of America; Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA, United States of America
| | - Lyndia C Brumback
- Department of Biostatistics, University of Washington, Seattle, WA, United States of America
| | - Erin K Kross
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle, WA, United States of America; Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA, United States of America
| | - Lois Downey
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle, WA, United States of America; Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA, United States of America
| | - Janaki Torrence
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle, WA, United States of America; Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA, United States of America
| | - Joanna Heywood
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle, WA, United States of America; Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA, United States of America
| | - Nicole LeDuc
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle, WA, United States of America; Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA, United States of America
| | - Kasey Mallon Andrews
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle, WA, United States of America; Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA, United States of America
| | - Jennifer Im
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle, WA, United States of America; Department of Health Systems and Population Health, University of Washington, Seattle, WA, United States of America
| | - Bryan J Weiner
- Department of Health Systems and Population Health, University of Washington, Seattle, WA, United States of America; Department of Global Health, University of Washington, Seattle, WA, United States of America
| | - Nita Khandelwal
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle, WA, United States of America; Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, United States of America
| | - Nauzley C Abedini
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle, WA, United States of America; Division of Gerontology and Geriatric Medicine, Department of Medicine, University of Washington, Seattle, WA, United States of America
| | - Ruth A Engelberg
- Cambia Palliative Care Center of Excellence at UW Medicine, University of Washington, Seattle, WA, United States of America; Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA, United States of America
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